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*** primarily this assignment is filling in the tables- attached all articles to use ****
- Use the attached “Literature Evaluation Table to complete this assignment (not a word document)
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All Rights Reserved. Copyright © 2020 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved. 1752 www.ccmjournal.org December 2020 • Volume 48 • Number 12 Objectives: Growing evidence supports the Awakening and Breathing Coordination, Delirium monitoring/management, and Early exercise/mobility (ABCDE) bundle processes as improving a number of short- and long-term clinical outcomes for patients requiring ICU care. To assess the cost-effectiveness of this inter- vention, we determined the impact of ABCDE bundle adherence on inpatient and 1-year mortality, quality-adjusted life-years, length of stay, and costs of care. Design: We conducted a 2-year, prospective, cost-effectiveness study in 12 adult ICUs in six hospitals belonging to a large, inte- grated healthcare delivery system. Setting: Hospitals included a large, urban tertiary referral center and five community hospitals. ICUs included medical/surgical, trauma, neurologic, and cardiac care units. Patients: The study included 2,953 patients, 18 years old or older, with an ICU stay greater than 24 hours, who were on a ventilator for more than 24 hours and less than 14 days. Intervention: ABCDE bundle. Measurements and Main Results: We used propensity score- adjusted regression models to determine the impact of high bundle adherence on inpatient mortality, discharge status, length of stay, and costs. A Markov model was used to estimate the potential effect of improved bundle adherence on health- care costs and quality-adjusted life-years in the year follow- ing ICU admission. We found that patients with high ABCDE bundle adherence (≥ 60%) had significantly decreased odds of inpatient mortality (odds ratio 0.28) and significantly higher costs ($3,920) of inpatient care. The incremental cost-effec- tiveness ratio of high bundle adherence was $15,077 (95% CI, $13,675–$16,479) per life saved and $1,057 per life-year saved. High bundle adherence was associated with a 0.12 in- crease in quality-adjusted life-years, a $4,949 increase in 1-year care costs, and an incremental cost-effectiveness ratio of $42,120 per quality-adjusted life-year. Conclusions: The ABCDE bundle appears to be a cost-effective means to reduce in-hospital and 1-year mortality for patients with an ICU stay. (Crit Care Med 2020; 48:1752–1759) Key Words: cost-effectiveness; critical care; delirium T he provision of critical care is associated with high rates of morbidity and mortality and is a major source of healthcare expenditures in the United States (1). Stud- ies have shown that 20–80% of patients in the ICU develop delirium as a complication of care (2). ICU-acquired delirium is independently associated with increased cognitive and phys- ical impairment, mortality, hospital length of stay (LOS), and healthcare costs (3–10). In a recent study, Vasilevskis et al (11) found that the additional costs of care attributable to ICU de- lirium ranged from $11,132 to $23,497 per patient, and a pre- vious study reported that delirium costs $152 billion dollars annually in the United States (12). Cost-effective, scalable interventions that ameliorate ICU- acquired delirium and facilitate ventilator liberation are important for improving delivery of care and outcomes in crit- ically ill patients. The Awakening and Breathing Coordination, Delirium monitoring/management, and Early exercise/mo- bility (ABCDE) bundle (Supplemental Table 1, Supplemental Digital Content 1, http://links.lww.com/CCM/F811) is an in- terdisciplinary, multicomponent patient safety intervention designed to reduce prevalence of delirium in ICUs by improv- ing collaboration among clinical team members, standardizing care processes, and breaking the cycle of oversedation and pro- longed ventilation (13–18). Studies examining the effectiveness of the ABCDE bundle have shown significant reductions in de- lirium prevalence, ventilator days, coma days, readmission, and in-hospital mortality, and a significant increase in the number of patients who were mobilized out of bed during their ICU stay and discharged home, but few have examined its cost ef- fectiveness (2, 19–23). The objective of this study was to deter – mine the impact of ABCDE processes on inpatient mortality, LOS, discharge status, and direct costs of care (from a payer perspective), with mortality and cost outcomes serving as a basis to evaluate the cost-effectiveness of bundle adherence. DOI: 10.1097/CCM.0000000000004609 *See also p. 1897. All authors: Center for Clinical Effectiveness, Baylor Scott & White Health, Dallas, TX. Copyright © 2020 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved. Evaluating the Cost-Effectiveness of the ABCDE Bundle: Impact of Bundle Adherence on Inpatient and 1-Year Mortality and Costs of Care* Ashley W. Collinsworth, ScD, MPH; Elisa L. Priest, DrPH; Andrew L. Masica, MD, MSCI Copyright © 2020 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.Clinical Investigations Critical Care Medicine www.ccmjournal.org 1753 MATERIALS AND METHODS Overview This cost-effective analysis was a component of a larger ABCDE bundle implementation study that began in July 2012 in 12 ICUs of six Baylor Scott & White Health (BSWH) hospitals in- cluding a large, urban tertiary referral center and five com- munity hospitals. ICUs included medical/surgical, trauma, neurologic, and cardiac care units. The ABCDE bundle incor – porates several individual evidence-based critical care processes. Since the prospective phase of the study was performed, the bundle has been modified slightly to include additional com- ponents (pain assessment/management and family engage- ment), and the ICU liberation approach endorsed by the Society of Critical Care Medicine is now the “ABCDEF bundle” (24). The number of care processes a patient is eligible for on a given day depends on whether or not the patient is ventilated and passes the appropriate screening criteria. Different strategies were em- ployed to improve bundle adherence in study ICUs during the first year of the study including modification of the electronic health record (EHR) to facilitate uptake and documentation of bundle elements, staff training, clinical champions, and monthly perfor – mance reports (25). Given the EHR modifications went live in July 2013, altered the documentation of bundle adherence, and likely improved the reliability and validity of the adherence data, we lim- ited this cost-effectiveness analysis to the observations obtained in the 2 years following the EHR modifications. This study was reviewed and approved by the BSWH Institutional Review Board. Patients The 2,953 patients admitted to study ICUs from July 2013 to June 2015 who were greater than or equal to 18 years old, had an ICU admission greater than 24 hours, and were on a ven- tilator for greater than 24 hours and less than 14 days were included. Patients were excluded if they were on comfort care; were awaiting a transfer order to a non-ICU bed; had a primary diagnosis of brain tumor, mental disorder, stroke, intracranial injury, or poisoning; or had a hospital stay greater than 30 days. Study Design We used a prospective, quasi-experimental design to examine differences in bundle adherence on in-hospital mortality, LOS, and cost outcomes. We then conducted an exploratory cost-ef- fectiveness analysis using a Markov model to estimate differ – ences in 1-year costs and quality-adjusted life-years (QALYs) for patients with low and high levels of bundle adherence from a payer perspective. Outcome Measures We examined differences in in-hospital mortality, LOS, dis- charge status, and direct costs of hospital care among patients with varying levels of bundle adherence. Bundle adherence was calculated as the total number of care processes a patient re- ceived divided by the total number of care processes the patient was eligible for during the ICU stay. Given that the bundle con- sisted of multiple daily care processes, few patients had 100% adherence. Recognizing the potential of partial adherence to improve outcomes, we examined differences between patients with high and low bundle adherence, with high adherence de- fined as receiving greater than or equal to 60% of bundle ele- ments based on the mean level of adherence obtained in sites following ABCDE bundle implementation efforts, rather than using an all-or-none adherence measure (26). We also esti- mated differences in costs and QALYs for patients with low and high bundle adherence in the year following ICU admission. Data Sources Process measures, demographics, and outcomes data were col- lected from the EHR and administrative databases. The cost of inpatient care was calculated as the direct care cost for each patient and was obtained from the Trendstar clinical costing system. These costs included the costs of any additional patient services, with the exception of overhead or physician fees, asso- ciated with bundle application. The cost of bundle implemen- tation was approximately $165,000 and included salary support (1.65 full-time equivalents) for the project lead, project man- ager, clinical champions, information technology personnel for EHR modifications, and data analysts plus the cost of trainings and visual aids. These sunk costs were excluded from the cost calculation. Costs were adjusted to 2013 dollars using the med- ical component of the consumer price index (27). Postacute care costs were estimated from 2014 Medicare average payments for patients based on discharge status (28). One-year mortality rates and QALYs based on discharge status were obtained from a 1-year prospective economic evaluation of patients who received prolonged ventilation in an academic medical center ICU (29, 30). Statistical Analysis We conducted a univariate analysis to examine unadjusted differences in patient characteristics and outcomes. We com- pared differences in continuous variables and outcomes that did not violate normality assumptions with independent t tests and differences in categorical variables and outcomes with chi- square and Fisher exact tests. Because patients with greater severity of illness and risk of mortality were more likely to have low levels of bundle ad- herence, propensity score adjustment was used to reduce the impact of selection bias on the association between bundle ad- herence and the outcomes of interest. The propensity score, the conditional probability of a patient having high bundle adher – ence, was determined from a multivariable logistic regression model based on findings from the literature (Supplemental Table 2, Supplemental Digital Content 2, http://links.lww.com/ CCM/F812). Propensity score-adjusted effects of bundle ad- herence on inpatient mortality and discharge status were mod- eled using logistic regression. A generalized linear model with a log link function and a gamma distribution was used to model direct costs due the highly skewed nature of the data (31). All statistical analyses were conducted using SAS 9.4 (SAS Institute, Cary, NC). Statistical significance was indicated at the α less than 0.05 level. Copyright © 2020 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved. Copyright © 2020 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved. Collinsworth et al 175 4 www.ccmjournal.org December 2020 • Volume 48 • Number 12 Cost-Effectiveness Analysis Potential patient life-years saved were calculated by estimating the number of life-years lost for each patient who died. Life expectancy was projected based on the age and sex of the pa- tient using the Social Security Administration’s actuarial life tables for 2010 (32), discounted based on the 5-year survival for patients discharged from ICUs compared with the general population (33). We calculated life-years saved as the differ – ence in projected life expectancy and the age of the patient at the time of death. We used recycled predictions to estimate the effect of high versus low adherence on outcomes. Outcomes were predicted from the modeled equations based on every patient having high adherence and every patient having low adherence. The difference between these two predictions constituted the pre- dicted mean differences in outcomes between groups. We gen- erated 1,000 bootstraps of this process to estimate the mean differences in outcomes and ses of these statistics. Bootstrap estimates obtained were used as inputs in the Markov model (Fig. 1) created with TreeAge Pro (TreeAge Software, LLC, Williamstown, MA) along with the 1-year mor – tality risks, QALYs, and costs of care obtained from the litera- ture (Supplemental Table 3, Supplemental Digital Content 3, http://links.lww.com/CCM/F813). Life expectancy estimates for the patients who died in the year following discharge were based on LOS averages for each discharge location. We assumed a life expectancy of 30 days for patients who died after being discharged home or to home health. We calculated the 1-year incremental cost effectiveness of high bundle adherence as the ratio of incremental healthcare costs in the year following ICU admission to the incremental effects (QALYs). RESULTS A total of 2,953 eligible patients received care in the study ICUs from July 2013 to June 2015. After excluding patients with missing data, we found that 1,710 (57.9%) had high (≥ 60%) bundle adherence. Patients in the low adherence group had significantly higher all patient refined diagnosis re- lated groups Severity, Risk of Mortality, and Acute Physiology and Chronic Health Evaluation II scores indicating greater ill- ness severity (Table 1). Among the 684 patients who died in the low (< 60%) bundle adherence group, 431 (63.0%) were eligible for 10 or fewer bundle elements. Of the 318 patients who died in the high adherence group, only 57 (17.9%) were eligible for 10 or fewer bundle elements. After risk-adjustment using propensity scores, patients with bundle adherence greater than or equal to 60% had decreased odds of mortality (0.28) (Table 2). Patients with higher levels of bundle adherence had significantly increased odds of being discharged home, to home health, inpatient rehabilitation, and to a skilled nursing facility. Hospital LOS and direct costs were significantly higher in patients with bundle adherence greater than or equal to 60%, after risk adjustment. Rates of risk-adjusted compliance varied across the 12 study ICUs, but patients in cardiac ICUs were significantly less likely to have high bundle adherence compared with patients in medical/ surgical ICUs (Supplemental Table 1, Supplemental Digital Content 1, http://links.lww.com/CCM/F811). The mean effect of ABCDE bundle adherence greater than or equal to 60% on inpatient mortality and costs obtained from the bootstrap analysis was a reduction in mortality (48% vs 22%) and a $4,949 increase in direct inpatient costs (Supplemental Table 3, Supplemental Digital Content 3, http:// links.lww.com/CCM/F813). Potential life-years saved were estimated at 14.3 years per patient. Based on the inpatient mortality rates observed in the included ICUs, the incremental cost-effectiveness ratio (ICER) was calculated as $15,077 per life saved and $1,057 per life-year saved (Table 3). The 95% CI per life saved calculated by applying Fieller’s method from the bootstrap estimates was $13,675–$16,479. In the exploratory cost-effectiveness analysis using a Markov model and QALY and cost inputs from the literature to estimate potential differ - ences in outcomes and costs at 1 year for the study population based on discharge status, we found high bundle adherence (≥ 60%) was associated with a 0.12 increase in QALYs and a $4,949 increase in costs (Table 3). Based on these differences, the ICER was calculated as $42,120 per QALY. One-way sensi- tivity analysis indicated that the ICERs were most sensitive to the probability of being discharged home and the cost of hos- pitalization (Fig. 2). DISCUSSION The ABCDE bundle has been identified as a patient safety in- tervention for critically ill patients that mitigates ICU delirium and is associated with reductions in mortality as well as other deleterious outcomes (20, 34). We found that higher levels (> 60%) of bundle adherence were associated with consider – ably lower risk-adjusted odds of mortality (odds ratio = 0.28). The results of our survival and cost analysis indicate that use of the ABCDE bundle is a cost-effective strategy for reducing mortality in ICU patients. The mortality reduction we observed in patients with higher bundle compliance is similar to findings in studies by Barnes-Daly et al (20) (odds of hospital survival increased by 7% for every 10% increase in total bundle compliance) and Pun et al (34) (hazard ratio, 0.32; 0.17–0.62 for mortality within 7 d with complete bundle compliance). Patients in our study with high bundle adherence had an increased likelihood of being discharged home or to other care facilities. Direct in- patient costs were higher for patients with higher adherence. We did not directly examine the sources of incremental cost difference according to bundle adherence level, but a portion of that increased cost likely stems from bed charges related to the higher LOS in the greater than 60% adherence group. Furthermore, patients with higher bundle adherence had sig- nificantly better likelihood of surviving to discharge, and that may have changed their inpatient spend trajectory (additional testing, monitoring, or other therapeutic interventions which would not have been indicated in the low adherence group). Based on the differences in inpatient mortality and costs, the ICER for high adherence to the ABCDE bundle was $15,077 per life saved and $1,057 per life-year saved. The estimated ICER for Copyright © 2020 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.Clinical Investigations Critical Care Medicine www.ccmjournal.org 1755 high bundle adherence in the year following hospital admission was $38,687 per QALY. These estimates are below the threshold of $50,000 per life-year or QALY frequently used as to assess the cost-effectiveness of health interventions in the United States (35). Not surprisingly, the ICERs were most sensitive to the probability of being discharged home and the cost of hospital- ization. The costs of being discharged home or to home health as opposed to a nursing facility are much lower from a health insurer’s perspective, and we observed a wide range in the per – centage of patients being discharged home across other ABCDE bundle studies. Although being discharged home may be linked to additional societal costs, we did not have the data needed to examine such costs. Hospital costs in patients with high adher – ence were approximately $4,000 greater than those with low ad- herence and served as the main source of differences in costs for patients discharged home. We used bootstrap estimates from our model to determine the range for hospital costs, as these costs were not available from previous ABCDE bundle studies. Few studies have examined the cost-effectiveness of the ABCDE bundle. Awissi et al (36) examined the cost-effectiveness Figure 1. Markov tree. Copyright © 2020 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved. Copyright © 2020 