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Machine Learning To Stratify Methicillin-Resistant Staphylococcus aureus Risk among Hospitalized Patients with
Nathaniel J Rhodes1,2,3, Roxane Rohani1,2,3, Paul R Yarnold4
1Department of Pharmacy Practice, Midwestern University, Chicago College of Pharmacy, Downers Grove, Illinois, USA.
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. We sought to develop robust models predicting the risk of MRSA CAP using machine learning using a population-based sample of hospitalized patients with CAP admitted to either a tertiary academic center or a community teaching hospital. Data were evaluated using a machine learning approach. Cases were CAP patients with MRSA isolated from blood or respiratory cultures within 72 h of admission; controls did not have MRSA CAP. The Classification Tree Analysis algorithm was used for model development. Model predictions were evaluated in sensitivity analyses. A total of 21 of 1,823 patients (1.2%) developed MRSA within 72 h of admission. MRSA risk was higher among patients admitted to the intensive care unit (ICU) in the first 24 h who required mechanical ventilation than among ICU patients who did not require ventilatory support (odds ratio [OR], 8.3; 95% confidence interval [CI], 2.4 to 32). MRSA risk was lower among patients admitted to ward units than among those admitted to the ICU (OR, 0.21; 95% CI, 0.07 to 0.56) and lower among ICU patients without a history of antibiotic use in the last 90 days than among ICU patients with antibiotic use in the last 90 days (OR, 0.03; 95% CI, 0.002 to 0.59). The final machine learning model was highly accurate (receiver operating characteristic [ROC] area = 0.775) in training and jackknife validity analyses. We identified a relatively simple machine learning model that predicted MRSA risk in hospitalized patients with CAP within 72 h postadmission.
Insights
Machine learning accurately predicted Methicillin-resistant Staphylococcus aureus (MRSA) community-acquired pneumonia (CAP) risk in hospitalized patients. This helps avoid unnecessary broad-spectrum antibiotic use by identifying high-risk individuals.
Area of Science:
- Infectious Diseases
- Medical Informatics
- Pulmonology
Background:
- Methicillin-resistant Staphylococcus aureus (MRSA) is a serious cause of community-acquired pneumonia (CAP).
- Lack of validated risk factors for MRSA CAP leads to overuse of broad-spectrum antibiotics.
- Accurate risk prediction models are needed to guide antibiotic stewardship.
Purpose of the Study:
- To develop and validate machine learning models for predicting MRSA CAP risk.
- To identify key clinical factors associated with MRSA CAP.
- To improve empirical antibiotic prescribing for CAP.
Main Methods:
- Utilized a population-based sample of hospitalized CAP patients from academic and community teaching hospitals.
- Employed machine learning, specifically Classification Tree Analysis, for model development.
- Defined cases as CAP with MRSA isolated within 72 hours of admission; controls lacked MRSA CAP.
Main Results:
- The machine learning model achieved high accuracy (ROC area = 0.775).
- MRSA CAP risk was higher in ICU patients requiring mechanical ventilation (OR, 8.3).
- Risk was lower in ward patients (OR, 0.21) and ICU patients without recent antibiotic use (OR, 0.03).
Conclusions:
- A simple, accurate machine learning model can predict MRSA CAP risk within 72 hours postadmission.
- Model facilitates targeted antibiotic use, potentially reducing unnecessary broad-spectrum prescriptions.
- Identified key predictors like ICU admission, mechanical ventilation, and recent antibiotic history.
