Clinical value of thromboelastography in predicting the risk of recurrence of acute ischemic stroke - Guo, Ruyue; Shen, Xiaoming; Lu, Jin; Zhou, Jiao; Hao, Shinan; Lan, Rui; Xu, Yumin (Frontiers in Neurology, 2024)
Background Thromboelastography (TEG) can objectively reflect the formation, development and rupture process of thrombosis in patients, but there are limited data on whether TEG can be used as a predictive tool for recurrence in patients with acute ischemic stroke. Objective To explore the TEG risk of recurrence in patients with acute ischemic stroke predictive value. Methods A total of 441 patients with acute ischemic stroke who met the research criteria in the First Affiliated Hospital of Henan University of Traditional Chinese Medicine from January 2020 to December 2021 were selected as the research objects. TEG was measured in all patients, and the main parameters of TEG (R value, indicating coagulation reaction time; K value and Angle, the rate of blood clot formation; MA value, indicating the maximum amplitude). The primary outcome of this study was ischemic stroke recurrence. Recurrent events included cerebral infarction, cerebral hemorrhage, TIA, and were determined by combining imaging events and clinical events. Logistic regression analysis was used to explore the influencing factors of recurrence in patients with acute ischemic stroke. Results Fifty-six patients (12.7%) had recurrence. Multivariate Logistic regression analysis showed that: Age [OR = 1.078, 95%CI(1.024, 1.135)], triglyceride [OR = 1.541, 95%CI(1.033, 2.298)], glycosylated hemoglobin [OR = 1.401, 95%CI(1.097, 1.790)], history of hypertension [OR = 16.046, p < 0.05], 95%CI(4.726, 54.489), R value [OR = 0.533, 95%CI(0.351, 0.809)], MA value [OR = 1.399, 95%CI(1.004, 1.949)] were independent influencing factors for hemorrhagic transformation in patients with acute ischemic stroke. Conclusion TEG has some value in predicting recurrence in patients with acute ischemic stroke, and the MA value in TEG [AUC = 0.806 (95%CI:0.747–0.867), with a sensitivity of 78.6% and a specificity of 70.4%], predicted the most significant efficiency of AIS recurrence.