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved. Collinsworth et al 175 6 www.ccmjournal.org December 2020 • Volume 48 • Number 12 TABLE 1. Characteristics of Patients Admitted to Study ICUs Characteristic Bundle Adherence < 60%, n = 1,243 ≥ 60%, n = 1,710 p Age, mean ( sd) 61.1 (15.1)61.7 (15.6)0.3652 Gender (male), n (%) 696 (56.0) 971 (56.8)0.6691 Race, n (%) White 825 (66.4)1,168 (68.3) 0.2415 Black 364 (29.3)469 (27.4) Asian 41 (3.3)45 (2.6) Other 13 (1.1)19 (1.6) Ethnicity, n (%) Hispanic 169 (13.6)194 (11.4)0.0693 Insurance, n (%) Private 163 (13.1)252 (14.7)0.4679 Medicare 666 (53.6)909 (53.2) Medicaid 71 (5.7)73 (4.3) Other federal 114 (9.2)156 (9.1) Self-pay 127 (10.2)182 (10.6) Other 102 (8.2)138 (8.1) Risk factors Charlson Comorbidity Index, mean ( sd) 5.04 (2.76)4.87 (2.83)0.1108 APR-dRG severity, n (%) 1 0 (0.0)1 (0.1)< 0.0001 a 2 4 (0.3)20 (1.2) 3 93 (7.5)233 (13.6) 4 1,145 (92.2)1,454 (85.1) APR-DRG mortality risk, n (%) 1 4 (0.3)7 (0.4)< 0.0001 a 2 9 (0.7)47 (3.0) 3 158 (12.7)450 (26.4) 4 1,071 (86.2)1,204 (70.5) Acute Physiology and Chronic Health Evaluation II score, mean ( sd) 20.6 (7.0) 18.4 (6.5)< 0.001 a surgical, n (%) 162 (13.0) 280 (16.4)0.0108 a Dementia, n (%) 76 (6.1) 136 (8.0) 0.0514 Alcohol, n (%) 28 (2.3) 35 (2.1)0.7025 Current smoker, n (%) 244 (19.6) 342 (20.0)0.8035 Inpatient mortality, n (%) 684 (54.7) 318 (18.4)< 0.001 a Discharge status, n (%) Home 206 (16.5)637 (37.3)< 0.001 a Home health 40 (3.2)117 (6.8) Hospice 117 (9.4)133 (7.8) Long-term care facility 54 (4.3)124 (7.2) Inpatient rehabilitation facility 57 (4.6)149 (8.7) Skilled nursing facility 86 (6.9)232 (13.6) Length of stay (d), mean ( sd) 9.9 (7.0)12.3 (6.8)< 0.001 a Cost difference ($), n (%) 25,685 (26,370) 31,170 (33,109)< 0.001 a APR-DRG = all patient refined diagnosis related groups.a p < 0.05. Copyright © 2020 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.Clinical Investigations Critical Care Medicine www.ccmjournal.org 1757 of a multifaceted care processes for management of seda- tion, analgesia, and delirium and found that mean hospital costs were $933 less in the group of ICU patients treated with a sedation, analgesia, and delirium management protocol (p = 0.022), primarily due to an average 1-day reduction in LOS. Although we found greater adherence to the ABCDE bundle to be associated with an increase rather than a decrease in LOS and inpatient costs, we have found evidence that use of bundle had a statistically significant impact on decreasing in-hospital mortality. This study has several limitations. Because this was a quasi-experimental study, differences in patient characteris- tics may have influenced bundle adherence rates, potentially overestimating the impact of improved bundle adherence on outcomes. This bias may have been due to improper appli- cation of bundle inclusion criteria, poor documentation, or differences in the provision of care for patients who were se- verely ill and had a high risk of mortality. We attempted to control for this selection bias and reduce potential endoge- neity by using a propensity score risk-adjustment approach to estimate the conditional probability of a patient having high bundle adherence. However, risk-adjustment can only account for observed confounders and does not ensure a bal- anced distribution of covariates between groups. In addition, all patients in this study were critically ill, and it is difficult to differentiate levels of illness severity within this population with existing measures. Overall bundle adherence observed during the study remained relatively low, as only 58% of patients received greater than 60% of bundle elements, and among patients who died, bundle process eligibility differed greatly in the low and high adherence groups (63.0% of patients in the low adher - ence group were eligible for 10 or fewer processes compared with 17.9% in the high adherence group). This may indicate that ABCDE bundle elements were not applied to patients who died early in their hospital stay and would not have accrued benefit from the care processes, as well as those perceived as being too acutely ill for the bundle to modify mortality risk. Removing the ICUs with the highest and lowest levels of compliance, controlling for cardiac ICUs, and controlling for bundle process eligibility at less than five and less than 10 pro- cesses, did not significantly impact the observed odds of inpa- tient mortality at the greater than 60% adherence threshold. Given that discernment of bundle process eligibility was re- liant on extractable structured documentation in the EHR, it is possible that there are unmeasured confounders pertaining to severity of illness in the low adherence group. Accordingly, the degree of mortality reduction attributable to bundle use in our analysis is likely overestimated. In spite of this limitation, our results directionally align with other recent studies showing a risk-adjusted mortality benefit associated with ABCDE bundle adherence. TABLE 2. The Unadjusted and Adjusted Effect of Bundle Adherence on Inpatient Outcomes Bundle Adherence Threshold 60% Unadjusted (95% CI) Adjusted (95% CI) Inpatient mortality (OR) 0.19 (0.16–0.22) a 0.28 (0.24–0.34) a Discharge status (OR) Home 2.99 (2.50–3.57) a 2.46 (2.02–2.89) a Home health 2.21 (1.53–3.19) a 1.76 (1.18–2.63) a Hospice 0.81 (0.63–1.05) 0.85 (0.64–1.14) Long-term care facility 1.72 (1.24–2.39) a 1.35 (0.94–1.94) Inpatient rehabilitation facility 1.99 (1.45–2.72) a 1.83 (1.30–2.57) a Skilled nursing facility 2.11 (1.63–2.74) a 1.61 (1.21–2.13) a Length of stay (d) 0.64 (0.51–0.76) a 0.57 (0.45–0.69) a Cost difference ($) 5,485 (2,689–8,283) a 4,067 (989–7,144) OR = odds ratio.a p < 0.05. TABLE 3. Cost-Effectiveness of High Versus Low Bundle Adherence in Terms of Inpatient Costs and Survival Inpatient Costs and Survival Cost Per Patient Incremental Cost Inpatient Survival Rate Incremental Effectiveness Cost/ Effectiveness Incremental Cost- Effectiveness Ratio Low bundle adherence (< 60%) $28,366 0.52 $54,550 High bundle adherence (≥ 60%) $32,256 $3,9200.78 0.26$41,353 $15,077 1-yr care costs and QALYS QALYs Low bundle adherence (< 60%) $34,181 0.2237 $152,799 High bundle adherence (≥ 60%) $39,130 $4,9490.3412 0.1175$115,088 $42,120 QALYS = quality-adjusted life-years. Copyright © 2020 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved. Copyright © 2020 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved. Collinsworth et al 175 8 www.ccmjournal.org December 2020 • Volume 48 • Number 12 As we did not have data for patients beyond their inpatient stay, we chose to model the impact of ABCDE bundle adher - ence on 1-year outcomes based on 1-year mortality and QALY estimates obtained from another study. The patient popula- tion of that study was similar to our patient population, but patients were 5 years older on average. While we excluded patients who were on the ventilator for greater than 14 days, the other study included 114 patients (14%) who were ventilated for greater than or equal to 21 days. Thus, the mortality esti- mates obtained from the study likely overestimated the 1-year mortality risk and underestimated QALYs. In addition, more rigorous research is needed to quantify health-related quality of life among ICU survivors (37). Our basic Markov model did not account for readmissions and transitions other than from hospital to home/discharge facility and from discharge facility to home or death. We recognize that there are inherent limita- tions in the Markov model, but have included it as an explor - atory analysis and as a starting point for future research given the current lack of cost-effectiveness studies pertaining to the ABCDE bundle. CONCLUSIONS Based on findings from our study and exploratory analysis, the ABCDE bundle appears to be a cost-effective means to improve outcomes for patients with ICU stays. There is building evi- dence that consistent use of the ABCDE bundle can favorably impact a range of clinical measures, including a reduction in the risk of mortality. Further research is needed to obtain bet- ter estimates of the effects of the ABCDE bundle on total costs of care over extended time periods, including an assessment of societal costs, for patients with an index admission to the ICU. Current address for Dr. Collin- sworth: 3M, Value Based Solutions Group, Medical Solutions Division, Dallas, TX; Dr. Priest: Baylor Scott & White Health Research Institute, Dallas, TX; and Dr. Masica: Texas Health Resources, Arlington, TX. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (http://journals. lww.com/ccmjournal). Supported, in part, by the Agency for Healthcare Research and Quality (R18HS021459) and operational funds from the Baylor Scott & White Health Center for Clinical Effective- ness. Drs. Collinsworth’s, Masica’s, and Priest’s institution received fund- ing from the Agency for Healthcare Research and Quality for article re- search. 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Rev Bras Ter Intensiva 2018; 30:496–507 Directions: please follow explicitly *** primarily this assignment is filling in the tables- attached all articles to use **** Use the attached "Literature Evaluation Table to complete this assignme ABCDE and ABCDEF care bundles: A systematic review protocol of the implementation process in intensive care units Fabio da Silva Moraes, MD a,∗ , Lívia Luize Marengo, MD a, Marcus Tolentino Silva, PhD a,b , Cristiane de Cássia Bergamaschi, PhD a, Luciane Cruz Lopes, PhD a, Mariana Del Grossi Moura, MD a, Fernando de Sá Del Fiol, PhD a, Silvio Barberato-Filho, PhD a,∗ Abstract Background: The awakening and breathing coordination of daily sedation and ventilator removal trials, delirium monitoring and management, and early mobility and exercise (ABCDE) and assessment, prevent and manage pain, both spontaneous awakening and spontaneous breathing trials, choice of analgesia and sedation, assess, prevent and manage delirium, early mobility and exercise, family engagement (ABCDEF) bundles are part of the science of the liberation of the intensive care unit (ICU). There are not enough studies that have evaluated the effectiveness and safety of the implementation of these bundles. This study will analyze the implementation process, estimate their effectiveness and safety, and identify barriers, facilitators and attitudes that have inﬂuenced the implementation process. Methods: Qualitative and quantitative studies will be eligible for our systematic review with adult patients who have been exposed to the implementation of the ABCDE or ABCDEF bundles compared to the usual care in the ICU. In order to search the implementation interventions of the bundles, we will search electronically: MEDLINE (PubMed); Excerpta Medica Database (Ovid); Cumulative Index to Nursing and Allied Health Literature (EBSCO); The Cochrane Library (Wiley); Web of Science; Virtual Health Library; and OpenGrey. We will not impose any language restrictions or publication status. Outcomes of interest include ICU and hospital length of stay; mechanical ventilation time; incidence and prevalence of delirium or coma; level of agitation and sedation; early mobilization; mortality in ICU and hospital; change in perception, attitude or behavior of the stakeholders; and change in knowledge of health professionals. The team of reviewers will independently screen search results, extract data from eligible studies, and assess risk of bias. Disagreements between the reviewers will be solved through consensus or arbitration by a third-party investigator. To assess the quality and risk of bias in randomized and quasi-randomized trials we will use the domain-based evaluation recommended by The Cochrane Handbook. Studies with other methodological designs will be evaluated using the Critical Appraisal Tools developed by The Joanna Briggs Institute. Other instruments may be used, if necessary. Results: The evidence derived from this study will increase the knowledge of effectiveness and safety of the implementation process of ABCDE and ABCDEF bundles. Conclusion: The results could guide patients and healthcare practitioners by helping to facilitate evidence-based shared care decision making. Protocol registration: PROSPERO CRD42019121307. Abbreviations: ABCDE=awakening and breathing coordination of daily sedation and ventilator removal trials, delirium monitoring and management, and early mobility and exercise, ABCDEF=assessment, prevent and manage pain, both spontaneous awakening and spontaneous breathing trials, choice of analgesia and sedation, assess, prevent and manage delirium, early mobility and exercise, family engagement, ICU=intensive care unit, PRISMA=preferred reporting items for systematic reviews and meta- analyses. Keywords: care bundles, effectiveness, implementation, intensive care units 1. Introduction Over time, a number of changes have occurred in the culture of intensive care and in the management of critical patients, who may be on the front line or at the rear of the process care.  During the 1980s and 1990s, the focus was almost exclusively on improving the clinical frontline, for example in the diagnosis and treatment of different forms of shock or in the management of mechanical ventilation.  Currently, in addition to addressing the patient’s liberation from the intensive care unit (ICU), many researches have extended to the management of the critical care rearguard, in other words, the period after the patient’s stabilization, until the last days of hospitalization before his return home.  The study of care bundles has also expanded because it is an important tool for obtaining short-, medium- and long-term This project is funded by governmental Program Graduate Education Institutions –PROSUC/CAPES/UNISO. The authors have no conﬂicts of interest to disclose. aGraduate Program in Pharmaceutical Sciences, University of Sorocaba, Sorocaba, São Paulo, bFaculty of Medicine, Federal University of Amazonas, Manaus, Amazonas, Brazil. ∗Correspondence: Fabio da Silva Moraes, Universidade de Sorocaba (UNISO), Rodovia Raposo Tavares, Km 92.5, Sorocaba, São Paulo 18023-000, Brazil (e-mail: [email protected]). Copyright©2019 the Author(s). Published by Wolters Kluwer Health, Inc. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Medicine (2019) 98:11(e14792) Received: 12 February 2019 / Accepted: 14 February 2019 http://dx.doi.org/10.1097/MD.0000000000014792 Study Protocol Systematic Review Medicine ® OPEN 1 results, as well as improving care indicators.  A care bundle can be deﬁned as“a small set of evidence-based interventions for a deﬁned patient group or population and a care setting that, when implemented together, will bring better results than when implemented individually.”  The awakening and breathing coordination of daily sedation and ventilator removal trials, delirium monitoring and manage- ment, and early mobility and exercise (ABCDE) bundle represents awakening and breathing coordination exercises, controlling daily sedation and removing mechanical ventilation; the choice of analgesics and sedatives; the monitoring and control of delirium; and mobilization and early exercise.  The assessment, prevent and manage pain, both spontaneous awakening and spontaneous breathing trials, choice of analgesia and sedation, assess, prevent and manage delirium, early mobility and exercise, family engagement (ABCDEF) bundle include protocols of spontaneous awakening and spontaneous breathing; choose analgesics and sedatives; evaluate, prevent and manage delirium; promote early mobility and exercise; and involve the family.  Protocols and guidelines are important tools for the develop- ment of health policies, since they guide planning, delivery, evaluation, and allow the improvement of the quality of health services.  Implementation methods can vary widely, depending on the intervention complexity and the theoretical model of knowledge transfer to practice, such as the consolidated framework for implementation research and the engage, educate, execute and evaluate framework. Thus, the implementation process ranges from simple interventions such as dissemination of educational material to the most complex and multifaceted ones like tutorials and consulting sessions.  In general, in theﬁeld of ICU practices, there is some resistance to change until the proposed concept is consistent and reproducible. It is very common for practitioners to change their practice when the results are consistent across types of studies and populations treated.  Thus, the intensivist community needs to be liberated from the key elements of critical patient care in the past, still belonging to today’s practices even after 50 years of evidence supporting the need to change.  In this context, the present study aims to analyze the implementation process of ABCDE or ABCDEF care bundles in ICU, estimate their effectiveness and safety, and identify barriers, facilitators and attitudes that have inﬂuenced. 2. Methods 2.1. Standards This review protocol was prepared using the preferred reporting items for systematic reviews and meta-analyses protocol (PRISMA-P) guidelines.  The systematic review will be performed and reported according to the PRISMA statement.  2.2. Protocol and registration We registered our review protocol with the international prospective register of systematic reviews (PROSPERO) CRD42019121307 (https://www.crd.york.ac.uk/PROSPERO/ display_record.php?RecordID=121307). Ethical approval is not required because this is a literature-based study. 2.3. Eligibility criteria 2.3.1. Inclusion criteria.Adults patients (>18 years old) admitted to the ICU and exposed to the implementation of theABCDE or ABCDEF care bundles compared to usual care. The type of study included will be qualitative and quantitative studies describing implementation process. 2.3.2. Exclusion criteria.Congress abstracts; protocols, sec- ondary and tertiary studies; other types of care bundles; evaluation of clinical tools; and institutional guidelines. 2.4. Measure outcomes We will include studies that report any of the following outcomes. 2.4.1. Primary outcomes. Length of stay in the ICU; Mechanical ventilation time; Incidence and prevalence of delirium or coma; Level of agitation and sedation; and Early mobilization. 2.4.2. Secondary outcomes. Mortality in the ICU and hospital; Hospital length of stay; Change in perception, attitude or behavior of the stakeholders; and Change in knowledge of health professionals. 2.5. Search methods for primary studies We will not impose any language restrictions or publication status. 2.5.1. Electronic searches.We will search the following electronic databases: MEDLINE (PubMed); Excerpta Medica Database (Ovid); Cumulative Index to Nursing and Allied Health Literature (EBSCO); The Cochrane Library (Wiley); Web of Science; OpenGrey (http://www.opengrey.eu/). 2.5.2. Searching other resources.Other studies described in full text and grey literature may be included if they contain sufﬁcient and relevant data. When needed, authors will be contacted for additional information. Reference lists of poten- tially eligible studies and systematic reviews will be analyzed to identify other relevant studies. 2.6. Search strategy The search strategy will be designed with the assistance of a trained librarian. We will use the following keywords ABCDE OR ABCDEF OR“PAD guideline”OR“ICU liberation”OR“PAD care bundle,”and methodologicalﬁlters will be applied to limit retrieval to primary studies. The search strategy will be adapted for each database and details will become available upon completion of the review. 