Structured Mobilization for Critically Ill Patients: A Pragmatic Cluster-randomized Trial.

Rationale: Small trials and professional recommendations support mobilization interventions to improve recovery among critically ill patients, but their real-world effectiveness is unknown. Objective: To evaluate a low-cost, multifaceted mobilization intervention. Methods: We conducted a stepped-wedge cluster-randomized trial across 12 ICUs with diverse case mixes. The primary and secondary samples included patients mechanically ventilated for ⩾48 hours who were ambulatory before admission, and all patients with ICU stays ⩾48 hours, respectively. The mobilization intervention included 1) designation and posting of daily mobilization goals; 2) interprofessional closed-loop communication coordinated by each ICU's facilitator; and 3) performance feedback. Measurements and Main Results: From March 4, 2019 through March 15, 2020, 848 and 1,069 patients were enrolled in the usual care and intervention phases in the primary sample, respectively. The intervention did not increase the primary outcome, patient's maximal Intensive Care Mobility Scale (range, 0-10) score within 48 hours before ICU discharge (estimated mean difference, 0.16; 95% confidence interval, -0.31 to 0.63; P = 0.51). More patients in the intervention (37.2%) than usual care (30.7%) groups achieved the prespecified secondary outcome of ability to stand before ICU discharge (odds ratio, 1.48; 95% confidence interval, 1.02 to 2.15; P = 0.04). Similar results were observed among the 7,115 patients in the secondary sample. The percentage of days on which patients received physical therapy mediated 90.1% of the intervention effect on standing. ICU mortality (31.5% vs. 29.0%), falls (0.7% vs. 0.4%), and unplanned extubations (2.0% vs. 1.8%) were similar between groups (all P > 0.3). Conclusions: A low-cost, multifaceted mobilization intervention did not improve overall mobility but improved patients' odds of standing and was safe. Clinical trial registered with www.clinicaltrials.gov (NCT03863470).

A Survey to Assess Primary Care Physician Awareness of Complications Following Critical Illness.

Background: Survivors of critical illness are at risk for post-intensive care syndrome (PICS, comprised of physical dysfunction, cognitive impairment, and neuropsychiatric disorders including anxiety, depression, and post-traumatic stress). Their family members and caregivers are at risk for PICS-F (PICS-family, comprised of anxiety, depression, post-traumatic stress). PICS and PICS-F are increasingly recognized in critical care; however, the awareness among primary providers of the domains and the terms of PICS/PICS-F is unknown. Objectives: To determine current practice patterns and knowledge among primary care physicians in regards to patients recovering from critical illness; to determine barriers to care of post-critically ill patients. Methods: A paper and electronic survey were developed and randomly distributed to a subset of North Carolina primary care physicians. Survey questions consisted of the following domains: demographics, current practice, barriers to providing care, knowledge of common issues/complications following critical illness, and interest in changing care for survivors of critical illness. Results: One hundred and ninety-six surveys were delivered and 77 completed surveys (39% response rate) were analyzed. Respondents confirmed significant barriers to care of post-critically ill patients including lack of awareness of PICS/PICS-F terminology, insufficient time to spend with patients, and inadequate education of patients/families about recovery after critical illness. Fifty-seven percent of respondents thought a specialized transitional post-ICU clinic would be helpful. Sixty-two percent reported feeling comfortable caring for patients after a critical illness and 75% felt they were aware of common problems encountered after critical illness. However, 84% also thought more education about PICS/PICS-F would be helpful as would a list of common problems seen after critical illness (91%). Conclusions: Significant gaps and barriers to providing optimal post-ICU care by PCPs exist. Providers identified time constraints and educational gaps as domains needing attention. Dedicated post-ICU clinics might provide a bridge to transition care post-critical illness back to primary care providers.

A Survey of Implementation of ABCDE Protocols.

