Development and validation of a triglyceride-glucose index-based nomogram for predicting risk in STEMI patients undergoing primary PCI

BACKGROUND: The triglyceride-glucose (TyG) index, a widely recognized surrogate marker of insulin resistance, has been extensively studied in the context of cardiovascular diseases. This study aimed to develop and validate a nomogram that predicts the prognosis of patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PCI), utilizing the TyG index alongside readily available clinical data. METHODS: This retrospective study analyzed 1022 STEMI patients, divided into a training cohort (n = 715) and a validation cohort (n = 307). Potential risk factors were identified, and a predictive nomogram was constructed using least absolute shrinkage and selection operator (LASSO)-penalized Cox regression. Model performance was evaluated through receiver operating characteristic (ROC) curve analysis, calibration plots, Harrell's C-index, and decision curve analysis (DCA). RESULTS: The nomogram incorporated five predictors: residual SYNTAX score (rSS), number of affected coronary vessels (VDn), high-sensitivity C-reactive protein (hsCRP), TyG index, and B-type natriuretic peptide (BNP). Harrell's C-index values of 0.73 (95% CI: 0.70-0.75) in the training cohort and 0.80 (95% CI: 0.76-0.83) in the validation cohort demonstrated strong discrimination. The area under the curve (AUC) for 1-year and 2-year major adverse cardiovascular events (MACE) predictions were 0.77 and 0.77 in the training cohort, and 0.86 and 0.85 in the validation cohort, highlighting excellent predictive accuracy. Subgroup analysis confirmed consistent model performance across age groups (< 65 years and ≥ 65 years), with AUC values ranging from 0.74 to 0.87. Calibration plots demonstrated excellent agreement between predicted and observed outcomes, and DCA highlighted the model's clinical utility by quantifying net benefits across a range of threshold probabilities. CONCLUSIONS: This study developed and validated a novel predictive nomogram that integrates the TyG index with key inflammatory (hsCRP), anatomical (rSS and VDn), and functional (BNP) markers to stratify MACE risk in STEMI patients after PCI. The model demonstrated robust predictive performance and clinical utility, offering a practical tool for precise risk stratification and facilitating personalized treatment strategies to improve long-term outcomes.

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Journal
BMC Cardiovascular Disorders
Published
2026-07-02
DOI
https://doi.org/10.1186/s12872-026-06156-2
Primary Topic
Cardiovascular Function and Risk Factors
Type
article
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article

Development and validation of a triglyceride-glucose index-based nomogram for predicting risk in STEMI patients undergoing primary PCI

Jinyong Huang, Junyi Zhang, Qing Wang, Jun Chen et al.
BMC Cardiovascular Disorders
Cardiovascular Function and Risk Factors
article

Development and validation of a triglyceride-glucose index-based nomogram for predicting risk in STEMI patients undergoing primary PCI

Jinyong Huang, Junyi Zhang, Qing Wang, Jun Chen, Li Li, Shaopeng Xu, Meiyan Chen, Qing Yang, Yongle Li, Shaozhuang Dong, Xiangdong Yu
article en

Abstract

BACKGROUND: The triglyceride-glucose (TyG) index, a widely recognized surrogate marker of insulin resistance, has been extensively studied in the context of cardiovascular diseases. This study aimed to develop and validate a nomogram that predicts the prognosis of patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PCI), utilizing the TyG index alongside readily available clinical data. METHODS: This retrospective study analyzed 1022 STEMI patients, divided into a training cohort (n = 715) and a validation cohort (n = 307). Potential risk factors were identified, and a predictive nomogram was constructed using least absolute shrinkage and selection operator (LASSO)-penalized Cox regression. Model performance was evaluated through receiver operating characteristic (ROC) curve analysis, calibration plots, Harrell's C-index, and decision curve analysis (DCA). RESULTS: The nomogram incorporated five predictors: residual SYNTAX score (rSS), number of affected coronary vessels (VDn), high-sensitivity C-reactive protein (hsCRP), TyG index, and B-type natriuretic peptide (BNP). Harrell's C-index values of 0.73 (95% CI: 0.70-0.75) in the training cohort and 0.80 (95% CI: 0.76-0.83) in the validation cohort demonstrated strong discrimination. The area under the curve (AUC) for 1-year and 2-year major adverse cardiovascular events (MACE) predictions were 0.77 and 0.77 in the training cohort, and 0.86 and 0.85 in the validation cohort, highlighting excellent predictive accuracy. Subgroup analysis confirmed consistent model performance across age groups (< 65 years and ≥ 65 years), with AUC values ranging from 0.74 to 0.87. Calibration plots demonstrated excellent agreement between predicted and observed outcomes, and DCA highlighted the model's clinical utility by quantifying net benefits across a range of threshold probabilities. CONCLUSIONS: This study developed and validated a novel predictive nomogram that integrates the TyG index with key inflammatory (hsCRP), anatomical (rSS and VDn), and functional (BNP) markers to stratify MACE risk in STEMI patients after PCI. The model demonstrated robust predictive performance and clinical utility, offering a practical tool for precise risk stratification and facilitating personalized treatment strategies to improve long-term outcomes.

BMC Cardiovascular Disorders
Tianjin Medical University General Hospital (CN), Tianjin Chest Hospital (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 8%
Cardiovascular Function and Risk Factors
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