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Updated: Jan 16, 2026
A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia
Published on: August 18, 2015
Nomogram Models Integrating TyG Index for Predicting Early Neurological Deterioration and 90-Day Outcomes in AIS
Lai Wei1, Zhihua Wu2, Xiang Zhou3,4
1Department of Radiology, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Purpose:
This study aimed to evaluate the influence of the triglyceride-glucose index (TyG index) on clinical outcomes and to develop nomogram models for predicting early neurological deterioration (END) and long-term prognosis in acute ischemic stroke (AIS) patients following intravenous thrombolytic (IVT) therapy.
Methods:
We conducted a multi-center retrospective cohort study involving 333 AIS patients treated with IVT. The short-term and long-term outcomes were defined as the occurrence of END and 90-day prognosis. Multivariate logistic regression was used to develop nomogram models for forecasting these clinical outcomes.
Results:
Patients in the high-TyG group exhibited significantly higher risks of END (P = 0.0010) and poor 90-day outcomes (P = 0.0012). Independent risk factors for END included a lower baseline NIHSS score, delayed door-to-needle time (DNT), reduced ASPECTS score, elevated TyG index, higher potassium (K+) levels, and incomplete Willis artery. Additionally, a higher initial NIHSS, increased TyG levels, presence of END, and a history of hypertension were predictors of poor prognosis. Based on the identified risk factors, two nomogram models yielded AUC values of 0.746 and 0.849 for predicting END and poor prognosis, respectively. NIHSS scores, TyG index, and admission glucose levels (Glu) emerged as prognostic indicators across all patients, while higher mean platelet volume (MPV) and history of stroke were identified as novel risk factors for poor prognosis in NO-END group.
Conclusion:
A higher TyG index correlates with poor clinical outcomes in AIS patients post-IVT. The nomograms combining the TyG index with various factors enhanced risk prediction for END and poor prognosis.