2.7. Eligibility determination Reviewers, working in pairs, will independently monitor potentially relevant citations and abstracts applying the selection criteria. We will obtain full texts of any article that is considered eligible. The same reviewers will independently evaluate the eligibility of each full-text article. In case of duplicate publication, we will use the article with the most complete data. Disagree- ments between the reviewers will be resolved through consensus or arbitration by a third-party investigator. Moraes et al. Medicine (2019) 98:11Medicine 2 2.8. Data extraction The same reviewers, working in pairs, will independently extract the data and will record information regarding patients, methods, interventions, outcomes, and missing outcome data using standardized and pretested data extraction forms with instruc- tions. Before starting data abstraction, we will conduct calibration exercises to ensure consistency between reviewers. We will contact study authors to resolve any uncertainties. Disagreements between the reviewers will be resolved through consensus or arbitration by a third-party investigator. 2.9. Risk of bias in individual studies We will critically appraise included papers, in duplicate and independently, for risk of bias. For randomized and quasi- randomized trials, we will use the domain-based evaluation recommended by The Cochrane Handbook. [12,13] These domains include the following: random sequence generation, allocation concealment, blinding, incomplete outcome data, selective reporting, and other biases. After completing the quality assessment of the 6 domains, we will classify the selected studies into the following categories: low risk of bias (low risk in all domains); high risk of bias (high risk in 1 or more domains); and unclear risk of bias (unclear risk in 1 or more domains). Studies with other methodological designs will be evaluated using the critical appraisal tools developed by The Joanna Briggs Institute.  If necessary, other instruments may be used. Disagreements between the reviewers will be resolved through consensus or arbitration by a third-party investigator. 2.10. Data synthesis The implementation strategies refer to the formal steps taken by different institutions to implement ABCDE and ABCDEF care bundles. These strategies will be identiﬁed in included studies and classiﬁed according to the taxonomy developed by the Cochrane effective practice and organization of care group.  This taxonomy provides criteria for different interventions using 4 main domains: organization,ﬁnance, governance, and imple- mentation strategies. Information related to barriers and facilitators of the implementation process will be identiﬁed and presented in table form. Whenever possible, for meta-analysis we will quantitatively pool the results at the patient level for the included studies and adopt statistical techniques to manage the difference in study quality and design. Meta-analyses will be conducted using STATA software (version 14.2). Statistical signiﬁcance was deﬁned asP<.05. The heterogeneity between the studies will be tested using the CochranQtest of heterogeneity and Higgins and Thompson I 2. The degree of heterogeneity was deﬁned as anI 2value: low (25% to 49%), moderate (50% to 74%) and high (>75%). Provided an appropriate number of studies are eligible for inclusion, we will assess for publication bias using an Egger plot or other statistical techniques. Summary tables will be presented and the narrative summary provided if the meta-analysis is not appropriate due to population heterogeneity, intervention, comparator, outcome or method. If appropriate, subgroup analysis will explore differences between groups, provided the data presented on literature allow the examination of such, type of care bundle (ABCDE or ABCDEF), number or type of bundle elements implemented, outcomes, for example.2.11. Ethics and dissemination Ethical approval is not needed for a systematic review that does not involve privacy concerns due to collection or presentation of data from individual patients. The systematic review will be published in a peer-reviewed journal and presented at confer- ences. 3. Discussion Our review will evaluate evidence of the effectiveness and safety of the implementation process of ABCDE and ABCDEF bundles for adult patients admitted to the ICU also provide estimates of the implementation process and associated risks and evaluate the quality of evidence from qualitative and quantitative studies. The results of our systematic review will be of interest to critical care managers and practitioners around the world. The information compiled on the implementation processes will inform patients and health professionals about their effectiveness and safety helping facilitate decision making for implementation of bundles in intensive care. This study will also identify gaps for future research. 3.1. Strengths and limitations of this study This systematic review will assess the effectiveness and safety of implementing ABCDE and ABCDEF care bundles to liberate ICU patients. The method of this review includes explicit eligibility criteria, comprehensive and extensive database research, independent and paired evaluation for study selection. We will assess the risk of bias in qualitative and quantitative studies that will be included. The quality of the primary studies to be included can be a limiting factor if there are uncontrolled studies, with low sample value and small effect size, and therefore they will be at high risk of bias. These limitations may decrease the quality of the evidence from the studyﬁndings regarding the effectiveness and safety of ICU care bundle implementation processes. Results can guide intensive care managers and practitioners about the effectiveness and safety of bundle implementation interventions helping facilitate decision making in the critical care setting. Author contributions FSM is the principal investigator and led the writing of the manuscript. SB-F and CCB are the project managers and coinvestigators and contributed to the writing and revision of the manuscript. MTS, LCL, LLM, MDG, and FDF are coinvestigators and contributed to the writing and revision of the manuscript. All authors read and approved theﬁnal manuscript. Conceptualization:Silvio Barberato-Filho, Fabio da Silva Moraes, Cristiane de Cássia Bergamaschi. Data curation:Silvio Barberato-Filho. Formal analysis:Fabio da Silva Moraes, Lívia Luize Marengo, Cristiane de Cássia Bergamaschi, Luciane Cruz Lopes, Mariana Del Grossi Moura, Fernando de Sá Del Fiol, Silvio Barberato-Filho. Funding acquisition:Fernando de Sá Del Fiol, Silvio Barberato-Filho. Investigation:Fabio da Silva Moraes, Lívia Luize Marengo, Marcus Tolentino Silva, Cristiane de Cássia Bergamaschi, Moraes et al. Medicine (2019) 98:11www.md-journal.com 3 Luciane Cruz Lopes, Mariana Del Grossi Moura, Fernando de Sá Del Fiol, Silvio Barberato-Filho. Methodology:Fabio da Silva Moraes, Lívia Luize Marengo, Marcus Tolentino Silva, Cristiane de Cássia Bergamaschi, Luciane Cruz Lopes, Mariana Del Grossi Moura, Silvio Barberato-Filho, Cristiane de Cássia Bergamaschi, Luciane Cruz Lopes, Marcus Tolentino Silva. Project administration:Fabio da Silva Moraes, Cristiane de Cássia Bergamaschi, Silvio Barberato-Filho. Resources:Silvio Barberato-Filho. Silvio Barberato-Filho – https://orcid.org/0000-0001-5179-3125 Supervision:Fabio da Silva Moraes, Silvio Barberato-Filho. Writing–original draft:Fabio da Silva Moraes, Lívia Luize Marengo, Marcus Tolentino Silva, Cristiane de Cássia Bergamaschi, Luciane Cruz Lopes, Fernando de Sá Del Fiol, Silvio Barberato-Filho, Cristiane de Cássia Bergamaschi, Marcus Tolentino Silva. Writing–review and editing:Fabio da Silva Moraes, Lívia Luize Marengo, Marcus Tolentino Silva, Cristiane de Cássia Bergamaschi, Luciane Cruz Lopes, Mariana Del Grossi Moura, Fernando de Sá Del Fiol, Silvio Barberato-Filho, Lívia Luize Marengo, Mariana Del Grossi Moura, Fernando de Sá Del Fiol, Luciane Cruz Lopes, Marcus Tolentino Silva. Fabio da Silva Moraes orcid: 0000-0002-1432-196X. References  Ely EW. The ABCDEF bundle: science and philosophy of how ICU liberation serves patients and families. Crit Care Med 2017;45:321–30.  Cook D, Swinton M, Toledo F, et al. Personalizing death in the intensive care unit: the 3 wishes project: a mixed-methods study. Ann Intern Med 2015;163:271–9.  Winterbottom F. Critical collaboration: the science of liberation in the intensive care unit. Ochsner J 2017;17:60–1. Colaço AD, Nascimento ER. Nursing intervention bundle for enteral nutrition in intensive care: a collective construction. Rev Esc Enferm USP 2014;48:844–50.  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Directions: please follow explicitly *** primarily this assignment is filling in the tables- attached all articles to use **** Use the attached “Literature Evaluation Table to complete this assignme
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All Rights Reserved. Critical Care Medicine www.ccmjournal.org 419 1Department of Intensive Care Adults, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands. 2Department of Public Health, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands. 3Department of Intensive Care, Albert Schweitzer Hospital, Dordrecht, The Netherlands. 4Department of Intensive Care, Ikazia Hospital, Rotterdam, The Nether-lands. 5Department of Intensive Care, IJsselland Hospital, Rotterdam, The Neth-erlands. 6Department of Intensive Care, Franciscus Gasthuis & Vlietland, Rot-terdam, The Netherlands. 7Department of Intensive Care, Maasstad Hospital, Rotterdam, The Neth-erlands. 8Department of Pulmonology and Critical Care, New York University – Langone, New York, NY. 9Department of Pulmonology and Critical Care, Columbia University Med-ical Center – New York Presbyterian, New York, NY. 10Department of Intensive Care, Pontificia Universidad Catolica de Chile, Santiago, Chile. 11Department of Pediatric Surgery, Intensive Care Unit, Erasmus MC – Sophia Children’s Hospital University Medical Center Rotterdam, Rot- terdam, The Netherlands. Drs. van der Jagt and Ista supervised the study and contributed equally. Supplemental digital content is available for this article. Direct URL cita- tions appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (http://journals.l ww.com/ ccmjournal). Supported, in part, by grant No. 171203008 from The Netherlands Orga- nization for Health Research and Development (ZonMw). ZonMw had no role in the statistical analyses or publication decisions. This work was performed at Department of Intensive Care Adults, Erasmus MC University Medical Center Rotterdam; Department of Intensive Care, Albert Schweitzer Hospital Dordrecht; Department of Intensive Care, Ika- zia Hospital Rotterdam; Department of Intensive Care, IJsselland Hospital Rotterdam; Department of Intensive Care, Sint Franciscus Gasthuis Rot- terdam; and Department of Intensive Care, Maasstad Hospital Rotterdam, all in the Netherlands. Drs. Trogrlic’s and van der Jagt’s institutions received funding from The Netherlands Organization for Health Research and Development (ZonMw) with the grant number: 171203008 awarded to Drs. van der Jagt and Ista. Drs. Verbrugge’s and Bakker’s institutions received funding from ZonMW. The remaining authors have disclosed that they do not have any potential conflicts of interest. Address requests for reprints to: Zoran Trogrlić , RN, MSc, Department of Intensive Care Adults, Erasmus MC University Medical Center Rotterdam‘s Gravendijkwal 230, P.O. Box 2040, 3000CA Rotterdam, the Netherlands, office: Ne-405. E-mail: [email protected] Objectives: Implementation of delirium guidelines at ICUs is sub- optimal. The aim was to evaluate the impact of a tailored mul- tifaceted implementation program of ICU delirium guidelines on processes of care and clinical outcomes and draw lessons re- garding guideline implementation. Design: A prospective multicenter, pre-post, intervention study. Setting: ICUs in one university hospital and five community hospitals. Patients: Consecutive medical and surgical critically ill patients were enrolled between April 1, 2012, and February 1, 2015. Interventions: Multifaceted, three-phase (baseline, delirium screening, and guideline) implementation program of delirium guidelines in adult ICUs. Measurements and Main Results: The primary outcome was adherence changes to delirium guidelines recommendations, based on the Pain, Agitation and Delirium guidelines. Secondary outcomes were brain dysfunction (delirium or coma), length of ICU stay, and hospital mortality. A total of 3,930 patients were included. Improvements after the implementation pertained to delirium screening (from 35% to 96%; p < 0.001), use of benzodiazepines for continuous sedation (from 36% to 17%; p < 0.001), light sedation of ventilated patients (from 55% to 61%; p < 0.001), physiotherapy (from 21% to 48%; p < 0.001), and early mobilization (from 10% to 19%; p < 0.001). Brain dys- function improved: the mean delirium duration decreased from 5.6 to 3.3 days (–2.2 d; 95% CI, –3.2 to –1.3; p < 0.001), and DOI: 10.1097/CCM.0000000000003596 Improved Guideline Adherence and Reduced Brain Dysfunction After a Multicenter Multifaceted Implementation of ICU Delirium Guidelines in 3,930 Patients Zoran Trogrlić, RN, MSc 1; Mathieu van der Jagt, MD, PhD 1; Hester Lingsma, PhD 2; Diederik Gommers, MD, PhD 1; Huibert H. Ponssen, MD 3; Jeannette F. J. Schoonderbeek, MD, PhD 4; Frodo Schreiner, MD 5; Serge J. Verbrugge, MD, PhD 6; Servet Duran, MD 7; Jan Bakker, MD, PhD 1,8,9,10 ; Erwin Ista, RN, PhD 11 Copyright © 2018 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved. Copyright © 2018 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved. 420 www.ccmjournal.org March 2019 • Volume 47 • Number 3 coma days decreased from 14% to 9% (risk ratio, 0.5; 95% CI, 0.4–0.6; p < 0.001). Other clinical outcome measures, such as length of mechanical ventilation, length of ICU stay, and hospital mortality, did not change. Conclusions: This large pre-post implementation study of delir- ium-oriented measures based on the 2013 Pain, Agitation, and Delirium guidelines showed improved health professionals’ adher- ence to delirium guidelines and reduced brain dysfunction. Our findings provide empirical support for the differential efficacy of the guideline bundle elements in a real-life setting and provide les- sons for optimization of guideline implementation programs. (Crit Care Med 2019; 47 :419–427) Key Words: critical care; delirium; guideline adherence; intensive care units D elirium is a common form of vital organ dysfunc- tion in critically ill adults, associated with increased morbidity, mortality, and long-term cognitive deteri- oration (1–3). Adequate delirium management is therefore an important component of intensive care—as substantiated in the Pain, Agitation, and Delirium (PAD) guidelines (4). Suc- cessful implementation of guidelines into daily practice is chal- lenging (5) although multifaceted implementation programs have the potential to facilitate success (6). Implementation of the PAD guidelines has had beneficial effects on pain, brain dysfunction, durations of mechanical ventilation and ICU stay, early mobilization, long-term cognitive dysfunction, func- tional recovery, and mortality in the critically ill (7–9). Still, “real-life” prospective multicenter implementation studies fo- cused on these delirium-oriented guidelines in hospitals with low use of the guidelines at baseline are needed to bring clinical evidence into practice on a wider scale, given the suboptimal implementation of these guidelines worldwide (10). We therefore performed the prospective multicenter “ICU DElirium in Clinical PracTice Implementation Evaluation” study (11), designed to evaluate the effectiveness of a multifac- eted implementation program tailored to improving adherence to delirium guidelines and to study patient-related benefits. MATERIALS AND METHODS Study Design and Participants We conducted a prospective, multicenter, before-after imple- mentation study in six ICUs in the Netherlands—one uni- versity and five community hospitals (three teaching and two nonteaching hospitals) (11). The size of the units varied be- tween eight and 32 ICU beds. Consecutive ICU patients 18 years old or older were included. Exclusion criteria were a primary neurologic diagnosis, home mechanical ventilation for chronic respiratory insufficiency, and burn injuries. The intervention, an implementation program focused at the im- plementation of the delirium-oriented recommendations de- rived from Dutch ICU Delirium Guidelines (12), and the PAD guidelines of the Society of Critical Care Medicine (4) was aimed at all ICU physicians and nurses. Results of this study were reported using the Standards for Quality Improvement Reporting Excellence guidelines (13). The study protocol was reviewed by the Medical Ethical Committees of participating hospitals (MEC-2012–063). Patients’ informed consent was not necessary according to Dutch legislation (14). The study was registered at ClinicalTrials.gov (Identifier: Nct01952899 2017). Procedures, Outcomes, and Data Collection The study duration was 36 months and consisted of three measurement periods between April 2012 and February 2015 (Fig. 1). The Implementation Model of Change of Grol and Wensing (15) was used to structure the guideline implemen- tation. This model is a seven steps approach and starts with identifying the problem and defining the aim of change fol- lowed by identification of potential barriers and facilitators for implementation; development of an implementation plan based on these barriers and facilitators; and finally execution, evaluation, and sustaining of the implementation plan. Phase I. The baseline phase started with a 4-month data collection period. To avoid the Hawthorne effect (11), staff of the participating ICUs were not informed about the study during data collection, with the exception of the local inten- sivist (principal investigator) and research nurses. Next, we performed an analysis of barriers and facilitators for delirium guideline adherence by means of a survey (16) and focus group interviews with stakeholders and development of the imple- mentation program (Fig. 1). We identified more than 30 bar - riers and facilitators for guideline adherence, to which we then tailored the implementation program following the model of Grol and Wensing and change theories (6, 11, 17) (Supple- mental Digital Content 1 and 2, http://links.lww.com/CCM/ E227). Important facilitators were realizing that delirium is a major problem, that treatment is essential, and that delirium is often underdiagnosed. The most important barriers were in- sufficient knowledge for screening, no integral delirium pro- tocol with a link to screening results (16). The implementation program consisted of different implementation strategies in accordance to the Effective Practice and Organization of Care (EPOC) group classification, mainly on organizational and professionals levels (18, 19). See details in Table 1 and Figure 1. Phase II. This phase was dedicated to reliable delirium screening, for which all nurses and physicians compulsory completed an e-learning program. We