Assessing capacity to social distance and neighborhood-level health disparities during the COVID-19 pandemic (preprint)

The COVID-19 pandemic has yielded disproportionate impacts on communities of color in New York City (NYC). Researchers have noted that social disadvantage may result in limited capacity to socially distance, and consequent disparities. Here, we investigate the role of neighborhood social disadvantage on the ability to socially distance, infections, and mortality. We combine Census Bureau and NYC open data with SARS-CoV-2 testing data using supervised dimensionality-reduction with Bayesian Weighted Quantile Sums regression. The result is a ZIP code-level index with relative weights for social factors facilitating infection risk. We find a positive association between neighborhood social disadvantage and infections, adjusting for the number of tests administered. Neighborhood infection risk is also associated with capacity to socially isolate, as measured by NYC subway data. Finally, infection risk is associated with COVID-19-related mortality. These analyses support that differences in capacity to socially isolate is a credible pathway between disadvantage and COVID-19 disparities.

Characterization of Patients Who Return to Hospital Following Discharge from Hospitalization For COVID-19 (preprint)

Background: Data on patients with coronavirus disease 2019 (COVID-19) who return to hospital after discharge are scarce. Characterization of these patients may inform post-hospitalization care. Methods and Findings: Retrospective cohort study of patients with confirmed SARS-CoV-2 discharged alive from five hospitals in New York City with index hospitalization between February 27th-April 12th, 2020, with follow-up of [≥]14 days. Significance was defined as P<0.05 after multiplying P by 125 study-wide comparisons. Of 2,864 discharged patients, 103 (3.6%) returned for emergency care after a median of 4.5 days, with 56 requiring inpatient readmission. The most common reason for return was respiratory distress (50%). Compared to patients who did not return, among those who returned there was a higher proportion of COPD (6.8% vs 2.9%) and hypertension (36% vs 22.1%). Patients who returned also had a shorter median length of stay (LOS) during index hospitalization (4.5 [2.9,9.1] vs. 6.7 [3.5, 11.5] days; Padjusted=0.006), and were less likely to have required intensive care on index hospitalization (5.8% vs 19%; Padjusted=0.001). A trend towards association between absence of in-hospital anticoagulation on index admission and return to hospital was also observed (20.9% vs 30.9%, Padjusted=0.064). On readmission, rates of intensive care and death were 5.8% and 3.6%, respectively. Conclusions: Return to hospital after admission for COVID-19 was infrequent within 14 days of discharge. The most common cause for return was respiratory distress. Patients who returned had higher proportion of COPD and hypertension with shorter LOS on index hospitalization, and a trend towards lower rates of in-hospital treatment-dose anticoagulation. Future studies should focus on whether these comorbid conditions, longer LOS and anticoagulation are associated with reduced readmissions.

Acute Kidney Injury in Hospitalized Patients with COVID-19 (preprint)

Importance: Preliminary reports indicate that acute kidney injury (AKI) is common in coronavirus disease (COVID)-19 patients and is associated with worse outcomes. AKI in hospitalized COVID-19 patients in the United States is not well-described. Objective: To provide information about frequency, outcomes and recovery associated with AKI and dialysis in hospitalized COVID-19 patients. Design: Observational, retrospective study. Setting: Admitted to hospital between February 27 and April 15, 2020. Participants: Patients aged [≥]18 years with laboratory confirmed COVID-19 Exposures: AKI (peak serum creatinine increase of 0.3 mg/dL or 50% above baseline). Main Outcomes and Measures: Frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aOR) with mortality. We also trained and tested a machine learning model for predicting dialysis requirement with independent validation. Results: A total of 3,235 hospitalized patients were diagnosed with COVID-19. AKI occurred in 1406 (46%) patients overall and 280 (20%) with AKI required renal replacement therapy. The incidence of AKI (admission plus new cases) in patients admitted to the intensive care unit was 68% (553 of 815). In the entire cohort, the proportion with stages 1, 2, and 3 AKI were 35%, 20%, 45%, respectively. In those needing intensive care, the respective proportions were 20%, 17%, 63%, and 34% received acute renal replacement therapy. Independent predictors of severe AKI were chronic kidney disease, systolic blood pressure, and potassium at baseline. In-hospital mortality in patients with AKI was 41% overall and 52% in intensive care. The aOR for mortality associated with AKI was 9.6 (95% CI 7.4-12.3) overall and 20.9 (95% CI 11.7-37.3) in patients receiving intensive care. 56% of patients with AKI who were discharged alive recovered kidney function back to baseline. The area under the curve (AUC) for the machine learned predictive model using baseline features for dialysis requirement was 0.79 in a validation test. Conclusions and Relevance: AKI is common in patients hospitalized with COVID-19, associated with worse mortality, and the majority of patients that survive do not recover kidney function. A machine-learned model using admission features had good performance for dialysis prediction and could be used for resource allocation.

