TY - JOUR A1 - Chan, Lili A1 - Jaladanki, Suraj K. A1 - Somani, Sulaiman A1 - Paranjpe, Ishan A1 - Kumar, Arvind A1 - Zhao, Shan A1 - Kaufman, Lewis A1 - Leisman, Staci A1 - Sharma, Shuchita A1 - He, John Cijiang A1 - Murphy, Barbara A1 - Fayad, Zahi A. A1 - Levin, Matthew A. A1 - Böttinger, Erwin A1 - Charney, Alexander W. A1 - Glicksberg, Benjamin A1 - Coca, Steven G. A1 - Nadkarni, Girish N. T1 - Outcomes of patients on maintenance dialysis hospitalized with COVID-19 JF - Clinical journal of the American Society of Nephrology : CJASN KW - chronic dialysis KW - COVID-19 KW - end-stage kidney disease Y1 - 2021 U6 - https://doi.org/10.2215/CJN.12360720 SN - 1555-9041 SN - 1555-905X VL - 16 IS - 3 SP - 452 EP - 455 PB - American Society of Nephrology CY - Washington ER - TY - JOUR A1 - Chan, Lili A1 - Chaudhary, Kumardeep A1 - Saha, Aparna A1 - Chauhan, Kinsuk A1 - Vaid, Akhil A1 - Zhao, Shan A1 - Paranjpe, Ishan A1 - Somani, Sulaiman A1 - Richter, Felix A1 - Miotto, Riccardo A1 - Lala, Anuradha A1 - Kia, Arash A1 - Timsina, Prem A1 - Li, Li A1 - Freeman, Robert A1 - Chen, Rong A1 - Narula, Jagat A1 - Just, Allan C. A1 - Horowitz, Carol A1 - Fayad, Zahi A1 - Cordon-Cardo, Carlos A1 - Schadt, Eric A1 - Levin, Matthew A. A1 - Reich, David L. A1 - Fuster, Valentin A1 - Murphy, Barbara A1 - He, John C. A1 - Charney, Alexander W. A1 - Böttinger, Erwin A1 - Glicksberg, Benjamin A1 - Coca, Steven G. A1 - Nadkarni, Girish N. T1 - AKI in hospitalized patients with COVID-19 JF - Journal of the American Society of Nephrology : JASN N2 - Background: Early reports indicate that AKI is common among patients with coronavirus disease 2019 (COVID-19) and associatedwith worse outcomes. However, AKI among hospitalized patients with COVID19 in the United States is not well described. Methods: This retrospective, observational study involved a review of data from electronic health records of patients aged >= 18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020. We describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aORs) with mortality. Results: Of 3993 hospitalized patients with COVID-19, AKI occurred in 1835 (46%) patients; 347 (19%) of the patientswith AKI required dialysis. The proportionswith stages 1, 2, or 3 AKIwere 39%, 19%, and 42%, respectively. A total of 976 (24%) patients were admitted to intensive care, and 745 (76%) experienced AKI. Of the 435 patients with AKI and urine studies, 84% had proteinuria, 81% had hematuria, and 60% had leukocyturia. Independent predictors of severe AKI were CKD, men, and higher serum potassium at admission. In-hospital mortality was 50% among patients with AKI versus 8% among those without AKI (aOR, 9.2; 95% confidence interval, 7.5 to 11.3). Of survivors with AKI who were discharged, 35% had not recovered to baseline kidney function by the time of discharge. An additional 28 of 77 (36%) patients who had not recovered kidney function at discharge did so on posthospital follow-up. Conclusions: AKI is common among patients hospitalized with COVID-19 and is associated with high mortality. Of all patients with AKI, only 30% survived with recovery of kidney function by the time of discharge. KW - acute renal failure KW - clinical nephrology KW - dialysis KW - COVID-19 Y1 - 2021 U6 - https://doi.org/10.1681/ASN.2020050615 SN - 1046-6673 SN - 1533-3450 VL - 32 IS - 1 SP - 151 EP - 160 PB - American Society of Nephrology CY - Washington ER - TY - JOUR A1 - Vaid, Akhil A1 - Somani, Sulaiman A1 - Russak, Adam J. A1 - De Freitas, Jessica K. A1 - Chaudhry, Fayzan F. A1 - Paranjpe, Ishan A1 - Johnson, Kipp W. A1 - Lee, Samuel J. A1 - Miotto, Riccardo A1 - Richter, Felix A1 - Zhao, Shan A1 - Beckmann, Noam D. A1 - Naik, Nidhi A1 - Kia, Arash A1 - Timsina, Prem A1 - Lala, Anuradha