formally appointed an intensivist and research nurse at each site to act as local champions during this and subsequent phases and encour - aged them to involve other ICU nurses or ICU physicians as “ambassadors”. Additional clinical lessons and bedside educa- tion were provided by the local implementation teams, which also performed delirium screening spot checks. Three of the ICUs preferred the Confusion Assessment Method for the ICU (CAM-ICU) (20); the other three preferred the Intensive Care Delirium Screening Checklist (ICDSC) (21). All implementa- tion elements are briefly explained in Table 1 and were catego- rized according to the Cochrane EPOC (18) and study phase. Trogrli et al Copyright © 2018 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.Neurologic Critical Care Critical Care Medicine www.ccmjournal.org 421 Phase III. This phase consisted of 8 months of implemen- tation followed by 4 months of data collection (Fig. 1). The nurses and physicians now completed a second e-learning pro- gram focused on the guideline. Everyone received a laminated pocket card summarizing the integrated measures based on the PAD guidelines (Supplemental Digital Content 3, a and b, http://links.lww.com/CCM/E227). Throughout the implementation phase, we regularly did bedside reliability spot checks on delirium screening, distrib- uted delirium screening adherence feedback posters, issued newsletters on study progression and practical experiences, assessed the perceived level of implementation of bundle elements, and the deployment of implementation elements as another feedback tool to the local implementation teams. Furthermore, experiences with the implementation program were shared in repeated focus group sessions. Outcomes The primary outcome was changes in adherence to guideline recommendations from before to after implementation. Sec- ondary outcomes were presence of brain dysfunction defined as days with delirium or coma, duration of mechanical venti- lation, ICU length of stay (LOS), ICU and hospital mortality. Study data were prospectively collected by research nurses at each site, using a data handling protocol (Supplemental Digital Content 4, http://links.lww.com/CCM/E227). Guideline ad- herence was measured using seven performance indicators ( Supplemental Digital Content 5, http://links.lww.com/CCM/ E227). During phase I, the presence of delirium was defined as treatment with any antipsychotic drug or documentation of a de- lirium diagnosis in the medical or nursing chart. During phases II and III, delirium was diagnosed with the CAM-ICU or ICDSC (20, 21). Coma was defined as a sedation level compatible with a Richmond Agitation-Sedation Scale (RASS) score (22) of –4 or –5 or a Ramsay Sedation Scale score (23) less than 5 or a Critically Ill Assessment score (24) less than 7. A “delirium day” was defined as at least one recorded delirium diagnosis in a 24-hour period. A coma day was defined as documented presence of coma with absence of documented delirium during a 24-hour period. Statistical Analysis Demographics are presented as numbers and percentages, medi- ans and interquartile ranges (IQRs), or means and sds where ap- propriate. differences in guideline adherence between the three phases, as expressed by crude numbers and percentages, were assessed with a chi-square test. To examine between-group dif- ferences, we used Kruskal-Wallis test for nonparametric analyses. differences in clinical outcomes between the three phases were assessed with adjusted regression models. Poisson regression was used for count data (e.g., number of delirium assessments per day), logistic regression for binary outcomes, and linear re- gression for continuous outcomes. Guideline adherence and presence of brain dysfunction were analyzed on day level, with random effect models with a random intercept for patient. du- ration of mechanical ventilation, ICU LOs, ICU and hospital mortality were analyzed on patient level with xed effect models. The adjusted models used severity of illness score Acute Phys- iology and Chronic Health Evaluation-II, hospital, age and ad- mission diagnosis (elective or acute surgery vs medical diagnosis) as covariables. differences between the periods were expressed as adjusted rate ratios, adjusted odds ratios (ORs), or betas. Miss- ing baseline data were imputed using single imputation with the Figure 1. Timeline ICU DElirium in Clinical PracTice Implementation Evaluation Study. See text for further details. Copyright © 2018 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved. 422 www.ccmjournal.org March 2019 • Volume 47 • Number 3 AregImpute function in R. Two-sided p values less than 0.05 were considered statistically significant. All analyses were performed with computer software programs R (extension packages: for - eign, lme4, and rms; R Foundation for Statistical Computing, Vienna, Austria; http://www.R-project.org/) and IBM SPSS Sta- tistics Version 23.0 (IBM Corp., Armonk, NY). RESULTS In total 4,853 patients were admitted during the three data col- lection periods. As 923 patients had to be excluded (Supple- mental Digital Content 6, http://links.lww.com/CCM/E227), data of 3,930 patients, with a total of 18,288 patient-days, were analyzed. Demographics are presented in Table 2. The e-learn- ing programs in phases II and III were completed by 90% of physicians (73/81) and 91% of nurses (374/409). Primary Outcomes—Guideline Adherence Figure 2 and Supplemental Digital Content 7 (http://links. lww.com/CCM/E227) show the crude performance indicator metrics presented as percentages. Delirium screening increased from 35% to 93% (p < 0.001) to 96% (p < 0.001). Contin- uous IV benzodiazepine sedation decreased from 36% to 31% TABLE 1. Description of Implementation Strategies Used, According to Effective Practice and Organization of Care classification Implementation Strategy InterventionPhase IIPhase III Audit and feedback Repeated evaluation of implementation process strategies used and level of perceived adherence to guideline recommendations. + a + Monitoring the performance of the delivery of healthcare Posters with delirium screening adherence and delirium incidence. ++ Educational materials Reader development and dissemination; interactive website e-learning (with instructional videos, e.g., on the use of screening instruments Confusion Assessment Method for ICU/ Intensive Care Delirium Screening Checklist). + + Educational meetings Education of expert teams at each hospital/ICU Education sessions. + + Educational outreach visits or academic detailing Interactive workshop sessions: education about the severity and impact of delirium on patient outcomes on short and long term. The importance of why screening for delirium is important and what may work as pre- ventive measures. + + Clinical Practice Guidelines Construction of general delirium guideline protocol by several “con- sensus group”—meetings with representatives from each ICU (physi- cians, nurses). During the sessions, various local protocols (if any) from each ICU would be made visible when discussing the interpretation and translation of the guideline into a workable and widely endorsed protocol among participating centers. – b + Interprofessional education Spot checks for screening were first done by expert-team members, but later by all nurses, checking and discussing each other’s delirium assessments. + – Local consensus processes Yes, see previous point under “Clinical Practice Guidelines”. –+ Local opinion leaders Medical and nursing stakeholders were recruited and involved in the stud y and its execution. They had the task to appeal to people, encourag- ing colleagues to work according to the guidelines (e.g., during daily rounds/visits). We appointed one participating intensivist and a dedi- cated research nurse as local opinion leaders/champion. + + Patient-mediated interventions Family involvement was encouraged: Delirium information poster and info booklet placed in family room. Instructions by nurses to family members on participation in daily care and communication in case of delirium. – + Reminders Operationalization of existing PDMS for integration of delirium guideline protocol. Reminders for screening was preferentially incorporated. One of the hospitals did not have a digital PDMS system which hampered the implementation process. + + Tailored interventions Yes: based on preimplementation assessment of barriers and facilitators. ++ PDMS = Patient Data Management System. a Plus (+) and and b minus (–) signs indicate whether individual implementation strategies were used (+) or not used (–) during: the phase II or phase III (Fig. 1). Trogrli et al Copyright © 2018 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.Neurologic Critical Care Critical Care Medicine www.ccmjournal.org 423 (p < 0.001) to 17% (p < 0.001). Administration of daily inter - mittent benzodiazepines boluses had not consistently increased over the three phases. The amounts given (mean of 0.22– 0.48 mg/d of diazepam equivalent; see legend of Supplemental Digital Content 7, http://links.lww.com/CCM/E227) seemed negligible compared with usual daily dosages of continuous IV benzodiazepines. Although the daily use of midazolam, fen- tanyl, and morphine had decreased, that of propofol, dexme- detomidine, and remifentanil had increased (Supplemental Digital Content 8, http://links.lww.com/CCM/E227). Applica- tion of physical therapy (PT), early mobilization of patients, sedation assessments, and light sedation improved signifi- cantly. The medians of all available daily maximum RASS scores in mechanically ventilated patients were significantly different between the study phases (p < 0.001), indicating less deep sedation after the implementation (Supplemental Dig- ital Content 9, http://links.lww.com/CCM/E227). Supplemental Digital Content 10 (http://links.lww.com/ CCM/E227) shows the adjusted effect changes of the per - formance indicators. Implementation of delirium screening resulted in a significant improvement in adherence to delirium screening, sedation assessments, light sedation, less use of continuous IV benzodiazepine sedation, and performing PT compared with the baseline period. These ORs indicate, for example, that for a random patient on a random admission day, the odds of getting sedated with continuous IV benzodi- azepines was 0.5 (or two times smaller) after implementation of delirium screening. These improvements in adherences rel- ative to the baseline period were maintained after implemen- tation of the guideline. Early mobilization (as opposed to PT) only improved after guideline implementation but not after screening implementation. Guideline implementation resulted in additional improvements compared with the screening im- plementation phase for delirium screening, use of benzodi- azepines, performing PT, and performing early mobilization when feasible. Secondary Outcomes—Clinical Outcomes Table 3 shows crude and adjusted clinical outcomes changes per study phase. The duration of delirium decreased over three periods from 5.6 days to 2.9 days (Beta: – 2.6 d; 95% CI, –3.5 to –1.6 d; p < 0.001) and to 3.3 days after guideline implementation TABLE 2. Patient Demographics and Baseline Clinical Characteristics Characteristics Data Collection Period a Phase I: Baseline Phase II: Screening Implementation Phase III: Guideline Implementation Number of patients, n 1,337 1,399 1,194 Number of ICU days, n 6,527 6,086 5,675 Gender, n (%) Male 775 (58)789 (56) 710 (60) Female 562 (42)610 (44) 484 (40) Age (yr), median (IQR) 66 (54–75)66 (53–75) 65 (5–74) Admission status, n (%) Elective surgery 401 (30)432 (31) 339 (28) Emergency surgery 188 (14)200 (14) 167 (14) Medical 748 (56)767 (55) 688 (58) Acute Physiology and Chronic Health Evaluation -II b, median (IQR) 16 (11–22)15 (10–21) 16 (11–21) Mechanically ventilated patients, n (%) 560 (42) 541 (39) 593 (50) Hospital, n (%) 1 145 (11)155 (11) 195 (16) 2 247 (19)248 (18) 242 (20) 3 231 (17)251 (18) 249 (18) 4 158 (12)166 (12) 76 (6) 5 251 (19)271 (19) 216 (18) 6 305 (23)308 (22) 216 (18) IQR = interquartile range .a See Figure 1 for further explanation.b Acute Physiology and Chronic Health Evaluation-II range is 0–71. Copyright © 2018 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved. 424 www.ccmjournal.org March 2019 • Volume 47 • Number 3 (Beta: –2.2 d; 95% CI, –3.2 to –1.3 d; p < 0.001). Implemen- tation of delirium screening resulted in 6% more patients detected with delirium in the third study period compared with the baseline period (OR, 1.4; 95% CI, 1.2–1.7; p < 0.001). Sup- plemental Digital Content 11 (http://links.lww.com/CCM/ E227) shows the cumulative proportions of delirium- and coma(-free) days as changes in percentages for the three study periods. In the adjusted analysis (Supplemental Digital Con- tent 12, http://links.lww.com/CCM/E227), only the coma days were significantly reduced in phases II and III relative to phase I (from 14% to 12%; OR, 0.6; 95% CI, 0.4–0.8; p < 0.001, and from 14% to 9%; OR, 0.5; 95% CI, 0.4–0.6; p < 0.001). There were no significant changes for the other study outcomes. DISCUSSION In this study, the implementation of delirium monitoring and other elements of delirium care recommended in the 2013 PAD guideline recommendations was associated with modest, although significant, improvements in six of the seven studied care processes, corresponding with fewer delirium or coma days. On the assumption that the participating ICUs already applied light sedation practices in general, we decided not to focus strongly on safety screens for Spontaneous Awakening Trials (SATs) and Spontaneous Breathing Trials (SBTs), which may have precluded improvements of the secondary outcomes, such as length of ventilation, ICU stay, or mortality. We found that delirium screening resulted in slightly higher delirium detection rates, probably on account of the phe- nomenon that the use of a vali- dated delirium screening tool increases the detection rate, es- pecially of hypoactive delirium (25). This may also explain that the cumulative number of de- lirium and coma free days in the entire population did not decrease significantly in spite of decreased mean duration of delirium and days with coma per patient. Several previous studies on delirium screening implementation (26–29) and PAD guidelines (7, 30–32) also have reported improvement in delirium screening adherence. Further, a recent systematic re- view reported that adherence to delirium screening was assessed in 15 of 21 implementation studies, 13 of which found improved adherence, with rates ranging from 14% to 92% (6). In a previous trial (SLEAP trial), SATs/SBTs did not have additional benefit for LOS or mortality in settings with rel- atively light sedation practices (33). The sedation levels we found (RASS –1 [IQR, –3 to 0) more closely resembled those of patients in the SLEAP trial (RASS between –2 and –1) than those of patients in the Awakening and Breathing Controlled trial (RASS between –4 and –1), which indeed found a positive effect on mortality (34). On the other hand, the implementa- tion studies by Balas et al (7, 35), that bared many methodo- logical similarities to our study, but was a single-center study, also had a mean RASS of –1 indicating light sedation rates, but still established lower length of mechanical ventilation, apply- ing awakening and breathing trials. Our lack of focus on SATs and SBTs may also be illustrative for the tension between the premises of the PAD guidelines (with moderate emphasis on SATs/SBTs), the Assess, Prevent, and Manage Pain, Both SAT and SBT, Choice of analgesia and sedation, Delirium: Assess, Prevent, and Manage, Early mobility and Exercise, and Family engagement concept (with strong emphasis) and more recent insights such as provided by the SLEAP study and as substanti- ated in the early Comfort using Analgesia, minimal Sedatives and maximal Humane care (eCASH) concept that has even questioned the value of daily sedation stops as opposed to goal- directed sedation (36). Furthermore, our results on patient outcomes are in line with a recent meta-analysis reporting that interventions that reduced delirium duration did not neces- sarily translate into reduced short-term mortality (37). Figure 2. Adherence to guideline recommendations. This figure graph shows adherence percentage per performance indicator for the three data collection periods. See Supplem ental Digital Content 7 (http://links. lww.com/CCM/E227) for crude numbers. *Indicates a significant change relative to the baseline period. #Indicates a significant change after guideline implementation relative to the screening implement ation period. For adjusted analyses: see Supplemental Digital Content 10 (http://links .lww.com/CCM/E227). LOS = length of stay, PT = physical therapy. Trogrli et al Copyright © 2018 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.Neurologic Critical Care Critical Care Medicine www.ccmjournal.org 425 From an implementation perspective, we learned several lessons on evidence-to-practice translation. First, our implicit assumption that other improvements such as SATs and SBTs would follow next to our efforts to implement delirium-ori- ented measures, not specifically aimed at safety screens, has been falsified. Second, ICU teams less experienced with use of the guideline bundles or relying solely on “local champions” rather than interprofessional implementation teams should not try to implement all PAD/ABCDE bundle elements “si- multaneously” within a limited time frame. Of note, our study deployed one or two local champions (intensivist or research nurse), but limited funding precluded appointment of full interprofessional teams (IPTs), existing of all relevant stakeholders, such as residents, respiratory therapists, physical therapists, and other dedicated healthcare workers. Deploying such IPTs has been shown in other implementation studies to be essential for multibundle implementation within a limited timeframe (7, 38, 39). A graded or phased implementation seems much more feasible in such relatively resource-limited settings, and we learned that “integration” of bundle elements should not be confused with their “simultaneous” adoption. Third, not only the caregivers but also the dedicated “role models” have a learning curve for providing education and the feedback, so patience is of the essence. Fourth, successful im- plementation of bundle elements requires taking into account the baseline situation and contextual issues, such as existing barriers and facilitators, because many have been identified and not all are pertinent to all settings (40). The strengths of our study include the prospective design, use of tailored multifaceted implementation strategies, the largest cohort to date outside of the United States, and the representative mix of ICU types supporting the translatability of our findings. Further, we deployed a pragmatic approach: implementation as part of daily clinical practice instead of deployment in a controlled research setting, which is also in contrast to most published studies. Several limitations need to be addressed. First, the Hawthorne effect was not avoided, seeing that delirium screening implementation alone resulted in improved adherence to several guideline recommendations. Second, duration of delirium might be a doubtful outcome pa- rameter due to the difference between a clinical diagnosis