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 - Garcia, A. L. A1 - Steiniger, J. A1 - Reich, S. C. A1 - Weickert, M. O. A1 - Harsch, I. A1 - Machowetz, A. A1 - Mohlig, M. A1 - Spranger, Joachim A1 - Rudovich, N. N. A1 - Meuser, F. A1 - Doerfer, J. A1 - Katz, N. A1 - Speth, M. A1 - Zunft, Hans-Joachim Franz A1 - Pfeiffer, Andreas F. H. A1 - Koebnick, Corinna T1 - Arabinoxylan fibre consumption improved glucose metabolism, but did not affect serum adipokines in subjects with impaired glucose tolerance JF - Hormone and metabolic research N2 - The consumption of arabinoxylan, a soluble fibre fraction, has been shown to improve glycemic control in type 2 diabetic subjects. Soluble dietary fibre may modulate gastrointestinal or adipose tissue hormones regulating food intake. The present study investigated the effects of arabinoxylan consumption on serum glucose, insulin, lipids, leptin, adiponectin and resistin in subjects with impaired glucose tolerance. In a randomized, single-blind, controlled, crossover intervention trial, 11 adults consumed white bread rolls as either placebo or supplemented with 15g arabinoxylan for 6 weeks with a 6-week washout period. Fasting serum glucose, insulin, triglycerides, unesterified fatty acids, apolipoprotein A1 and B, adiponectin, resistin and leptin were assessed before and after intervention. Fasting serum glucose, serum triglycerides and apolipoprotein A-1 were significantly lower during arabinoxylan consumption compared to placebo (p = 0.029, p = 0.047; p = 0.029, respectively). No effects of arabinoxylan were observed for insulin, adiponectin, leptin and resistin as well as for apolipoprotein B, and unesterified fatty acids. In conclusion, the consumption of AX in subjects with impaired glucose tolerance improved fasting serum glucose, and triglycerides. However, this beneficial effect was not accompanied by changes in fasting adipokine concentrations. KW - dietary fibre KW - arabinoxylan KW - adiponectin KW - resistin KW - leptin Y1 - 2006 U6 - https://doi.org/10.1055/s-2006-955089 SN - 0018-5043 VL - 38 IS - 2 SP - 761 EP - 766 PB - Thieme CY - Stuttgart ER -