BACKGROUND: Spontaneous awakening trials (SATs), spontaneous breathing trials (SBTs), delirium assessment/management, early mobility have been termed the ABCDE bundle. The ABCDE bundle has been proven to improve patient outcomes. However, there is often a long gap in dissemination and implementation of evidence-based medicine. OBJECTIVES: To determine the prevalent implementation of and determinants for ABCDE protocol adoption in Pennsylvania. METHODS: We developed a survey of ABCDE bundle protocols. We surveyed factors around implementation including written protocol presence, standardized assessments to guide protocols, timing of creation of protocols, and estimated adherence to protocols. We also collected data on factors that might be determinants for protocol adoption including ICU staffing models, hospital and ICU level factors. We validated the survey tool using the Michigan Health and Hospital Association Keystone ICU collaborative. We then administered the validated survey to a leader of the medical ICU or mixed medical-surgical ICU of all Pennsylvania Hospitals. Multivariable logistic and ordinal regression were used to determine associations between ICU staffing models and hospital and ICU level factors with the presence of ABCDE bundle protocols. RESULTS: In the study cohort of Pennsylvania ICUs (n = 144), we had 100 respondents (69% response). The median number of hospital beds among the respondents was 185 (IQR 111-355) with a median of 14 ICU beds (IQR 10-20). 86% reported spontaneous awakening trial protocols, 60% reported spontaneous breathing trial protocols, 43% reported delirium assessment/management protocols, and 27% reported early mobility protocols. Being a medical ICU compared to a mixed medical-surgical ICU (OR 3.48, 95% CI 1.19-10.21, P = .02) and presence of multidisciplinary rounds (OR 4.97, 95% CI 2.07-11.94, P < .001) were associated with increasing number of ABCDE bundle protocol components. CONCLUSIONS: Variable implementation of ABCDE bundle protocols was present across Pennsylvania. Team communication is important to implementation of these protocols.

Association of Time to Rapid Response Team Activation With Patient Outcomes Using a Range of Physiologic Deterioration Thresholds.

Clinical deterioration of hospitalized patients is common and can lead to critical illness and death. Rapid response teams (RRTs) assess and treat high-risk patients with signs of clinical deterioration to prevent further worsening and subsequent adverse outcomes. Whether activation of the RRT early in the course of clinical deterioration impacts outcomes, however, remains unclear. We sought to characterize the relationship between increasing time to RRT activation after physiologic deterioration and short-term patient outcomes. DESIGN: Retrospective multicenter cohort study. SETTING: Three academic hospitals in Pennsylvania. PATIENTS: We included the RRT activation of a hospitalization for non-ICU inpatients greater than or equal to 18 years old. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary exposure was time to RRT activation after physiologic deterioration. We selected four Cardiac Arrest Risk Triage (CART) score thresholds a priori from which to measure time to RRT activation (CART score ≥ 12, ≥ 16, ≥ 20, and ≥ 24). The primary outcome was 7-day mortality-death or discharge to hospice care within 7 days of RRT activation. For each CART threshold, we modeled the association of time to RRT activation duration with 7-day mortality using multivariable fractional polynomial regression. Increased time from clinical decompensation to RRT activation was associated with higher risk of 7-day mortality. This relationship was nonlinear, with odds of mortality increasing rapidly as time to RRT activation increased from 0 to 4 hours and then plateauing. This pattern was observed across several thresholds of physiologic derangement. CONCLUSIONS: Increasing time to RRT activation was associated in a nonlinear fashion with increased 7-day mortality. This relationship appeared most marked when using a CART score greater than 20 threshold from which to measure time to RRT activation. We suggest that these empirical findings could be used to inform RRT delay definitions in further studies to determine the clinical impact of interventions focused on timely RRT activation.

Defining Physiological Decompensation: An Expert Consensus and Retrospective Outcome Validation.

