Latent class analysis identifies three biologically distinct phenotypes among patients undergoing endovascular thrombectomy: a derivation and temporal validation study
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background Despite successful vascular recanalization, approximately half of patients undergoing endovascular thrombectomy (EVT) fail to achieve functional independence at 3 months. The biological determinants of this heterogeneity remain poorly characterized. Methods Latent class analysis (LCA) was applied to 825 consecutive EVT-treated patients (development cohort, January 2021–December 2024) using 12 routine post-procedural clinical and laboratory indicators. Phenotype associations with favorable 3-month outcome (modified Rankin Scale 0–2) were assessed using multivariable logistic regression. Reproducibility was evaluated in an independent temporal validation cohort ( n = 211). Results LCA identified three phenotypes. The Low-burden phenotype (Class 1, 44.4%) had the lowest inflammatory and coagulatory burden and achieved favorable outcome in 70.8% of patients. The Metabolic phenotype (Class 2, 42.7%), characterized by hyperglycemia and elevated neutrophil-to-lymphocyte ratio, had a favorable outcome rate of 44.9% and symptomatic intracranial hemorrhage (sICH) rate of 7.1%. The hypercoagulable phenotype (Class 3, 13.0%), defined by markedly elevated D-dimer and atrial fibrillation, had a favorable outcome rate of 40.2% and sICH rate of 8.4%. After multivariable adjustment, both Class 2 (OR 0.49, 95% CI 0.35–0.69) and Class 3 (OR 0.41, 95% CI 0.25–0.67) were independently associated with unfavorable outcome (both p < 0.001). Class distribution and outcome associations were reproduced in the validation cohort. Conclusion LCA identified three biologically distinct phenotypes among EVT-treated patients with divergent functional outcomes. Multicenter prospective validation is needed before these findings can inform clinical practice.
Source: Frontiers in Neurology
Authors: Li-Li Yang, Chao Chen, Li-Li Sun, Meng-Xia Li, Li Han
Institution: Wenzhou Medical University
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7222022155