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Background <p>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.</p> Methods <p>This retrospective study analyzed 1022 STEMI patients, divided into a training cohort (<i>n</i> = 715) and a validation cohort (<i>n</i> = 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).</p> Results <p>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 (&lt; 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.</p> Conclusions <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月2日

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

摘要

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.