OBJECTIVES: Physiological decompensation of hospitalized patients is common and is associated with substantial morbidity and mortality. Research surrounding patient decompensation has been hampered by the absence of a robust definition of decompensation and lack of standardized clinical criteria with which to identify patients who have decompensated. We aimed to: 1) develop a consensus definition of physiological decompensation and 2) to develop clinical criteria to identify patients who have decompensated. DESIGN: We utilized a three-phase, modified electronic Delphi (eDelphi) process, followed by a discussion round to generate consensus on the definition of physiological decompensation and on criteria to identify decompensation. We then validated the criteria using a retrospective cohort study of adult patients admitted to the Hospital of the University of Pennsylvania. SETTING: Quaternary academic medical center. PATIENTS: Adult patients admitted to the Hospital of the University of Pennsylvania who had triggered a rapid response team (RRT) response between January 1, 2019, and December 31, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Sixty-nine experts participated in the eDelphi. Participation was high across the three survey rounds (first round: 93%, second round: 94%, and third round: 98%). The expert panel arrived at a consensus definition of physiological decompensation, "An acute worsening of a patient's clinical status that poses a substantial increase to an individual's short-term risk of death or serious harm." Consensus was also reached on criteria for physiological decompensation. Invasive mechanical ventilation, severe hypoxemia, and use of vasopressor or inotrope medication were bundled as criteria for our novel decompensation metric: the adult inpatient decompensation event (AIDE). Patients who met greater than one AIDE criteria within 24 hours of an RRT call had increased adjusted odds of 7-day mortality (adjusted odds ratio [aOR], 4.1 [95% CI, 2.5-6.7]) and intensive care unit transfer (aOR, 20.6 [95% CI, 14.2-30.0]). CONCLUSIONS: Through the eDelphi process, we have reached a consensus definition of physiological decompensation and proposed clinical criteria with which to identify patients who have decompensated using data easily available from the electronic medical record, the AIDE criteria.

A Retrospective Analysis of Malpractice-Related Procedure Rates for Internal Medicine Specialists at an Academic Medical Center.

BACKGROUND: Although malpractice litigation is common in the United States, the risk of a malpractice claim for procedures performed by internal medical practitioners is unknown. This study determined the frequency of malpractice claims related to procedures in a large department of medicine at an academic medical center over a five-year period. METHODS: Researchers retrospectively reviewed all malpractice claims and procedures performed by internal medicine practitioners of all specialties between July 1, 2014, and June 30, 2019, in a department of medicine at a large academic medical center. A list of all procedures and Current Procedural Terminology codes performed by internal medicine practitioners was compiled. Active procedure-related malpractice claims and the total number of procedures performed during the study period were counted. RESULTS: During the study period, 353,661 procedures were performed by internal medicine practitioners. During the same period, 76 active malpractice claims were identified, of which only 13 (17.1%) were procedure-related. For 2 different malpractice claims, a single patient had 2 procedures; thus 13 total claims related to the performance of 15 procedures. The proportion of procedure-related claims per total number of procedures performed was 0.37 claims/10,000 cases. The frequency of procedure-related malpractice claims per number of procedures performed ranged from 1 in 38 for pulmonary artery thrombolytic therapy to 1 in 137,325 for colonoscopy. CONCLUSION: Procedure-related malpractice claims against internal medicine practitioners at a large academic medical center over a five-year period were infrequent despite significant procedural volume. Contextualizing procedure-related malpractice claims in terms of procedure-specific volume reframes the reporting of malpractice risk.

Impact of COVID-19 on inpatient clinical emergencies: A single-center experience.

