• Predicting post-operative infections could allow proactive actions to reduce the prevalence of infections in surgical wards, reducing antibiotics prescriptions, and potentially reducing antimicrobial resistance. • Machine learning could potentially flag high risk patients to post-surgical infections, informing patients consultations about surgical outcomes and risk reduction. • Identification of modifiable/controllable predictors could improve the clinical usefulness and transition of predictive machine learning models into clinical practice. Abstract : Postoperative infections are a common surgical complication. Machine learning (ML) models have been developed to help predict the likelihood of these infections occurring; however, their accuracy is often low, and surgical patients have not been represented in past studies. We developed and internally validated a ML model for predicting postoperative infection likelihood after elective general abdominal surgery (SMART), among 2716 patients. : The United Kingdom Health Data Research (UKHDR) Hub for Acute Care (PIONEER) supplied retrospective pseudonymised data for model training. These data contained demographic information, vital signs, microbiological investigations, comorbidities, surgical information and infection diagnosis for elective general surgical patients (n=2,716). Predictors were selected using an integrated approach of ML methods (feature elimination) and expert input. Recursive feature elimination with cross-validation was run on these predictors using Python(v3.8.2). Twelve algorithms were used and an ensemble model with the three highest performing models was developed. Nineteen predictors were selected to build the model including: demographics (e.g. age), comorbidities, microbiology data (e.g. MDR-infections), and laboratory investigation (C-reactive protein). Gradient boosting classifier was found to be the best-performing model. The ensemble model showed high performance during training with 85.3% sensitivity, 74.6% specificity, and AUC=0.89, and during internal validation, with 96.9% sensitivity, 74.1% specificity, and AUC=0.86. : SMART showed high performance predicting postoperative infections in elective surgery. The model used modifiable predicators that aided its clinical application. Identifying patients at higher risk of postoperative infections before surgery can promote early interventions and reduce antimicrobial resistance risk. External validation and testing are necessary for successful clinical implementation.
Hassan et al. (Sun,) studied this question.