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CovRNN - A recurrent neural network model for predicting outcomes of COVID-19 patients: model development and validation using EHR data
Laila Rasmy; Masayuki Nigo; Bijun Sai Kannadath; Ziqian Xie; Bingyu Mao; Khush Patel; Yujia Zhou; Wanheng Zhang; Angela M. Ross; Hua Xu; Degui Zhi.
Affiliation
  • Laila Rasmy; School of Biomedical Informatics, University of Texas Health Science Center at Houston
  • Masayuki Nigo; McGovern Medical School, University of Texas Health Science Center at Houston
  • Bijun Sai Kannadath; College of Medicine, University of Arizona - Phoenix
  • Ziqian Xie; School of Biomedical Informatics, University of Texas Health Science Center at Houston
  • Bingyu Mao; School of Biomedical Informatics, University of Texas Health Science Center at Houston
  • Khush Patel; School of Biomedical Informatics, University of Texas Health Science Center at Houston
  • Yujia Zhou; School of Biomedical Informatics, University of Texas Health Science Center at Houston
  • Wanheng Zhang; School of Public Health, University of Texas Health Science Center at Houston
  • Angela M. Ross; School of Biomedical Informatics, University of Texas Health Science Center at Houston
  • Hua Xu; School of Biomedical Informatics, University of Texas Health Science Center at Houston
  • Degui Zhi; School of Biomedical Informatics, University of Texas Health Science Center at Houston
Preprint in English | medRxiv | ID: ppmedrxiv-21264121
ABSTRACT
BackgroundPredicting outcomes of COVID-19 patients at an early stage is critical for optimized clinical care and resource management, especially during a pandemic. Although multiple machine learning models have been proposed to address this issue, based on the need for extensive data pre-processing and feature engineering, these models have not been validated or implemented outside of the original study site. MethodsIn this study, we propose CovRNN, recurrent neural network (RNN)-based models to predict COVID-19 patients outcomes, using their available electronic health record (EHR) data on admission, without the need for specific feature selection or missing data imputation. CovRNN is designed to predict three

outcomes:

in-hospital mortality, need for mechanical ventilation, and long length of stay (LOS >7 days). Predictions are made for time-to-event risk scores (survival prediction) and all-time risk scores (binary prediction). Our models were trained and validated using heterogeneous and de-identified data of 247,960 COVID-19 patients from 87 healthcare systems, derived from the Cerner(R) Real-World Dataset (CRWD). External validation was performed using three test sets (approximately 53,000 patients). Further, the transferability of CovRNN was validated using 36,140 de-identified patients data derived from the Optum(R) de-identified COVID-19 Electronic Health Record v. 1015 dataset (2007-2020). FindingsCovRNN shows higher performance than do traditional models. It achieved an area under the receiving operating characteristic (AUROC) of 93% for mortality and mechanical ventilation predictions on the CRWD test set (vs. 91{middle dot}5% and 90% for light gradient boost machine (LGBM) and logistic regression (LR), respectively) and 86.5% for prediction of LOS > 7 days (vs. 81{middle dot}7% and 80% for LGBM and LR, respectively). For survival prediction, CovRNN achieved a C-index of 86% for mortality and 92{middle dot}6% for mechanical ventilation. External validation confirmed AUROCs in similar ranges. InterpretationTrained on a large heterogeneous real-world dataset, our CovRNN model showed high prediction accuracy, good calibration, and transferability through consistently good performance on multiple external datasets. Our results demonstrate the feasibility of a COVID-19 predictive model that delivers high accuracy without the need for complex feature engineering.
License
cc_by_nc_nd
Full text: Available Collection: Preprints Database: medRxiv Type of study: Prognostic study Language: English Year: 2021 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Prognostic study Language: English Year: 2021 Document type: Preprint
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