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Objective: To determine whether surge conditions were associated with increased mortality., Design: Multicenter cohort study., Setting: U.S. ICUs participating in STOP-COVID., Patients: Consecutive adults with COVID-19 admitted to participating ICUs between March 4 and July 1, 2020., Interventions: None., Measurements and Main Results: The main outcome was 28-day in-hospital mortality. To assess the association between admission to an ICU during a surge period and mortality, we used two different strategies: (1) an inverse probability weighted difference-in-differences model limited to appropriately matched surge and non-surge patients and (2) a meta-regression of 50 multivariable difference-in-differences models (each based on sets of randomly matched surge- and non-surge hospitals). In the first analysis, we considered a single surge period for the cohort (March 23 - May 6). In the second, each surge hospital had its own surge period (which was compared to the same time periods in matched non-surge hospitals).Our cohort consisted of 4342 ICU patients (average age 60.8 [sd 14.8], 63.5% men) in 53 U.S. hospitals. Of these, 13 hospitals encountered surge conditions. In analysis 1, the increase in mortality seen during surge was not statistically significant (odds ratio [95% CI]: 1.30 [0.47-3.58], p = .6). In analysis 2, surge was associated with an increased odds of death (odds ratio 1.39 [95% CI, 1.34-1.43], p < .001)., Conclusions: Admission to an ICU with COVID-19 in a hospital that is experiencing surge conditions may be associated with an increased odds of death. Given the high incidence of COVID-19, such increases would translate into substantial excess mortality.

Objectives: Critically ill patients with coronavirus disease 2019 have variable mortality. Risk scores could improve care and be used for prognostic enrichment in trials. We aimed to compare machine learning algorithms and develop a simple tool for predicting 28-day mortality in ICU patients with coronavirus disease 2019., Design: This was an observational study of adult patients with coronavirus disease 2019. The primary outcome was 28-day inhospital mortality. Machine learning models and a simple tool were derived using variables from the first 48 hours of ICU admission and validated externally in independent sites and temporally with more recent admissions. Models were compared with a modified Sequential Organ Failure Assessment score, National Early Warning Score, and CURB-65 using the area under the receiver operating characteristic curve and calibration., Setting: Sixty-eight U.S. ICUs., Patients: Adults with coronavirus disease 2019 admitted to 68 ICUs in the United States between March 4, 2020, and June 29, 2020., Interventions: None., Measurements and Main Results: The study included 5,075 patients, 1,846 (36.4%) of whom died by day 28. eXtreme Gradient Boosting had the highest area under the receiver operating characteristic curve in external validation (0.81) and was well-calibrated, while k-nearest neighbors were the lowest performing machine learning algorithm (area under the receiver operating characteristic curve 0.69). Findings were similar with temporal validation. The simple tool, which was created using the most important features from the eXtreme Gradient Boosting model, had a significantly higher area under the receiver operating characteristic curve in external validation (0.78) than the Sequential Organ Failure Assessment score (0.69), National Early Warning Score (0.60), and CURB-65 (0.65; p < 0.05 for all comparisons). Age, number of ICU beds, creatinine, lactate, arterial pH, and Pao 2 /Fio 2 ratio were the most important predictors in the eXtreme Gradient Boosting model., Conclusions: eXtreme Gradient Boosting had the highest discrimination overall, and our simple tool had higher discrimination than a modified Sequential Organ Failure Assessment score, National Early Warning Score, and CURB-65 on external validation. These models could be used to improve triage decisions and clinical trial enrichment., Competing Interests: Dr. Churpek is supported by an R01 from National Institute of General Medical Sciences (NIGMS) (R01 GM123193), has a patent pending (ARCD. P0535US.P2) for risk stratification algorithms for hospitalized patients, and