Development and Validation of Risk Nomogram Model Predicting Coronary Microvascular Obstruction in Patients with

Yuyang Xiao1, Xianghua Fu1, Yanbo Wang1

  • 1Department of Cardiology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China (mainland).

Insights

A new nomogram model accurately predicts coronary microvascular obstruction (CMVO) risk in ST-segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention. This tool uses six key factors to identify high-risk individuals.

Area of Science:

  • Cardiology
  • Interventional Cardiology
  • Biostatistics

Background:

  • Coronary microvascular functional and structural obstruction (CMVO) is a significant complication post-ST-elevation myocardial infarction (STEMI).
  • Predicting CMVO risk is crucial for optimizing patient management during primary percutaneous catheterization.

Purpose of the Study:

  • To develop and validate a predictive nomogram model for CMVO risk in STEMI patients.
  • To identify independent predictors of CMVO to aid clinical decision-making.

Main Methods:

  • A cohort of STEMI patients undergoing primary percutaneous catheterization was divided into training (n=446) and validation (n=99) sets.
  • Multivariate logistic regression identified significant factors for the nomogram.
  • Coronary microvascular obstruction (CMVO) was assessed using TIMI myocardial perfusion grade.

Main Results:

  • A nomogram incorporating age, symptom onset to balloon time, Killip classification, admission activated clotting time, neutrophil/lymphocyte ratio, and glucose was developed.
  • The model demonstrated high predictive accuracy with a concordance index of 0.925 (internal) and an area under the curve of 0.939 (external).
  • Significant differences in left ventricular ejection fraction (LVEF) were observed between CMVO and non-CMVO subgroups.

Conclusions:

  • The developed nomogram effectively predicts CMVO risk in STEMI patients undergoing primary percutaneous catheterization.
  • The model's accuracy supports its potential clinical utility in risk stratification and management.

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