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.

Background Diabetes affects more than 30 million patients across the United States. With such a large disease burden, even a small error in classification can be significant. Currently billing codes, assigned at the time of a medical encounter, are the “gold standard” reflecting the actual diseases present in an individual, and thus in aggregate reflect disease prevalence in the population. These codes are generated by highly trained coders and by health care providers but are not always accurate. Objective This work provides a scalable deep learning methodology to more accurately classify individuals with diabetes across multiple health care systems. Methods We leveraged a long short-term memory-dense neural network (LSTM-DNN) model to identify patients with or without diabetes using data from 5 acute care facilities with 187,187 patients and 275,407 encounters, incorporating data elements including laboratory test results, diagnostic/procedure codes, medications, demographic data, and admission information. Furthermore, a blinded physician panel reviewed discordant cases, providing an estimate of the total impact on the population. Results When predicting the documented diagnosis of diabetes, our model achieved an 84% F1 score, 96% area under the curve–receiver operating characteristic curve, and 91% average precision on a heterogeneous data set from 5 distinct health facilities. However, in 81% of cases where the model disagreed with the documented phenotype, a blinded physician panel agreed with the model. Taken together, this suggests that 4.3% of our studied population have either missing or improper diabetes diagnosis. Conclusions This study demonstrates that deep learning methods can improve clinical phenotyping even when patient data are noisy, sparse, and heterogeneous.

Introduction: Severe AKI is strongly associated with poor outcomes in COVID-19, but data on renal recovery is lacking. Methods: We retrospectively analyzed these associations in 3,299 hospitalized patients (1,338 with COVID-19 and 1,961 with acute respiratory illness but tested negative for COVID-19). Uni- and multi-variable analyses were used to study mortality and recovery after KDIGO Stage 2&3 AKI and Machine Learning (ML) for predicting AKI and recovery using admission data. Long-term renal function and other outcomes were studied in a sub-group of AKI-2/3 survivors. Results: Among the 172 COVID-19 negative patients with AKI-2/3, 74.4% had partial & 44.2% complete renal recovery, while 11.6% died. Among 255 COVID-19 positive patients with AKI-2/3, lower recovery and higher mortality were noted (50.6% partial, 24.7% complete renal recovery, 23.9% died). On multivariable analysis, ICU admission and ARDS were associated with non-recovery, and recovery was significantly associated with survival in COVID-19 positive patients. With ML, we were able to predict recovery from COVID-19-associated AKI-2/3 with an average precision of 0.62 and the strongest predictors of recovery were initial arterial paO2 & CO2, SCr, K, lymphocyte count, & CPK. At 12 months follow-up, among 52 survivors with AKI-2/3, 25.7% COVID-19 positive and 23.5% COVID-19 negative had incident or progressive CKD. Conclusions: Recovery from COVID-19-associated moderate/severe AKI, can be predicted using admission data and is associated with severity of respiratory disease and in-hospital death. The risk of CKD might be similar between COVID-19 positive and negative patients.