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Competing Interests: I have received a grant from the Department of Defense (grant number W81XWH2110217). I have also received a grant from the National Institutes of Health (grant number R01HL164777), unrelated to this work.

Recent Bayesian reanalyses of prominent trials in critical illness have generated controversy by contradicting the initial conclusions based on conventional frequentist analyses. Many clinicians might be sceptical that Bayesian analysis, a philosophical and statistical approach that combines prior beliefs with data to generate probabilities, provides more useful information about clinical trials than the frequentist approach. In this Personal View, we introduce clinicians to the rationale, process, and interpretation of Bayesian analysis through a systematic review and reanalysis of interventional trials in critical illness. In the majority of cases, Bayesian and frequentist analyses agreed. In the remainder, Bayesian analysis identified interventions where benefit was probable despite the absence of statistical significance, where interpretation depended substantially on choice of prior distribution, and where benefit was improbable despite statistical significance. Bayesian analysis in critical care medicine can help to distinguish harm from uncertainty and establish the probability of clinically important benefit for clinicians, policy makers, and patients., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: Over 40 000 patients with COVID-19 have been hospitalised in New York City (NY, USA) as of April 28, 2020. Data on the epidemiology, clinical course, and outcomes of critically ill patients with COVID-19 in this setting are needed., Methods: This prospective observational cohort study took place at two NewYork-Presbyterian hospitals affiliated with Columbia University Irving Medical Center in northern Manhattan. We prospectively identified adult patients (aged ≥18 years) admitted to both hospitals from March 2 to April 1, 2020, who were diagnosed with laboratory-confirmed COVID-19 and were critically ill with acute hypoxaemic respiratory failure, and collected clinical, biomarker, and treatment data. The primary outcome was the rate of in-hospital death. Secondary outcomes included frequency and duration of invasive mechanical ventilation, frequency of vasopressor use and renal replacement therapy, and time to in-hospital clinical deterioration following admission. The relation between clinical risk factors, biomarkers, and in-hospital mortality was modelled using Cox proportional hazards regression. Follow-up time was right-censored on April 28, 2020 so that each patient had at least 28 days of observation., Findings: Between March 2 and April 1, 2020, 1150 adults were admitted to both hospitals with laboratory-confirmed COVID-19, of which 257 (22%) were critically ill. The median age of patients was 62 years (IQR 51-72), 171 (67%) were men. 212 (82%) patients had at least one chronic illness, the most common of which were hypertension (162 [63%]) and diabetes (92 [36%]). 119 (46%) patients had obesity. As of April 28, 2020, 101 (39%) patients had died and 94 (37%) remained hospitalised. 203 (79%) patients received invasive mechanical ventilation for a median of 18 days (IQR 9-28), 170 (66%) of 257 patients received vasopressors and 79 (31%) received renal replacement therapy. The median time to in-hospital deterioration was 3 days (IQR 1-6). In the multivariable Cox model, older age (adjusted hazard ratio [aHR] 1·31 [1·09-1·57] per 10-year increase), chronic cardiac disease (aHR 1·76 [1·08-2·86]), chronic pulmonary disease (aHR 2·94 [1·48-5·84]), higher concentrations of interleukin-6 (aHR 1·11 [95%CI 1·02-1·20] per decile increase), and higher concentrations of D-dimer (aHR 1·10 [1·01-1·19] per decile increase) were independently associated with in-hospital mortality., Interpretation: Critical illness among patients hospitalised with COVID-19 in New York City is common and associated with a high frequency of invasive mechanical ventilation, extrapulmonary organ dysfunction, and substantial in-hospital mortality., Funding: National Institute of Allergy and Infectious Diseases and the National Center for Advancing Translational Sciences, National Institutes of Health, and the Columbia University Irving Institute for Clinical and Translational Research., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: We aimed to examine short- and long-term mortality in a mixed population of patients with interstitial lung disease (ILD) with acute respiratory failure, and to identify those at lower vs higher risk of in-hospital death., Methods: We conducted a single-center retrospective cohort study of 126 consecutive adults with ILD admitted to an ICU for respiratory failure at a tertiary care hospital between 