Abstract Number: Esoc2026a2357 Individualizing Rescue Intracranial Stenting Decisions After Failed Mechanical Thrombectomy: a Causal Machine Learning Approach
Year: 2026
Type: conference-abstract
Abstract: Background and aims Rescue intracranial stenting after failed mechanical thrombectomy remains a clinical dilemma, with observational data suggesting benefit but significant procedural risks and lifelong antiplatelet requirements. Current practice lacks tools to identify which patients are most likely to benefit. We developed a causal machine learning framework to estimate individualized treatment effects for rescue stenting decisions. Methods We applied T-Learner methodology with stacking ensemble models to the SAINT multicenter cohort (n=393; 195 rescue stenting, 198 failed MT only; baseline mRS 0-2). Separate models predicted outcomes under each treatment scenario (μ0: failed MT, μ1: rescue stenting), with Conditional Average Treatment Effect (CATE) quantifying individual-level benefit. Outcomes included functional independence (mRS 0-2), favorable outcome (mRS 0-3), symptomatic ICH, and 90-day mortality. Internal validation used Harrell’s optimism-corrected bootstrap (5000 iterations). A ≥15% absolute benefit threshold (NNT≤7) defined clinically meaningful effect given procedural risks. Results Optimism-corrected AUCs were excellent: functional independence 0.90(95%CI:0.77-0.99) for failed MT and 0.89(0.84-0.93) for rescue stenting models. Mean CATE for functional independence was +29.0 percentage points, with 91.7% of patients showing predicted benefit. However, only 75% exceeded the ≥15% clinically meaningful threshold. Patients with shorter time-to-puncture(importance:0.26), younger age(0.25), higher ASPECTS(0.16), fewer thrombectomy passes(0.15), and moderate NIHSS(0.13) demonstrated greatest predicted benefit. Mortality reduction paralleled functional gains(+14.3%). Conclusions Causal machine learning enables individualized rescue stenting. Approximately one-quarter of patients do not meet the threshold for clinically meaningful benefit. This approach may inform both clinical decision-making and future trial designs requiring prospective validation. Conflict of interest None
Source: European Stroke Journal
Institutions: UPMC Health System, University of Pittsburgh Medical Center, Emory University, Icahn School of Medicine at Mount Sinai, University of Iowa, University of Iowa Hospitals and Clinics, The University of Texas Rio Grande Valley, Hospital of the University of Pennsylvania, The University of Texas Health Science Center at Houston, Boston University, Boston Medical Center
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,400
OpenAlex ID: W7160440435
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