Geospatial Distribution and Predictors of Mortality in Hospitalized Patients with COVID-19: A Cohort Study

Background The global Coronavirus Disease 2019 (COVID-19) pandemic offers the opportunity to assess how hospitals managed the care of hospitalized patients with varying demographics and clinical presentation. The goal of this study is to demonstrate the impact of densely populated residential areas on hospitalization and to identify predictors of length of stay and mortality in hospitalized patients with COVID-19 in one of the hardest hit counties internationally. Methods This is a single-center cohort study of 1325 sequentially hospitalized patients with COVID-19 in New York between March 2, 2020 to May 11, 2020. Geospatial distribution of study patients’ residence relative to population density in the region were mapped and data analysis included hospital length of stay, need and duration of invasive mechanical ventilation (IMV), and mortality. Logistic regression models were constructed to predict discharge dispositions in the remaining active study patients. Results The median age of the study cohort was 62 years (IQR - 49-75), and more than half were male (57%) with history of hypertension (60%), obesity (41%), and diabetes (42%). Geographic residence of the study patients was disproportionately associated with areas of higher population density (rs=0.235, p=0.004), with noted “hot spots” in the region. Study patients were predominantly hypertensive (MAP>90mmHg (670, 51%)) on presentation with lymphopenia (590, 55%), hyponatremia (411, 31%), and kidney dysfunction (eGFR&60ml/min/1.73m 2 (381, 29%)). Of the patients with a disposition (1188/1325), 15% (182/1188) required IMV and 21% (250/1188) developed acute kidney injury. In patients on IMV, median hospital length of stay in survivors (22 days; 16.5-29.5) was significantly longer than non-survivors (15 days; 10-23.75), but this was not due to prolonged time on the ventilator. The overall mortality in all hospitalized patients was 15% and in patients receiving IMV was 48%, which is predicted to minimally rise from 48% to 49% based on logistic regression models constructed to project the disposition in the remaining patients on the ventilator. Acute kidney injury during hospitalization (ORE=3.23) was the strongest predictor of mortality in patients requiring IMV. Conclusions This is the first study to collectively utilize the demographics, clinical characteristics and hospital course of COVID-19 patients to identify predictors of poor outcomes that can be used for resource allocation in future waves of the pandemic.