Novel Machine Learning Approach for the Prediction of Hernia Recurrence, Surgical Complication, and 30-Day Readmission after Abdominal Wall Reconstruction
Abbas M. Hassan, Sheng-Chieh Lu, Malke Asaad, Jun Liu, Anaeze C. Offodile, Chris Sidey‐Gibbons, Charles E. Butler
Abstract
BACKGROUND: Despite advancements in abdominal wall reconstruction (AWR) techniques, hernia recurrences (HRs), surgical site occurrences (SSOs), and unplanned hospital readmissions persist. We sought to develop, validate, and evaluate machine learning (ML) algorithms for predicting complications after AWR. METHODS: We conducted a comprehensive review of patients who underwent AWR from March 2005 to June 2019. Nine supervised ML algorithms were developed to preoperatively predict HR, SSOs, and 30-day readmission. Patient data were partitioned into training (80%) and testing (20%) sets. RESULTS: We identified 725 patients (52% women), with a mean age of 60 ± 11.5 years, mean body mass index of 31 ± 7 kg/m2, and mean follow-up time of 42 ± 29 months. The HR rate was 12.8%, SSO rate was 30%, and 30-day readmission rate was 10.9%. ML models demonstrated good discriminatory performance for predicting HR (area under the receiver operating characteristic curve [AUC] 0.71), SSOs (AUC 0.75), and 30-day readmission (AUC 0.74). ML models achieved mean accuracy rates of 85% (95% CI 80% to 90%), 72% (95% CI 64% to 80%), and 84% (95% CI 77% to 90%) for predicting HR, SSOs, and 30-day readmission, respectively. ML identified and characterized 4 unique significant predictors of HR, 12 of SSOs, and 3 of 30-day readmission. Decision curve analysis demonstrated that ML models have a superior net benefit regardless of the probability threshold. CONCLUSIONS: ML algorithms trained on readily available preoperative clinical data accurately predicted complications of AWR. Our findings support incorporating ML models into the preoperative assessment of patients undergoing AWR to provide data-driven, patient-specific risk assessment.