Prediction of In-hospital Mortality Among Intensive Care Unit Patients Using Modified Daily Laboratory-based Acute Physiology Score, Version 2.

BACKGROUND: Mortality prediction for intensive care unit (ICU) patients frequently relies on single ICU admission acuity measures without accounting for subsequent clinical changes. OBJECTIVE: Evaluate novel models incorporating modified admission and daily, time-updating Laboratory-based Acute Physiology Score, version 2 (LAPS2) to predict in-hospital mortality among ICU patients. RESEARCH DESIGN: Retrospective cohort study. PATIENTS: ICU patients in 5 hospitals from October 2017 through September 2019. MEASURES: We used logistic regression, penalized logistic regression, and random forest models to predict in-hospital mortality within 30 days of ICU admission using admission LAPS2 alone in patient-level and patient-day-level models, or admission and daily LAPS2 at the patient-day level. Multivariable models included patient and admission characteristics. We performed internal-external validation using 4 hospitals for training and the fifth for validation, repeating analyses for each hospital as the validation set. We assessed performance using scaled Brier scores (SBS), c -statistics, and calibration plots. RESULTS: The cohort included 13,993 patients and 107,699 ICU days. Across validation hospitals, patient-day-level models including daily LAPS2 (SBS: 0.119-0.235; c -statistic: 0.772-0.878) consistently outperformed models with admission LAPS2 alone in patient-level (SBS: 0.109-0.175; c -statistic: 0.768-0.867) and patient-day-level (SBS: 0.064-0.153; c -statistic: 0.714-0.861) models. Across all predicted mortalities, daily models were better calibrated than models with admission LAPS2 alone. CONCLUSIONS: Patient-day-level models incorporating daily, time-updating LAPS2 to predict mortality among an ICU population performs as well or better than models incorporating modified admission LAPS2 alone. The use of daily LAPS2 may offer an improved tool for clinical prognostication and risk adjustment in research in this population.

A Data-Driven Analysis of Ward Capacity Strain Metrics That Predict Clinical Outcomes Among Survivors of Acute Respiratory Failure.

Supply-demand mismatch of ward resources ("ward capacity strain") alters care and outcomes. Narrow strain definitions and heterogeneous populations limit strain literature. Evaluate the predictive utility of a large set of candidate strain variables for in-hospital mortality and discharge destination among acute respiratory failure (ARF) survivors. In a retrospective cohort of ARF survivors transferred from intensive care units (ICUs) to wards in five hospitals from 4/2017-12/2019, we applied 11 machine learning (ML) models to identify ward strain measures during the first 24 hours after transfer most predictive of outcomes. Measures spanned patient volume (census, admissions, discharges), staff workload (medications administered, off-ward transports, transfusions, isolation precautions, patients per respiratory therapist and nurse), and average patient acuity (Laboratory Acute Physiology Score version 2, ICU transfers) domains. The cohort included 5,052 visits in 43 wards. Median age was 65 years (IQR 56-73); 2,865 (57%) were male; and 2,865 (57%) were white. 770 (15%) patients died in the hospital or had hospice discharges, and 2,628 (61%) were discharged home and 964 (23%) to skilled nursing facilities (SNFs). Ward admissions, isolation precautions, and hospital admissions most consistently predicted in-hospital mortality across ML models. Patients per nurse most consistently predicted discharge to home and SNF, and medications administered predicted SNF discharge. In this hypothesis-generating analysis of candidate ward strain variables' prediction of outcomes among ARF survivors, several variables emerged as consistently predictive of key outcomes across ML models. These findings suggest targets for future inferential studies to elucidate mechanisms of ward strain's adverse effects.

Wellness and Coping of Physicians Who Worked in ICUs During the Pandemic: A Multicenter Cross-Sectional North American Survey.

