ABSTRACT NUMBER: ESOC2026A2357 INDIVIDUALIZING RESCUE INTRACRANIAL STENTING DECISIONS AFTER FAILED MECHANICAL THROMBECTOMY: A CAUSAL MACHINE LEARNING APPROACH

Mohamed F Doheim; Mahmoud H Mohammaden; Johanna Fifi; Santiago Ortega-Gutierrez; Ameer Hassan; Jan-Karl Burkhardt; Sunil Sheth; Thanh N Nguyen; Diogo C Haussen; Raul Nogueira

doi:10.1093/esj/aakag023.1803

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ABSTRACT NUMBER: ESOC2026A2357 INDIVIDUALIZING RESCUE INTRACRANIAL STENTING DECISIONS AFTER FAILED MECHANICAL THROMBECTOMY: A CAUSAL MACHINE LEARNING APPROACH

Mohamed F Doheim, Mahmoud H Mohammaden, Johanna Fifi, Santiago Ortega-Gutierrez, Ameer Hassan, Jan-Karl Burkhardt, Sunil Sheth, Thanh N Nguyen, Diogo C Haussen and Raul Nogueira

European stroke journal, Vol.11(Suppl 1), pp.i964-i964

05/06/2026

DOI: 10.1093/esj/aakag023.1803

PMCID: PMC13146313

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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.

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Title: Subtitle: ABSTRACT NUMBER: ESOC2026A2357 INDIVIDUALIZING RESCUE INTRACRANIAL STENTING DECISIONS AFTER FAILED MECHANICAL THROMBECTOMY: A CAUSAL MACHINE LEARNING APPROACH
Creators: Mohamed F Doheim - UPMC Health System
Mahmoud H Mohammaden - Emory University
Johanna Fifi - Icahn School of Medicine at Mount Sinai
Santiago Ortega-Gutierrez - University of Iowa
Ameer Hassan - The University of Texas Rio Grande Valley
Jan-Karl Burkhardt - Hospital of the University of Pennsylvania
Sunil Sheth - The University of Texas Health Science Center at Houston
Thanh N Nguyen - Boston Medical Center
Diogo C Haussen - Emory University
Raul Nogueira - UPMC Health System
Resource Type: Abstract
Publication Details: European stroke journal, Vol.11(Suppl 1), pp.i964-i964
DOI: 10.1093/esj/aakag023.1803
PMCID: PMC13146313
ISSN: 2396-9873
eISSN: 2396-9881
Publisher: Oxford University Press
Language: English
Date published: 05/06/2026
Academic Unit: Neurology; Radiology; Iowa Neuroscience Institute; Neurosurgery
Record Identifier: 9985161448702771

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