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Updated: Aug 5, 2026

Thrombus Profiling Assay: A Microfluidics-Based Platform for Comprehensively Characterizing Biomechanical Thrombogenesis
Published on: January 9, 2026
Baseline ASPECTS, Fibrinogen Level, and Platelet Count for Predicting Hemorrhagic Transformation After Mechanical
Nguyen Van Tuyen1,2, Nguyen Van Tuan1, Tran Tien Dung1
1Department of Stroke, 108 Military Central Hospital, 1 Tran Hung Dao Street, Hai Ba Trung Ward, Hanoi 10000, Vietnam.
Abstract:
Background: Hemorrhagic transformation (HT) is a major complication after mechanical thrombectomy (MT) in acute ischemic stroke (AIS). This study evaluated the predictive value of baseline ASPECTS, fibrinogen level, and platelet count for HT after MT. Methods: We retrospectively analyzed 274 AIS patients treated with MT. HT within 24 h was identified using follow-up imaging. Logistic regression, ROC analysis, DeLong tests, and bootstrap resampling (1000 iterations) were performed to evaluate predictors and model performance. Results: HT occurred in 52/274 patients (19.0%). Baseline ASPECTS (adjusted OR 0.73, 95% CI 0.61-0.88; p = 0.001), fibrinogen level (adjusted OR 0.51, 95% CI 0.32-0.79; p = 0.003), and platelet count (adjusted OR 0.99, 95% CI 0.99-1.00; p = 0.018) were independently associated with HT. Baseline ASPECTS showed the highest discriminative ability among individual predictors (AUC = 0.684), whereas baseline NIHSS score showed poor performance (AUC = 0.539). The reduced model combining baseline ASPECTS score, fibrinogen level, and platelet count achieved a balanced discriminative performance (AUC = 0.739), with no significant difference compared with the full model (p = 0.804) or baseline ASPECTS alone (p = 0.112). Similarly, the full model did not significantly outperform baseline ASPECTS (p = 0.120). Bootstrap validation confirmed model stability. Conclusions: Baseline ASPECTS, fibrinogen level, and platelet count are independent predictors of HT after MT. A reduced model incorporating these variables demonstrated modest-to-acceptable and parsimonious predictive performance, although external validation is required before clinical implementation.
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