ReviewJournal of translational medicine2025
Development and validation of a novel coronary artery disease risk prediction model.
Review in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- The Prognostic Ability of ECG Findings in Predicting Cardiovascular Events: A Five-Year Nested Case-Cohort Study in an Iranian Population (Shiraz Heart Study).Health science reports · 2026Article
- Global Myocardial Work-Derived Nomogram for Coronary Stenosis Assessment in Stable Coronary Artery Disease: Development and External Validation.Diagnostics (Basel, Switzerland) · 2026Article
- Early metoprolol use in ICU patients with congestive heart failure is associated with increased 30-day mortality: a causal machine learning study.Frontiers in pharmacology · 2026Article
- Translating the effect of dual lipid reduction with PCSK9 inhibitors on a mechanical vascular instrumental biomarker in familial hypercholesterolemia subjects.Journal of translational medicine · 2025Observational
- Integrative Bulk and Single-Cell Transcriptome Analyses Reveal Mitochondrial Metabolism-Related Biomarkers in IgA Nephropathy with Experimental Validation.Journal of inflammation research · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectiveThis study aims to develop a novel risk assessment tool for coronary artery disease (CAD) based on data of patients with chest pain in outpatient and emergency department, thereby facilitating the effective identification and management of high-risk patients.
methodsA retrospective analysis was conducted on patients hospitalized for chest pain. Patients were divided into a control group and a CAD group based on angiographic results. Logistic regression was used to identify factors associated with CAD, and R-Studio was utilized to construct the CAD risk prediction model.
resultsMultivariate logistic regression analysis indicated that age, gender, diabetes, ECG (electrocardiogram) ST-T changes, neutrophils (NE), coronary artery calcification (CAC), and typical chest pain were independent factors associated with CAD. Based on the results of multifactorial logistic analysis, the CAD risk prediction model built with R-Studio had a highest C-index of 0.909, and a validation cohort C-index of 0.897, demonstrating excellent predictive ability. Decision Curve Analysis showed that the model significantly outperformed others in terms of clinical net benefit.
conclusionThe present study successfully developed a CAD risk assessment model based on Chinese population. This novel model could be used to assess CAD risk in patients with chest pain, optimize clinical decision making, and improve patient outcomes, regardless of whether it is applied in large hospitals or resource-limited Community Healthcare Center.
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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.