AIM: Determine changes in rapid response team (RRT) activations and describe institutional adaptations made during a surge in hospitalizations for coronavirus disease 2019 (COVID-19). METHODS: Using prospectively collected data, we compared characteristics of RRT calls at our academic hospital from March 7 through May 31, 2020 (COVID-19 era) versus those from January 1 through March 6, 2020 (pre-COVID-19 era). We used negative binomial regression to test differences in RRT activation rates normalized to floor (non-ICU) inpatient census between pre-COVID-19 and COVID-19 eras, including the sub-era of rapid COVID-19 census surge and plateau (March 28 through May 2, 2020). RESULTS: RRT activations for respiratory distress rose substantially during the rapid COVID-19 surge and plateau (2.38 (95% CI 1.39-3.36) activations per 1000 floor patient-days v. 1.27 (0.82-1.71) during the pre-COVID-19 era; p = 0.02); all-cause RRT rates were not significantly different (5.40 (95% CI 3.94-6.85) v. 4.83 (3.86-5.80) activations per 1000 floor patient-days, respectively; p = 0.52). Throughout the COVID-19 era, respiratory distress accounted for a higher percentage of RRT activations in COVID-19 versus non-COVID-19 patients (57% vs. 28%, respectively; p = 0.001). During the surge, we adapted RRT guidelines to reduce in-room personnel and standardize personal protective equipment based on COVID-19 status and risk to providers, created decision-support pathways for respiratory emergencies that accounted for COVID-19 status uncertainty, and expanded critical care consultative support to floor teams. CONCLUSION: Increased frequency and complexity of RRT activations for respiratory distress during the COVID-19 surge prompted the creation of clinical tools and strategies that could be applied to other hospitals.

Characteristics, Outcomes, and Trends of Patients With COVID-19-Related Critical Illness at a Learning Health System in the United States.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic continues to surge in the United States and globally. OBJECTIVE: To describe the epidemiology of COVID-19-related critical illness, including trends in outcomes and care delivery. DESIGN: Single-health system, multihospital retrospective cohort study. SETTING: 5 hospitals within the University of Pennsylvania Health System. PATIENTS: Adults with COVID-19-related critical illness who were admitted to an intensive care unit (ICU) with acute respiratory failure or shock during the initial surge of the pandemic. MEASUREMENTS: The primary exposure for outcomes and care delivery trend analyses was longitudinal time during the pandemic. The primary outcome was all-cause 28-day in-hospital mortality. Secondary outcomes were all-cause death at any time, receipt of mechanical ventilation (MV), and readmissions. RESULTS: Among 468 patients with COVID-19-related critical illness, 319 (68.2%) were treated with MV and 121 (25.9%) with vasopressors. Outcomes were notable for an all-cause 28-day in-hospital mortality rate of 29.9%, a median ICU stay of 8 days (interquartile range [IQR], 3 to 17 days), a median hospital stay of 13 days (IQR, 7 to 25 days), and an all-cause 30-day readmission rate (among nonhospice survivors) of 10.8%. Mortality decreased over time, from 43.5% (95% CI, 31.3% to 53.8%) to 19.2% (CI, 11.6% to 26.7%) between the first and last 15-day periods in the core adjusted model, whereas patient acuity and other factors did not change. LIMITATIONS: Single-health system study; use of, or highly dynamic trends in, other clinical interventions were not evaluated, nor were complications. CONCLUSION: Among patients with COVID-19-related critical illness admitted to ICUs of a learning health system in the United States, mortality seemed to decrease over time despite stable patient characteristics. Further studies are necessary to confirm this result and to investigate causal mechanisms. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality.

Preparedness Tested: Severe Cerebral Malaria Presenting as a High-Risk Person Under Investigation for Ebola Virus Disease at a US Hospital.

In 2019, a 42-year-old African man who works as an Ebola virus disease (EVD) researcher traveled from the Democratic Republic of Congo (DRC), near an ongoing EVD epidemic, to Philadelphia and presented to the Hospital of the University of Pennsylvania Emergency Department with altered mental status, vomiting, diarrhea, and fever. He was classified as a "wet" person under investigation for EVD, and his arrival activated our hospital emergency management command center and bioresponse teams. He was found to be in septic shock with multisystem organ dysfunction, including circulatory dysfunction, encephalopathy, metabolic lactic acidosis, acute kidney injury, acute liver injury, and diffuse intravascular coagulation. Critical care was delivered within high-risk pathogen isolation in the ED and in our Special Treatment Unit until a diagnosis of severe cerebral malaria was confirmed and EVD was definitively excluded.This report discusses our experience activating a longitudinal preparedness program designed for rare, resource-intensive events at hospitals physically remote from any active epidemic but serving a high-volume international air travel port-of-entry.

