January 10, 2025Journal of Translational MedicineOpen Access

Development and validation of a novel coronary artery disease risk prediction model

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Why the study?

The study aimed to develop a novel CAD risk assessment tool using data from outpatient and emergency department chest pain patients to facilitate effective identification and management.

Does a novel CAD risk prediction model accurately predict CAD risk in Chinese patients with chest pain?

Structured PICO

Does a novel CAD risk prediction model accurately predict CAD risk in Chinese patients with chest pain?

Population

Chinese patients hospitalized for chest pain.

Intervention

Novel CAD risk prediction model incorporating age, gender, diabetes, ECG ST-T changes, neutrophils, coronary artery calcification, and typical chest pain.

Comparator

Other risk prediction models.

Outcome

Predictive ability for CAD (measured by C-index and Decision Curve Analysis).

A novel risk prediction model incorporating clinical, ECG, and laboratory parameters demonstrated excellent predictive ability for CAD in Chinese patients presenting with chest pain.

Implication

May aid CAD identification in chest pain; leaves open prospective validation before clinical use.

Abstract

OBJECTIVE: This 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. METHODS: A 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. RESULTS: Multivariate 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. CONCLUSION: The 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.