Analysis of hospitalized COVID-19 patients in the Mount Sinai Health System using electronic medical records (EMR) reveals important prognostic factors for improved clinical outcomes (preprint)

COVID-19 is a novel threat to human health worldwide. There is an urgent need to understand patient characteristics of having COVID-19 disease and evaluate markers of critical illness and mortality. Objective: To assess association of clinical features on patient outcomes. Design, Setting, and Participants: In this observational case series, patient-level data were extracted from electronic medical records for 28,336 patients tested for SARS-CoV-2 at the Mount Sinai Health System from 2/24/ to 4/15/2020, including 6,158 laboratory-confirmed cases. Exposures: Confirmed COVID-19 diagnosis by RT-PCR assay from nasal swabs. Main Outcomes and Measures: Effects of race on positive test rates and mortality were assessed. Among positive cases admitted to the hospital (N = 3,273), effects of patient demographics, hospital site and unit, social behavior, vital signs, lab results, and disease comorbidities on discharge and death were estimated. Results: Hispanics (29%) and African Americans (25%) had disproportionately high positive case rates relative to population base rates (p<2e-16); however, no differences in mortality rates were observed in the hospital. Outcome differed significantly between hospitals (Gray's T=248.9; p<2e-16), reflecting differences in average baseline age and underlying comorbidities. Significant risk factors for mortality included age (HR=1.05 [95% CI, 1.04-1.06]; p=1.15e-32), oxygen saturation (HR=0.985 [95% CI, 0.982-0.988]; p=1.57e-17), care in ICU areas (HR=1.58 [95% CI, 1.29-1.92]; p=7.81e-6), and elevated creatinine (HR=1.75 [95% CI, 1.47-2.10]; p=7.48e-10), alanine aminotransferase (ALT) (HR=1.002, [95% CI 1.001-1.003]; p=8.86e-5) and body-mass index (BMI) (HR=1.02, [95% CI 1.00-1.03]; p=1.09e-2). Asthma (HR=0.78 [95% CI, 0.62-0.98]; p=0.031) was significantly associated with increased length of hospital stay, but not mortality. Deceased patients were more likely to have elevated markers of inflammation. Baseline age, BMI, oxygen saturation, respiratory rate, white blood cell (WBC) count, creatinine, and ALT were significant prognostic indicators of mortality. Conclusions and Relevance: While race was associated with higher risk of infection, we did not find a racial disparity in inpatient mortality suggesting that outcomes in a single tertiary care health system are comparable across races. We identified clinical features associated with reduced mortality and discharge. These findings could help to identify which COVID-19 patients are at greatest risk and evaluate the impact on survival.

Clinical Characteristics of Hospitalized Covid-19 Patients in New York City (preprint)

ABSTRACT Background: The coronavirus 2019 (Covid-19) pandemic is a global public health crisis, with over 1.6 million cases and 95,000 deaths worldwide. Data are needed regarding the clinical course of hospitalized patients, particularly in the United States. Methods Demographic, clinical, and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed Covid-19 between February 27 and April 2, 2020 were identified through institutional electronic health records. We conducted a descriptive study of patients who had in-hospital mortality or were discharged alive. Results A total of 2,199 patients with Covid-19 were hospitalized during the study period. As of April 2nd, 1,121 (51%) patients remained hospitalized, and 1,078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 ug/ml, C-reactive protein was 162 mg/L, and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 ug/ml, C-reactive protein was 79 mg/L, and procalcitonin was 0.09 ng/mL. Conclusions This is the largest and most diverse case series of hospitalized patients with Covid-19 in the United States to date. Requirement of intensive care and mortality were high. Patients who died typically had pre-existing conditions and severe perturbations in inflammatory markers.