Machine learning models accurately predicted complications following colorectal surgery, achieving a pooled AUC of 0.813 (95% CI 0.753-0.873) for anastomotic leak and 0.867 for mortality.
Meta-Analysis
Do machine learning models accurately predict postoperative complications in patients undergoing colorectal surgery?
Machine learning models show promising clinical utility with high predictive accuracy for complications such as mortality and anastomotic leak following colorectal surgery.
Effect estimate: AUC 0.813 (95% CI 0.753-0.873)
BACKGROUND: To systematically evaluate the clinical utility of machine learning in predicting postoperative 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 postoperative 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 postoperative complications, namely anastomotic leak, mortality, prolonged length of hospitalization, 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-center studies are needed to investigate the usefulness and generalizability of these novel approaches in optimizing peri-operative surgical care.
Mohamedahmed et al. (Tue,) conducted a meta-analysis in Postoperative complications following colorectal surgery. Machine learning models was evaluated on Area under the curve for predicting anastomotic leak (AUC 0.813, 95% CI 0.753-0.873). Machine learning models accurately predicted complications following colorectal surgery, achieving a pooled AUC of 0.813 (95% CI 0.753-0.873) for anastomotic leak and 0.867 for mortality.