OBJECTIVES: Few surveys have focused on physician moral distress, burnout, and professional fulfilment. We assessed physician wellness and coping during the COVID-19 pandemic. DESIGN: Cross-sectional survey using four validated instruments. SETTING: Sixty-two sites in Canada and the United States. SUBJECTS: Attending physicians (adult, pediatric; intensivist, nonintensivist) who worked in North American ICUs. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: We analysed 431 questionnaires (43.3% response rate) from 25 states and eight provinces. Respondents were predominantly male (229 [55.6%]) and in practice for 11.8 ± 9.8 years. Compared with prepandemic, respondents reported significant intrapandemic increases in days worked/mo, ICU bed occupancy, and self-reported moral distress (240 [56.9%]) and burnout (259 [63.8%]). Of the 10 top-ranked items that incited moral distress, most pertained to regulatory/organizational ( n = 6) or local/institutional ( n = 2) issues or both ( n = 2). Average moral distress (95.6 ± 66.9), professional fulfilment (6.5 ± 2.1), and burnout scores (3.6 ± 2.0) were moderate with 227 physicians (54.6%) meeting burnout criteria. A significant dose-response existed between COVID-19 patient volume and moral distress scores. Physicians who worked more days/mo and more scheduled in-house nightshifts, especially combined with more unscheduled in-house nightshifts, experienced significantly more moral distress. One in five physicians used at least one maladaptive coping strategy. We identified four coping profiles (active/social, avoidant, mixed/ambivalent, infrequent) that were associated with significant differences across all wellness measures. CONCLUSIONS: Despite moderate intrapandemic moral distress and burnout, physicians experienced moderate professional fulfilment. However, one in five physicians used at least one maladaptive coping strategy. We highlight potentially modifiable factors at individual, institutional, and regulatory levels to enhance physician wellness.

The impact of resource limitations on care delivery and outcomes: routine variation, the coronavirus disease 2019 pandemic, and persistent shortage.

PURPOSE OF REVIEW: Resource limitation, or capacity strain, has been associated with changes in care delivery, and in some cases, poorer outcomes among critically ill patients. This may result from normal variation in strain on available resources, chronic strain in persistently under-resourced settings, and less commonly because of acute surges in demand, as seen during the coronavirus disease 2019 (COVID-19) pandemic. RECENT FINDINGS: Recent studies confirmed existing evidence that high ICU strain is associated with ICU triage decisions, and that ICU strain may be associated with ICU patient mortality. Studies also demonstrated earlier discharge of ICU patients during high strain, suggesting that strain may promote patient flow efficiency. Several studies of strain resulting from the COVID-19 pandemic provided support for the concept of adaptability - that the surge not only caused detrimental strain but also provided experience with a novel disease entity such that outcomes improved over time. Chronically resource-limited settings faced even more challenging circumstances because of acute-on-chronic strain during the pandemic. SUMMARY: The interaction between resource limitation and care delivery and outcomes is complex and incompletely understood. The COVID-19 pandemic provides a learning opportunity for strain response during both pandemic and nonpandemic times.

Gender Differences in Authorship of Critical Care Literature.

Rationale: Gender gaps exist in academic leadership positions in critical care. Peer-reviewed publications are crucial to career advancement, and yet little is known regarding gender differences in authorship of critical care research.Objectives: To evaluate gender differences in authorship of critical care literature.Methods: We used a validated database of author gender to analyze authorship of critical care articles indexed in PubMed between 2008 and 2018 in 40 frequently cited journals. High-impact journals were defined as those in the top 5% of all journals. We used mixed-effects logistic regression to evaluate the association of senior author gender with first and middle author gender, as well as association of first author gender with journal impact factor.Measurements and Main Results: Among 18,483 studies, 30.8% had female first authors, and 19.5% had female senior authors. Female authorship rose slightly over the last decade (average annual increases of 0.44% [P < 0.01] and 0.51% [P < 0.01] for female first and senior authors, respectively). When the senior author was female, the odds of female coauthorship rose substantially (first author adjusted odds ratio [aOR], 1.93; 95% confidence interval [CI], 1.71-2.17; middle author aOR, 1.48; 95% CI, 1.29-1.69). Female first authors had higher odds than men of publishing in lower-impact journals (aOR, 1.30; 95% CI, 1.16-1.45).Conclusions: Women comprise less than one-third of first authors and one-fourth of senior authors of critical care research, with minimal increase over the past decade. When the senior author was female, the odds of female coauthorship rose substantially. However, female first authors tend to publish in lower-impact journals. These findings may help explain the underrepresentation of women in critical care academic leadership positions and identify targets for improvement.

