Abbasi, Mehdi

Introduction: Embolic stroke of undetermined source (ESUS) is a clinical construct encompassing heterogenous possible mechanisms of incident stroke, potentially with variable responsiveness to anticoagulation and antiplatelet treatment. This study aims to identify differences in recurrent stroke risk based on phenotypic clusters in an ESUS population, and whether some clusters may benefit relatively more from anticoagulation over antiplatelet therapy. Methods: We identified patient phenotypes from a multicenter, retrospective cohort study of 1909 ESUS patients by dividing them into groups using Partitioning Around Medoids (PAM) with correlation-weighted Gower distance over 23 baseline demographic, clinical, laboratory, radiographic, and cardiac features. We confirmed optimal clustering at 3 groups. We assessed cluster associations with time to recurrent stroke using Kaplan-Meier survival analysis. We then employed a causal survival forest to estimate conditional average treatment effects (CATE) of anticoagulation versus antiplatelet therapy using the same baseline features as predictors with a 365-day horizon for restricted mean survival time differences. Finally, we used best linear projection to identify baseline features predictive of treatment effect and assessed whether effects differed significantly by cluster. Results: Cluster 1 (n=542) identified a low-risk, younger sample with minimal baseline disability and comorbidity burden. Cluster 2 (n=743) comprised a moderate-risk phenotype with intermediate comorbidity. Cluster 3 (n=626) represented the oldest and highest-risk phenotype. Kaplan-Meier analysis demonstrated significant differences in stroke-free survival across patient clusters (p < 0.001), where Cluster 1 experienced the lowest recurrent stroke rate and Cluster 3 had the highest. The clustering algorithm successfully identified clinically meaningful phenotypes with distinct risk profiles. While CATE varied across clusters, the differences were not statistically significant. Conclusion: In a multicenter ESUS population, we identified 3 distinct phenotypic clusters, each comprising nearly one-third of the sample and with a differential risk of recurrent stroke. Although cluster was not a significant effect modifier, substantial CATE variability by cluster was observed. Future clinical trials may consider evaluating secondary stroke prevention therapies among phenotypically similar patients who may respond differentially to novel therapies.

Background: Optimal Blood pressure management after thrombectomy for acute ischemic stroke and its association with clinical outcomes remains unclear. We performed this study to compare clinical outcomes between intensive systolic blood pressure (SBP) control (<120-140mmHg) and conventional SBP control (< 180mmHg). Methods: In September 2023, this registered systematic review (CRD42023464176) was performed using multiple databases for the following keywords: stroke, thrombectomy, and Blood Pressure from 2010 to present. Randomized control studies were included if they had the reported the outcome for adults aged >18 who underwent successful thrombectomy (defined as mTICI 2b or higher) for acute ischemic stroke. The primary outcome of interest was the rate of functional independence, defined as a modified Rankin scale of 0-2 at 90 days. The secondary outcomes of interest included rates of mortality, and symptomatic intracranial hemorrhage (sICH). For all outcome events, corresponding odds ratios (OR) and 95% confidence intervals (CI) were calculated using a random-effects meta-analysis model. Results: Four studies comprising 1497 patients were included. Compared to conventional SBP control, intensive SBP control was associated with lower rates of functional independence (OR 0.67, 95% CI 0.51- 0.88 p=0.0). There were comparable rates of sICH (OR 1.12, 95% CI 0.75-1.67; p = 0.56) and mortality (OR 1.21, 95% CI 0.90-1.64; p = 0.19) between intensive and conventional SBP control groups. Conclusions: Our findings suggest that intensive blood pressure control does not improve clinical outcomes. More robust data is needed to draw definite conclusions.