IMPORTANCEThe National COVID Cohort Collaborative (N3C) is a centralized, harmonized, highgranularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy.OBJECTIVES To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. DESIGN, SETTING, AND PARTICIPANTSIn a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). MAIN OUTCOMES AND MEASURESPatient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. RESULTSThe cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472(18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, (continued) Key Points Question In a US data resource large enough to adjust for multiple confounders, what risk factors are associated with COVID-19 severity and severity trajectory over time, and can machine learning models predict clinical severity? Findings In this cohort study of 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized and 6565 (20.2%) were severely ill, and first-day machine learning models accurately predicted clinical severity. Mortality was 11.6%

Background Heterogeneous respiratory system static compliance (CRS) values and levels of hypoxemia in patients with novel coronavirus disease (COVID-19) requiring mechanical ventilation have been reported in previous small-case series or studies conducted at a national level. Methods We designed a retrospective observational cohort study with rapid data gathering from the international COVID-19 Critical Care Consortium study to comprehensively describe CRS—calculated as: tidal volume/[airway plateau pressure-positive end-expiratory pressure (PEEP)]—and its association with ventilatory management and outcomes of COVID-19 patients on mechanical ventilation (MV), admitted to intensive care units (ICU) worldwide. Results We studied 745 patients from 22 countries, who required admission to the ICU and MV from January 14 to December 31, 2020, and presented at least one value of CRS within the first seven days of MV. Median (IQR) age was 62 (52–71), patients were predominantly males (68%) and from Europe/North and South America (88%). CRS, within 48 h from endotracheal intubation, was available in 649 patients and was neither associated with the duration from onset of symptoms to commencement of MV (p = 0.417) nor with PaO2/FiO2 (p = 0.100). Females presented lower CRS than males (95% CI of CRS difference between females-males: − 11.8 to − 7.4 mL/cmH2O p < 0.001), and although females presented higher body mass index (BMI), association of BMI with CRS was marginal (p = 0.139). Ventilatory management varied across CRS range, resulting in a significant association between CRS and driving pressure (estimated decrease − 0.31 cmH2O/L per mL/cmH20 of CRS, 95% CI − 0.48 to − 0.14, p < 0.001). Overall, 28-day ICU mortality, accounting for the competing risk of being discharged within the period, was 35.6% (SE 1.7). Cox proportional hazard analysis demonstrated that CRS (+ 10 mL/cm H2O) was only associated with being discharge from the ICU within 28 days (HR 1.14, 95% CI 1.02–1.28, p = 0.018). Conclusions This multicentre report provides a comprehensive account of CRS in COVID-19 patients on MV. CRS measured within 48 h from commencement of MV has marginal predictive value for 28-day mortality, but was associated with being discharged from ICU within the same period. Trial documentation: Available at https://www.covid-critical.com/study. Trial registration: ACTRN12620000421932.

The four-shock Bayesian up-down protocol is the first clinical protocol to accurately predict an ED80 voltage. A 100 V increment above the ED80 provides an adequate safety margin. This simple and accurate method for estimating a highly effective defibrillation dose may be a valuable tool for population-based clinical hypothesis testing, as well as defibrillator implantation.

Introduction: To examine the effects of coronavirus disease 2019 (COVID-19) on patients in an academic psychiatric ambulatory clinic, data from a measurement-based care (MBC) system were analyzed to evaluate impacts on psychiatric functioning in patients using telemedicine. Psychiatric functioning was evaluated for psychological distress (brief adjustment scale [BASE]-6), depression (patient health questionnaire [PHQ]-9), and anxiety (generalized anxiety disorder [GAD]-7), including initial alcohol (U.S. alcohol use disorders identification test) and substance use (drug abuse screening test-10) screening. Methods: This observational study included MBC data collected from November 2019 to March 2021. Patient-Reported Outcome Measures (PROMs) were examined to determine changes in symptomatology over the course of treatment, as well as symptom changes resulting from the pandemic. Patients were included in analyses if they completed at least one PROM in the MBC system. Results: A total of 2,145 patients actively participated in the MBC system completing at least one PROM, with engagement ranging from 35.07% to 83.50% depending on demographic factors, where completion rates were significantly different for age, payor status, and diagnostic group. Average baseline scores for new patients varied for the GAD-7, PHQ-9, and BASE-6. Within-person improvements in mental health before and after the pandemic were statistically significant for anxiety, depression, and psychological adjustment. Discussion: MBC is a helpful tool in determining treatment progress for patients engaging in telemedicine. This study showed that patients who engaged in psychiatric services incorporating PROMs had improvements in mental health during the COVID-19 pandemic. Additional research is needed exploring whether PROMs might serve as a protective or facilitative factor for those with mental illness during a crisis when in-person visits are not possible.