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Research Article: The association between the C-reactive protein-triglyceride glucose index and myocardial injury after acute ischemic stroke: a machine learning analysis of the brain-heart axis

Date Published: 2026-05-21

Abstract:
Myocardial injury is a common and serious complication following acute ischemic stroke (AIS), that contributes to poor functional outcomes. The C-reactive protein-triglyceride glucose index (CTI), a novel composite marker reflecting inflammation and insulin resistance (IR), has shown prognostic value in cardio-cerebrovascular diseases. This study used machine learning (ML) for feature selection to investigate the association between the CTI and myocardial injury following AIS. A retrospective cohort study was conducted, in which patients with AIS within 72?h of symptom onset were enrolled. The primary endpoint was myocardial injury after AIS. The CTI was calculated via the formula 0.412?×?ln [Hs-CRP (mg/L)]?+?ln [TG (mg/dL)?×?FBG (mg/dL) / 2]. Four ML algorithms were applied to identify predictive variables. Multivariate logistic regression and restricted cubic spline (RCS) analyses were used to assess the independent associations and dose-response relationships between the CTI and myocardial injury. Among the 842 patients, 288 (34.2%) experienced myocardial injury. The CTI was significantly greater in the myocardial injury group ( P <?0.01). The ML models consistently identified the CTI as the top predictor. Multivariate analysis revealed that the CTI was independently associated with myocardial injury ( OR =?2.61, 95% CI : 1.99–3.42). RCS analysis revealed a positive linear relationship ( P for nonlinea r = 0.443). Compared with the TyG index ( AUC =?0.596) and Hs-CRP ( AUC =?0.689) alone, the CTI demonstrated moderate discriminative ability and showed improved performance ( AUC =?0.713). The CTI was significantly associated with myocardial injury and its integration of IR and inflammatory status suggests that it may serve as a moderately discriminative indicator for risk stratification.

Introduction:
Myocardial injury is a common and serious complication following acute ischemic stroke (AIS), that contributes to poor functional outcomes. The C-reactive protein-triglyceride glucose index (CTI), a novel composite marker reflecting inflammation and insulin resistance (IR), has shown prognostic value in cardio-cerebrovascular diseases. This study used machine learning (ML) for feature selection to investigate the association between the CTI and myocardial injury following AIS.

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