as assessed by chart review at baseline compared with the second and third phases (based on validated screening instruments). Long-term outcomes, such as cognition or posttraumatic stress disorder, may be more relevant outcomes. Last, certain changes over time may have been overestimated in the pres- ence of secular trends (41). In conclusion, this largest pre-post implementation study outside of the United States of delirium-oriented measures based on the 2013 PAD guidelines showed that implementa- tion had improved health professionals’ adherence to delirium guidelines, which was linked to reduced brain dysfunction. TABLE 3. Secondary (Clinical) Outcomes Outcomes Crude analysis Adjusted a Effect Values Phase I: Baseline Phase II: Screening Implementation Phase III: Guideline Implementation Adjusted OR/Rate Ratio/Beta a (95% CI; p) a) Phase I vs Phase II b) Phase I vs Phase III c) Phase II vs Phase III Patients (n) Patients (n) Patients (n) Delirium duration (d), mean ( sd) 2 74 5.6 (8.6) 3002.9 (3.3) 3193.3 (4.5) a) 2.6 3.5 to 1.6; p < 0.001) b) –2.2 (–3.2 to –1.3; p < 0.001) c) 0.3 (–0.6 to 1.2; p = 0.46) Patients with delirium during ICU admission, n (%) 1,337 274 (21) 1,399300 (21) 1,194319 (27) a) 1.2 (0.9–1.4; p = 0.16) b) 1.4 (1.2–1.7; p < 0.001) c) 1.2 (1.0–1.5; p = 0.25) Duration of mechanical ventilation (d), mean ( sd) 560 4.6 (8.2) 5414.9 (6.4) 5934.7 (6.5) a) 0.5 0.3 to 1.3; p = 0.23) b) 0.4 (–0.4 to 1.2; p = 0.36) c) –0.1 (–0.9 to 0.7; p = 0.75) ICU length of stay (d), mean ( sd) 1,337 4.9 (6.9) 1,3994.3 (6.0) 1,1944.8 (5.9) a) 0.3 0.8 to 0.1; p = 0.19) b) –0.1 (–0.6 to 0.3; p = 0.56) c) 0.2 (–0.3 to 0.6; p = 0.49) ICU mortality, n (%) 1,337 135 (10.1) 1,399140 (10.0) 1,194126 (10.6) a) 1.3 (1.0–1.7; p = 0.08) b) 1.3 (0.9–1.7; p = 0.13) c) 1.0 (0.7–1.3; p = 0.88) Hospital mortality, n (%) 1,337 216 (16.2) 1,399226 (16.2) 1,194194 (16.2) a) 1.3 (1.0–1.6; p = 0.057) b) 1.1 (0.9–1.5; p = 0.31) c) 0.9 (0.7–1.1; p = 0.39) OR = odds ratio.a Differences are expressed as adjusted OR or adjusted rate ratios with the Phase I: Baseline (for a and b) and Phase II: After screening implementation (for c) as the reference. Adjusted for Acute Physiology and Chronic Health Evaluati on-II, hospital, age, and admission type. Copyright © 2018 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved. 426 www.ccmjournal.org March 2019 • Volume 47 • Number 3 Our data add to existing implementation literature due to the non-U.S. setting, strongly enhancing translatability of findings. Furthermore, implementation lessons learned that are unique for our study pertain to 1) the feasibility of staggered versus simultaneous implementation of bundle elements, that seem strongly dependent on local resources (e.g., “local champions” vs interprofessional implementation teams or level of previous experience with the guidelines), and 2) the fact that our “error of omission” of daily safety screens for SATs and SBTs may have precluded concurrently improved clinical outcomes, adding strong empirical support from a “real-life setting” for effective- ness of individual ABCDE bundle elements. ACKNOWLEDGMENTS The authors thank the coordinating nurses: A. van Wijk van Brievingh, I. van Doremalen, T. Schravesande, H. van Embden, and M. Campo; ICU pharmacist N. Hunfeld; psy- chiatrist R.J. Osse; all those who have contributed to data collection, all nurses and physicians for participating in ed- ucation; all site data managers; and all those who provided help during the study. The authors thank Ko Hagoort for editing the article. REFERENCES 1. Pandharipande PP, Girard TD, Jackson JC, et al; BRAIN-ICU Study Investigators: Long-term cognitive impairment after critical illness. N Engl J Med 2013; 369:1306–1316 2. Pisani MA, Kong SY, Kasl SV, et al: Days of delirium are associated with 1-year mortality in an older intensive care unit population. Am J Respir Crit Care Med 2009; 180:1092–1097 3. Ely EW, Shintani A, Truman B, et al: Delirium as a predictor of mor- tality in mechanically ventilated patients in the intensive care unit. JAMA 2004; 291:1753–1762 4. 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Balas MC, Burke WJ, Gannon D, et al: Implementing the awakening and breathing coordination, delirium monitoring/management, and early exercise/mobility bundle into everyday care: Opportunities, chal- lenges, and lessons learned for implementing the ICU Pain, Agitation, and Delirium Guidelines. Crit Care Med 2013; 41:S116–S127 36. Vincent JL, Shehabi Y, Walsh TS, et al: Comfort and patient-centred care without excessive sedation: The eCASH concept. Intensive Care Med 2016; 42:962–971 37. Al-Qadheeb NS, Balk EM, Fraser GL, et al: Randomized ICU trials do not demonstrate an association between interventions that reduce delirium duration and short-term mortality: A systematic review and meta-analysis. Crit Care Med 2014; 42:1442–1454 38. Barnes-Daly MA, Phillips G, Ely EW: Improving hospital survival and reducing brain dysfunction at seven california community hospitals: Implementing pad guidelines via the ABCDEF bundle in 6,064 patients. Crit Care Med 2017; 45:171–178 39. 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BMJ Qual Saf 2016; 25:303–310 Directions: please follow explicitly *** primarily this assignment is filling in the tables- attached all articles to use **** Use the attached "Literature Evaluation Table to complete this assignme Downloaded from<004B005700570053001D00120012004D00520058005500510044004F00560011004F005A005A001100460052005000120046 00460050004D00520058005500510044004F> by<0047004E004F005B00480034003300170053003300360017004D0019004A002D004F001700300059004B004D004B00190026 003A00460026001B0047002E0026003100250024002B00170048004E002B004C0059004B001A003A0013005900450053 003100360054002500480059004A005D0036001300320017004C00350057005800560044003400350028002E00540012 004C00490052001C0033005A004B00250026003B00520025003D004D00180039001C004B00500030002C0024002A0050 005D001800160016003B004D002D0058002C002F003D0032004E0058002D0014004F003A0035005800360056004D0055 0028004400390035005800470026004B005100190038001C005C0029001C0037002F003300170024005A001A00570058 0027003400440045001300130044005000240055003C001C001700450019> on02/20/2022 Downloadedfrom<004B005700570053001D00120012004D00520058005500510044004F00560011004F005A005A001100460052005000120046 00460050004D00520058005500510044004F> by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on02/20/2022Critical Care Medicine www.ccmjournal.org 335 DOI: 10.1097/CCM.0000000000004773 *See also p. 380. Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution- Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. OBJECTIVE: To evaluate the impact of bundle interventions on ICU de- lirium prevalence, duration, and other patients’ adverse outcomes. DATA SOURCES: The Cochrane Library, PubMed, CINAHL, EMBASE, PsychINFO, and MEDLINE from January 2000 to July 2020. The protocol of the study was registered in International prospective register of sys�tem- atic reviews (CRD42020163147). STUDY SELECTION: Randomized clinical trials or cohort studies that examined the following outcomes were included in the current study: ICU delirium prevalence and duration, proportion of patient-days with coma, � ventilator-free days, mechanical ventilation days, ICU or hospital length of stay, and ICU or inhospital or 28-day mortality. DATA EXTRACTION: Using a standardized data-collection form, two authors screened the studies and extracted the data independently, and assessed the studies’ quality using the Modified Jadad Score Scale �for ran- domized clinical trials and the Newcastle-Ottawa Scale for cohort studies. DATA SYNTHESIS: Eleven studies with a total of 26,384 adult partici- pants were included in the meta-analysis. Five studies (three randomized clinical trials and two cohort studies) involving 18,638 patients demon- strated that ICU delirium prevalence was not reduced (risk ratio = 0.92; 95% CI, 0.68–1.24). Meta-analysis showed that the use of bundle inter- ventions was not associated with shortening the duration of ICU delirium (mean difference = –1.42 d; 95% CI, –3.06 to 0.22; two randomized clin- ical trials and one cohort study), increasing ventilator-free days (me�an dif- ference = 1.56 d; 95% CI, –1.56 to 4.68; three randomized clinical trials), decreasing mechanical ventilation days (mean difference = –0.83 d; 95% CI, –1.80 to 0.14; four randomized clinical trials and two cohort studies), ICU length of stay (mean difference = –1.08 d; 95% CI, –2.16 to 0.00; seven randomized clinical trials and two cohort studies), and inhospital mortality (risk ratio = 0.86; 95% CI, 0.70–1.06; five randomized clinical trials and four cohort studies). However, bundle interventions are effective in reducing the proportion of patient-days experiencing coma (risk rati�o = 0.47; 95% CI, 0.39–0.57; two cohort studies), hospital length of stay (mean difference = –1.47 d; 95% CI, –2.80 to –0.15; four randomized clinical trials and one cohort study), and 28-day mortality by 18% (risk ratio = 0.82; 95% CI, 0.69–0.99; three randomized clinical trials). CONCLUSIONS: This meta-analysis fails to support that bundle interven- tions are effective in reducing ICU delirium prevalence and duration, but supports that bundle interventions are effective in reducing the propor- tion of patient-days with coma, hospital length of stay, and 28-day mor- tality. Larger randomized clinical trials are needed to evaluate the impact of bundle interventions on ICU delirium and other clinical outcomes. Shan Zhang, PhD 1 Yuan Han, MD 1 Qian Xiao, PhD 1 Haibin Li, PhD 2 Ying Wu, PhD, RN, ACNP, ANP, NFESC 1 Effectiveness of Bundle Interventions on ICU Delirium: A Meta-Analysis* LW W Zhang et al 336 www.ccmjournal.org February 2021 • Volume 49 • Number 2 KEY WORDS: bundle interventions; delirium; intensive care unit; meta-analysis D elirium is a common but mostly preventable complication among patients in the ICUs, with the incidence ranged as high as 70–87% (1, 2). ICU patients complicated with delirium have been identified with prolonged mechanical ventilation (MV), longer hospital stay, and increased mortality (2, 3). The severity of adverse outcomes was also associated with delirium duration, the longer the duration, and the worse the adverse outcomes (3, 4). Therefore, pre- vention of delirium from its happening or early man- agement to reverse ICU delirium is critical to minimize the adverse effects on clinical outcomes associated with ICU delirium among identified patients (5–7). Although the pathogenesis of ICU delirium is not completely clear, it is proposed that multiple risk factors collectively contributed to the onset and persistence of ICU delirium (7, 8). Therefore, clinical guidelines, in- cluding the pain, agitation, delirium, immobility, and sleep (PADIS) guidelines, have recommended to use a bundle approach, such as the “ABCDEF bundle” to target on eliminating multiple modifiable risk factors of ICU delirium to reduce the chances of or shorten the duration of delirium to be occurred in critically ill adults (6, 9). Among the different components of the ABCDEF bundle, A stands for Assess, prevent, and manage pain, which is a major risk factor of ICU de- lirium; B represents Both spontaneous awakening trials (SATs) for sedative patients and spontaneous breathing trials (SBTs) if patients were on mechanical ventilators; C refers to the Choice of analgesics and sedatives, as the use of analgesics and sedatives is a major risk factor of ICU delirium; D denotes for Delirium monitoring or management, which includes reorientation, improv- ing sleep and wakefulness, as well as reducing hearing and/or visual impairment, etc; E implies Early exercise/ mobility as immobility is a major risk factor of ICU de- lirium; and F refers to Family engagement and empow- erment (restrictive ICU visit is a major risk factor of ICU delirium) (9). Not every ICU patient has all the above-mentioned risk factors; therefore, the appro- priate subset of interventions from the ABCDEF bundle should be tailored to patients’ specific risk factors. It has been proposed that the ABCDEF bundle maybe more effective than any single-component strategy in preventing and managing ICU delirium with its evidence largely driven from before-after stud- ies (10–13) or pilot studies (14, 15). After the PADIS Guidelines were released, a number of well-designed robust randomized clinical trials (RCTs) (16–18) have been conducted to evaluate the bundle interventions in minimizing modifiable risk factors related to ICU delirium, therefore reducing its prevalence or dura- tion. However, their findings have been inconsistent or even contradictory among different studies (19–21). Therefore, we conducted a meta-analysis to assess the overall effectiveness of bundle interventions on the prevalence and duration of ICU delirium, and other important adverse outcomes, such as the hospital length of stay (LOS) and mortality. MATERIALS AND METHODS The meta-analysis was conducted and reported in ac- cordance with the criteria identified by the preferred reporting items for systematic reviews and meta-anal- yses (PRISMA) guidelines (Appendix File 1, http:// links.lww.com/CCM/G21) (22). The current study was a retrospective analysis of published research litera- ture only, and no human were involved. Therefore, the Institution Review Board approval was not required based on the institutional policies. The protocol of the study was registered in International prospective reg- ister of systematic reviews (CRD42020163147). Search Strategy We performed a comprehensive literature search to identify RCTs and cohort studies related to delirium bundle interventions between January 2000 and July 2020. Following a preliminary PubMed search using combined key terms (delirium OR ICU delirium) AND (intervention OR critical care), the earliest published work by Slomka et al (23) relevant to the topic of this meta-analysis was identified in the year of 2000; there- fore, the year of 2000 was chosen as the starting point to search available relevant published works. Databases including the Cochrane Library, PubMed, CINAHL, EMBASE, PsychINFO, and MEDLINE were searched for published articles with no language restriction applied. The search terms included a combination of key terms related to delirium: (delirium OR confusion OR acute confusional syndrome OR postoperative de- lirium OR cognitive dysfunction OR ICU delirium OR ICU psychosis OR ICU syndrome OR deliri*) AND Review Articles Critical Care Medicine www.ccmjournal.org 337 (ABCDE bundle OR ABCDEF bundle OR bundle OR PAD OR critical care* OR intensive care* OR preven- tion OR intervention). We also searched ongoing and unpublished trials using the clinicaltrials.gov data- bases. Additional relevant articles were identified by manually reviewing the reference lists of all included research articles as well as published review articles and meta-analyses. The authors of original studies were also contacted to acquire missed data to be in- cluded in the final analysis. Study Selection The title and abstract of all articles were screened ini- tially, and the full text of potential studies was retrieved and further reviewed by two reviewers (S.Z. and Y.H.) independently to assess the eligibility. Articles were eli- gible for inclusion in the meta-analysis if they met all of the following inclusion criteria: 1) RCTs or cohort stud- ies, (the Cochrane Handbook for Systematic Reviews of Interventions, Version 6.0 , identifies that the re- view may include nonrandomized studies, such as co- hort studies, when the question of interest cannot be answered by RCTs), 2) study participants were adults (18 years old or older) administered in the ICUs, and 3) application of at least three of the components identified in the ABCDEF bundle, which includes assessment and pain management, SAT or SAT plus SBT for patients supported by ventilator, choice of analgesia and seda- tion, delirium monitoring/management, early exercise/ mobility, and family engagement and empowerment. Articles were excluded if they were presented with any of the following reasons: 1) nonrelevant topics, 2) study protocols or case reports, 3) commentary or meta-anal- ysis and systemic review, and 4) nonhuman study. For articles that met the above initial criteria, the following second-level inclusion criteria were applied: 1) study must be designed with control groups, 2) ICU delirium was measured by validated instruments including the diagnostic and statistical methods IV criteria, confu- sion assessment method (CAM), CAM for the ICU (CAM-ICU), or the intensive care delirium screening checklist (ICDSC), and 3) the study reported selected clinical outcomes of our interest (Fig. 1). Outcome Measures The primary endpoint assessed in this study was the prevalence and duration of ICU delirium. The prevalence of ICU delirium was defined as the presence of delirium among patients at the end of follow-up, and the duration of ICU delirium is defined as the total hospital days in which the patient was diagnosed with ICU delirium. The secondary endpoints included pro- portion of patient-days with coma, ICU and hospital LOS, number of ventilator-free days (VFDs) and MV days, as well as the ICU, inhospital, and 28-day mor – talities (Supplementary File 2 , http://links.lww.com/ CCM/G22). Quality Assessment The quality of RCT studies was examined by two reviewers (S.Z. and Y.H.) separately, using the Modified Jadad Scale (25). The score on the Modified Jadad Scale was ranged from 0 to 7, with a score of greater than or equal to 4 being defined as high-quality stud- ies. The quality of cohort studies was examined using the Newcastle-Ottawa Scale (NOS) (26). The score on the NOS was ranged from 0 to 9 with a score of greater than or equal to 6 being identified as an acceptable methodological design. Risk of bias of each study was further assessed based on the six domains identified by the “Cochrane Handbook for Systematic Reviews of Interventions” (27). Data Collection Using a predesigned standardized data-collection form, relevant data from original studies were extracted and collected independently by two researchers (S.Z. and Y.H.), including study characteristics (primary au- thor, publication year, study design, and sample size), participant demographics (age and gender), interven- tions and comparisons, as well as information on the intended outcome variables. For each outcome, the reviewers extracted the means (sd s) of the variable or number of patients in each study. Statistical Analysis Meta-analysis was performed using the Review Manager (RevMan) Version 5.3 (The Nordic Cochrane Centre, The Cochrane Collaboration, 2014, Copenhagen, Denmark). Heterogeneity among studies was assessed using the chi- square test, and Ι 2 values were used to deter – mine heterogeneity across studies, attributing to Zhang et al 338 www.ccmjournal.org February 2021 • Volume 49 • Number 2 the proportion of total variation, in which the Ι 2 > 50% indicated substantial heterogeneity of effects and random-effects models were applied. If the Ι 2 < 50% was identified, which represented homogeneity, fixed-effects models were selected. For continuous data, mean difference (MD) and 95% CI were used for outcomes pooled. For dichotomous outcomes, risk ratios (RRs) with 95% CI were evaluated in ac- cordance with intent-to-treat principles. The for - est plot was applied to represent the