Development and validation of a simple model to predict functionally significant coronary artery disease in Chinese populations: A two-center retrospective study
Objectives: This study sought to derive and validate a simple model combining traditional clinical risk factors with biomarkers and imaging indicators easily obtained from routine preoperative examinations to predict functionally significant coronary artery disease (CAD) in Chinese populations. Meth...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023078519 |
| _version_ | 1797646514877628416 |
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| author | Wen-Qian Shen Guo-Qing Du Xin Duan Yi-Tong Li Shuang Chen Yu-Ming Huang Jun-Qing Yang Li-Wen Li Jing-Yi Xue Jia-Wei Tian |
| author_facet | Wen-Qian Shen Guo-Qing Du Xin Duan Yi-Tong Li Shuang Chen Yu-Ming Huang Jun-Qing Yang Li-Wen Li Jing-Yi Xue Jia-Wei Tian |
| author_sort | Wen-Qian Shen |
| collection | DOAJ |
| description | Objectives: This study sought to derive and validate a simple model combining traditional clinical risk factors with biomarkers and imaging indicators easily obtained from routine preoperative examinations to predict functionally significant coronary artery disease (CAD) in Chinese populations. Methods: We developed five models from a derivation cohort of 320 patients retrospective collected. In the derivation cohort, we assessed each model discrimination using the area under the receiver operating characteristic curve (AUC), reclassification using the integrated discrimination improvement (IDI) and net reclassification improvement (NRI), calibration using the Hosmer-Lemeshow test, and clinical benefit using decision curve analysis (DCA) to derive the optimal model. The optimal model was internally validated by bootstrapping, and external validation was performed in another cohort including 96 patients. Results: The optimal model including 5 predictors (age, sex, hyperlipidemia, hs-cTnI and LVEF) achieved an AUC of 0.807 with positive NRI and IDI in the derivation cohort. Moreover, the Hosmer-Lemeshow test showed a good fit, and the DCA demonstrated good clinical net benefit. The C-statistic calculated by bootstrapping internal validation was 0.798, and the calibration curve showed adequate calibration (Brier score = 0.179). In the external validation cohort, the optimal model performance was acceptable (AUC = 0.704; Brier score = 0.20). Finally, a nomogram based on this model was constructed to facilitate its use in clinical practice. Conclusions: A simple model combined clinical risk factors with hs-cTnI and LVEF improving the prediction of functionally significant CAD in Chinese populations. This attractive model may be a choice for clinicians to risk stratification for CAD. |
| first_indexed | 2024-03-11T15:02:44Z |
| format | Article |
| id | doaj.art-5bf1955cc865491da0976d7e37996cb1 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-11T15:02:44Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-5bf1955cc865491da0976d7e37996cb12023-10-30T06:06:52ZengElsevierHeliyon2405-84402023-10-01910e20643Development and validation of a simple model to predict functionally significant coronary artery disease in Chinese populations: A two-center retrospective studyWen-Qian Shen0Guo-Qing Du1Xin Duan2Yi-Tong Li3Shuang Chen4Yu-Ming Huang5Jun-Qing Yang6Li-Wen Li7Jing-Yi Xue8Jia-Wei Tian9Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Ultrasound Molecular Imaging Joint Laboratory of Heilongjiang Province, Harbin, ChinaDepartment of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Ultrasound Molecular Imaging Joint Laboratory of Heilongjiang Province, Harbin, ChinaDepartment of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Ultrasound Molecular Imaging Joint Laboratory of Heilongjiang Province, Harbin, ChinaDepartment of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Ultrasound Molecular Imaging Joint Laboratory of Heilongjiang Province, Harbin, ChinaDepartment of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University, Guangzhou, ChinaDepartment of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University, Guangzhou, ChinaDepartment of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University, Guangzhou, ChinaDepartment of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University, Guangzhou, China; Corresponding author. Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University, Guangzhou, 510030, China.Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Ultrasound Molecular Imaging Joint Laboratory of Heilongjiang Province, Harbin, China; Corresponding author. Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China.Objectives: This study sought to derive and validate a simple model combining traditional clinical risk factors with biomarkers and imaging indicators easily obtained from routine preoperative examinations to predict functionally significant coronary artery disease (CAD) in Chinese populations. Methods: We developed five models from a derivation cohort of 320 patients retrospective collected. In the derivation cohort, we assessed each model discrimination using the area under the receiver operating characteristic curve (AUC), reclassification using the integrated discrimination improvement (IDI) and net reclassification improvement (NRI), calibration using the Hosmer-Lemeshow test, and clinical benefit using decision curve analysis (DCA) to derive the optimal model. The optimal model was internally validated by bootstrapping, and external validation was performed in another cohort including 96 patients. Results: The optimal model including 5 predictors (age, sex, hyperlipidemia, hs-cTnI and LVEF) achieved an AUC of 0.807 with positive NRI and IDI in the derivation cohort. Moreover, the Hosmer-Lemeshow test showed a good fit, and the DCA demonstrated good clinical net benefit. The C-statistic calculated by bootstrapping internal validation was 0.798, and the calibration curve showed adequate calibration (Brier score = 0.179). In the external validation cohort, the optimal model performance was acceptable (AUC = 0.704; Brier score = 0.20). Finally, a nomogram based on this model was constructed to facilitate its use in clinical practice. Conclusions: A simple model combined clinical risk factors with hs-cTnI and LVEF improving the prediction of functionally significant CAD in Chinese populations. This attractive model may be a choice for clinicians to risk stratification for CAD.http://www.sciencedirect.com/science/article/pii/S2405844023078519Functionally significant coronary artery diseaseQuantitative flow ratioHigh-sensitivity cardiac troponinLeft ventricular ejection fractionPrediction model |
| spellingShingle | Wen-Qian Shen Guo-Qing Du Xin Duan Yi-Tong Li Shuang Chen Yu-Ming Huang Jun-Qing Yang Li-Wen Li Jing-Yi Xue Jia-Wei Tian Development and validation of a simple model to predict functionally significant coronary artery disease in Chinese populations: A two-center retrospective study Heliyon Functionally significant coronary artery disease Quantitative flow ratio High-sensitivity cardiac troponin Left ventricular ejection fraction Prediction model |
| title | Development and validation of a simple model to predict functionally significant coronary artery disease in Chinese populations: A two-center retrospective study |
| title_full | Development and validation of a simple model to predict functionally significant coronary artery disease in Chinese populations: A two-center retrospective study |
| title_fullStr | Development and validation of a simple model to predict functionally significant coronary artery disease in Chinese populations: A two-center retrospective study |
| title_full_unstemmed | Development and validation of a simple model to predict functionally significant coronary artery disease in Chinese populations: A two-center retrospective study |
| title_short | Development and validation of a simple model to predict functionally significant coronary artery disease in Chinese populations: A two-center retrospective study |
| title_sort | development and validation of a simple model to predict functionally significant coronary artery disease in chinese populations a two center retrospective study |
| topic | Functionally significant coronary artery disease Quantitative flow ratio High-sensitivity cardiac troponin Left ventricular ejection fraction Prediction model |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023078519 |
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