A1 - Paranjpe, Manish A1 - Golden, Eddye A1 - Danieletto, Matteo A1 - Singh, Manbir A1 - Meyer, Dara A1 - O'Reilly, Paul F. A1 - Huckins, Laura A1 - Kovatch, Patricia A1 - Finkelstein, Joseph A1 - Freeman, Robert M. A1 - Argulian, Edgar A1 - Kasarskis, Andrew A1 - Percha, Bethany A1 - Aberg, Judith A. A1 - Bagiella, Emilia A1 - Horowitz, Carol R. A1 - Murphy, Barbara A1 - Nestler, Eric J. A1 - Schadt, Eric E. A1 - Cho, Judy H. A1 - Cordon-Cardo, Carlos A1 - Fuster, Valentin A1 - Charney, Dennis S. A1 - Reich, David L. A1 - Böttinger, Erwin A1 - Levin, Matthew A. A1 - Narula, Jagat A1 - Fayad, Zahi A. A1 - Just, Allan C. A1 - Charney, Alexander W. A1 - Nadkarni, Girish N. A1 - Glicksberg, Benjamin S. T1 - Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation JF - Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR N2 - 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. KW - machine learning KW - COVID-19 KW - electronic health record KW - TRIPOD KW - clinical KW - informatics KW - prediction KW - mortality KW - EHR KW - cohort KW - hospital KW - performance Y1 - 2020 U6 - https://doi.org/10.2196/24018 SN - 1439-4456 SN - 1438-8871 VL - 22 IS - 11 PB - Healthcare World CY - Richmond, Va. ER - TY - JOUR A1 - Sigel, Keith Magnus A1 - Swartz, Talia H. A1 - Golden, Eddye A1 - Paranjpe, Ishan A1 - Somani, Sulaiman A1 - Richter, Felix A1 - De Freitas, Jessica K. A1 - Miotto, Riccardo A1 - Zhao, Shan A1 - Polak, Paz A1 - Mutetwa, Tinaye A1 - Factor, Stephanie A1 - Mehandru, Saurabh A1 - Mullen, Michael A1 - Cossarini, Francesca A1 - Böttinger, Erwin A1 - Fayad, Zahi A1 - Merad, Miriam A1 - Gnjatic, Sacha A1 - Aberg, Judith A1 - Charney, Alexander A1 - Nadkarni, Girish A1 - Glicksberg, Benjamin S. T1 - Coronavirus 2019 and people living with human immunodeficiency virus BT - outcomes for hospitalized patients in New York City JF - Clinical infectious diseases : electronic edition N2 - Background: There are limited data regarding the clinical impact of coronavirus disease 2019 (COVID-19) on people living with human immunodeficiency virus (PLWH). In this study, we compared outcomes for PLWH with COVID-19 to a matched comparison group. Methods: We identified 88 PLWH hospitalized with laboratory-confirmed COVID-19 in our hospital system in New York City between 12 March and 23 April 2020. We collected data on baseline clinical characteristics, laboratory values, HIV status, treatment, and outcomes from this group and matched comparators (1 PLWH to up to 5 patients by age, sex, race/ethnicity, and calendar week of infection). We compared clinical characteristics and outcomes (death, mechanical ventilation, hospital discharge) for these 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. PLWH hospitalized with COVID-19 had high proportions of HIV virologic control on antiretroviral therapy. PLWH had greater proportions of smoking (P < .001) and comorbid illness than uninfected comparators. There was no difference in COVID-19 severity on admission by HIV status (P = .15). Poor outcomes for hospitalized PLWH were frequent but similar to proportions in comparators; 18% required mechanical ventilation and 21% died during follow-up (compared with 23% and 20%, respectively). There was similar cumulative incidence of death over time by HIV status (P = .94). Conclusions: We found no differences in adverse outcomes associated with HIV infection for hospitalized COVID-19 patients compared with a demographically similar patient group. KW - human immunodeficiency virus KW - coronavirus 2019 KW - severe acute respiratory KW - syndrome coronavirus 2 Y1 - 2020 U6 - https://doi.org/10.1093/cid/ciaa880 SN - 1058-4838 SN - 1537-6591 VL - 71 IS - 11 SP - 2933 EP - 2938 PB - Oxford Univ. Press CY - Cary, NC ER -