Burnout and Joy in the Profession of Critical Care Medicine.

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2020. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2020. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901.

Determining the Association Between End-of-Life Care Resources and Patient Outcomes in Pennsylvania ICUs.

OBJECTIVES: As ICUs are increasingly a site of end-of-life care, many have adopted end-of-life care resources. We sought to determine the association of such resources with outcomes of ICU patients. DESIGN: Retrospective cohort study. SETTING: Pennsylvania ICUs. PATIENTS: Medicare fee-for-service beneficiaries. INTERVENTIONS: Availability of any of one hospital-based resource (palliative care consultants) or four ICU-based resources (protocol for withdrawal of life-sustaining therapy, triggers for automated palliative care consultation, protocol for family meetings, and palliative care clinicians embedded in ICU rounds). MEASUREMENTS AND MAIN RESULTS: In mixed-effects regression analyses, admission to a hospital with end-of-life resources was not associated with mortality, length of stay, or treatment intensity (mechanical ventilation, hemodialysis, tracheostomy, gastrostomy, artificial nutrition, or cardiopulmonary resuscitation); however, it was associated with a higher likelihood of discharge to hospice (odds ratio, 1.58; 95% CI, 1.11-2.24), an effect that was driven by ICU-based resources (odds ratio, 1.37; 95% CI, 1.04-1.81) rather than hospital-based resources (odds ratio, 1.19; 95% CI, 0.83-1.71). Instrumental variable analysis using differential distance (defined as the additional travel distance beyond the hospital closest to a patient's home needed to reach a hospital with end-of-life resources) demonstrated that among those for whom differential distance would influence receipt of end-of-life resources, admission to a hospital with such resources was not associated with any outcome. CONCLUSIONS: ICU-based end-of-life care resources do not appear to change mortality but are associated with increased hospice utilization. Given that this finding was not confirmed by the instrumental variable analysis, future studies should attempt to verify this finding, and identify specific resources or processes of care that impact the care of ICU patients at the end of life.

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.

The Association of ICU Acuity With Outcomes of Patients at Low Risk of Dying.

OBJECTIVE: Many ICU patients do not require critical care interventions. Whether aggressive care environments increase risks to low-acuity patients is unknown. We evaluated whether ICU acuity was associated with outcomes of low mortality-risk patients. We hypothesized that admission to high-acuity ICUs would be associated with worse outcomes. This hypothesis was based on two possibilities: 1) high-acuity ICUs may have a culture of aggressive therapy that could lead to potentially avoidable complications and 2) high-acuity ICUs may focus attention toward the many sicker patients and away from the fewer low-risk patients. DESIGN: Retrospective cohort study. SETTING: Three hundred twenty-two ICUs in 199 hospitals in the Philips eICU database between 2010 and 2015. PATIENTS: Adult ICU patients at low risk of dying, defined as an Acute Physiology and Chronic Health Evaluation-IVa-predicted mortality of 3% or less. EXPOSURE: ICU acuity, defined as the mean Acute Physiology and Chronic Health Evaluation IVa score of all admitted patients in a calendar year, stratified into quartiles. MEASUREMENTS AND MAIN RESULTS: We used generalized estimating equations to test whether ICU acuity is independently associated with a primary outcome of ICU length of stay and secondary outcomes of hospital length of stay, hospital mortality, and discharge destination. The study included 381,997 low-risk patients. Mean ICU and hospital length of stay were 1.8 ± 2.1 and 5.2 ± 5.0 days, respectively. Mean Acute Physiology and Chronic Health Evaluation IVa-predicted hospital mortality was 1.6% ± 0.8%; actual hospital mortality was 0.7%. In adjusted analyses, admission to low-acuity ICUs was associated with worse outcomes compared with higher-acuity ICUs. Specifically, compared with the highest-acuity quartile, ICU length of stay in low-acuity ICUs was increased by 0.24 days; in medium-acuity ICUs by 0.16 days; and in high-acuity ICUs by 0.09 days (all p < 0.001). Similar patterns existed for hospital length of stay. Patients in lower-acuity ICUs had significantly higher hospital mortality (odds ratio, 1.28 [95% CI, 1.10-1.49] for low-; 1.24 [95% CI, 1.07-1.42] for medium-, and 1.14 [95% CI, 0.99-1.31] for high-acuity ICUs) and lower likelihood of discharge home (odds ratio, 0.86 [95% CI, 0.82-0.90] for low-, 0.88 [95% CI, 0.85-0.92] for medium-, and 0.95 [95% CI, 0.92-0.99] for high-acuity ICUs). CONCLUSIONS: Admission to high-acuity ICUs is associated with better outcomes among low mortality-risk patients. Future research should aim to understand factors that confer benefit to patients with different risk profiles.

