Neuroanesthesia & neurocritical care
Machine Learning analysis of functional outcome and hemodynamic parameters during stroke thrombectomy under general anesthesia
Authors' conclusion
These findings highlight distinct intraoperative hemodynamic patterns associated with outcomes after thrombectomy under general anesthesia
Study context
- Population
- Consecutive patients with anterior circulation acute ischemic stroke (AIS) undergoing mechanical thrombectomy under GA at a tertiary stroke center, March 2014–June 2019
- Participants
- 229 patients
- Primary endpoint
- Functional independence (mRS score 0-2)
- Follow-up
- Three months
Methodological detailsmachine learning (XGBoost, SHAP), single tertiary stroke centre, consecutive patients, internal validation
- Machine learning (XGBoost, SHAP)
- Single tertiary stroke centre
- Consecutive patients
- Internal validation
Primary result
As reported in the abstract
Functional independence (mRS ≤2)
Achieved mRS score ≤2
101
44.1%
Model predictive performance (AUC ROC)
0.85
Original publication
Machine Learning analysis of functional outcome and hemodynamic parameters during stroke thrombectomy under general anesthesia. Anaesth Crit Care Pain Med. Published 7 November 2025. doi:10.
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