A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.

OBJECTIVES: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes. DESIGN: Retrospective cohort for algorithm derivation and validation, pre-post impact evaluation. SETTING: Tertiary teaching hospital system in Philadelphia, PA. PATIENTS: All non-ICU admissions; algorithm derivation July 2011 to June 2014 (n = 162,212); algorithm validation October to December 2015 (n = 10,448); silent versus alert comparison January 2016 to February 2017 (silent n = 22,280; alert n = 32,184). INTERVENTIONS: A random-forest classifier, derived and validated using electronic health record data, was deployed both silently and later with an alert to notify clinical teams of sepsis prediction. MEASUREMENT AND MAIN RESULT: Patients identified for training the algorithm were required to have International Classification of Diseases, 9th Edition codes for severe sepsis or septic shock and a positive blood culture during their hospital encounter with either a lactate greater than 2.2 mmol/L or a systolic blood pressure less than 90 mm Hg. The algorithm demonstrated a sensitivity of 26% and specificity of 98%, with a positive predictive value of 29% and positive likelihood ratio of 13. The alert resulted in a small statistically significant increase in lactate testing and IV fluid administration. There was no significant difference in mortality, discharge disposition, or transfer to ICU, although there was a reduction in time-to-ICU transfer. CONCLUSIONS: Our machine learning algorithm can predict, with low sensitivity but high specificity, the impending occurrence of severe sepsis and septic shock. Algorithm-generated predictive alerts modestly impacted clinical measures. Next steps include describing clinical perception of this tool and optimizing algorithm design and delivery.

Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.

OBJECTIVE: To assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0). DESIGN: Prospective observational study. SETTING: Tertiary teaching hospital in Philadelphia, PA. PATIENTS: Non-ICU admissions November-December 2016. INTERVENTIONS: During a 6-week study period conducted 5 months after Early Warning System 2.0 alert implementation, nurses and providers were surveyed twice about their perceptions of the alert's helpfulness and impact on care, first within 6 hours of the alert, and again 48 hours after the alert. MEASUREMENTS AND MAIN RESULTS: For the 362 alerts triggered, 180 nurses (50% response rate) and 107 providers (30% response rate) completed the first survey. Of these, 43 nurses (24% response rate) and 44 providers (41% response rate) completed the second survey. Few (24% nurses, 13% providers) identified new clinical findings after responding to the alert. Perceptions of the presence of sepsis at the time of alert were discrepant between nurses (13%) and providers (40%). The majority of clinicians reported no change in perception of the patient's risk for sepsis (55% nurses, 62% providers). A third of nurses (30%) but few providers (9%) reported the alert changed management. Almost half of nurses (42%) but less than a fifth of providers (16%) found the alert helpful at 6 hours. CONCLUSIONS: In general, clinical perceptions of Early Warning System 2.0 were poor. Nurses and providers differed in their perceptions of sepsis and alert benefits. These findings highlight the challenges of achieving acceptance of predictive and machine learning-based sepsis alerts.

Sleep and Work in ICU Physicians During a Randomized Trial of Nighttime Intensivist Staffing.

