Heterogeneity of Clazosentan Benefit after Aneurysmal Subarachnoid Hemorrhage Demonstrated in an Externally Validated Causal Policy Tree.

BACKGROUND: Clazosentan reduces angiographic vasospasm after aneurysmal subarachnoid hemorrhage (aSAH), but functional benefit may vary across patients. We used causal machine learning to explore heterogeneity and derive an interpretable rule. METHODS: In a secondary analysis of the RECOVER dataset [multicenter retrospective cohort of aSAH treated by clipping or coiling within 48 h (N = 506)], we compared clazosentan-containing management (with or without fasudil) with fasudil-only prophylaxis. After applying inverse probability of treatment weighting for prespecified confounders [age, World Federation of Neurosurgical Societies (WFNS) grade, Fisher grade, and body mass index (BMI)], we used a causal forest to estimate conditional average treatment effects (CATEs) on favorable discharge outcome (modified Rankin Scale 0-2 at discharge). A policy tree summarized CATEs, and external validation was performed in an independent cohort (N = 181). RESULTS: CATEs were heterogeneous (mean 0.18 ± 0.14). The policy tree split first on BMI (≤ 20.03 kg/m2): patients with BMI ≤ 20.03 and WFNS ≤ 2 showed no clear estimated benefit (mean CATE - 0.058), whereas those with BMI > 20.03 or WFNS > 2 showed higher estimated benefit (CATE 0.24-0.25). In external validation, the same rule identified a subgroup with higher odds of favorable recovery with clazosentan; estimates in the low-benefit subgroup were imprecise (n = 27). CONCLUSIONS: In observational cohorts with limited overlap in treatment assignment, causal machine learning suggested heterogeneity in the estimated association of a clazosentan-containing strategy with discharge outcomes and produced a simple BMI/WFNS policy tree. These findings are hypothesis-generating and require prospective validation including safety endpoints and longer-term functional outcomes.