Predicting Colonic Neoplasia Surgical Complications: A Machine Learning Approach

BACKGROUND: A range of statistical approaches have been used to help predict outcomes associated with colectomy. The multifactorial nature of complications suggest that machine learning algorithms may be more accurate in determining postoperative outcomes by detecting nonlinear associations, which are not readily measured by traditional statistics. OBJECTIVE: The aim of this study was to investigate the utility of machine learning algorithms to predict complications in patients undergoing colectomy for colonic neoplasia. DESIGN: Retrospective analysis using decision tree, random forest, and artificial neural network classifiers to predict postoperative outcomes. SETTINGS: National Inpatient Sample database (2003-2017). PATIENTS: Adult patients who underwent elective colectomy with anastomosis for neoplasia. INTERVENTIONS(S) IF ANY: N/A. MAIN OUTCOME MEASURES: Performance was quantified using sensitivity, specificity, accuracy, and area-under-the-curve-receiver-operator-characteristic to predict the incidence of anastomotic leak, prolonged length of stay, and inpatient mortality. RESULTS: A total of 14,935 patients (4,731 laparoscopic, 10,204 open) were included. They had an average age of 67±12.2 years and 53% were female. The three machine learning models successfully identified patients who developed the measured complications. Although differences between model performance were largely insignificant, the neural network scored highest for most outcomes: predicting anastomotic leak, area-under-the-curve-receiver-operator-characteristic 0.88/0.93 (open/laparoscopic, 95% CI, 0.73-0.92/0.80-0.96); prolonged length of stay, area-under-the-curve-receiver-operator-characteristic 0.84/0.88 (open/laparoscopic, 95% CI, 0.82-0.85/0.85-0.91); and inpatient mortality, area-under-the-curve-receiver-operator-characteristic 0.90/0.92 (open/laparoscopic, 95% CI, 0.85-0.96/0.86-0.98). LIMITATIONS: The patients from the National Inpatient Sample database may not be an accurate sample of the population of all patients undergoing colectomy for colonic neoplasia and does not account for specific institutional and patient factors. CONCLUSIONS: Machine learning predicted postoperative complications in patients with colonic neoplasia undergoing colectomy with good performance. Though validation using external data and optimization of data quality will be required, these machine learning tools show great promise in assisting surgeons with risk-stratification of perioperative care to improve postoperative outcomes. See Video Abstract .