ArticleScientific reports2025
Construction of a prediction model for coronary heart disease in type 2 diabetes mellitus: a cross-sectional study.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Enhanced prediction of coronary heart disease risk in diabetic patients via Machine learning incorporating multiple inflammatory and metabolic indices: A study with Dual-Cohort validation.International journal of cardiology. Heart & vasculature · 2026Article
- Prognostic Models for Predicting Coronary Heart Disease Risk in Patients with Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Development and validation of a deep neural network for predicting coronary heart disease in hypertensive patients using 24-hour ambulatory blood pressure monitoring: a retrospective study.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.