2010 and 2014 and who did not undergo lung transplantation during their hospitalization. We examined associations of ICU-day 1 characteristics with in-hospital and 1-year mortality, using Poisson regression, and examined survival using Kaplan-Meier curves. We created a risk score for in-hospital mortality, using a model developed with penalized regression., Results: In-hospital mortality was 66%, and 1-year mortality was 80%. Those with connective tissue disease-related ILD had better short-term and long-term mortality compared with unclassifiable ILD (adjusted relative risk, 0.6; 95% CI, 0.3-0.9; and relative risk, 0.6; 95% CI, 0.4-0.9, respectively). Our prediction model includes male sex, interstitial pulmonary fibrosis diagnosis, use of invasive mechanical ventilation and/or extracorporeal life support, no ambulation within 24 h of ICU admission, BMI, and Simplified Acute Physiology Score-II. The optimism-corrected C-statistic was 0.73, and model calibration was excellent (P = .99). In-hospital mortality rates for the low-, moderate-, and high-risk groups were 33%, 65%, and 96%, respectively., Conclusions: We created a risk score that classifies patients with ILD with acute respiratory failure from low to high risk for in-hospital mortality. The score could aid providers in counseling these patients and their families., (Copyright © 2018 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Background: Frail lung transplant candidates are more likely to be delisted or die without receiving a transplant. Further knowledge of what frailty represents in this population will assist in developing interventions to prevent frailty from developing. We set out to determine whether frail lung transplant candidates have reduced exercise capacity independent of disease severity and diagnosis., Methods: Sixty-eight adult lung transplant candidates underwent cardiopulmonary exercise testing (CPET) and a frailty assessment (Fried's Frailty Phenotype (FFP)). Primary outcomes were peak workload and peak aerobic capacity (V˙O 2 ). We used linear regression to adjust for age, gender, diagnosis, and lung allocation score (LAS)., Results: The mean ± SD age was 57 ± 11 years, 51% were women, 57% had interstitial lung disease, 32% had chronic obstructive pulmonary disease, 11% had cystic fibrosis, and the mean LAS was 40.2 (range 19.2-94.5). In adjusted models, peak workload decreased by 10 W (95% CI 4.7 to 14.6) and peak V˙O 2 decreased by 1.8 mL/kg/min (95% CI 0.6 to 2.9) per 1 unit increment in FFP score. After adjustment, exercise tolerance was 38 W lower (95% CI 18.4 to 58.1) and peak V˙O 2 was 8.5 mL/kg/min lower (95% CI 3.3 to 13.7) among frail participants compared to non-frail participants. Frailty accounted for 16% of the variance (R 2 ) of watts and 19% of the variance of V˙O 2 in adjusted models., Conclusion: Frailty contributes to reduced exercise capacity among lung transplant candidates independent of disease severity., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Background: Although 1.4 million elderly Americans survive hospitalization involving intensive care annually, many are at risk for early mortality following discharge. No models that predict the likelihood of death after discharge exist explicitly for this population. Therefore, we derived and externally validated a 6-month postdischarge mortality prediction model for elderly ICU survivors., Methods: We derived the model from medical record and claims data for 1,526 consecutive patients aged ≥ 65 years who had their first medical ICU admission in 2006 to 2009 at a tertiary-care hospital and survived to discharge (excluding those patients discharged to hospice). We then validated the model in 1,010 patients from a different tertiary-care hospital., Results: Six-month mortality was 27.3% and 30.2% in the derivation and validation cohorts, respectively. Independent predictors of mortality (in descending order of contribution to the model's predictive power) were a do-not-resuscitate order, older age, burden of comorbidity, admission from or discharge to a skilled-care facility, hospital length of stay, principal diagnoses of sepsis and hematologic malignancy, and male sex. For the derivation and external validation cohorts, the area under the receiver operating characteristic curve was 0.80 (SE, 0.01) and 0.71 (SE, 0.02), respectively, with good calibration for both (P = 0.31 and 0.43)., Conclusions: Clinical variables available at hospital discharge can help predict 6-month mortality for elderly ICU survivors. Variables that capture elements of frailty, disability, the burden of comorbidity, and patient preferences regarding resuscitation during the hospitalization contribute most to this model's predictive power. The model could aid providers in counseling elderly ICU survivors at high risk of death and their families.