Nomogram to Predict Outcomes After Staged Revascularization in ST-Segment Elevation Myocardial Infarction and

Huaigen Wang1, Aiqun Ma1,2, Tingzhong Wang1,2

  • 1Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.

Insights

This study developed predictive models and nomograms for patients with ST-segment elevation myocardial infarction (STEMI) and multivessel coronary artery disease (MVD). These tools help identify low-risk patients, improving outcomes after percutaneous coronary intervention (PCI).

Area of Science:

  • Cardiology
  • Medical Informatics
  • Predictive Modeling

Background:

  • Multivessel coronary artery disease (MVD) affects approximately 50% of ST-segment elevation myocardial infarction (STEMI) patients.
  • Management strategies for STEMI patients with MVD are controversial, necessitating improved risk stratification.

Purpose of the Study:

  • To develop predictive models and a nomogram for outcomes in STEMI patients with MVD.
  • To aid in the identification and classification of patient risk for better management.

Main Methods:

  • Least Absolute Shrinkage and Selection Operator (LASSO) regression for feature selection.
  • Cox regression modeling and nomogram construction.
  • Kaplan-Meier survival analysis to compare low- and high-risk groups.

Main Results:

  • Models demonstrated good predictive accuracy with C-indices of 0.68 for MACE-free survival and 0.75 for overall survival.
  • Low-risk patients exhibited significantly lower 1- and 3-year MACE and all-cause death rates compared to high-risk patients.
  • Nomogram predictions correlated well with observed patient outcomes.

Conclusions:

  • Developed nomograms provide a practical tool for predicting outcomes in STEMI patients with MVD undergoing primary and staged PCI.
  • These nomograms can assist clinicians in risk stratification and treatment planning for improved patient management.
Abstract

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