Anticoagulation Treatment Response Among Phenotypic ESUS Clusters: Evidence From CASPR.

Authors

Elena Badillo Goicoechea, Patrick Ellsworth, James E Siegler, James R Brorson, Christina M Lineback, Frank G Conyers, Deborah Kerrigan, Kelsey Eklund, Shadi Yaghi, Farid Khasiyev, Emiliya Melkumova, Collin J Culbertson, Marissa D'Souza, Dinesh V Jillella, Patrick Glover, Hassan Aboul-Nour, Fadi Nahab, Mahan Shahrivari, Simona Nedelcu, David S Liebeskind, Matthew M Smith, Aaron Rothstein, Anna Bowman, Alicia Zha, Narendra Kala, Ahmad Abu Qdais, Sami Al Kasab, Mudassir Farooqui, Diana Rojas-Soto, Yasmin Aziz, Franziska Herpich, Jean-Philippe Daniel, Sean L Thompson, Vivek Patel, Thanh N Nguyen, Siddharth Sehgal, Wenzheng Yu, Wayneho Kam, Russel Van Coevering, Oana M Dumitrascu, Muhammad Alvi, Balaji Krishnaiah, Setareh Salehi Omran, Guillermo Linares, Eesha Singh, Hamid Ali, Mary Penckofer, Jesse M Thon, Shani Mannion, Dalia Chahien, Lucia Chen, Fawad Talat, Mehdi Abbasi, Richa Sharma

Published in

Annals of clinical and translational neurology. Oct 06, 2026. Epub Oct 06, 2026.

Abstract

To evaluate treatment effect heterogeneity across phenotypic clusters of patients with cryptogenic embolic stroke using causal inference methods.
From a retrospective, multicenter cohort of patients with cryptogenic embolic stroke, we estimated the treatment effect of anticoagulation versus antiplatelet therapy in patients with ESUS in two ways: (1) Partitioning Around Medoids and (2) causal survival forest modeling. We assessed cluster-level associations with time to primary outcome (recurrent stroke, major bleeding, or death) using Kaplan-Meier survival analysis to assess cluster differences. We then extracted SHapley Additive exPlanations (SHAP) feature importances from the causal forest model to identify candidate effect modifiers from the causal forest.
Among 1869 patients with ESUS, three phenotypically distinct clusters were extracted with significant differences in event-free survival across clusters (log-rank p < 0.0001). There was no significant population-level benefit of anticoagulation over antiplatelet therapy (p = 0.23). SHAP feature importance analysis identified several candidate effect modifiers; however, only higher LVEF (β = -327, 95% CI: -580 to -74, p = 0.011) and cancer history (β = -226, 95% CI: -441 to -11, p = 0.039) were independent predictors of response to antiplatelet over anticoagulation using best linear projections. Phenotypic cluster assignment did not significantly predict treatment effect heterogeneity.
Three distinct phenotypic clusters of cryptogenic stroke patients emerged with a differential risk of the composite outcome, but they did not predict responsiveness to anticoagulation. LVEF and cancer history, however, showed differential outcomes across antithrombotic strategies. These findings demonstrate the potential of causal inference methods for identifying effect heterogeneity and individualizing treatment strategies.

PMID:
42839308
Bibliographic data and abstract were imported from PubMed on 07 Oct 2026.

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