External Validation of an Extreme Gradient Boosting Model for Prediction of Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage

Abstract

Background: Delayed cerebral ischemia (DCI) may significantly worsen the functional status of patients with aneurysmal subarachnoid hemorrhage (aSAH). Several authors have designed predictive models for early identification of patients at risk of post-aSAH DCI. In this study, we externally validate an extreme gradient boosting (EGB) forecasting model for post-aSAH DCI prediction.

Methods: A 9-year institutional retrospective review of patients with aSAH was performed. Patients were included if they underwent surgical or endovascular treatment and had available follow-up data. DCI was diagnosed as new-onset neurologic deficits at 4-12 days after aneurysm rupture, defined as worsening Glasgow Coma Scale score for ≥2 points, and new ischemic infarcts at imaging.

Results: We collected 267 patients with aSAH. At admission, median Hunt-Hess score was 2 (range, 1-5), median Fisher score 3 (range, 1-4), and median modified Fisher score 3 (range, 1-4). One-hundred and forty-five patients underwent external ventricular drainage placement for hydrocephalus (54.3%). The ruptured aneurysms were treated with clipping (64%), coiling (34.8%), and stent-assisted coiling (1.1%). Fifty-eight patients (21.7%) were diagnosed with clinical DCI and 82 (30.7%) with asymptomatic imaging vasospasm. The EGB classifier correctly predicted 19 cases of DCI (7.1%) and 154 cases of no-DCI (57.7%), achieving sensitivity of 32.76% and specificity of 73.68%. The calculated F1 score and accuracy were 0.288% and 64.8%, respectively.

Conclusions: We validated that the EGB model is a potential assistant tool to predict post-aSAH DCI in clinical practice, finding moderate-high specificity but low sensitivity. Future research should investigate the underlying pathophysiology of DCI to allow the development of high-performing forecasting models.

Keywords: Aneurysmal subarachnoid hemorrhage; Delayed cerebral ischemia; Machine learning; Predictive analysis; Vasospasm.