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Background: Increasingly, genomics is informing clinical practice, but challenges remain for medical professionals lacking genetics expertise, and in access to and clinical utility of genomic testing for minority and underrepresented populations. The latter is a particularly pernicious problem due to the historical lack of inclusion of racially and ethnically diverse populations in genomic research and genomic medicine. A further challenge is the rapidly changing landscape of genetic tests and considerations of cost, interpretation, and diagnostic yield for emerging modalities like whole-genome sequencing., Methods: The NYCKidSeq project is a randomized controlled trial recruiting 1130 children and young adults predominantly from Harlem and the Bronx with suspected genetic disorders in three disease categories: neurologic, cardiovascular, and immunologic. Two clinical genetic tests will be performed for each participant, either proband, duo, or trio whole-genome sequencing (depending on sample availability) and proband targeted gene panels. Clinical utility, cost, and diagnostic yield of both testing modalities will be assessed. This study will evaluate the use of a novel, digital platform (GUÍA) to digitize the return of genomic results experience and improve participant understanding for English- and Spanish-speaking families. Surveys will collect data at three study visits: baseline (0 months), result disclosure visit (ROR1, + 3 months), and follow-up visit (ROR2, + 9 months). Outcomes will assess parental understanding of and attitudes toward receiving genomic results for their child and behavioral, psychological, and social impact of results. We will also conduct a pilot study to assess a digital tool called GenomeDiver designed to enhance communication between clinicians and genetic testing labs. We will evaluate GenomeDiver's ability to increase the diagnostic yield compared to standard practices, improve clinician's ability to perform targeted reverse phenotyping, and increase the efficiency of genetic testing lab personnel., Discussion: The NYCKidSeq project will contribute to the innovations and best practices in communicating genomic test results to diverse populations. This work will inform strategies for implementing genomic medicine in health systems serving diverse populations using methods that are clinically useful, technologically savvy, culturally sensitive, and ethically sound., Trial Registration: ClinicalTrials.gov NCT03738098 . Registered on November 13, 2018 Trial Sponsor: Icahn School of Medicine at Mount Sinai Contact Name: Eimear Kenny, PhD (Principal Investigator) Address: Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., Box 1003, New York, NY 10029 Email: eimear.kenny@mssm.edu.

Objective: The COVID-19 pandemic is a global public health crisis, with over 33 million cases and 999 000 deaths worldwide. Data are needed regarding the clinical course of hospitalised patients, particularly in the USA. We aimed to compare clinical characteristic of patients with COVID-19 who had in-hospital mortality with those who were discharged alive., Design: Demographic, clinical and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed COVID-19 between 27 February and 2 April 2020 were identified through institutional electronic health records. We performed a retrospective comparative analysis of patients who had in-hospital mortality or were discharged alive., Setting: All patients were admitted to the Mount Sinai Health System, a large quaternary care urban hospital system., Participants: Participants over the age of 18 years were included., Primary Outcomes: We investigated in-hospital mortality during the study period., Results: A total of 2199 patients with COVID-19 were hospitalised during the study period. As of 2 April, 1121 (51%) patients remained hospitalised, and 1078 (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 μg/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 μg/mL, C reactive protein was 79 mg/L and procalcitonin was 0.09 ng/mL., Conclusions: In our cohort of hospitalised patients, requirement of intensive care and mortality were high. Patients who died typically had more pre-existing conditions and greater perturbations in inflammatory markers as compared with those who were discharged., Competing Interests: Competing interests: GNN reports grants, personal fees and non-financial support from Renalytix AI, non-financial support from Pensieve Health, personal fees from AstraZeneca, BioVie, GLG Consulting, from outside the submitted work. AL reports personal fees from Zoll, outside the submitted work. ZAF reports grants from Daiichi Sankyo, grants from Amgen, Bristol Myers Squibb and Siemens Healthineers, personal fees from Alexion, GlaxoSmithKline, Trained Therapeutix Discovery, outside the submitted work. In addition, ZAF has patents licensed to Trained Therapeutix Discovery. The other authors have nothing to disclose., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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., (©Akhil Vaid, Sulaiman Somani, Adam J Russak, Jessica K De Freitas, Fayzan F Chaudhry, Ishan Paranjpe, Kipp W Johnson, Samuel J Lee, Riccardo Miotto, Felix Richter, Shan Zhao, Noam D Beckmann, Nidhi Naik, Arash Kia, Prem Timsina, Anuradha Lala, Manish Paranjpe, Eddye Golden, Matteo Danieletto, Manbir Singh, Dara Meyer, Paul F O'Reilly, Laura Huckins, Patricia Kovatch, Joseph Finkelstein, Robert M. Freeman, Edgar Argulian, Andrew Kasarskis, Bethany Percha, Judith A Aberg, Emilia Bagiella, Carol R Horowitz, Barbara Murphy, Eric J Nestler, Eric E Schadt, Judy H Cho, Carlos Cordon-Cardo, Valentin Fuster, Dennis S Charney, David L Reich, Erwin P Bottinger, Matthew A Levin, Jagat Narula, Zahi A Fayad, Allan C Just, Alexander W Charney, Girish N Nadkarni, Benjamin S Glicksberg. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 06.11.2020.)

Objective: To assess association of clinical features on COVID-19 patient outcomes., Design: Retrospective observational study using electronic medical record data., Setting: Five member hospitals from the Mount Sinai Health System in New York City (NYC)., Participants: 28 336 patients tested for SARS-CoV-2 from 24 February 2020 to 15 April 2020, including 6158 laboratory-confirmed COVID-19 cases., Main Outcomes and Measures: Positive test rates and in-hospital mortality were assessed for different racial groups. Among positive cases admitted to the hospital (N=3273), we estimated HR for both discharge and death across various explanatory variables, including patient demographics, hospital site and unit, smoking status, vital signs, lab results and comorbidities., Results: Hispanics (29%) and African Americans (25%) had disproportionately high positive case rates relative to their representation in the overall NYC population (p<0.05); however, no differences in mortality rates were observed in hospitalised patients based on race. Outcomes differed significantly between hospitals (Gray's T=248.9; p<0.05), reflecting differences in average baseline age and underlying comorbidities. Significant risk factors for mortality included age (HR 1.05, 95% CI 1.04 to 1.06; p=1.15e-32), oxygen saturation (HR 0.985, 95% CI 0.982 to 0.988; p=1.57e-17), care in intensive care unit areas (HR 1.58, 95% CI 1.29 to 1.92; p=7.81e-6) and elevated creatinine (HR 1.75, 95% CI 1.47 to 2.10; p=7.48e-10), white cell count (HR 1.02, 95% CI 1.01 to 1.04; p=8.4e-3) and body mass index (BMI) (HR 1.02, 95% CI 1.00 to 1.03; p=1.09e-2). Deceased patients were more likely to have elevated markers of inflammation., Conclusions: While race was associated with higher risk of infection, we did not find racial disparities in inpatient mortality suggesting that outcomes in a single tertiary care health system are comparable across races. In addition, we identified key clinical features associated with reduced mortality and discharge. These findings could help to identify which COVID-19 patients are at greatest risk of a severe infection response and predict survival., Competing Interests: Competing interests: WKO is a paid consultant to Astellas, Astra Zeneca, Bayer, Janssen, Sanofi, Sema4, and TeneoBio., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)