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February 27, 2025Scientific Reports5 citationsOpen Access

Construction of a prediction model for coronary heart disease in type 2 diabetes mellitus: a cross-sectional study

HZHuiling ZhangHSHui Shi

Structured PICO

P
Population
423 hospitalized patients with type 2 diabetes mellitus (T2DM), including 193 with coronary heart disease (CHD) and 230 without CHD, from a tertiary hospital in Anhui Province, China, between February 1, 2023, and February 1, 2024.
O
Outcome
Presence of coronary heart disease (CHD) for the development and validation of a diagnostic prediction model

A novel nomogram incorporating eight clinical and metabolic risk factors can effectively predict the risk of coronary heart disease in patients with type 2 diabetes mellitus.

Limitations

  • Sample size limitations
  • Potential collinearity between variables
  • Single-center data

Abstract

Type 2 diabetes mellitus (T2DM), as a globally prevalent metabolic disorder, is continuously rising in prevalence and significantly increases the risk of developing coronary heart disease (CHD). Studies have shown that the risk of CHD is higher in T2DM patients compared to those without diabetes, making early identification and prevention essential. Therefore, establishing an effective prediction model to identify high-risk individuals for CHD among T2DM patients is crucial. This study aims to develop and validate a prediction model for coronary heart disease in patients with type 2 diabetes mellitus, accurately identifying high-risk individuals to support early intervention and personalized treatment. The study included 423 patients with type 2 diabetes mellitus (T2DM) who were hospitalized in the endocrinology department of a tertiary hospital in Anhui Province between February 1, 2023, and February 1, 2024. Based on the presence of hypertension, patients were divided into a T2DM with coronary heart disease (CHD) group (193 patients) and a T2DM group (230 patients). Data were collected through questionnaires and clinical indicators. Univariate and multivariate logistic regression analyses were used to identify significant predictors, and the model was validated. Model performance was evaluated using the ROC curve and AUC value. Hypertension, smoking, neuropathy, vascular complications, cerebral infarction, bilateral lower extremity arteriosclerosis, microalbuminuria, and elevated uric acid levels. were identified as significant predictors for T2DM with hypertension. The AUC of the prediction model was 0.83, indicating good predictive performance. The prediction model developed in this study effectively identifies high-risk patients with T2DM and CHD, providing a reliable tool for clinical use. This model facilitates early intervention and personalized treatment for hypertension, smoking, neuropathy, vascular complications, cerebral infarction, bilateral lower extremity arteriosclerosis, microalbuminuria, and elevated uric acid levels, improving overall health outcomes for patient.

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Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/6a70d5b175498292b70a4e45https://doi.org/10.1038/s41598-025-85692-x
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