Nomogram model for predicting multivessel disease in coronary artery disease using cardiac function parameters and...

Area of Science:

• Cardiology
• Medical Diagnostics
• Predictive Modeling

Background:

• Multivessel disease (MVD) is a severe coronary artery disease phenotype linked to poor prognosis.
• Early, non-invasive identification of MVD is a significant clinical challenge.
• Existing methods for MVD detection lack sufficient accuracy and accessibility.

Purpose of the Study:

• To develop and validate a nomogram for the individualized, non-invasive prediction of MVD risk.
• To integrate cardiac function parameters and clinical characteristics into a predictive model.
• To improve early identification of patients with MVD for timely intervention.

Main Methods:

• Retrospective collection of clinical data from 353 patients with confirmed coronary artery disease.
• Utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression for risk factor screening.
• Developed a predictive model using multivariate logistic regression and validated it with AUC, calibration curves, and decision curve analysis.

Main Results:

• A nomogram incorporating age, gender, prior stent, total cholesterol, LVEF, HDL, and albumin was created.
• The model demonstrated good discrimination with AUCs of 0.84 (training) and 0.79 (validation).
• The nomogram accurately stratified patients into high- and low-risk groups for MVD.

Conclusions:

• The developed nomogram offers an accurate, individualized, and non-invasive tool for predicting MVD risk.
• This tool can assist clinicians in identifying high-risk MVD patients who may benefit from intensified treatment.
• The nomogram enhances clinical decision-making for coronary artery disease management.