has received research support from EarlySense (Tel Aviv, Israel). Dr. Gupta is a scientific coordinator for the A Study of Cardiovascular Events in Diabetes trial (GlaxoSmithKline). Dr. Shaefi is supported by a K08 from NIGMS (K08GM134220) and an R03 from National Institute of Aging (R03AG060179). Dr. Leaf is supported by an R01 from National Heart, Lung, and Blood Institute (R01HL144566). The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Rationale: Variation in hospital mortality has been described for coronavirus disease 2019 (COVID-19), but the factors that explain these differences remain unclear., Objective: Our objective was to utilize a large, nationally representative dataset of critically ill adults with COVID-19 to determine which factors explain mortality variability., Methods: In this multicenter cohort study, we examined adults hospitalized in intensive care units with COVID-19 at 70 United States hospitals between March and June 2020. The primary outcome was 28-day mortality. We examined patient-level and hospital-level variables. Mixed-effects logistic regression was used to identify factors associated with interhospital variation. The median odds ratio (OR) was calculated to compare outcomes in higher- vs. lower-mortality hospitals. A gradient boosted machine algorithm was developed for individual-level mortality models., Measurements and Main Results: A total of 4,019 patients were included, 1537 (38%) of whom died by 28 days. Mortality varied considerably across hospitals (0-82%). After adjustment for patient- and hospital-level domains, interhospital variation was attenuated (OR decline from 2.06 [95% CI, 1.73-2.37] to 1.22 [95% CI, 1.00-1.38]), with the greatest changes occurring with adjustment for acute physiology, socioeconomic status, and strain. For individual patients, the relative contribution of each domain to mortality risk was: acute physiology (49%), demographics and comorbidities (20%), socioeconomic status (12%), strain (9%), hospital quality (8%), and treatments (3%)., Conclusion: There is considerable interhospital variation in mortality for critically ill patients with COVID-19, which is mostly explained by hospital-level socioeconomic status, strain, and acute physiologic differences. Individual mortality is driven mostly by patient-level factors. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Objectives: Therapies for patients with respiratory failure from coronavirus disease 2019 are urgently needed. Early implementation of prone positioning ventilation improves survival in patients with acute respiratory distress syndrome, but studies examining the effect of proning on survival in patients with coronavirus disease 2019 are lacking. Our objective was to estimate the effect of early proning initiation on survival in patients with coronavirus disease 2019-associated respiratory failure., Design: Data were derived from the Study of the Treatment and Outcomes in Critically Ill Patients with coronavirus disease 2019, a multicenter cohort study of critically ill adults with coronavirus disease 2019 admitted to 68 U.S. hospitals. Using these data, we emulated a target trial of prone positioning ventilation by categorizing mechanically ventilated hypoxemic (ratio of Pao2 over the corresponding Fio2 ≤ 200 mm Hg) patients as having been initiated on proning or not within 2 days of ICU admission. We fit an inverse probability-weighted Cox model to estimate the mortality hazard ratio for early proning versus no early proning. Patients were followed until death, hospital discharge, or end of follow-up., Setting: ICUs at 68 U.S. sites., Patients: Critically ill adults with laboratory-confirmed coronavirus disease 2019 receiving invasive mechanical ventilation with ratio of Pao2 over the corresponding Fio2 less than or equal to 200 mm Hg., Interventions: None., Measurements and Main Results: Among 2,338 eligible patients, 702 (30.0%) were proned within the first 2 days of ICU admission. After inverse probability weighting, baseline and severity of illness characteristics were well-balanced between groups. A total of 1,017 (43.5%) of the 2,338 patients were discharged alive, 1,101 (47.1%) died, and 220 (9.4%) were still hospitalized at last follow-up. Patients proned within the first 2 days of ICU admission had a lower adjusted risk of