Machine Learning–Enhanced Surveillance for Surgical Site Infections in Patients Undergoing Colon Surgery: Model Development and Evaluation Study (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Surgical site infections (SSIs) are one of the most common health care–associated infections, accounting for nearly 20% of all health care–associated infections in hospitalized patients. SSIs are associated with longer hospital stays, increased readmission rates, higher health care costs, and a mortality rate twice that of patients without infections. </sec> <sec> <title>OBJECTIVE</title> This study aimed to develop and evaluate machine learning (ML) models for augmenting SSI surveillance after colon surgery with the goal of improving the efficiency of infection control practices by prioritizing patients at high risk. </sec> <sec> <title>METHODS</title> We conducted a retrospective study using data from 1508 patients undergoing colon surgery treated between 2018 and 2023 at a single academic medical center. Of these 1508 patients, 66 (4.4%) developed SSIs as adjudicated by infection control practitioners following Centers for Disease Control and Prevention National Healthcare Safety Network criteria. Data included 78 structured variables (eg, demographics, comorbidities, vital signs, laboratory tests, medications, and operative details) and 2 features derived from unstructured clinical notes using natural language processing. ML models&amp;lt;strong&amp;gt;―&amp;lt;/strong&amp;gt;logistic regression, random forest, and Extreme Gradient Boosting (XGBoost)&amp;lt;strong&amp;gt;―&amp;lt;/strong&amp;gt;were trained using stratified 80/20 train-test splits. Class imbalance was addressed using cost-sensitive learning and the synthetic minority oversampling technique. Model performance was evaluated using precision, recall, &lt;i&gt;F&lt;/i&gt;&lt;sub&gt;1&lt;/sub&gt;-score, area under the receiver operating characteristic curve, and Brier scores for calibration. </sec> <sec> <title>RESULTS</title> Of the 1508 patients, those who developed SSIs had longer hospital stays (mean 8.1, SD 6.8 days vs mean 6.3, SD 10.5 days; &lt;i&gt;P&lt;/i&gt;&amp;lt;.001), higher rates of an American Society of Anesthesiologists score of 3 (52/66, 79% vs 653/1442, 45.3%; &lt;i&gt;P&lt;/i&gt;&amp;lt;.001), and elevated white blood cell counts (51/66, 77% vs 734/1442, 50.9%; &lt;i&gt;P&lt;/i&gt;&amp;lt;.001). XGBoost achieved the best overall performance with an area under the receiver operating characteristic curve of 0.788, precision of 50%, recall of 38%, and Brier score of 0.035. Random forest yielded perfect precision (100%) but lower recall (23%), with a Brier score of 0.034. Logistic regression showed the highest recall (46%) but the lowest precision (10%), with a Brier score of 0.139. Feature importance analysis using Shapley additive explanations (SHAP) values revealed that the top predictors included recovery duration (SHAP=1.18), SSI keyword frequency (SHAP=1.12), patient age (SHAP=1.12), and American Society of Anesthesiologists score (SHAP=0.94), with natural language processing–derived features ranking among the top 10. </sec> <sec> <title>CONCLUSIONS</title> ML models can augment traditional SSI surveillance by improving early identification of patients at high risk. The XGBoost model offered the best trade-off between discrimination and calibration, suggesting its utility in clinical workflows. Incorporating structured and unstructured electronic health record data enhances model accuracy and clinical relevance, supporting scalable and efficient infection control practices. </sec>

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