meta-analysis results, and the funnel plots were constructed to identify publication bias using the Begg and Egger Figure 1. Flowchart of literature identification, review, and selection. Review Articles Critical Care Medicine www.ccmjournal.org 339 tests with Stata software (Stata/SE 12.0; StataCorp LP, College Station, TX). Sensitivity analysis was also performed by assessing whether random-effects and fixed-effects models would bring about the same re- sult. All statistical tests were two-tailed, and p value of less than 0.05 was considered statistically significant. RESULTS Study Identification Our initial search yielded 7,190 publications based on the defined search terms (Fig. 1). After screening of the titles and abstracts, 37 potential studies with TABLE 1. Characteristics of Included Studies Source Study Type Setting Sample Size (n) Intervention Group/ Control Group ICU Delirium Assessment Tool Interventions Quality Assessment a Risk of Bias b Girard et al (29) (2008) RCT ICU 167/168 CAM-ICU 3/6 (A, B, D) 5 5/6 (A, C, S, R, O) Schweickert et al (30) (2009) RCT MICU 49/55CAM-ICU 4/6 (A, B, D, E) 7 6/6 (A, B, C, S, R, O) Mehta et al (28) (2012) RCT SICU and MICU 214/209 ICDSC 3/6 (A, B, D) 7 6/6 (A, B, C, S, R, O) Mansouri et al (31) (2013) RCT SICU and MICU 96/105 CAM-ICU 3/6 (A, C, D) 4 4/6 (A, C, S, O) Moon and Lee (18) (2015) RCT SICU and MICU 60/63 CAM-ICU 4/6 (A, C, D, E) 5 5/6 (A, C, S, R, O) Sosnowski et al (17) (2018) RCT ICU 15/15CAM-ICU 5/6 (A, B, C, D, E) 5 5/6 (A, C, S, R, O) Olsen et al (16) (2020) RCT ICU 351/349 CAM-ICU 5/6 (A, B, C, D, E) 5 4/6 (A, C, S, O) Barnes-Daly et al (32) (2017) CS MICU and SICU 6,064 CAM-ICU 5/6 (A, B, C, D, E) 8 3/6 (C, S, O) Hsieh et al (20) (2019) CS MICU 281/366 CAM-ICU 3/6 (B, D, E) 8 3/6 (C, S, O) Pun et al (21) (2019) CS ICU NACAM-ICU or ICDSC 6/6 (A, B, C, D, E, F) 8 3/6 (C, S, O) Trogrlić et al (19) (2019) CS SICU and MICU 1,194/ 1,337 CAM-ICU or ICDSC 5/6 (A, C, D, E, F) 8 3/6 (C, S, O) A = assess, prevent, and manage pain, B = both spontaneous awakening trials and spontaneous breathing trials, C = c�hoice of analgesia and sedation, CAM-ICU = confusion assessment method for the ICU, CS = cohort study, D = delirium monitoring/management, E = early exercise/mobility, F = family engagement and empowerment, ICDSC = intensive care delirium screening checklist, MICU = medical ICU, NR = not report, RCT = randomized clinical trial, SICU = surgical ICU. a The quality of included RCTs articles was examined using the Modified Jadad Scale (range, 0–7). The quality of included cohort studies was examined using the Newcastle-Ottawa Scale (range, 0–9). b Risk of bias include the following: A = allocation concealment, B = blinding of participants, personnel, and outcome assessors, C = complet�e- ness of outcome data, O = other sources of bias, R = random-sequence gen�eration or balanced allocation, S = selective outcome reporting. Zhang et al 340 www.ccmjournal.org February 2021 • Volume 49 • Number 2 full-text were retrieved, in which 26 studies did not meet the second-level inclusion criteria. Therefore, a total of 11 studies (seven RCTs and four cohort stud- ies) were included in the final analysis according to the selection criteria (Ta b l e 1 ). Two datasets were ac- quired from the principal investigators of the original studies (16, 28) as the data included in the articles were inadequate for analysis. Study Characteristics The 11 original studies included in the current study were published between 2008 and 2020, with a total of 26,384 adult participants. The reported ICU delirium prevalence varied from 20.49% (19) to 74.25% (29). All studies (with supplementary data obtained from authors of two original studies) provided relevant data on one or more targeted outcomes that were suitable for final analysis (Table 1). The selected elements of the bundle intervention used in each study were listed in Supplementary Table 1 (http://links.lww.com/ CCM/G24). Pooled Outcomes ICU Delirium Prevalence . Five studies (three RCTs and two cohort studies) reported on the prevalence of ICU delirium, which included a total of 18,638 patients in the meta-analysis (Ta b l e 2 ). A random- effect model showed that, when compared with con- trol groups, the bundle interventions lowered the odds of ICU delirium prevalence by 8% (RR = 0.92; 95% CI, 0.68–1.24; p = 0.57), but not statistically sig- nificant (Fig. 2). The ICU delirium prevalence was stratified by the study design, with three RCTs com- prising 441 ICU patients in intervention groups and 440 in control groups, and the pooled result showed that the bundle interventions had no effect on low- ering the odds of ICU delirium (RR = 1.01; 95% CI, 0.91–1.13; p = 0.81) (Table 2). The two cohort stud- ies that applied bundle interventions lowered the ICU delirium prevalence by 8% (RR = 0.92; 95% CI, 0.40–2.11; p = 0.84) (Table 2), but no significant dif- ferences were detected. ICU Delirium Duration . There was no difference identified on the length of ICU delirium between the par - ticipants in the bundle-intervention group (n = 1,410) and usual care (n = 1,560) group (three studies [two RCTs and one cohort study]; MD = –1.42 d; 95% CI, –3.06 to 0.22; p = 0.09) (Table 2; and Supplementary Fig. 1, http://links.lww.com/CCM/G23). Proportion of Patient-Days With Coma. Patients in the bundle-intervention group were associated with lower likelihood on the proportion of patient-days experiencing coma (RR = 0.47; 95% CI, 0.39–0.57; p < 0.001; two cohort studies; fixed-effects model) (Table 2; and Supplementary Fig. 2, http://links.lww. com/CCM/G23). Mechanical Ventilation Days and Ventilator-Free Days . The length of MV was 0.83 days shorter among 1,849 ICU patients who received the bundle interven- tions (MD = –0.83 d; 95% CI, –1.80 to 0.14; p = 0.09; six studies [four RCTs and two cohort studies]) (Table 2; and Supplementary Fig. 3, http://links.lww.com/ CCM/G23) compared with those in the control group (n = 2,087), but the outcome was not statistically sig- nificant. Regarding VFDs, no difference was found be- tween the intervention group (n = 567) and the control group ( n = 572) (MD = 1.56 d; 95% CI, –1.56 to 4.68; p = 0.33; three RCTs) (Table 2; and Supplementary Fig. 4, http://links.lww.com/CCM/G23). ICU and Hospital Length of Stay. There were nine studies (seven RCTs and two cohort studies) re- porting results on the ICU LOS. With a total of 5,184 ICU patients included in the meta-analysis using a random-effects model, the pooled result showed that the MD was 1.08 days shorter (95% CI, –2.16 to 0.00; p = 0.05) (Table 2; and Supplementary Fig. 5, http:// links.lww.com/CCM/G23) among patients in the in- tervention group compared with those in the control group. In addition, five studies (four RCTs and one co- hort study) measured hospital LOS (Table 2), and the meta-analysis using a fixed-effects model (I 2 = 42%; p = 0.14) found that the MD of hospital LOS was 1.47 (95% CI, –2.80 to –0.15; p = 0.03) days shorter among 726 ICU patients in the intervention group compared with patients in the control group (Table 2; and Supplementary Fig. 6, http://links.lww.com/ CCM/G23). Mortality . Two (one RCT and one cohort study), nine (five RCTs and four cohort studies), and three (all RCTs) studies reported results on the ICU, inhos- pital, and 28-day mortalities, respectively (Table 2; and Supplementary Figs. 7–9, http://links.lww.com/ CCM/G23). Meta-analysis using a fixed-effects model (I 2 = 0%; p = 0.61) found that the bundle interven- tions did not decrease ICU mortality (RR = 1.01; 95% Review Articles Critical Care Medicine www.ccmjournal.org 341 TABLE 2. Meta-Analysis of the Effect of Bundle Interventions VariableStatistical Method Risk Ratio or Mean Difference (95% CI) I 2 Value (%)p ICU delirium prevalence RCTs (18, 28, 29) (3) M-H, fixed1.01 (0.91–1.13) 310.81 Cohort studies (19, 21) (2) M-H, random0.92 (0.40–2.11) 980.84 Combined M-H, random0.92 (0.68–1.24) 910.57 ICU delirium duration RCTs (29, 30) (2) IV, random–0.89 (–2.82 to 1.06) 790.37 Cohort studies (19) (1) IV, random–2.30 (–2.83 to –1.77) NA< 0.001 Combined IV, random–1.42 (-–3.06 to 0.22) 900.09 Coma RCTs (0) NANANANA Cohort studies (19, 21) (2) M-H, fixed0.47 (0.39–0.57) 47< 0.001 Combined M-H, fixed0.47 (0.39–0.57) 47< 0.001 Ventilator-free days RCTs (16, 29, 30) (3) IV, random1.56 (–1.56 to 4.68) 760.33 Cohort studies (0) NANANANA Combined IV, random1.56 (–1.56 to 4.68) 760.33 Mechanical ventilation days RCTs (17, 28, 30, 31) (4) IV, random–0.74 (–2.22 to 0.74) 790.33 Cohort studies (19, 20) (2) IV, random–0.94 (–2.99 to 1.12) 950.37 Combined IV, random–0.83 (–1.80 to 0.14) 860.09 ICU LOS RCTs (16–18, 28, 29–31) (7) IV, random–1.07 (–2.62 to 0.48) 630.18 Cohort studies (19, 20) (2) IV, random–0.96 (–2.72 to 0.80) 910.29 Combined IV, random–1.08 (–2.16 to 0.00) 740.05 Hospital LOS RCTs (17, 28, 29, 30) (4) IV, fixed–2.24 (–4.11 to –0.37) 470.02 Cohort studies (20) (1) IV, fixed–0.70 (–2.58 to 1.18) NA0.47 Combined IV, fixed–1.47 (–2.80 to –0.15) 420.03 ICU mortality RCTs (28) (1) M-H, fixed0.94 (0.67–1.32) NA0.72 Cohort studies (19) (1) M-H, fixed1.05 (0.83–1.32) NA0.71 Combined M-H, fixed1.01 (0.84–1.23) 00.89 Inhospital mortality RCTs (17, 18, 28, 30, 31) (5) M-H, random0.73 (0.45–1.17) 530.19 Cohort studies (19–21, 32 (4) M-H, random0.92 (0.71–1.19) 800.52 Combined M-H, random0.86 (0.70–1.06) 670.16 28-d mortality RCTs (16, 18, 29) (3) M-H, fixed0.83 (0.71–0.98) 110.02 Cohort studies (0) NANANANA Combined M-H, fixed0.83 (0.71–0.98) 110.02 IV = inverse variance, LOS = length of stay, M-H = Mantel-Haenszel, NA = not applicable, RCT = randomized clinical trial. Zhang et al 342 www.ccmjournal.org February 2021 • Volume 49 • Number 2 CI, 0.84–1.23; p = 0.89) among 2,954 ICU patients. Additionally, the RR for inhospital mortality was 0.86 (95% CI, 0.70–1.06; p = 0.16) among 25,349 ICU patients, with nonsignificant findings. However, the 28-day mortality was decreased by 18% (RR = 0.82; 95% CI, 0.69–0.99; p = 0.04) among 1,158 ICU patients in the intervention group. Sensitivity Analysis . In the sensitivity analysis on the ICU delirium prevalence, there was still heterogeneity among studies (p < 0.001; I 2 = 93%) after excluding the study from Mehta et al (28), which used the ICDSC to assess ICU delirium. The RRs obtained by the random- effect model were 0.89 (95% CI, 0.59–1.33), Z = 0.59, and p = 0.56, with no substantial changes observed in the results. Meanwhile, the sensitivity analysis was also per - formed by excluding the study from Pun et al(21), which underwent extensive modeling and adjusted for 18 confounding factors, and therefore could have af- fected the result on ICU delirium prevalence. However, the result was similar to the general pooled analysis (RR = 1.08; 95% CI, 0.87–1.34; p = 0.49). The ICU delirium duration was stratified based on the number of bundle interventions, which was dichot- omized using the median of 4 as the cutoff point and was divided into two groups among patients received the intervention (Supplementary Table 2 , http://links. lww.com/CCM/G24). In patients who received inter - ventions with equal or less than four elements iden- tified in the bundle approach (two studies), the MD obtained by the random-effect model was –0.89 (95% CI, –2.83 to 1.06; p = 0.37), and the MD was –2.30 (95% CI, –2.83 to –1.77; p < 0.01) among those who received more than four elements of the bundle interventions (one study). In the sensitivity analysis on inhospital mortality, heterogeneity was still identified among studies ( I 2 = 69%; p = 0.002) after excluding the study from Sosnowski et al (17), which is a pilot study reported with very high inhospital mortality in the interven- tion group. The result was in line with that from the general pooled data (RR = 0.85; 95% CI, 0.70–1.04; p = 0.12). The sensitivity analysis shows that regardless of which effect model was applied, the outcomes remained similar. Publication Bias . As shown in Supplementary Fig. 10 (http://links.lww.com/CCM/G23), the funnel plot is generally symmetric, which implied no pub- lication bias existed for the ICU delirium prevalence (Egger test, p = 0.66; Begg test, p = 0.46). Similar find- ings were observed for ICU LOS, MV days, hospital LOS, and inhospital mortality (Supplementary Figs. 11–14, http://links.lww.com/CCM/G23). However, the number of studies reported on the relationship of bundle interventions with other outcomes was too small and a funnel plot analysis was not performed. Association Between Quality Ratings and Effectiveness. The quality assessment based on the Modified Jadad Scale showed that seven RCTs were rated as high-quality study designs (Modified Jadad Score 4–7) (Table 1). The four cohort studies were also identified with high quality, among which the NOSs score were ranged from 6 to 8 (Table 1). No significant difference was observed between the score on risk of bias and the effectiveness of bundle interventions. DISCUSSION In this meta-analysis, we included 11 studies with a total of 26,384 adult ICU patients to evaluate the ef- fectiveness of bundle interventions on either pre- vention and/or management of ICU delirium. Our findings failed to provide evidence in supporting that Figure 2. Meta-analysis of ICU delirium prevalence. RR = risk ratio. Review Articles Critical Care Medicine www.ccmjournal.org 343 the bundle interventions were effective measures on reducing either ICU delirium prevalence or duration. However, the result should be interpreted with cau- tion, as there was substantial heterogeneity among studies even though sensitivity analysis was applied in terms of the result on ICU delirium prevalence and duration, but the results were not changed from the pooled effects. To the best of our knowledge, this is the first meta-analysis conducted to evaluate the effect of the ABCDEF bundle on ICU delirium prevalence and duration and other related adverse outcomes. The PADIS Guidelines have recommended to use all components of the ABCDEF bundle to reduce the modifiable risk factors (e.g., pain, deep sedation, use of MV, analgesics and sedatives, and immobility) relevant to the development of the ICU delirium. However, the majority of the studies in the current analysis only used selected elements from the bundle. Among all the studies included, four studies used three elements, two studies used four elements, another four studies used five elements, and only one study reported the use of all ABCDEF bundle elements. This indicates that most of the interventions described by authors may not tailor to patients’ every specific risk factors targeted by the ABCDEF bundle. Our meta-analysis found that there were no signif- icant differences in reducing the prevalence and dura- tion of ICU delirium between the bundle-intervention group and the control group in the pooled analysis. These findings may be explained by the following pos- sible reasons. First, the majority of the included studies in this meta-analysis did not focus on all the elements identified in the ABCDEF bundle, so not all modifiable ICU delirium risk factors were appropriately addressed by the interventions applied. For example, the PADIS Guidelines recommend to use nonbenzodiazepine seda- tives (e.g., dexmedetomidine) over benzodiazepines for sedation in ICU patients (9, 33). However, as identified by authors in three of the included studies (28, 29, 30), benzodiazepines were commonly prescribed for seda- tion in patients in the ICU. In addition, one study (19) used five elements of the bundle interventions, which significantly decreased the ICU delirium duration by 2.30 days among patients in the intervention group. However, the result must be interpreted with caution, as only one study used interventions that included more than four elements of the bundle approach among those examined the effects of the intervention on ICU delirium duration. In addition, the cohort study conducted by Pun et al (21) used all ABCDEF bundle elements and demonstrated that the bundle approach significantly reduced the delirium prevalence and improved selected outcomes such as coma and MV use. However, there is a lack of sufficient evidence from RCTs to support the effectiveness of ABCDEF bundle in improving ICU de- lirium prevalence and duration. Future well-designed RCTs are needed to evaluate the effects of all the ABCDEF bundle components as a whole intervention on ICU delirium. Second, due to the complexity of the ABCDEF bundle approach, the adoption and adherence of the bundle interventions were suboptimal among included studies (28, 34). Healthcare providers were often reluc- tant to implement fully the bundle interventions in ICU patients, concerning practical difficulty, patient safety, workload burden, etc (5, 35); therefore, even the bundle interventions were implemented and they were not executed in their full extent (such as the dosage of sedatives is not adequately titrated) (28). The proportion of patient-days experiencing coma was reduced by 53% in the bundle-intervention group. Although only two cohort studies (19, 21) reported the proportion of patient-days with coma in the current meta-analysis, there are one RCT study and two cohort studies revealed that the bundle intervention signifi- cantly shortened the duration of coma, decreased the proportion of patients with coma, or experienced more days free of coma, respectively. However, we failed to combine these coma-related outcomes due to incon- sistent data formats among studies (20, 29, 32). The pos- sible effect of bundle approach on coma improvement may be explained in part by the application of bundle intervention targeting on daily awakening, which attempts to stimulate the reticular activating system in the brain of comatose patient, and therefore promoted arousal (36, 37). Evidences have shown that the awak- ening trial is necessary for sustaining cortical arousal, which promoted further recovery of the nervous system and improved functional efficiency of the brain (36, 38). In addition, as demonstrated in previous research, pas- sive range-of-motion activities (the “E” element) could also stimulate the brain activities that might have con- tributed to the decrease in the proportion of patient- days with coma (39, 40). Further studies are necessary to verify this result. The improvement of coma by the Zhang et al 344 www.ccmjournal.org February 2021 • Volume 49 • Number 2 bundle intervention may also explained our result on decreased LOS (1.47 d) in the current analysis. As indicated by the results on mortality, the 28-day mortality was reduced after implementation of the bundle intervention but not with the ICU or inhospital mortality. The possible reason may be that the preva- lence and duration of delirium were not changed in patients receiving the bundle intervention in the cur - rent meta-analysis; therefore, it could not reverse the adverse effect of delirium such as ICU and inhospital mortality. The other reason may be that none of the in- cluded studies were designed to test the effectiveness of bundle intervention on ICU mortality as primary out- comes; therefore, they were not powered to test the dif- ferences between the groups. However, a longer follow up period, such as 28 days, will increase the power to test the differences on 28-day mortality (1, 29). One of the strengths of this study is that our meta- analysis strictly followed the PRISMA statement and used a comprehensive search