Development and validation of a novel coronary artery disease risk prediction model
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 retrospecti...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | https://doi.org/10.1186/s12967-024-05789-1 |
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| author | Zu-Fei Wu Si-Xiao Tao Wen-Tao Su Shi Chen Bai-Da Xu Gang-Jun Zong Gang-Yong Wu |
| author_facet | Zu-Fei Wu Si-Xiao Tao Wen-Tao Su Shi Chen Bai-Da Xu Gang-Jun Zong Gang-Yong Wu |
| author_sort | Zu-Fei Wu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2703cc15fb3c4a87b479a7dd2c04d54a |
| institution | Kabale University |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-2703cc15fb3c4a87b479a7dd2c04d54a2025-01-12T12:37:31ZengBMCJournal of Translational Medicine1479-58762025-01-0123111310.1186/s12967-024-05789-1Development and validation of a novel coronary artery disease risk prediction modelZu-Fei Wu0Si-Xiao Tao1Wen-Tao Su2Shi Chen3Bai-Da Xu4Gang-Jun Zong5Gang-Yong Wu6Department of Cardiology, Xuancheng People’s HospitalDepartment of Cardiology, Xuancheng People’s HospitalDepartment of Cardiology, The 904th Hospital of the PLA Joint Logistics Support ForceDepartment of Cardiology, Wuxi No.5 People’s HospitalDepartment of Cardiology, The 904th Hospital of the PLA Joint Logistics Support ForceDepartment of Cardiology, The 904th Hospital of the PLA Joint Logistics Support ForceDepartment of Cardiology, The 904th Hospital of the PLA Joint Logistics Support ForceAbstract 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.https://doi.org/10.1186/s12967-024-05789-1Coronary artery diseaseRisk assessmentInflammation |
| spellingShingle | Zu-Fei Wu Si-Xiao Tao Wen-Tao Su Shi Chen Bai-Da Xu Gang-Jun Zong Gang-Yong Wu Development and validation of a novel coronary artery disease risk prediction model Journal of Translational Medicine Coronary artery disease Risk assessment Inflammation |
| title | Development and validation of a novel coronary artery disease risk prediction model |
| title_full | Development and validation of a novel coronary artery disease risk prediction model |
| title_fullStr | Development and validation of a novel coronary artery disease risk prediction model |
| title_full_unstemmed | Development and validation of a novel coronary artery disease risk prediction model |
| title_short | Development and validation of a novel coronary artery disease risk prediction model |
| title_sort | development and validation of a novel coronary artery disease risk prediction model |
| topic | Coronary artery disease Risk assessment Inflammation |
| url | https://doi.org/10.1186/s12967-024-05789-1 |
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