death compared with nonproned patients (hazard ratio, 0.84; 95% CI, 0.73-0.97)., Conclusions: In-hospital mortality was lower in mechanically ventilated hypoxemic patients with coronavirus disease 2019 treated with early proning compared with patients whose treatment did not include early proning., Competing Interests: Dr. Mathews reported receiving grants from the National Institute of Health (NIH) and the National Heart, Lung, and Blood Institute (NHLBI) during the conduct of the study and serves on the steering committee for A Multi-Center, Adaptive, Randomized, Double-blind, Placebo-controlled Study to Assess the Efficacy and Safety of Gimsilumab in Subjects With Lung Injury or Acute Respiratory Distress Syndrome Secondary to COVID-19 (BREATHE) trial, funded by Roivant/Kinevant Sciences; she received support for article research from NIH. Dr. Shaefi reported receiving grants from the NIH and the National Institute on Aging and the National Institute of General Medical Sciences; he received support for article research from NIH. Dr. Coca received funding from RenalytixAI, Relypsa, Takeda Pharmaceuticals, CHF Solutions, Bayer, Boehringer Ingelheim, Akebia, inRegen, Renal Research Institute, and XORTX Therapeutics, Inc.; he owns equities in RenalytixAI and pulseData. Dr. Gupta reported receiving grants from the NIH and is a scientific coordinator for GlaxoSmithKline’s Anemia Studies in Chronic Kidney Disease: Erythropoiesis via a Novel Prolyl Hydroxylase Inhibitor Daprodustat trial. Dr. Srivastava’s institution received funding from the NIH and National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK); he reported receiving funding from the NIH, NIDDK, Horizon Therapeutics PLC, AstraZeneca, Tate & Latham, and CVS Caremark. Dr. Hernán reported receiving grants from the NIH. Dr. Chan’s institution received funding from NIH and Renal Research Institute; she received funding from Gerson Lehrman Group consulting and NIH; she received support for article research from NIH. Dr. Leaf’s institution received funding from NIH, NIDDK, and NHLBI; he received funding from BioPorto. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Objectives: Hypercoagulability may be a key mechanism for acute organ injury and death in patients with severe coronavirus disease 2019, but the relationship between elevated plasma levels of d-dimer, a biomarker of coagulation activation, and mortality has not been rigorously studied. We examined the independent association between d-dimer and death in critically ill patients with coronavirus disease 2019., Design: Multicenter cohort study., Setting: ICUs at 68 hospitals across the United States., Patients: Critically ill adults with coronavirus disease 2019 admitted to ICUs between March 4, 2020, and May 25, 2020, with a measured d-dimer concentration on ICU day 1 or 2., Interventions: None., Measurements and Main Results: The primary exposure was the highest normalized d-dimer level (assessed in four categories: < 2×, 2-3.9×, 4-7.9×, and ≥ 8× the upper limit of normal) on ICU day 1 or 2. The primary endpoint was 28-day mortality. Multivariable logistic regression was used to adjust for confounders. Among 3,418 patients (63.1% male; median age 62 yr [interquartile range, 52-71 yr]), 3,352 (93.6%) had a d-dimer concentration above the upper limit of normal. A total of 1,180 patients (34.5%) died within 28 days. Patients in the highest compared with lowest d-dimer category had a 3.11-fold higher odds of death (95% CI, 2.56-3.77) in univariate analyses, decreasing to a 1.81-fold increased odds of death (95% CI, 1.43-2.28) after multivariable adjustment for demographics, comorbidities, and illness severity. Further adjustment for therapeutic anticoagulation did not meaningfully attenuate this relationship (odds ratio, 1.73; 95% CI, 1.36-2.19)., Conclusions: In a large multicenter cohort study of critically ill patients with coronavirus disease 2019, higher d-dimer levels were independently associated with a greater risk of death., Competing Interests: Dr. Gupta’s institution received funding from the Foundation for the National Institutes of Health (NIH) 5 F32 DC 17342-2; she received funding from GlaxoSmithKline; she is a scientific coordinator for the Anemia Studies in CKD: Erythropoiesis via a Novel Prolyl Hydroxylase Inhibitor Daprodustat (ASCEND) trial (GlaskoSmithKline). Dr. Srivastava received funding from CVS Caremark, AstraZeneca, Horizon Therapeutics, PLC, and Tate & Latham. Dr. Shaefi’s institution received funding from the National Institute on Aging/NIH R03AG060179 and the National Institute of General Medical Sciences/NIH K08GM134220; he received support for article research from the NIH. Dr. Bagchi's institution received funding from the American Heart Association 20IPA35360009. He received funding from Lungpacer Medical, Inc. Dr. Al-Samkari received funding from Agios, Dova, Rigel, Argenx, and Amgen. Dr. Zakai’s institution received funding from the NIH and Centers for Disease Control and Prevention. Dr. Leaf received research support from BioPorto. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Background: Hypercoagulability may be a key mechanism of death in patients with coronavirus disease 2019 (COVID-19)., Objective: To evaluate the incidence of venous thromboembolism (VTE) and major bleeding in critically ill patients with COVID-19 and examine the observational effect of early therapeutic anticoagulation on survival., Design: In a multicenter cohort study of 3239 critically ill adults with COVID-19, the incidence of VTE and major bleeding within 14 days after intensive care unit (ICU) admission was evaluated. A target trial emulation in which patients were categorized according to receipt or no receipt of therapeutic anticoagulation in the first 2 days of ICU admission was done to examine the observational effect of early therapeutic anticoagulation on survival. A Cox model with inverse probability weighting to adjust for confounding was used., Setting: 67 hospitals in the United States., Participants: Adults with COVID-19 admitted to a participating ICU., Measurements: Time to death, censored at hospital discharge, or date of last follow-up., Results: Among the 3239 patients included, the median age was 61 years (interquartile range, 53 to 71 years), and 2088 (64.5%) were men. A total of 204 patients (6.3%) developed VTE, and 90 patients (2.8%) developed a major bleeding event. Independent predictors of VTE were male sex and higher D-dimer level on ICU admission. Among the 2809 patients included in the target trial emulation, 384 (11.9%) received early therapeutic anticoagulation. In the primary analysis, during a median follow-up of 27 days, patients who received early therapeutic anticoagulation had a similar risk for death as those who did not (hazard ratio, 1.12 [95% CI, 0.92 to 1.35])., Limitation: Observational design., Conclusion: Among critically ill adults with COVID-19, early therapeutic anticoagulation did not affect survival in the target trial emulation., Primary Funding Source: None.

Purpose: Limited data are available on venovenous extracorporeal membrane oxygenation (ECMO) in patients with severe hypoxemic respiratory failure from coronavirus disease 2019 (COVID-19)., Methods: We examined the clinical features and outcomes of 190 patients treated with ECMO within 14 days of ICU admission, using data from a multicenter cohort study of 5122 critically ill adults with COVID-19 admitted to 68 hospitals across the United States. To estimate the effect of ECMO on mortality, we emulated a target trial of ECMO receipt versus no ECMO receipt within 7 days of ICU admission among mechanically ventilated patients with severe hypoxemia (PaO 2 /FiO 2  < 100). Patients were followed until hospital discharge, death, or a minimum of 60 days. We adjusted for confounding using a multivariable Cox model., Results: Among the 190 patients treated with ECMO, the median age was 49 years (IQR 41-58), 137 (72.1%) were men, and the median PaO 2 /FiO 2 prior to ECMO initiation was 72 (IQR 61-90). At 60 days, 63 patients (33.2%) had died, 94 (49.5%) were discharged, and 33 (17.4%) remained hospitalized. Among the 1297 patients eligible for the target trial emulation, 45 of the 130 (34.6%) who received ECMO died, and 553 of the 1167 (47.4%) who did not receive ECMO died. In the primary analysis, patients who received ECMO had lower mortality than those who did not (HR 0.55; 95% CI 0.41-0.74). Results were similar in a secondary analysis limited to patients with PaO 2 /FiO 2  < 80 (HR 0.55; 95% CI 0.40-0.77)., Conclusion: In select patients with severe respiratory failure from COVID-19, ECMO may reduce mortality.