OBJECTIVES: To compare sleep, work hours, and behavioral alertness in faculty and fellows during a randomized trial of nighttime in-hospital intensivist staffing compared with a standard daytime intensivist model. DESIGN: Prospective observational study. SETTING: Medical ICU of a tertiary care academic medical center during a randomized controlled trial of in-hospital nighttime intensivist staffing. PATIENTS: Twenty faculty and 13 fellows assigned to rotations in the medical ICU during 2012. INTERVENTIONS: As part of the parent study, there was weekly randomization of staffing model, stratified by 2-week faculty rotation. During the standard staffing model, there were in-hospital residents, with a fellow and faculty member available at nighttime by phone. In the intervention, there were in-hospital residents with an in-hospital nighttime intensivist. Fellows and faculty completed diaries detailing their sleep, work, and well-being; wore actigraphs; and performed psychomotor vigilance testing daily. MEASUREMENTS AND MAIN RESULTS: Daily sleep time (mean hours [SD]) was increased for fellows and faculty in the intervention versus control (6.7 [0.3] vs 6.0 [0.2]; p < 0.001 and 6.7 [0.1] vs 6.4 [0.2]; p < 0.001, respectively). In-hospital work duration did not differ between the models for fellows or faculty. Total hours of work done at home was different for both fellows and faculty (0.1 [< 0.1] intervention vs 1.0 [0.1] control; p < 0.001 and 0.2 [< 0.1] intervention vs 0.6 [0.1] control; p < 0.001, respectively). Psychomotor vigilance testing did not demonstrate any differences. Measures of well-being including physical exhaustion and alertness were improved in faculty and fellows in the intervention staffing model. CONCLUSIONS: Although no differences were measured in patient outcomes between the two staffing models, in-hospital nighttime intensivist staffing was associated with small increases in total sleep duration for faculty and fellows, reductions in total work hours for fellows only, and improvements in subjective well-being for both groups. Staffing models should consider how work duration, sleep, and well-being may impact burnout and sustainability.

Implementing the ABCDEF Bundle: Top 8 Questions Asked During the ICU Liberation ABCDEF Bundle Improvement Collaborative.

The ABCDEF bundle (A, assess, prevent, and manage pain; B, both spontaneous awakening and spontaneous breathing trials; C, choice of analgesic and sedation; D, delirium: assess, prevent, and manage; E, early mobility and exercise; and F, family engagement and empowerment) improves intensive care unit patient-centered outcomes and promotes interprofessional teamwork and collaboration. The Society of Critical Care Medicine recently completed the ICU Liberation ABCDEF Bundle Improvement Collaborative, a 20-month, multicenter, national quality improvement initiative that formalized dissemination and implementation strategies to promote effective adoption of the ABCDEF bundle. The purpose of this article is to describe 8 of the most frequently asked questions during the Collaborative and to provide practical advice from leading experts to other institutions implementing the ABCDEF bundle.

Common Challenges to Effective ABCDEF Bundle Implementation: The ICU Liberation Campaign Experience.

Although growing evidence supports the safety and effectiveness of the ABCDEF bundle (A, assess, prevent, and manage pain; B, both spontaneous awakening and spontaneous breathing trials; C, choice of analgesic and sedation; D, delirium: assess, prevent, and manage; E, early mobility and exercise; and F, family engagement and empowerment), intensive care unit providers often struggle with how to reliably and consistently incorporate this interprofessional, evidence-based intervention into everyday clinical practice. Recently, the Society of Critical Care Medicine completed the ICU Liberation ABCDEF Bundle Improvement Collaborative, a 20-month, nationwide, multicenter quality improvement initiative that formalized dissemination and implementation strategies and tracked key performance metrics to overcome barriers to ABCDEF bundle adoption. The purpose of this article is to discuss some of the most challenging implementation issues that Collaborative teams experienced, and to provide some practical advice from leading experts on ways to overcome these barriers.

Clinical Impact of an Electronic Dashboard and Alert System for Sedation Minimization and Ventilator Liberation: A Before-After Study.

