Machine learning based preoperative analytics for the prediction of anastomotic insufficiency in colorectal surgery: a single-centre pilot study

Abstract

BackgroundAnastomotic insufficiency (AI) is a relatively common but grave complication after colorectal surgery. This study aims to determine whether AI can be predicted from simple preoperative data using machine learning (ML) algorithms.MethodsIn this retrospective analysis, patients undergoing colorectal surgery with creation of a bowel anastomosis from the University Hospital of Basel were included. Data was split into a training set (80%) and a test set (20%). The group of patients with AI was oversampled to a ratio of 50:50 in the training set and missing values were imputed. Known predictors of AI were included as inputs: age, gender, BMI, smoking status, alcohol abuse, prior abdominal surgery, leukocytosis, haemoglobin and albumin levels, steroid use, the Charlson Comorbidity Index, the American Society of Anesthesiologists score, and renal function.ResultsOf the 593 included patients, 88 experienced AI. At internal validation on unseen patients from the test set, area under the curve (AUC) was 0.64 (95% confidence interval [CI]: 0.44-0.82), calibration slope was 0.21 (95% CI: -0.02-0.46) and calibration intercept was 0.06 (95% CI: 0.01-0.1). We observed a specificity of 0.76 (95% CI: 0.68-0.84), sensitivity of 0.36 (95% CI: 0.08-0.7), and accuracy of 0.72 (95% CI: 0.65-0.8).ConclusionBy using 13 patient-related risk factors associated with AI, we demonstrate the feasibility of ML-based prediction of AI after colorectal surgery. Nevertheless, it is crucial to include multicenter data and higher sample sizes to develop a robust and generalisable model, which will subsequently allow for deployment of the algorithm in a web-based application.