Interpretable Machine Learning Predicts Postoperative Risks in Cranioplasty

Overview

This multicenter study developed a machine learning model using nine clinical features to predict postoperative complications after cranioplasty with high accuracy. The random forest algorithm showed excellent performance across internal and external validations, and causal analysis identified protective intraoperative factors such as subcutaneous negative-pressure drainage and titanium mesh.

Background

Cranioplasty, a surgical procedure to repair cranial defects, carries a significant risk of postoperative complications that can impact patient outcomes. Accurate risk stratification is essential to optimize surgical decisions and improve recovery. Traditional predictive methods have limitations in handling complex clinical data, prompting the use of machine learning approaches. This study aimed to create an interpretable, validated model to support individualized clinical decision-making in cranioplasty.

Data Highlights

Key Findings

• The random forest model outperformed 14 other algorithms in predicting postoperative complications.
• Model performance was robust across age and sex subgroups with AUROC as low as 0.927 and good calibration (O/E ratio 1.16, 95% CI: 0.97–1.40).
• Causal inference methods revealed subcutaneous negative-pressure drainage reduced complication risk (average treatment effect [ATE] = −0.241).
• Titanium mesh use was also protective against complications (ATE = −0.191).
• The model is accessible via a web-based tool for real-time, individualized surgical risk assessment.

Clinical Implications

This validated machine learning tool enables clinicians to accurately stratify postoperative risk in cranioplasty patients, facilitating personalized surgical planning. Identification of modifiable intraoperative factors such as drainage technique and implant material offers actionable targets to reduce complications. Integration of this decision-support system into clinical workflows may improve patient outcomes and optimize resource utilization.

Conclusion

An interpretable machine learning model reliably predicts postoperative complications after cranioplasty and supports intraoperative decision-making. Its deployment as a web-based tool provides a practical framework for individualized risk management in neurosurgical practice.

References

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