Machine Learning To Stratify Methicillin-Resistant Staphylococcus aureus Risk among Hospitalized Patients with Community-Acquired Pneumonia

Rhodes, Nathaniel J.; Rohani, Roxane; Yarnold, Paul R.; Pawlowski, Anna; Malczynski, Michael; Qi, Chao; Sutton, Sarah H.; Zembower, Teresa; Wunderink, Richard G.

doi:10.1128/aac.01023-22

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…Rhodes et al used a machine learning model to predict community-acquired MRSA pneumonia. 29 Although the time frame and patient population differed from our study, their model achieved AUC of 77.5%, which was a lower AUC than ours. Additionally, some risk factor-based models heavily rely on certain tests, such as the nasal MRSA PCR test from nare 30 , which hampers the model's generalizability due to the tests' availability and applicability to other types of infections.…”

Section: Discussioncontrasting

confidence: 82%

Deep-Learning Model for Personalized Prediction of Positive MRSA Culture Results Using Patient’s Time-Series Electronic Health Records

Nigo

Rasmy

Xie

et al. 2023

Preprint

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Methicillin-resistant Staphylococcus aureus (MRSA) is a common bacterial cause of morbidity and mortality. Our deep-learning model (PyTorch_EHR) processes time-series structured electronic health record (EHR) data, including previous cultures and antimicrobial exposures, to predict the lab result of MRSA culture positivity over the next two weeks. After training and evaluation on data from 8,164 MRSA and 22,563 non-MRSA patient events from Memorial Hermann Hospital System, Houston, Texas, the PyTorch_EHR outperformed traditional machine learning methods logistic regression and light GBM (Area Under the Curve of Receiver Operating Curve [AUC]PyTorch_EHR=91.12%, AUCLR=85.91%, AUCLGBM=89.11%). External validation using the MIMIC-IV dataset of 393,713 patient events from a tertiary care center in Boston, Massachusetts, confirmed PyTorch_EHR's accuracy (AUCPyTorch_EHR=85.50%, AUCLR=83.24%, AUCLGBM=82.48%). The model maintained its accuracy across most subgroup analyses based on infection type. The cumulative incidence curves based on our model successfully high-, medium-, and low-risk patients. This study demonstrates the potential of deep-learning models to predict the presence of MRSA-positive cultures to optimize MRSA antimicrobial therapy.

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Section: Discussioncontrasting

confidence: 82%

Deep-Learning Model for Personalized Prediction of Positive MRSA Culture Results Using Patient’s Time-Series Electronic Health Records

Nigo

Rasmy

Xie

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The models have differing degrees of accuracy but often focus on a certain type of infection, such as pneumonia, to achieve and simplify the risk factors and models. Rhodes et al used a machine learning model to predict community-acquired MRSA pneumonia 26 . Although the time frame and patient population differed from our study, their model achieved an AUROC of 0.775, which was lower than ours.…”

Section: Discussionmentioning

confidence: 99%

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

Nigo,

Rasmy,

Mao

et al. 2024

Nat Commun

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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.

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Antibiotic treatment of community-acquired and hospital-acquired pneumonia

Simon

Rademacher

2023

Med Klin Intensivmed Notfmed

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Machine Learning To Stratify Methicillin-Resistant Staphylococcus aureus Risk among Hospitalized Patients with Community-Acquired Pneumonia

Abstract: Methicillin-resistant Staphylococcus aureus (MRSA) is an uncommon but serious cause of community-acquired pneumonia (CAP). A lack of validated MRSA CAP risk factors can result in overuse of empirical broad-spectrum antibiotics.

Cited by 8 publications

References 23 publications

Deep-Learning Model for Personalized Prediction of Positive MRSA Culture Results Using Patient’s Time-Series Electronic Health Records

Deep-Learning Model for Personalized Prediction of Positive MRSA Culture Results Using Patient’s Time-Series Electronic Health Records

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

Antibiotic treatment of community-acquired and hospital-acquired pneumonia

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