Systematic review and meta-analysis of the role of machine learning in predicting postoperative complications following colorectal surgery: how far has machine learning come?

Background: To systematically evaluate the clinical utility of machine learning in predicting post-operative outcomes following colorectal surgery.

Methods: A systematic literature search was conducted using PubMed, MEDLINE, Embase, and Google Scholar. Clinical studies investigating the role of machine learning models in predicting post-operative complications following colorectal surgery were included. Outcome measure was area under the curve for the model under investigation. The area under the curve and standard error were pooled using a random effects model to estimate the overall effect size. Statistical analyses were performed using the MedCalc (version 23) software, and the results presented as forest plots.

Results: Eighteen eligible articles were included. These reported outcomes on post-operative complications, namely anastomotic leak, mortality, prolonged length of hospitalisation, re-admission rates, risk of bleeding, paralytic ileus occurrence and surgical site infection. Pooled area under the curve for anastomotic leak was 0.813 [standard error: 0.031, 95% confidence interval (0.753-0.873)]; mortality 0.867 [standard error: 0.015, 95% confidence interval (0.838-0.896)]; prolonged length of stay 0.810 [standard error: 0.042, 95% confidence interval (0.728-0.892)]; and surgical site infection 0.802 [standard error: 0.031, 95% confidence interval (0.742-0.862)], respectively.

Conclusion: Machine learning methods and techniques are displaying promising clinical utility and applicability in accurately predicting the risk of developing complications following colorectal surgery. Future well-designed, adequately powered, multi-centre studies are needed to investigate the usefulness and generalisability of these novel approaches in optimising peri-operative surgical care.