A Causal and interpretable machine learning framework for postcranioplasty risk prediction and surgical decision support

Abstract

Cranioplasty is associated with a substantial burden of postoperative complications. In this multicenter study, we developed a machine learning-based clinical decision-support tool to predict the risk of postoperative complications following cranioplasty. A set of nine features was selected for model development. Among the 15 algorithms evaluated, the random forest model demonstrated the best overall performance and was validated on data from both spatial and temporal external cohorts (AUROC = 0.949, internal cross-validation; 0.930, geographical validation; and 0.932, temporal validation). Subgroup analyses by age and sex demonstrated consistently high discriminative performance (lowest AUROC = 0.927) and good calibration (O/E ratio = 1.16, 95% CI: 0.97-1.40). Analysis of causal effects of modifiable intraoperative variables on postoperative complications, with diverse counterfactual explanations and causal inference methods, including double machine learning and the T-learner framework, revealed a protective effect of subcutaneous negative-pressure drainage (ATE = -0.241) and titanium mesh (ATE = -0.191). Finally, we present the model as an accessible web-based tool for individualized, real-time clinical decision-making ( http://www.cranioplastycomplicationprediction.top ). These findings provide a practical framework for postoperative risk stratification and support the optimization of intraoperative decision-making in cranioplasty.

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