An Official American Thoracic Society Systematic Review: The Effect of Nighttime Intensivist Staffing on Mortality and Length of Stay among Intensive Care Unit Patients.

BACKGROUND: Studies of nighttime intensivist staffing have yielded mixed results. GOALS: To review the association of nighttime intensivist staffing with outcomes of intensive care unit (ICU) patients. METHODS: We searched five databases (2000-2016) for studies comparing in-hospital nighttime intensivist staffing with other nighttime staffing models in adult ICUs and reporting mortality or length of stay. We abstracted data on staffing models, outcomes, and study characteristics and assessed study quality, using standardized tools. Meta-analyses used random effects models. RESULTS: Eighteen studies met inclusion criteria: one randomized controlled trial and 17 observational studies. Overall methodologic quality was high. Studies included academic hospitals (n = 10), community hospitals (n = 2), or both (n = 6). Baseline clinician staffing included residents (n = 9), fellows (n = 4), and nurse practitioners or physician assistants (n = 2). Studies included both general and specialty ICUs and were geographically diverse. Meta-analysis (one randomized controlled trial; three nonrandomized studies with exposure limited to nighttime intensivist staffing with adjusted estimates of effect) demonstrated no association with mortality (odds ratio, 0.99; 95% confidence interval, 0.75-1.29). Secondary analyses including studies without risk adjustment, with a composite exposure of organizational factors, stratified by intensity of daytime staffing and by ICU type, yielded similar results. Minimal or no differences were observed in ICU and hospital length of stay and several other secondary outcomes. CONCLUSIONS: Notwithstanding limitations of the predominantly observational evidence, our systematic review and meta-analysis suggests nighttime intensivist staffing is not associated with reduced ICU patient mortality. Other outcomes and alternative staffing models should be evaluated to further guide staffing decisions.

Validation of an Administrative Definition of ICU Admission Using Revenue Center Codes.

OBJECTIVES: Describe the operating characteristics of a proposed set of revenue center codes to correctly identify ICU stays among hospitalized patients. DESIGN: Retrospective cohort study. We report the operating characteristics of all ICU-related revenue center codes for intensive and coronary care, excluding nursery, intermediate, and incremental care, to identify ICU stays. We use a classification and regression tree model to further refine identification of ICU stays using administrative data. The gold standard for classifying ICU admission was an electronic patient location tracking system. SETTING: The University of Pennsylvania Health System in Philadelphia, PA, United States. PATIENTS: All adult inpatient hospital admissions between July 1, 2013, and June 30, 2015. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among 127,680 hospital admissions, the proposed combination of revenue center codes had 94.6% sensitivity (95% CI, 94.3-94.9%) and 96.1% specificity (95% CI, 96.0-96.3%) for correctly identifying hospital admissions with an ICU stay. The classification and regression tree algorithm had 92.3% sensitivity (95% CI, 91.6-93.1%) and 97.4% specificity (95% CI, 97.2-97.6%), with an overall improved accuracy (χ = 398; p < 0.001). CONCLUSIONS: Use of the proposed combination of revenue center codes has excellent sensitivity and specificity for identifying true ICU admission. A classification and regression tree algorithm with additional administrative variables offers further improvements to accuracy.

