denotes emergency department, and IQR interquartile range. † Race was determined by the clinical team. ‡ Obesity was defined as a body-mass index (the weight in kilograms divided by the square of the height in meters) of 30 or higher.

Background: Some reports suggest that obesity could be a risk factor for complications in coronavirus disease 2019 (COVID-19) (1). Several mechanisms could explain this. First, adipocytes, which activate the inflammatory cascade, can increase risk for thromboembolism and susceptibility to the cytokine storm described in COVID-19 (2). Second, obesity negatively affects lung mechanics, which could predispose obese persons to more severe respiratory distress and failure (3). Finally, obesity can alter mitochondrial bioenergetics in lung epithelial cells and increase risk for acute lung injury (4). However, some have suggested an obesity paradox in some critical illnesses, including acute respiratory distress syndrome, where patients with obesity may have improved outcomes; whether this phenomenon occurs in patients with COVID-19 is unclear (5). Objective: To study the association between obesity and outcomes among a diverse cohort of 1687 persons hospitalized with confirmed COVID-19 at 2 New York City hospitals. Methods and Findings: This retrospective observational cohort study included consecutive adults with confirmed COVID-19 who were hospitalized between 3 March and 15 May 2020 at an 862-bed quaternary referral center or a 180bed community hospital in New York City. We excluded 46 patients who did not have height or weight data available to calculate body mass index (BMI). Patient data were manually abstracted (1) from the electronic health record through 6 June 2020. We determined BMI on the basis of the most recent height and weight listed in the electronic health record. Height and weight were collected during hospitalization for 95.5% of the cohort; the remaining BMIs were collected during ambulatory encounters within 3 months of hospitalization. We defined BMI categories as underweight (<18.5 kg/m 2), normal (18.5 to 24.9 kg/m 2), overweight (25.0 to 29.9 kg/m 2), mild to moderate obesity (30.0 to 39.9 kg/m 2), and morbid obesity (≥40.0 kg/m 2). To examine the association between BMI and in-hospital mortality, we used a Cox proportional hazards model adjusted for age, sex, race, smoking, diabetes, hypertension, chronic obstructive pulmonary disease, asthma, end-stage renal disease, coronary artery disease, heart failure, and cancer. These characteristics were chosen on the basis of risk factors for severe COVID-19 identified by the Centers for Disease Control and Prevention. We also examined for effect modification by age, sex, and race. To examine the association between BMI and respiratory failure, defined as a need for invasive mechanical ventilation, we used a Fine and Gray model to account for the competing risk for death and adjusted for the same 12 variables used in the model for mortality. We excluded the underweight group from this analysis because of low numbers. Finally, we repeated the adjusted Cox proportional hazards model analysis for mortality among persons with respiratory failure, again excluding the underweight group. To account for missing data (12% for race), we did multiple imputation.

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Background The independent prognostic value of troponin and other biomarker elevation among patients with coronavirus‐19 (COVID‐19) are unclear. We sought to characterize biomarker levels in patients hospitalized with COVID‐19 and develop and validate a mortality risk score. Methods and Results An observational cohort study of 1053 patients with COVID‐19 was conducted. Patients with all of the following biomarkers measured: troponin‐I (TnI), B‐type natriuretic peptide, C‐reactive protein, ferritin and D‐dimer (n = 446) were identified. Maximum levels for each biomarker were recorded. Primary endpoint was 30‐day in‐hospital mortality. Multivariable logistic regression was used to construct a mortality risk score. Validation of the risk score was performed using an independent patient cohort (n = 440). Mean age of patients was 65.0 ± 15.2 years and 65.3% were men. Overall, 444 (99.6%) had elevation of any biomarker. Among tested biomarkers, TnI ≥ 0.34 ng/ml was the only independent predictor of 30‐day mortality (adjusted OR 4.38; P < 0.001). Patients with a mortality score using hypoxia on presentation, age and TnI elevation, age (HA 2 T 2 ) ≥ 3 had a 30‐day mortality of 43.7% while those with a score < 3 had mortality of 5.9%. Area under the receiver operating characteristic curve of the HA 2 T 2 score was 0.834 for the derivation cohort and 0.784 for the validation cohort. Conclusions Elevated troponin and other biomarker levels are commonly seen in patients hospitalized with COVID‐19. High troponin levels are a potent predictor of 30‐day in‐hospital mortality. A simple risk score can stratify patients at risk for COVID‐19‐associated mortality.

