Improved Prediction of Surgical-Site Infection After Colorectal Surgery Using Machine Learning
Creators: name: Stem, Jonathan M.
name: Gomez, Shawn M.
name: Guillem, Jose G.
name: Kapadia, Muneera R.
name: Chen, Kevin A.
name: Joisa, Chinmaya U.
Publisher: name: Wolters Kluwer
Year: 2022

description: <p>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 . </p>
description: <p>PREDICCIN MEJORADA DE LA INFECCIN DEL SITIO QUIRRGICO DESPUS DE LA CIRUGA COLORRECTAL MEDIANTE EL APRENDIZAJE AUTOMTICO:&nbsp; ANTECEDENTES: La infecci&oacute;n del sitio quir&uacute;rgico es una fuente de morbilidad significativa despu&eacute;s de la cirug&iacute;a colorrectal. Los esfuerzos anteriores para desarrollar modelos que predijeran la infecci&oacute;n del sitio quir&uacute;rgico han tenido una precisi&oacute;n limitada. El aprendizaje autom&aacute;tico se ha mostrado prometedor en la predicci&oacute;n de los resultados posoperatorios mediante la identificaci&oacute;n de patrones no lineales dentro de grandes conjuntos de datos. OBJETIVO: Intentamos utilizar el aprendizaje autom&aacute;tico para desarrollar un modelo predictivo m&aacute;s preciso para las infecciones del sitio quir&uacute;rgico colorrectal. DISE&Ntilde;O: Los pacientes que se sometieron a cirug&iacute;a colorrectal se identificaron en la base de datos del Programa Nacional de Mejoramiento de la Calidad del Colegio Estadounidense de Cirujanos de los a&ntilde;os 2012 a 2019 y se dividieron en conjuntos de capacitaci&oacute;n, validaci&oacute;n y prueba. Las t&eacute;cnicas de aprendizaje autom&aacute;tico incluyeron conjunto aleatorio, aumento de gradiente y red neuronal artificial. Tambi&eacute;n se cre&oacute; un modelo de regresi&oacute;n log&iacute;stica. El rendimiento del modelo se evalu&oacute; utilizando el &aacute;rea bajo la curva caracter&iacute;stica operativa del receptor. CONFIGURACI&Oacute;N: Un conjunto de datos multic&eacute;ntrico nacional. PACIENTES: Pacientes intervenidos de cirug&iacute;a colorrectal. PRINCIPALES MEDIDAS DE RESULTADO: El resultado primario (infecci&oacute;n del sitio quir&uacute;rgico) incluy&oacute; pacientes que experimentaron infecciones superficiales, profundas o del espacio de &oacute;rganos del sitio quir&uacute;rgico. RESULTADOS: El conjunto de datos incluy&oacute; 275.152 pacientes despu&eacute;s de la aplicaci&oacute;n de los criterios de exclusi&oacute;n. El 10,7% de los pacientes present&oacute; infecci&oacute;n del sitio quir&uacute;rgico. La red neuronal artificial mostr&oacute; el mejor rendimiento con el &aacute;rea bajo la curva caracter&iacute;stica operativa del receptor de 0,769 (IC del 95 %: 0,762 - 0,777), en comparaci&oacute;n con 0,766 (IC del 95 %: 0,759 - 0,774) para el aumento de gradiente, 0,764 (IC del 95 %: 0,756 - 0,772) para conjunto aleatorio y 0,677 (IC 95% 0,669 - 0,685) para regresi&oacute;n log&iacute;stica. Para el modelo de red neuronal artificial, los predictores m&aacute;s fuertes de infecci&oacute;n del sitio quir&uacute;rgico fueron la infecci&oacute;n del sitio quir&uacute;rgico del espacio del &oacute;rgano presente en el momento de la cirug&iacute;a, el tiempo operatorio, la preparaci&oacute;n intestinal con antibi&oacute;ticos orales y el abordaje quir&uacute;rgico. LIMITACIONES: No se realiz&oacute; validaci&oacute;n institucional local. CONCLUSIONES: Las t&eacute;cnicas de aprendizaje autom&aacute;tico predicen infecciones del sitio quir&uacute;rgico colorrectal con mayor precisi&oacute;n que la regresi&oacute;n log&iacute;stica. Estas t&eacute;cnicas se pueden usar para identificar a los pacientes con mayor riesgo y para orientar las intervenciones preventivas para la infecci&oacute;n del sitio quir&uacute;rgico. Consulte Video Resumen en http://links.lww.com/DCR/C88 . (Traducci&oacute;n-Dr Yolanda Colorado ).</p>