Dynamic, Explainable Artificial Intelligence Framework for Sequential Prediction of Symptomatic Intracranial Hemorrhage after Reperfusion Therapy in Acute Ischemic Stroke.

INTRODUCTION

Symptomatic intracranial hemorrhage (sICH) remains a devastating complication undermining reperfusion therapy in acute ischemic stroke (AIS). Current prediction models rely on static baseline parameters, neglecting dynamic post-treatment physiological changes. We aimed to develop and validate a dynamic, explainable risk prediction framework integrating high-resolution temporal data.

METHODS

In this prospective multicenter study, 2,341 AIS patients with anterior circulation large vessel occlusion receiving reperfusion therapy were consecutively enrolled (January 2023-June 2024). High-frequency vital signs and serial laboratory tests were collected over 72 h post-treatment. Time-series features (trend, volatility, stability) were engineered, and XGBoost-based dynamic models were constructed at five timepoints (6, 12, 24, 48, 72 h). Performance was evaluated in a held-out validation cohort (n = 576) against a static baseline model. SHAP was applied for interpretability.

RESULTS

Of 2,158 analyzed patients, 218 (10.1%) developed sICH. The dynamic model consistently outperformed the static model across all timepoints (AUC 0.83-0.90 vs. 0.71-0.75; all p < 0.001). SHAP identified three core risk drivers: hemodynamic volatility (24-h SBP standard deviation ranked highest), metabolic-inflammatory trajectories, and early tissue injury markers. Unsupervised clustering revealed three risk phenotypes: persistent low-risk (71.5%, sICH 0.8%), early rapid-riser (18.2%, 52.3%), and delayed-riser (10.3%, 38.1%).

CONCLUSION

This dynamic, explainable framework shifts sICH risk prediction from static snapshots to continuous, interpretable monitoring. Hemodynamic volatility emerged as the strongest modifiable predictor, offering clinically actionable insights for personalized post-procedural care in AIS.