A risk predictive model for determining the severity of coronary artery lesions in older postmenopausal women with...

Area of Science:

• Cardiology
• Geriatrics
• Medical Informatics

Background:

• Coronary artery disease (CAD) poses a significant health challenge in older postmenopausal women.
• Identifying specific risk factors for severe CAD in this demographic is crucial for effective management.
• Current risk stratification methods may not fully capture the nuances of CAD severity in elderly women.

Purpose of the Study:

• To determine the risk factors associated with the severity of CAD in older postmenopausal women diagnosed with coronary heart disease (CHD).
• To develop and validate a personalized risk prediction model for severe CAD in this patient population.

Main Methods:

• Retrospective analysis of clinical records from 527 female patients (≥60 years) with CHD.
• CAD severity assessed using Gensini scores derived from coronary angiography.
• Logistic regression identified independent predictors; a nomogram model was constructed and validated using ROC, calibration curves, and decision curve analysis (DCA).

Main Results:

• High-sensitivity C-reactive protein, RBC count, WBC count, BMI, and diabetes mellitus were identified as independent risk factors for severe CAD.
• The developed nomogram model demonstrated good predictive efficiency (ROC AUC 0.846) and excellent predictive agreement.
• Decision curve analysis confirmed the clinical utility of the nomogram for identifying high-risk patients.

Conclusions:

• A personalized risk assessment model for severe CAD in older menopausal women with CHD was successfully developed.
• The model exhibits strong predictive performance, aiding in the screening of high-risk individuals.
• This tool can assist healthcare professionals in implementing targeted interventions for severe CAD in this vulnerable population.