ArticleBMC cardiovascular disorders2025
A multimodal nomogram for predicting disease progression in diabetic patients with coronary artery disease: integrating clinical, ultrasound, and angiographic data.
Article in BMC cardiovascular disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveThe 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.
methodsWe 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).
resultsMultivariate 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.
conclusionWe 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.
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