Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records

Masayuki Nigo (), Laila Rasmy, Bingyu Mao, Bijun Sai Kannadath, Ziqian Xie and Degui Zhi
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Masayuki Nigo: University of Texas Health Science Center at Houston
Laila Rasmy: University of Texas Health Science Center at Houston
Bingyu Mao: University of Texas Health Science Center at Houston
Bijun Sai Kannadath: University of Arizona College of Medicine
Ziqian Xie: University of Texas Health Science Center at Houston
Degui Zhi: University of Texas Health Science Center at Houston

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract Methicillin-resistant Staphylococcus aureus (MRSA) poses significant morbidity and mortality in hospitals. Rapid, accurate risk stratification of MRSA is crucial for optimizing antibiotic therapy. Our study introduced a deep learning model, PyTorch_EHR, which leverages electronic health record (EHR) time-series data, including wide-variety patient specific data, to predict MRSA culture positivity within two weeks. 8,164 MRSA and 22,393 non-MRSA patient events from Memorial Hermann Hospital System, Houston, Texas are used for model development. PyTorch_EHR outperforms logistic regression (LR) and light gradient boost machine (LGBM) models in accuracy (AUROCPyTorch_EHR = 0.911, AUROCLR = 0.857, AUROCLGBM = 0.892). External validation with 393,713 patient events from the Medical Information Mart for Intensive Care (MIMIC)-IV dataset in Boston confirms its superior accuracy (AUROCPyTorch_EHR = 0.859, AUROCLR = 0.816, AUROCLGBM = 0.838). Our model effectively stratifies patients into high-, medium-, and low-risk categories, potentially optimizing antimicrobial therapy and reducing unnecessary MRSA-specific antimicrobials. This highlights the advantage of deep learning models in predicting MRSA positive cultures, surpassing traditional machine learning models and supporting clinicians’ judgments.

Date: 2024
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DOI: 10.1038/s41467-024-46211-0

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