Adaptation of the Acute Organ Failure Score for Use in a Medicare Population.

OBJECTIVES: Without widely available physiologic data, a need exists for ICU risk adjustment methods that can be applied to administrative data. We sought to expand the generalizability of the Acute Organ Failure Score by adapting it to a commonly used administrative database. DESIGN: Retrospective cohort study. SETTING: One hundred fifty-one hospitals in Pennsylvania. PATIENTS: A total of 90,733 ICU admissions among 77,040 unique patients between January 1, 2009, and December 1, 2009, in the Medicare Provider Analysis and Review database. MEASUREMENTS AND MAIN RESULTS: We used multivariable logistic regression on a random split cohort to predict 30-day mortality, and to examine the impact of using different comorbidity measures in the model and adding historical claims data. Overall 30-day mortality was 17.6%. In the validation cohort, using the original Acute Organ Failure Score model's ß coefficients resulted in poor discrimination (C-statistic, 0.644; 95% CI, 0.639-0.649). The model's C-statistic improved to 0.721 (95% CI, 0.711-0.730) when the Medicare cohort was used to recalibrate the ß coefficients. Model discrimination improved further when comorbidity was expressed as the COmorbidity Point Score 2 (C-statistic, 0.737; 95% CI, 0.728-0.747; p < 0.001) or the Elixhauser index (C-statistic, 0.748; 95% CI, 0.739-0.757) instead of the Charlson index. Adding historical claims data increased the number of comorbidities identified, but did not enhance model performance. CONCLUSIONS: Modification of the Acute Organ Failure Score resulted in good model discrimination among a diverse population regardless of comorbidity measure used. This study expands the use of the Acute Organ Failure Score for risk adjustment in ICU research and outcomes reporting using standard administrative data.

A randomized trial of nighttime physician staffing in an intensive care unit.

BACKGROUND: Increasing numbers of intensive care units (ICUs) are adopting the practice of nighttime intensivist staffing despite the lack of experimental evidence of its effectiveness. METHODS: We conducted a 1-year randomized trial in an academic medical ICU of the effects of nighttime staffing with in-hospital intensivists (intervention) as compared with nighttime coverage by daytime intensivists who were available for consultation by telephone (control). We randomly assigned blocks of 7 consecutive nights to the intervention or the control strategy. The primary outcome was patients' length of stay in the ICU. Secondary outcomes were patients' length of stay in the hospital, ICU and in-hospital mortality, discharge disposition, and rates of readmission to the ICU. For length-of-stay outcomes, we performed time-to-event analyses, with data censored at the time of a patient's death or transfer to another ICU. RESULTS: A total of 1598 patients were included in the analyses. The median Acute Physiology and Chronic Health Evaluation (APACHE) III score (in which scores range from 0 to 299, with higher scores indicating more severe illness) was 67 (interquartile range, 47 to 91), the median length of stay in the ICU was 52.7 hours (interquartile range, 29.0 to 113.4), and mortality in the ICU was 18%. Patients who were admitted on intervention days were exposed to nighttime intensivists on more nights than were patients admitted on control days (median, 100% of nights [interquartile range, 67 to 100] vs. median, 0% [interquartile range, 0 to 33]; P<0.001). Nonetheless, intensivist staffing on the night of admission did not have a significant effect on the length of stay in the ICU (rate ratio for the time to ICU discharge, 0.98; 95% confidence interval [CI], 0.88 to 1.09; P=0.72), ICU mortality (relative risk, 1.07; 95% CI, 0.90 to 1.28), or any other end point. Analyses restricted to patients who were admitted at night showed similar results, as did sensitivity analyses that used different definitions of exposure and outcome. CONCLUSIONS: In an academic medical ICU in the United States, nighttime in-hospital intensivist staffing did not improve patient outcomes. (Funded by University of Pennsylvania Health System and others; ClinicalTrials.gov number, NCT01434823.).