strategy to identify po- tential studies in all available databases to ensure the generalizability of the results. Meanwhile, we included a relevantly large number of studies in the meta-anal- ysis to extend the conclusion beyond the population contained in previous meta-analyses and systematic reviews. In our meta-analysis, bundle interventions were applied in all 11 studies that were identified as methodologically high-quality studies. Therefore, our findings appear to be largely driven by the findings from high-quality RCTs, allowing us to draw more re- liable and valid conclusions. In addition, we avoided publication bias by following comprehensive search strategies that included studies with a large sample size. Several limitations should be noted in this meta- analysis. First, we included both RCT and cohort studies in the current analysis, and heterogeneity was identified among studies in terms of results on the ICU delirium prevalence and duration, MV days, ICU, or hospital LOS. These could be due to differentiation existed in terms of study designs and inconsistent in- clusion and exclusion criteria among studies; these all restricted the power to draw conclusions. However, we rigorously limited the heterogeneity by including only high-quality studies in the analysis and used sensi- tivity analysis that applied random-effects models and fixed-effects models simultaneously to examine the effect of bundle interventions. In addition, the results of the sensitivity analyses on related outcomes showed no different findings from the pooled effects. Second, the number of studies included in the current analysis reporting outcomes on ICU mortality is small, which may have insufficient power to assess the differences and limited the interpretation of our pooled data. Third, although some studies reported coma-related outcomes, we failed to combine these data for anal- ysis due to different presented data formats. Although authors of the original studies were contacted several times, no responses were obtained. Therefore, this lim- ited the reliability when interpreting this result. Finally, as majority of the studies in this analysis did not in- clude all elements of the bundle approach, the modi- fiable risk factors identified by the PADIS Guidelines are not fully addressed in the interventions. Therefore, it is limited to draw conclusions on the collective effect of the full bundle approach with current evidence. Further studies are needed to examine the full imple- mentation of the ABCDEF bundle on the prevalence and duration of ICU delirium in the future. Despite these limitations, the results of this meta-analysis are clinically relevant and reliable for the prevention and management of delirium in the ICU settings. CONCLUSIONS The current meta-analysis did not support the effects of bundle interventions on decreasing the prevalence and shortening the duration of ICU delirium, although there is clear evidence in supporting that the bundle interventions are effective in reducing the proportion of patient-days with coma, hospital LOS, and 28-day mortality in ICU patients. The modifiable risk factors for ICU delirium were not fully addressed by interven- tions in the majority of the included studies, which may limit the effectiveness of bundle interventions to be shown on ICU delirium prevalence and duration. Future studies, especially well and rigorously designed RCTs and full implementation of ABCDEF bundle in- tervention, should be considered to test the effect of bundle interventions on ICU delirium prevalence and duration, as well as other related adverse outcomes. ACKNOWLEDGMENTS We thank for the support from Karen V. Lamb (Associate Professor, College of Nursing, Rush University, United States) and Meihua Ji (Associate Professor, School of Nursing, Capital Medical University, Beijing, China) Review Articles Critical Care Medicine www.ccmjournal.org 345 for her editing assistance. We also thank Prof. Palle Toft (Department of Anaesthesiology and Intensive Care, Odense University Hospital) and Prof. Sangeeta Mehta (Medical Surgical ICU, Mount Sinai Hospital) for providing additional dataset on related outcomes. 1 Department of Adult Care, School of Nursing, Capital Medical University, Beijing, China. 2 Department of Epidemiology and Health Statistics, School of Public Heath, Capital Medical University, Beijing, China. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (http://journals.lww.com/ccmjournal). The funding source had no role in the study design, data collec- tion, data analysis, data explanation, or article writing. Dr. Wu is receiving a grant (#71661167008) from “the National Natural Science Foundation of China.” The remaining authors have disclosed that they do not have any potential conflicts of interest. 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J Rehabil Med 2017; 49:715–722 Directions: please follow explicitly *** primarily this assignment is filling in the tables- attached all articles to use **** Use the attached "Literature Evaluation Table to complete this assignme Levels of Evidence in Research Evidence Level Types of Evidence Primary Research Secondary Research LEVEL 1 Randomized-Controlled Trial: Subjects randomly assigned to intervention or control groups. Intervention group receives treatment/intervention. Comparison group receives no treatment/intervention. Clinician conducting study is unaware which group participants are assigned to which typically leads to unbiased results. X Systematic Review: Comprehensive review of existing literature which involves analyzing all articles related to the research question and summarizing findings. Researchers then make recommendations for clinical practice based on evidence from articles reviewed. X Meta-Analysis: Synthesis of findings from all single, independent studies to calculate an effect. X LEVEL 2 Cohort Studies: Studies observe large groups of people that record exposure to risk factors to find possible causes of disease. Studies gather data either moving forward (prospective) or review past data already recorded (retrospective). X LEVEL 3 Case Report Studies: Studies used to determine if there is an association between exposure and specific health outcome. Frequently used when studying rare health outcomes or diseases. X LEVEL 4 Case Report: Provides detailed report of diagnosis, treatment, response to treatment, and follow-up care of an individual patient. Case Series: Group of case reports involving patients who were given the same treatment. LEVEL 5 Animal or Laboratory Studies Primary Research: Involves active participation/observation by researchers themselves. Secondary Research: Involves summary or synthesis of data/literature that has been organized by others. *Adapted from Johns Hopkins Nursing Evidence-Based Practice: Models and Guidelines and University of Michigan Library © 2022. Grand Canyon University. All Rights Reserved. Directions: please follow explicitly *** primarily this assignment is filling in the tables- attached all articles to use **** Use the attached "Literature Evaluation Table to complete this assignme Literature Evaluation Table – DPI Intervention Learner Name: Instructions: Use this table to evaluate and record the literature gathered for your DPI Project. Refer to the assignment instructions for guidance on completing the various sections. Empirical research articles must be published within 5 years of your anticipated graduation date. Add or delete rows as needed. PICOT-D Question: In adult patients in a high observation unit (HOU) at a long-term acute care hospital will the translation of Balas’s et al. research implementing the ABCDEF bundle, compared to current practice impact length of stay over an eight-week period? Table 1: Primary Quantitative Research – Intervention (5 Articles) complete table with listed articles APA Reference (Include the GCU permalink or working link used to access the article.) Research Questions/ Hypothesis, and Purpose/Aim of Study Type of Primary Research Design Research Methodology Setting/Sample (Type, country, number of participants in study) Methods (instruments used; state if instruments can be used in the DPI project) How was the data collected? Interpretation of Data (State p-value: acceptable range is p= 0.000 – p= 0.05) Outcomes/Key Findings (Succinctly states all study results applicable to the DPI Project.) Limitations of Study and Biases Recommendations for Future Research Explanation of How the Article Supports Your Proposed Intervention Balas, M. C., Tan, A., Pun, B. T., Ely, E. W., Carson, S. S., Mion, L., Barnes-Daly, M. A., & Vasilevskis, E. E. (2022). Effects of a national quality improvement collaborative on ABCDEF bundle implementation. American Journal of Critical Care, 31(1), 54–64. https://doi-org.lopes.idm.oclc.org/10.4037/ajcc2022768 https://aacnjournals.org/ajcconline/article-abstract/31/1/54/31644/Effects-of-a-National-Quality-Improvement?redirectedFrom=fulltext What are the effect of quality improvement collaborative participation on ABCDEF bundle performance? This study examined the NQIC's impact on the implementation of the six components of the ABCDEF Bundle in four types of hospitals: The authors hypothesized that with an increase in safety culture, there would be an increased implementation of the ABCDEF Bundle. The purpose of this study was to determine whether the ABCDEF Bundle could be implemented in a variety of hospitals across the United States with a focus on safety culture. Quasi-experimental design This study used a non-experimental design to determine the impact of the ABCDEF Bundle on safety culture, defined as the degree to which a system is characterized by attention to safety in tasks, relationships, and attitudes. The study included 114 acute care hospitals that were participating in the NQIC. P > 0.05 In the ARISE and ProCESS trials, ABCDEF Bundle reduced ICU mortality by 12.6% (P=0.04) and hospital mortality by 15.1% (P=0.007) with no difference in new organ failure or adverse events. The greatest benefit was seen in patients with septic shock. “Conclusion: These studies showed that the ABCDEF Bundle is associated with lower ICU and hospital mortality The first limitation is that the study involved observational studies, and residual confounding cannot be omitted as an explanation for the observed changes in bundle performance. Secondly, conclusions cannot be made on long-term sustainability despite ICUs demonstrating improvements during a 20-month period. Authors should use an experimental research design The language used should be simplified for easier understanding by all audience. The article provides information on reducing the use of common potentially preventable complications (PPCs) in acute care hospitals, connected to my DPI project. The Central Line Bundle demonstrated a 19% reduction in complications, and the ABCDEF Bundle demonstrated a 21% reduction. The ABCDEF Bundle can be implemented in various hospitals across the United States with a focus on safety culture, defined as the degree to which a system is characterized by attention to safety in tasks, relationships, and attitudes. Barnes-Daly, M. A., Phillips, G., & Ely, E. W. (2017). Improving hospital survival and reducing brain dysfunction at seven California community hospitals: Implementing PAD guidelines via the ABCDEF bundle in 6,064 patients. Critical Care Medicine, 45(2), 171–178. https://doi-org.lopes.idm.oclc.org/10.1097/CCM.0000000000002149 https://ubccriticalcaremedicine.ca/academic/jc_article/Improving%20Hospital%20Survival%20and%20Reducing%20Brain%20Dysfunction%20(Jan-19-17).pdf The research question was tailored on tracking compliance by an interprofessional team with the (ABCDEF) bundle in enforcing the Agitation, Pain, and Delirium procedures. The aim was to examine the connection between ABCDEF bundle compliance and consequences, including clinic survival and delirium-free and coma-free days in community infirmaries A prospective cohort quality improvement initiative involving ICU patients. 1. Random selection of 1 patient from the daily census at each hospital 2. Study included patients who were 66 years or older with a diagnosis of AMI. Exclusion criteria included age <66 years, primary diagnosis of a noncardiac etiology (e.g., sepsis), and a transfer from another acute care hospital. Data collection Data on patient characteristics, processes of care, and outcomes were collected during the baseline period (January 1, 2008, to July 31, 2009) and during the follow-up period (August 1, 2009, to September 30, 2011) for a total of 2 years of data. P > 0.05 The mortality rate for patients with sepsis was decreased by 42 percent (from 20.7 percent to 12.1 percent) in the 23 months after implementation of the ABCDEF bundle, compared with the 21 months before the institution of the bundle. Mortality rates for patients with pneumonia were also lower after bundle implementation (35.4 percent before the intervention vs. 28 percent afterward) The number of days’ patients spent in the intensive care unit within 30 days after arriving at the hospital was reduced by an average of 1.7 days for patients who had sepsis, and by an average of 1.5 days for those with pneumonia The number of brain dysfunction events (such as coma, seizures, and infection) within 30 days after an ICU admission dropped by 36 percent improving Hospital Survival and Reducing Brain Dysfunction at Seven California Community Hospitals: Implementing PAD Guidelines Via the ABCDEF Bundle in 6,064 Patients. First, this QI project lacked the strict protocols found in randomized, controlled trials. The design and sample size benefits of the investigation did not trump other statistical concerns. – Physicians need further education on guidelines and protocols, as well as how to collaborate with other physicians and experts. – Physical environment needs to be improved along with an organized system for transferring patients. – Physicians should be more open to changing their thought process. – Better communication between nurse and physician needs to be encouraged, as well as between physicians and experts such as cardiologists. The article describes the implementation of acute care for older adults’ guidelines at seven California community hospitals and has been used to determine whether a regional quality improvement initiative is associated with improved hospital survival, functional status, and intensive care unit (ICU) length of stay after acute myocardial infarction (AMI). The article also determined whether a regional quality improvement initiative is associated with improved hospital survival, functional status, and ICU length of stay after AMI. Devlin, J. W., Skrobik, Y., Gélinas, C., Needham, D. M., Slooter, A. J. C., Pandharipande, P. P., Watson, P. L., Weinhouse, G. L., Nunnally, M. E., Rochwerg, B., Balas, M. C., van den Boogaard, M., Bosma, K. J., Brummel, N. E., Chanques, G., Denehy, L., Drouot, X., Fraser, G. L., Harris, J. E., … Alhazzani, W. (2018). Clinical Practice Guidelines for the prevention and management of pain, agitation/sedation, delirium, immobility, and sleep disruption in adult patients in the ICU. Critical Care Medicine, 46(9), 825–873. https://doi-org.lopes.idm.oclc.org/10.1097/CCM.0000000000003299 https://lopes.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=cmedm&AN=30113379&site=eds-live&scope=site&custid=s8333196&groupid=main&profile=eds1 The research question tested the compliance of clinical practice guidelines in the prevention and management of pain, agitation, delirium, immobility and sleep disruption in adult patients in the ICU. Research aimed at expanding the clinical practice guidelines to prevent pain, agitation, delirium, immobility and sleep disruption in adult patients in ICU Clinical Practice Guideline The study applied grading of recommendations assessment development and evaluation (GRADE) working group’s methodology to determine clinical practice guideline development The study involved content experts, methodologists and ICU survivors represented in each of the five sections of the guidelines. Chairs, group heads, panel members and 11 ICU survivors selected topics that are relevant to patients and practicing clinicians. P > 0.05 Pooled analysis demonstrated neuropathic agents reduced pain intensity. Patients taking gabapentin also demonstrated decrease in pain compared to patients taking carbamazepine. Reduced use of opioids resulted to improvement in patients outcomes for critically ill adults in ICU The study failed to use a validated pain intensity scale and the methodologic limitations inherent to observation led to an overall very low quality of evidence. Numerous factors related to resources make this intervention possibly infeasible to implement. Clinicians need to be provided with basic education regarding the pain control and management for adults in ICU Physicians should attend regular on-the-job training to improve their clinical management accuracy for the patients. This article describes clinical practice guidelines for the prevention and management of pain agitation, delirium, immobility, and sleep disruption in adult patients in ICU. The article also highlights some the clinical precautions that physicians ca use to management and prevent pain, agitation, delirium, immobility and sleep disruption to improve effectiveness for patients in and post ICU. Hsieh, S. J., Otusanya, O., Gershengorn, H. B., Hope, A. A., Dayton, C., Levi, D., Garcia, M., Prince, D., Mills, M., Fein, D., Colman, S., & Gong, M. N. (2019). Staged implementation of awakening and breathing, coordination, delirium monitoring and management, and early mobilization bundle improves patient outcomes and reduces hospital costs. Critical Care Medicine, 47(7), 885–893. https://doi-org.lopes.idm.oclc.org/10.1097/CCM.0000000000003765 https://lopes.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=cmedm&AN=30985390&site=eds-live&scope=site&custid=s8333196&groupid=main&profile=eds1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6579661/ The research question aimed at measuring the impact of staged implementation of complete versus virtual ABCDE bundle on mechanical ventilation (MV) duration, intensive care unit (ICU) and hospital length of stay(LOS), and cost Prospective cohort study The study included two medical ICUs within Montefiore Healthcare Center (Bronx, New York). The study also included 1855 mechanically ventilated patients admitted to ICUs between July 2011 – July 2014. p<0.05 Early mobilization and coordination (EC) portrayed improvement of patients in ICU by 30% Implementation of full (B-AD-EC) vs (B-AD) resulted to a decrease in MV duration. Implementation of ABCDE bundle reduced total ICU and hospital cost by 24.2% and 30.2% respectively. The study experienced the challenge of unmeasured changes which could have affected the results The study also was conducted in a single medical center hence limiting generalizability. The study also may have experienced cross-contamination of practices between two ICUs The study was unable to compare costs between two seasonal periods due to cost-to-charge ratios changes hence study used smaller cohort for cost analyses. The study did not collect all the data in the partial bundle ICU for comparison There is need for physicians to acquire training on implementing ABCDE bundle to improve patient’s conditions on ICU and reduce length of hospital stay. There is need for teamwork between physicians in ICU to enhance patient’s health and medication adherence. There is need for improvement of working conditions in health facilities to safeguard patient’s health. This article accessed the impact of implementing complete versus virtual ABCDE bundle on mechanical ventilation(MV) duration, intensive care Unit (ICU)and hospital length of stay(LOS), and cost. However, the article has also determined that early mobilization and structured condition of ABCDE bundle results to a spontaneous awakening, breathing, and delirium management leading to reduced mechanical duration(MV), length of hospital stay and the cost. Pun, B. T., Balas, M. C., Barnes-Daly, M. A., Thompson, J. L., Aldrich, J. M., Barr, J., Byrum, D., Carson, S. S., Devlin, J. W., Engel, H. J., Esbrook, C. L., Hargett, K. D., Harmon, L., Hielsberg, C., Jackson, J. C., Kelly, T. L., Kumar, V., Millner, L., Morse, A., … Ely, E. W. (2019). Caring for critically ill patients with the ABCDEF bundle: Results of the ICU liberation collaborative in Over 15,000 adults. Critical Care Medicine, 47(1), 3–14. https://doi-org.lopes.idm.oclc.org/10.1097/CCM.0000000000003482 https://lopes.