Background: AKI is a common sequela of coronavirus disease 2019 (COVID-19). However, few studies have focused on AKI treated with RRT (AKI-RRT)., Methods: We conducted a multicenter cohort study of 3099 critically ill adults with COVID-19 admitted to intensive care units (ICUs) at 67 hospitals across the United States. We used multivariable logistic regression to identify patient-and hospital-level risk factors for AKI-RRT and to examine risk factors for 28-day mortality among such patients., Results: A total of 637 of 3099 patients (20.6%) developed AKI-RRT within 14 days of ICU admission, 350 of whom (54.9%) died within 28 days of ICU admission. Patient-level risk factors for AKI-RRT included CKD, men, non-White race, hypertension, diabetes mellitus, higher body mass index, higher d-dimer, and greater severity of hypoxemia on ICU admission. Predictors of 28-day mortality in patients with AKI-RRT were older age, severe oliguria, and admission to a hospital with fewer ICU beds or one with greater regional density of COVID-19. At the end of a median follow-up of 17 days (range, 1-123 days), 403 of the 637 patients (63.3%) with AKI-RRT had died, 216 (33.9%) were discharged, and 18 (2.8%) remained hospitalized. Of the 216 patients discharged, 73 (33.8%) remained RRT dependent at discharge, and 39 (18.1%) remained RRT dependent 60 days after ICU admission., Conclusions: AKI-RRT is common among critically ill patients with COVID-19 and is associated with a hospital mortality rate of >60%. Among those who survive to discharge, one in three still depends on RRT at discharge, and one in six remains RRT dependent 60 days after ICU admission., (Copyright © 2021 by the American Society of Nephrology.)

Importance: Therapies that improve survival in critically ill patients with coronavirus disease 2019 (COVID-19) are needed. Tocilizumab, a monoclonal antibody against the interleukin 6 receptor, may counteract the inflammatory cytokine release syndrome in patients with severe COVID-19 illness., Objective: To test whether tocilizumab decreases mortality in this population., Design, Setting, and Participants: The data for this study were derived from a multicenter cohort study of 4485 adults with COVID-19 admitted to participating intensive care units (ICUs) at 68 hospitals across the US from March 4 to May 10, 2020. Critically ill adults with COVID-19 were categorized according to whether they received or did not receive tocilizumab in the first 2 days of admission to the ICU. Data were collected retrospectively until June 12, 2020. A Cox regression model with inverse probability weighting was used to adjust for confounding., Exposures: Treatment with tocilizumab in the first 2 days of ICU admission., Main Outcomes and Measures: Time to death, compared via hazard ratios (HRs), and 30-day mortality, compared via risk differences., Results: Among the 3924 patients included in the analysis (2464 male [62.8%]; median age, 62 [interquartile range {IQR}, 52-71] years), 433 (11.0%) received tocilizumab in the first 2 days of ICU admission. Patients treated with tocilizumab were younger (median age, 58 [IQR, 48-65] vs 63 [IQR, 52-72] years) and had a higher prevalence of hypoxemia on ICU admission (205 of 433 [47.3%] vs 1322 of 3491 [37.9%] with mechanical ventilation and a ratio of partial pressure of arterial oxygen to fraction of inspired oxygen of <200 mm Hg) than patients not treated with tocilizumab. After applying inverse probability weighting, baseline and severity-of-illness characteristics were well balanced between groups. A total of 1544 patients (39.3%) died, including 125 (28.9%) treated with tocilizumab and 1419 (40.6%) not treated with tocilizumab. In the primary analysis, during a median follow-up of 27 (IQR, 14-37) days, patients treated with tocilizumab had a lower risk of death compared with those not treated with tocilizumab (HR, 0.71; 95% CI, 0.56-0.92). The estimated 30-day mortality was 27.5% (95% CI, 21.2%-33.8%) in the tocilizumab-treated patients and 37.1% (95% CI, 35.5%-38.7%) in the non-tocilizumab-treated patients (risk difference, 9.6%; 95% CI, 3.1%-16.0%)., Conclusions and Relevance: Among critically ill patients with COVID-19 in this cohort study, the risk of in-hospital mortality in this study was lower in patients treated with tocilizumab in the first 2 days of ICU admission compared with patients whose treatment did not include early use of tocilizumab. However, the findings may be susceptible to unmeasured confounding, and further research from randomized clinical trials is needed.