The preventability of ventilator-associated events. The CDC Prevention Epicenters Wake Up and Breathe Collaborative.

RATIONALE: The CDC introduced ventilator-associated event (VAE) definitions in January 2013. Little is known about VAE prevention. We hypothesized that daily, coordinated spontaneous awakening trials (SATs) and spontaneous breathing trials (SBTs) might prevent VAEs. OBJECTIVES: To assess the preventability of VAEs. METHODS: We nested a multicenter quality improvement collaborative within a prospective study of VAE surveillance among 20 intensive care units between November 2011 and May 2013. Twelve units joined the collaborative and implemented an opt-out protocol for nurses and respiratory therapists to perform paired daily SATs and SBTs. The remaining eight units conducted surveillance alone. We measured temporal trends in VAEs using generalized mixed effects regression models adjusted for patient-level unit, age, sex, reason for intubation, Sequential Organ Failure Assessment score, and comorbidity index. MEASUREMENTS AND MAIN RESULTS: We tracked 5,164 consecutive episodes of mechanical ventilation: 3,425 in collaborative units and 1,739 in surveillance-only units. Within collaborative units, significant increases in SATs, SBTs, and percentage of SBTs performed without sedation were mirrored by significant decreases in duration of mechanical ventilation and hospital length-of-stay. There was no change in VAE risk per ventilator day but significant decreases in VAE risk per episode of mechanical ventilation (odds ratio [OR], 0.63; 95% confidence interval [CI], 0.42-0.97) and infection-related ventilator-associated complications (OR, 0.35; 95% CI, 0.17-0.71) but not pneumonias (OR, 0.51; 95% CI, 0.19-1.3). Within surveillance-only units, there were no significant changes in SAT, SBT, or VAE rates. CONCLUSIONS: Enhanced performance of paired, daily SATs and SBTs is associated with lower VAE rates. Clinical trial registered with www.clinicaltrials.gov (NCT 01583413).

Outcomes and statistical power in adult critical care randomized trials.

RATIONALE: Intensive care unit (ICU)-based randomized clinical trials (RCTs) among adult critically ill patients commonly fail to detect treatment benefits. OBJECTIVES: Appraise the rates of success, outcomes used, statistical power, and design characteristics of published trials. METHODS: One hundred forty-six ICU-based RCTs of diagnostic, therapeutic, or process/systems interventions published from January 2007 to May 2013 in 16 high-impact general or critical care journals were studied. MEASUREMENT AND MAIN RESULTS: Of 146 RCTs, 54 (37%) were positive (i.e., the a priori hypothesis was found to be statistically significant). The most common primary outcomes were mortality (n = 40 trials), infection-related outcomes (n = 33), and ventilation-related outcomes (n = 30), with positive results found in 10, 58, and 43%, respectively. Statistical power was discussed in 135 RCTs (92%); 92 cited a rationale for their power parameters. Twenty trials failed to achieve at least 95% of their reported target sample size, including 11 that were stopped early due to insufficient accrual/logistical issues. Of 34 superiority RCTs comparing mortality between treatment arms, 13 (38%) accrued a sample size large enough to find an absolute mortality reduction of 10% or less. In 22 of these trials the observed control-arm mortality rate differed from the predicted rate by at least 7.5%. CONCLUSIONS: ICU-based RCTs are commonly negative and powered to identify what appear to be unrealistic treatment effects, particularly when using mortality as the primary outcome. Additional concerns include a lack of standardized methods for assessing common outcomes, unclear justifications for statistical power calculations, insufficient patient accrual, and incorrect predictions of baseline event rates.