Machine learning to predict adverse outcomes after cardiac surgery: A systematic review and meta-analysis

Abstract

Background: Machine learning (ML) models are promising tools for predicting adverse postoperative outcomes in cardiac surgery, yet have not translated to routine clinical use. We conducted a systematic review and meta-analysis to assess the predictive performance of ML approaches. Methods: We conducted an electronic search to find studies assessing ML and traditional statistical models to predict postoperative outcomes. Our primary outcome was the concordance (C-) index of discriminative performance. Using a Bayesian meta-analytic approach we pooled the C-indices with the 95% credible interval (CrI) across multiple outcomes comparing ML methods to logistic regression (LR) and clinical scoring tools. Additionally, we performed critical difference and sensitivity analysis. Results: We identified 2792 references from the search of which 51 met inclusion criteria. Two postoperative outcomes were amenable for meta-analysis: 30-day mortality and in-hospital mortality. For 30-day mortality, the pooled C-index and 95% CrI were 0.82 (0.79−0.85), 0.80 (0.77−0.84), 0.78 (0.74−0.82) for ML models, LR, and scoring tools respectively. For in-hospital mortality, the pooled C-index was 0.81 (0.78−0.84) and 0.79 (0.73−0.84) for ML models and LR, respectively. There were no statistically significant results indicating ML superiority over LR. Conclusion: In cardiac surgery patients, for the prediction of mortality, current ML methods do not have greater discriminative power over LR as measured by the C-index.