OBJECTIVE

The long-term prognosis of diabetic patients with coronary artery disease (CAD) is influenced by various clinical variables and biomarkers. This study aimed to develop and validate a prognostic model that integrates clinical, echocardiographic, and angiographic data to predict disease progression.

METHODS

We retrospectively analyzed 396 diabetic CAD patients with a 3-year follow-up starting from their first coronary angiography. Outcome variables included recurrent myocardial infarction, unstable angina rehospitalization, heart failure, ischemic stroke, cardiovascular death, and all-cause death. Non-progression was defined as the absence of these events. Variables included clinical data, echocardiographic parameters, coronary angiography results, and biomarkers. A multivariate Cox regression model was developed, incorporating key factors (coronary lesion number, myocardial infarction history, ejection fraction, and creatinine).

RESULTS

Multivariate analysis identified the number of obstructed coronary arteries, history of myocardial infarction, ejection fraction, and creatinine level as independent predictors of disease progression. The model showed good predictive performance, with AUC values of 0.742, 0.782, and 0.816 at 3, 6, and 9 months, respectively. The C-index was 0.669 (95% CI: 0.5959-0.7196) in the training set and 0.695 (95% CI: 0.5781-0.7436) in the validation set, reflecting consistent predictive performance. Calibration curves showed excellent agreement between predicted and observed outcomes.

CONCLUSION

We developed and validated a practical nomogram integrating clinical, biochemical, and imaging data to predict short-term disease progression in diabetic patients with CAD. This tool may assist clinicians in early risk stratification and individualized management planning.