Background There have been limited data regarding the clinical impact of COVID-19 disease on people with HIV (PWH). In this study we compared outcomes for PWH with COVID-19 disease to a matched comparison group. Design We identified 88 PWH hospitalized with laboratory confirmed COVID-19 in our hospital system in New York between March 12 and April 23, 2020. We collected data on baseline clinical characteristics, laboratory values, HIV infection status, COVID-19 treatment, and outcomes from this group and matched comparators (one PWH to up to five patients by age, sex, race/ethnicity and calendar week of infection). We compared baseline clinical characteristics and outcomes (death, mechanical ventilation, hospital discharge) for these two groups, as well as cumulative incidence of death by HIV status. Results Patients did not differ significantly by HIV status by age, sex or race/ethnicity due to the matching algorithm. PWH hospitalized with COVID-19 had high proportions of HIV virologic control on antiretroviral therapy. PWH had greater proportions of smoking (p<0.001) and comorbid illness than demographically similar uninfected comparators. There was no difference in COVID-19 severity on admission by HIV status (p=0.15). Poor outcomes for hospitalized PWH were frequent but similar to proportions in comparators; 18% required mechanical ventilation and ultimately 21% died during follow-up (compared with 23% and 20% respectively). There was similar cumulative incidence of death over time by HIV status (p=0.94). Interpretation We found no differences in adverse outcomes associated with HIV infection for hospitalized COVID-19 patients compared to a demographically similar patient group.

Background COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19–positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Background Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. Objective We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. Methods Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. Results Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19–related symptom compared to all other symptom-free days (P=.01). Conclusions Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19–related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

Background:The coronavirus 2019 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. MethodsDemographic, 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. ResultsA total of 2,199 patients with Covid-19 were hospitalized during the study period. As of April 2 nd , 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. Preexisting 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. ConclusionsThis 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. All rights reserved. No reuse allowed without permission.

85Coronavirus 2019 , caused by the SARS-CoV-2 virus, has become the 86 deadliest pandemic in modern history, reaching nearly every country worldwide and 87 overwhelming healthcare institutions. As of April 20, there have been more than 2.4 88 million confirmed cases with over 160,000 deaths. Extreme case surges coupled with 89 challenges in forecasting the clinical course of affected patients have necessitated 90 thoughtful resource allocation and early identification of high-risk patients. However, 91 136 (11.1) 158 (8.6) Diabetes (%) 313 (25.5) 466 (25.5) Asthma (%) 115 (9.4) 132 (7.2) Chronic Obstructive Pulmonary Disease (%) 65 (5.3) 103 (5.6) Cancer (%) 112 (9.1) 94 (5.1) Vital Signs at Hospital Admission, Median (IQR) Heart Rate (bpm) 89 (78 -100) 89 (78 -100) Pulse Oximetry (%) 96 (94 -98) 96 (94 -98) Respiration Rate (breaths / minute) 20 (18 -20) 18 (18 -20) Temperature (F) 98.7 (98.1 -99.9) 97.9 (98.6 -99.5) Systolic Blood Pressure (mmHg) 124 (112 -138) 127 (112 -142) Diastolic Blood Pressure (mmHg) 69 (61 -78) 72 (65 -81) Weight (kg) 80.9 (68.9 -95.3) 78.9 (68.04 -91.7) Admission Laboratory Parameters, Median (IQR) Metabolic markers Sodium (mEq/L) 137 (135 -140) 138 (135 -141) Potassium (mEq/L) 4 (3.6 -4.5) 4.2 (3.9 -4.7) Creatinine (mg/dL) 0.9 (0.7 -1.4) 1.0 (0.8 -1.6) Lactate (mg/dL) 1.7 (1.3 -2.