Sedation minimization and ventilator liberation protocols improve outcomes but are challenging to implement. We sought to demonstrate proof-of-concept and impact of an electronic application promoting sedation minimization and ventilator liberation. DESIGN: Multi-ICU proof-of-concept study and a single ICU before-after study. SETTING: University hospital ICUs. PATIENTS: Adult patients receiving mechanical ventilation. INTERVENTIONS: An automated application consisting of 1) a web-based dashboard with real-time data on spontaneous breathing trial readiness, sedation depth, sedative infusions, and nudges to wean sedation and ventilatory support and 2) text-message alerts once patients met criteria for a spontaneous breathing trial and spontaneous awakening trial. Pre-intervention, sedation minimization, and ventilator liberation were reviewed daily during a multidisciplinary huddle. Post-intervention, the dashboard was used during the multidisciplinary huddle, throughout the day by respiratory therapists, and text alerts were sent to bedside providers. MEASUREMENTS AND MAIN RESULTS: We enrolled 115 subjects in the proof-of-concept study. Spontaneous breathing trial alerts were accurate (98.3%), usually sent while patients were receiving mandatory ventilation (88.5%), and 61.9% of patients received concurrent spontaneous awakening trial alerts. We enrolled 457 subjects in the before-after study, 221 pre-intervention and 236 post-intervention. After implementation, patients were 28% more likely to be extubated (hazard ratio, 1.28; 95% CI, 1.01-1.63; p = 0.042) and 31% more likely to be discharged from the ICU (hazard ratio, 1.31; 95% CI, 1.03-1.67; p = 0.027) at any time point. After implementation, the median duration of mechanical ventilation was 2.20 days (95% CI, 0.09-4.31 d; p = 0.042) shorter and the median ICU length of stay was 2.65 days (95% CI, 0.13-5.16 d; p = 0.040) shorter, compared with the expected durations without the application. CONCLUSIONS: Implementation of an electronic dashboard and alert system promoting sedation minimization and ventilator liberation was associated with reductions in the duration of mechanical ventilation and ICU length of stay.

Caring for Critically Ill Patients with the ABCDEF Bundle: Results of the ICU Liberation Collaborative in Over 15,000 Adults.

OBJECTIVE: Decades-old, common ICU practices including deep sedation, immobilization, and limited family access are being challenged. We endeavoured to evaluate the relationship between ABCDEF bundle performance and patient-centered outcomes in critical care. DESIGN: Prospective, multicenter, cohort study from a national quality improvement collaborative. SETTING: 68 academic, community, and federal ICUs collected data during a 20-month period. PATIENTS: 15,226 adults with at least one ICU day. INTERVENTIONS: We defined ABCDEF bundle performance (our main exposure) in two ways: 1) complete performance (patient received every eligible bundle element on any given day) and 2) proportional performance (percentage of eligible bundle elements performed on any given day). We explored the association between complete and proportional ABCDEF bundle performance and three sets of outcomes: patient-related (mortality, ICU and hospital discharge), symptom-related (mechanical ventilation, coma, delirium, pain, restraint use), and system-related (ICU readmission, discharge destination). All models were adjusted for a minimum of 18 a priori determined potential confounders. MEASUREMENTS AND RESULTS: Complete ABCDEF bundle performance was associated with lower likelihood of seven outcomes: hospital death within 7 days (adjusted hazard ratio, 0.32; CI, 0.17-0.62), next-day mechanical ventilation (adjusted odds ratio [AOR], 0.28; CI, 0.22-0.36), coma (AOR, 0.35; CI, 0.22-0.56), delirium (AOR, 0.60; CI, 0.49-0.72), physical restraint use (AOR, 0.37; CI, 0.30-0.46), ICU readmission (AOR, 0.54; CI, 0.37-0.79), and discharge to a facility other than home (AOR, 0.64; CI, 0.51-0.80). There was a consistent dose-response relationship between higher proportional bundle performance and improvements in each of the above-mentioned clinical outcomes (all p < 0.002). Significant pain was more frequently reported as bundle performance proportionally increased (p = 0.0001). CONCLUSIONS: ABCDEF bundle performance showed significant and clinically meaningful improvements in outcomes including survival, mechanical ventilation use, coma, delirium, restraint-free care, ICU readmissions, and post-ICU discharge disposition.