Importance: The US is currently an epicenter of the coronavirus disease 2019 (COVID-19) pandemic, yet few national data are available on patient characteristics, treatment, and outcomes of critical illness from COVID-19., Objectives: To assess factors associated with death and to examine interhospital variation in treatment and outcomes for patients with COVID-19., Design, Setting, and Participants: This multicenter cohort study assessed 2215 adults with laboratory-confirmed COVID-19 who were admitted to intensive care units (ICUs) at 65 hospitals across the US from March 4 to April 4, 2020., Exposures: Patient-level data, including demographics, comorbidities, and organ dysfunction, and hospital characteristics, including number of ICU beds., Main Outcomes and Measures: The primary outcome was 28-day in-hospital mortality. Multilevel logistic regression was used to evaluate factors associated with death and to examine interhospital variation in treatment and outcomes., Results: A total of 2215 patients (mean [SD] age, 60.5 [14.5] years; 1436 [64.8%] male; 1738 [78.5%] with at least 1 chronic comorbidity) were included in the study. At 28 days after ICU admission, 784 patients (35.4%) had died, 824 (37.2%) were discharged, and 607 (27.4%) remained hospitalized. At the end of study follow-up (median, 16 days; interquartile range, 8-28 days), 875 patients (39.5%) had died, 1203 (54.3%) were discharged, and 137 (6.2%) remained hospitalized. Factors independently associated with death included older age (≥80 vs <40 years of age: odds ratio [OR], 11.15; 95% CI, 6.19-20.06), male sex (OR, 1.50; 95% CI, 1.19-1.90), higher body mass index (≥40 vs <25: OR, 1.51; 95% CI, 1.01-2.25), coronary artery disease (OR, 1.47; 95% CI, 1.07-2.02), active cancer (OR, 2.15; 95% CI, 1.35-3.43), and the presence of hypoxemia (Pao2:Fio2<100 vs ≥300 mm Hg: OR, 2.94; 95% CI, 2.11-4.08), liver dysfunction (liver Sequential Organ Failure Assessment score of 2-4 vs 0: OR, 2.61; 95% CI, 1.30-5.25), and kidney dysfunction (renal Sequential Organ Failure Assessment score of 4 vs 0: OR, 2.43; 95% CI, 1.46-4.05) at ICU admission. Patients admitted to hospitals with fewer ICU beds had a higher risk of death (<50 vs ≥100 ICU beds: OR, 3.28; 95% CI, 2.16-4.99). Hospitals varied considerably in the risk-adjusted proportion of patients who died (range, 6.6%-80.8%) and in the percentage of patients who received hydroxychloroquine, tocilizumab, and other treatments and supportive therapies., Conclusions and Relevance: This study identified demographic, clinical, and hospital-level risk factors that may be associated with death in critically ill patients with COVID-19 and can facilitate the identification of medications and supportive therapies to improve outcomes.

Objective: To systematically review studies assessing the effects of health information technology (health IT) on patient safety outcomes., Materials and Methods: The authors employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement methods. MEDLINE, Cumulative Index to Nursing Allied Health (CINAHL), EMBASE, and Cochrane Library databases, from 2001 to June 2012, were searched. Descriptive and comparative studies were included that involved use of health IT in a clinical setting and measured effects on patient safety outcomes., Results: Data on setting, subjects, information technology implemented, and type of patient safety outcomes were all abstracted. The quality of the studies was evaluated by 2 independent reviewers (scored from 0 to 10). A total of 69 studies met inclusion criteria. Quality scores ranged from 1 to 9. There were 25 (36%) studies that found benefit of health IT on direct patient safety outcomes for the primary outcome measured, 43 (62%) studies that either had non-significant or mixed findings, and 1 (1%) study for which health IT had a detrimental effect. Neither the quality of the studies nor the rate of randomized control trials performed changed over time. Most studies that demonstrated a positive benefit of health IT on direct patient safety outcomes were inpatient, single-center, and either cohort or observational trials studying clinical decision support or computerized provider order entry., Discussion and Conclusion: Many areas of health IT application remain understudied and the majority of studies have non-significant or mixed findings. Our study suggests that larger, higher quality studies need to be conducted, particularly in the long-term care and ambulatory care settings., (© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)