Improved Prediction of Surgical Site Infection after Colorectal Surgery Using Machine Learning

Abstract

BACKGROUND: Surgical-site infection is a source of significant morbidity after colorectal surgery. Previous efforts to develop models that predict surgical-site infection have had limited accuracy. Machine learning has shown promise in predicting postoperative outcomes by identifying nonlinear patterns within large data sets. OBJECTIVE: This study aimed to seek usage of machine learning to develop a more accurate predictive model for colorectal surgical-site infections. DESIGN: Patients who underwent colorectal surgery were identified in the American College of Surgeons National Quality Improvement Program database from years 2012 to 2019 and were split into training, validation, and test sets. Machine-learning techniques included random forest, gradient boosting, and artificial neural network. A logistic regression model was also created. Model performance was assessed using area under the receiver operating characteristic curve. SETTINGS: A national, multicenter data set. PATIENTS: Patients who underwent colorectal surgery. MAIN OUTCOME MEASURES: The primary outcome (surgical-site infection) included patients who experienced superficial, deep, or organ-space surgical-site infections. RESULTS: The data set included 275,152 patients after the application of exclusion criteria. Of all patients, 10.7% experienced a surgical-site infection. Artificial neural network showed the best performance with area under the receiver operating characteristic curve of 0.769 (95% CI, 0.762–0.777), compared with 0.766 (95% CI, 0.759–0.774) for gradient boosting, 0.764 (95% CI, 0.756–0.772) for random forest, and 0.677 (95% CI, 0.669–0.685) for logistic regression. For the artificial neural network model, the strongest predictors of surgical-site infection were organ-space surgical-site infection present at time of surgery, operative time, oral antibiotic bowel preparation, and surgical approach. LIMITATIONS: Local institutional validation was not performed. CONCLUSIONS: Machine-learning techniques predict colorectal surgical-site infections with higher accuracy than logistic regression. These techniques may be used to identify patients at increased risk and to target preventive interventions for surgical-site infection. See Video Abstract at http://links.lww.com/DCR/C88. PREDICCIÓN MEJORADA DE LA INFECCIÓN DEL SITIO QUIRÚRGICO DESPUÉS DE LA CIRUGÍA COLORRECTAL MEDIANTE EL APRENDIZAJE AUTOMÁTICO ANTECEDENTES: La infección del sitio quirúrgico es una fuente de morbilidad significativa después de la cirugía colorrectal. Los esfuerzos anteriores para desarrollar modelos que predijeran la infección del sitio quirúrgico han tenido una precisión limitada. El aprendizaje automático se ha mostrado prometedor en la predicción de los resultados posoperatorios mediante la identificación de patrones no lineales dentro de grandes conjuntos de datos. OBJETIVO: Intentamos utilizar el aprendizaje automático para desarrollar un modelo predictivo más preciso para las infecciones del sitio quirúrgico colorrectal. DISEÑO: Los pacien...

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A commentary on ‘Prediction models of surgical site infection after gastrointestinal surgery: a nationwide prospective cohort study’

“…First, some residual confounding factors may not have been considered. When analyzed with SHAP (SHapley Additive exPlanations), several research found that organ-space SSI present at the time of surgery, operative time, oral antibiotic bowel prep, surgical technique, procedure CPT (Current Procedural Terminology) code, body mass index, ASA (American Society of Anesthesiologists) classification, and age had the most impact on model decision-making 4 . The model’s decision-making was influenced by preoperative care measures such as hair trimming, OA, MBP, antiseptic wash the night before and the day of the operation, antibiotic prophylaxis, and more.…”