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=cmedm&AN=30339549&site=eds-live&scope=site&custid=s8333196&groupid=main&profile=eds1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6298815/ The study aim at evaluating the relationship between ABCDEF bundle performance and patient-centered outcomes in critical care. Prospective cohort study from national quality improvement collaborative The research collected a 20-month period data on 68 academics, community, and federal ICUs The study also included 15226 patient adults and at least one ICU every day. p < 0.002 Complete ABCDE bundle performance demonstrated a reduction in mortality rate within 7 days, mechanical ventilation, delirium and physical restraint use. Patients also demonstrated an increased dose response relationship between higher proportion bundle performance. Frequent pain was reported with increased bundle performance. The study did not use a randomized study design, nor did it have access to concurrent control. ICU liberation collaborative included numerous ICU types as part of a larger effort to understand the impact of the ABCDE bundle on various types of critically ill patients while understanding the implementation strategies unique to each setting. The patient-level outcomes are not wholly independent of one another and are assessed within a short time frame during which patients did not experience those outcomes. The ICU liberation collaborative study lacked sufficient funds to support data accuracy auditing. Cohort analysis is from patient data collected within a larger QI project that collected a minimum and de-identified dataset, limiting the study’s ability to answer some questions. Physicians ought to familiarize with ABCDE bundle performance to enhance patients’ dose adherence to the critically ill adults in ICU. Physicians need to collaborate with other professionals in health sector and attend to ICU cases with open minded ready to learn from others. The article analyzes measures to take in caring for the critically ill patients in ICU with ABCDEF bundle with reference to the results of the ICU liberation collaborative of over 15000 adults. The article however outlined the relationship between ABCDEF bundle performance and patient centered outcomes in critical care. Therefore, it is clear that ABCDEF bundle performance portray significant clinical improvements in patient survival, mechanical ventilation use, coma and delirium, restraint free care, ICU re-admissions and post ICU discharge disposition. Table 2: Additional Primary and Secondary Quantitative Research (10 Articles) complete table with listed articles APA Reference (Include the GCU permalink or working link used to access the article.) Research Questions/ Hypothesis, and Purpose/Aim of Study Type of Primary or Secondary Research Design Research Methodology Setting/Sample (Type, country, number of participants in study) Methods (instruments used; state if instruments can be used in the DPI project) How was the data collected? Interpretation of Data (State p-value: acceptable range is p= 0.000 – p= 0.05) Outcomes/Key Findings (Succinctly states all study results applicable to the DPI Project.) Limitations of Study and Biases Recommendations for Future Research Explanation of How the Article Supports Your Proposed DPI Project Collinsworth, A. W., Brown, R., Cole, L., Jungeblut, C., Kouznetsova, M., Qiu, T., Richter, K. M., Smith, S., & Masica, A. L. (2021). Implementation and routinization of the ABCDE bundle: A mixed methods evaluation. Dimensions of Critical Care Nursing : DCCN, 40(6), 333–344. https://doi-org.lopes.idm.oclc.org/10.1097/DCC.0000000000000495 https://lopes.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=cmedm&AN=34606224&site=eds-live&scope=site&custid=s8333196&groupid=main&profile=eds1 https://pubmed.ncbi.nlm.nih.gov/34606224/ The study determines how to facilitate ABCDE bundle adoption by analyzing different implementation strategies on bundle adherence rates. The study also aims at assessing clinician’s perception of the bundle and the implementation effort. Mixed method eval The study examined effect of 2 bundle implementation on 8 patient adults in ICU. Electronic Health Record(EHR) modification was used as the primary strategy while enhanced strategy uses HER plus additional bundle training 84 nurses, therapists and physicians participated in the survey. (p <0.05) The response from the participants show that bundle use resulted in best care and patient outcomes. After bundle implementation process, ICUs in both interventions showed improvement in bundle adherence ICUs in the basic intervention outperformed others after initiating own implementation strategies. Data collection was time consuming The study acquired data through HER hence limited to evaluating some elements such as pain and sedation Physicians response on bundle perception may be biased. There is need for adequate training for physicians on how best to implement ABCDE bundle to improve care for patients Promote teamwork to enhance coordination between healthcare professionals for easier implementation of ABCDE bundle. The article highlights the effects of applying ABCDE bundle in healthcare for the patients in ICU It scores the fact that proper implementation of ABCDE bundles results to improvement in nursing care and patient outcomes. DeMellow, J. M., Kim, T. Y., Romano, P. S., Drake, C., & Balas, M. C. (2020). Factors associated with ABCDE bundle adherence in critically ill adults requiring mechanical ventilation: An observational design. Intensive & Critical Care Nursing, 60. https://doi-org.lopes.idm.oclc.org/10.1016/j.iccn.2020.102873 https://lopes.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edselp&AN=S0964339720300768&site=eds-live&scope=site&custid=s8333196&groupid=main&profile=eds1 https://pubmed.ncbi.nlm.nih.gov/32414557/ The study aim at identifying factors associated with ABCDEF bundle adherence in critically ill patients during the first 96hours of ventilation. Observational using electronic health record data The study used 15 ICUs located in seven community hospitals in western United States The study also included 977 adult patients who were on mechanical ventilation for more than 24hours and admitted to an intensive care unit over the six months. (p <0.05) The observational results from the data identified that modifiable factors improved team’s performance of the ABCDEF bundle in critically patients in need of mechanical ventilation. The study was restricted to EHR clinical data available hence managed to only evaluate assessment for pain, sedation, delirium, and mobility elements. The study did not use analgesic infusions as sedation to determine duration of sedation and adherence of awakening trials. The study was limited to the examination of the early 96hours on MV adherence to bundle by the care unit. There is need for openness in data sharing among the physicians to develop a complete system that can identify all the factors associated with ABCDEF bundle adherence in severely ill patients The article supports my DPI project since the article identifies the factors associated with ABCDEF bundle adherence in critically ill patients during the first 96 hours of ventilation. The article supports the results that modifiable factors improve team’s performance of the ABCDE bundle in critically ill patients in mechanical ventilation. Loberg, R. A., Smallheer, B. A., & Thompson, J. A. (2022). A quality improvement initiative to evaluate the effectiveness of the ABCDEF bundle on Sepsis outcomes. Critical Care Nursing Quarterly, 45(1), 42–53. https://doi-org.lopes.idm.oclc.org/10.1097/CNQ.0000000000000387 https://lopes.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=cmedm&AN=34818297&site=eds-live&scope=site&custid=s8333196&groupid=main&profile=eds1 https://pubmed.ncbi.nlm.nih.gov/34818297/ The study aims to determine how quality improvement initiative can evaluate the effectiveness of the ABCDEF bundle elements to improve clinical outcomes Quality Improvement Secondary research through sampling Interventions was done in (609-bed) Midwest metropolitan hospital. Pre-implementation data were collected between January 2019 and March 2019. A pre/posttest design was used, and a convenience sample of all patients with sepsis admitted (p <0.05 The study results indicated overall implementation of ABCDEF bundle in the setting resulted to enhanced care delivery and improved clinical outcomes. The QI initiative has problem with its generalizability Lower than desired rate with bundle elements was experienced The intervention was not designed as randomized controlled study but rather utilized as convenient sampling. There is need to provide nursing care education to healthcare workers to implement the ABCDEF bundle since its implementation has a direct impact on enhancing care giving and clinical outcomes. The government should support the implementation of the QI initiative to enhance quality care for patients. The article is relevant to my DPI project since it outlines the guidelines on how best ABCDEF bundle can be applied in nursing to improve clinical outcomes. Otusanya, O. T., Hsieh, S. J., Gong, M. N., & Gershengorn, H. B. (2021). Impact of ABCDE bundle implementation in the intensive care unit on specific patient costs. Journal of Intensive Care Medicine, 8850666211031813. https://doi-org.lopes.idm.oclc.org/10.1177/08850666211031813 https://lopes.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=cmedm&AN=34286609&site=eds-live&scope=site&custid=s8333196&groupid=main&profile=eds1 https://pubmed.ncbi.nlm.nih.gov/34286609/#:~:text=Conclusions%3A%20Full%20ABCDE%20bundle%20implementation,increase%20in%20physical%20therapy%20costs. The study objective is to measure the impact of full versus partial ABCDE bundle implementation on specific cost centers and related resource utilization. Retrospective cohort study The study was conducted in two medical ICUs in Montefiore Health Systems The study also involved 472 mechanically ventilated patients admitted in the ICU between 1st January 2013 and 31st December 2013. (p <0.05) There was a relationship between ABCDE bundle implementation and the cost Relative to the comparison ICU, implementation of the entire bundle in the intervention resulted to a decrease of 27.3%in total hospital laboratory cost Total hospital resource use resource use decreased in the intervention ICU. The research data collection and analysis was only limited to two ICU centers. There is need for teamwork between professionals in nursing to fully implement ABCDE bundle intervention to increase ICU discharges and reduce total hospitalization cost Physicians also need conducive environment and support to fully implement ABCDE bundle in health centers The article supports my DPI project as it focuses on how fully implementation of ABCDE bundle significantly reduces hospital laboratory costs and the hospital resource use also decreased. van den Boogaard, M., Wassenaar, A., van Haren, F. M. P., Slooter, A. J. C., Jorens, P. G., van der Jagt, M., Simons, K. S., Egerod, I., Burry, L. D., Beishuizen, A., Pickkers, P., & Devlin, J. W. (2020). Influence of sedation on delirium recognition in critically ill patients: A multinational cohort study. Australian Critical Care, 33(5), 420–425. https://doi-org.lopes.idm.oclc.org/10.1016/j.aucc.2019.12.002 https://lopes.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145414398&site=eds-live&scope=site&custid=s8333196&groupid=main&profile=eds1 https://www.australiancriticalcare.com/article/S1036-7314(19)30131-6/pdf The study aim to determine the association between level of sedation and delirium occurrence in critically ill patients Observation of cohort study. Patients aged above 18years from multinational ICUs participated since ICU patients are at risk of developing outcome of interest and delirium. The study was a secondary analysis of a multinational prospective cohort study performed in 9 ICUs in different countries Patients were assessed either through CAM-ICU or ICDSC 1660 patients were involved in the study. (p <0.05) At a RASS of 0, assessment with the CAM-ICU (vs. the ICDSC) was associated with fewer positive delirium evaluations The influence of level of sedation on delirium assessment depends on whether the CAM-ICU or ICDSC is used The study based on comparison between sedation and delirium hence need to compare both CAM-ICU to ICDSC simultaneously and determine its impact on critically ill patients. There is need to compare the CAM-ICU and ICDSC simultaneously in sedated and non-sedated ICU patients There is need to offer training to nurses in intensive care units on how best sedation and delirium influence affects critically ill patients in ICU. The article is relevant since it focuses on determining the influence of sedation on delirium which aligns with DPI project as heath care personnel. Part 2 Collingsworth Marra Moraes Shallom sinvani Trogrlic Zhang Table 3: Theoretical Framework Aligning to DPI Project Nursing Theory Selected APA Reference – Seminal Research References (Include the GCU permalink or working link used to access each article.) Explanation for the Nursing Theory Guides the Practice Aspect of the DPI Project Virginia Henderson’s Nursing Needs Theory Ahtisham, Y., & Jacoline, S. (2015). Integrating Nursing Theory and Process into Practice; Virginia’s Henderson Need Theory. International Journal of Caring Sciences, 8(2), 443–450. https://lopes.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=102972280&site=eds-live&scope=site&custid=s8333196&groupid=main&profile=eds1 Virginia Henderson came up with a modern nursing theory named ‘need theory.’ Virginia named her theory need to emphasize the importance of increasing the patient's independence to ensure the nursing progress can continue even after hospitalization. Virginia further categorized nursing into fourteen components based on human needs. In her theory, Virginia described the nurse’s role as a substitute doing on behalf of the patient, helper, and working for the patient to help the patient become independent. According to Virginia, a nurse’s role is to assist the person sick or healthy in performing activities that contribute to healthy recovery that the person would have performed individually if they had the strength to do it. In her theory on individual care, Virginia emphasized assisting individuals with essential activities to maintain health or help the person attain a peaceful death. Henderson, in her fourteen components of nursing, the first nine are physiological, 10th and 14th are psychological aspects of learning and communication, 11th as spiritual and moral while the remaining she categorized them as sociological oriented to work and recreation. The fourteen components effective for nursing, according to Henderson, included breathing normally, eating and drinking adequately, excretion, mobility and maintaining body postures, enough sleep and rest, suitable clothing, maintaining body temperatures by wearing different clothes in different environments, maintaining body hygiene and avoiding dangers both personal and from endangering others, expression of emotions, fears or needs through communication, worshipping, working in a way to express a sense of accomplishment, participating in various recreational activities and lastly the curiosity to discover and learn Change Theory Selected APA Reference - Seminal Research References (Include the GCU permalink or working link used to access each article.) Explanation for How the Change Theory Outlines the Strategies for Implementing the Proposed Intervention John Kotter’s Change Model Kang, S. P., Chen, Y., Svihla, V., Gallup, A., Ferris, K., & Datye, A. K. (2022). Guiding change in higher education: an emergent, iterative application of Kotter’s change model. Studies in Higher Education, 47(2), 270–289. https://doi-org.lopes.idm.oclc.org/10.1080/03075079.2020.1741540 https://lopes.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=155185571&site=eds-live&scope=site&custid=s8333196&groupid=main&profile=eds1 Kotter came up with 8 step change processes applied to implement change successfully. These strategies can be applied in implementing proposed interventions in nursing. The first step is creating urgency. First, there is a need to develop urgency for the proposed interventions. This is possible by identifying the existing threats caring for patients in ICUs. Therefore, discuss the weaknesses with the stakeholders and colleagues and ask for their support to implement the change. Secondly, put together a guiding coalition. Come up with a group of competent leaders and professionals to steer the agenda to influence the stakeholders. Thirdly develop vision and strategies. In this step, come up with a clear vision of how the organization will look if the change is implemented. A clear vision of how the health sector would look after implementing intervention will enhance action and decision-making. The next step is communicating the change vision. In this step, communicate to capture the hearts of other health workers to support the change. The next step is avoiding barriers. The guiding team avoids barriers from the change to drum up support for the change. The next step is accomplishing short-term wins. These short-term wins serve as encouragement and should be related to the change. E.g., win by demonstrating the effectiveness of the proposed intervention. The next step is building on the change. This step ensures the team is overworking to achieve the change and measure progress. The last step is to make change stick. Here ensure that everyone adapts to new change by illustrating its importance, training them the skills necessary to maintain the new change. Table 4: Clinical Practice Guidelines (If applicable to your project/practice) APA Reference - Clinical Guideline (Include the GCU permalink or working link used to access the article.) APA Reference - Original Research (All) (Include the GCU permalink or working link used to access the article.) Explanation for How Clinical Practice Guidelines Align to DPI Project © 2022. Grand Canyon University. All Rights Reserved.
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