Background The long-term prevalence and risk factors for post-acute COVID-19 sequelae (PASC) are not well described and may have important implications for unvaccinated populations and policy makers. Objective To assess health status, persistent symptoms, and effort tolerance approximately 1 year after COVID-19 infection Design Retrospective observational cohort study using surveys and clinical data Participants Survey respondents who were survivors of acute COVID-19 infection requiring Emergency Department presentation or hospitalization between March 3 and May 15, 2020. Main Measure(s) Self-reported health status, persistent symptoms, and effort tolerance Key Results The 530 respondents (median time between hospital presentation and survey 332 days [IQR 325–344]) had mean age 59.2±16.3 years, 44.5% were female and 70.8% were non-White. Of these, 41.5% reported worse health compared to a year prior, 44.2% reported persistent symptoms, 36.2% reported limitations in lifting/carrying groceries, 35.5% reported limitations climbing one flight of stairs, 38.1% reported limitations bending/kneeling/stooping, and 22.1% reported limitations walking one block. Even those without high-risk comorbid conditions and those seen only in the Emergency Department (but not hospitalized) experienced significant deterioration in health, persistent symptoms, and limitations in effort tolerance. Women (adjusted relative risk ratio [aRRR] 1.26, 95% CI 1.01–1.56), those requiring mechanical ventilation (aRRR 1.48, 1.02–2.14), and people with HIV (aRRR 1.75, 1.14–2.69) were significantly more likely to report persistent symptoms. Age and other risk factors for more severe COVID-19 illness were not associated with increased risk of PASC. Conclusions PASC may be extraordinarily common 1 year after COVID-19, and these symptoms are sufficiently severe to impact the daily exercise tolerance of patients. PASC symptoms are broadly distributed, are not limited to one specific patient group, and appear to be unrelated to age. These data have implications for vaccine hesitant individuals, policy makers, and physicians managing the emerging longer-term yet unknown impact of the COVID-19 pandemic. Supplementary Information The online version contains supplementary material available at 10.1007/s11606-021-07379-z.

Eosinophils influence antitumor immunity and may predict response to treatment with immune checkpoint inhibitors (ICIs). To examine the association between blood eosinophil counts and outcomes in patients with advanced or metastatic urothelial carcinoma (mUC) treated with ICIs, we identified 2 ICI-treated cohorts: discovery (n = 60) and validation (n = 111). Chemotherapy cohorts were used as comparators (first-line platinum-based chemotherapy, n = 75; second-line or more pemetrexed, n = 77). The primary endpoint was overall survival (OS). Secondary endpoints were time on treatment (ToT) and progression-free survival. Univariate and multivariate analyses were performed using Cox proportional hazard models. Associations between changes in eosinophil count at weeks 2/3 and 6 after the start of ICI treatment were analyzed using landmark analyses. Baseline characteristics of the ICI cohorts were similar. In the discovery cohort, an optimal cutoff for pretreatment eosinophil count was determined [Eos-Lo: < 100 cells/µL; n = 9 (15%); Eos-Hi: ≥ 100 cells/µL; n = 51 (85%)]. Eos-Lo was associated with inferior outcomes [OS: hazard ratio (HR)

Background The clinical course of COVID-19 includes multiple disease phases. Data describing post-hospital discharge outcomes may provide insight into disease course. Studies describing post-hospitalization outcomes of adults following COVID-19 infection are limited to electronic medical record review, which may underestimate the incidence of outcomes. Objective To determine 30-day post-hospitalization outcomes following COVID-19 infection. Design Retrospective cohort study Setting Quaternary referral hospital and community hospital in New York City. Participants COVID-19 infected patients discharged alive from the emergency department (ED) or hospital between March 3 and May 15, 2020. Measurement Outcomes included return to an ED, re-hospitalization, and mortality within 30 days of hospital discharge. Results Thirty-day follow-up data were successfully collected on 94.6% of eligible patients. Among 1344 patients, 16.5% returned to an ED, 9.8% were re-hospitalized, and 2.4% died. Among patients who returned to the ED, 50.0% (108/216) went to a different hospital from the hospital of the index presentation, and 61.1% (132/216) of those who returned were re-hospitalized. In Cox models adjusted for variables selected using the lasso method, age (HR 1.01 per year [95% CI 1.00–1.02]), diabetes (1.54 [1.06–2.23]), and the need for inpatient dialysis (3.78 [2.23–6.43]) during the index presentation were independently associated with a higher re-hospitalization rate. Older age (HR 1.08 [1.05–1.11]) and Asian race (2.89 [1.27–6.61]) were significantly associated with mortality. Conclusions Among patients discharged alive following their index presentation for COVID-19, risk for returning to a hospital within 30 days of discharge was substantial. These patients merit close post-discharge follow-up to optimize outcomes. Supplementary Information The online version contains supplementary material available at 10.1007/s11606-021-06924-0.

As the coronavirus disease 2019 (COVID-19) pandemic hit the United States in March 2020, there was widespread disruption of clinical medical education: Hospital clerkships were suspended nationwide and students were moved out of the hospital and continued their studies remotely through virtual learning systems. Frustrated by not being able to directly care for patients, medical students across the country formed diverse volunteer initiatives to help frontline clinicians. In this article, the authors describe the essential role of medical students at Weill Cornell Medicine in quickly designing and acknowledge their faculty mentors,