{"title":"开发可预测冠心病患者心房颤动风险的提名图","authors":"Xinfu Cao, Yi Sun, Yuqiao Chen, Chao Tang, Hongwen Yu, Xiaolong Li, Zhenhua Gu","doi":"10.2147/rmhp.s466205","DOIUrl":null,"url":null,"abstract":"<strong>Objective:</strong> To explore the risk factors of atrial fibrillation (AF) in patients with coronary heart disease (CHD), and to construct a risk prediction model.<br/><strong>Methods:</strong> The participants in this case-control study were from the cardiovascular Department of Changzhou Affiliated Hospital of Nanjing University of Chinese Medicine from June 2016 to June 2023, and they were divided into AF group and non-AF group according to whether AF occurred during hospitalization. The clinical data of the two groups were compared by retrospective analysis. Multivariate Logistic regression analysis was used to investigate the risk factors of AF occurrence in CHD patients. The nomogram model was constructed with R 4.2.6 language “rms” package, and the model’s differentiation, calibration and effectiveness were evaluated by drawing ROC curve, calibration curve and decision curve.<br/><strong>Results:</strong> A total of 1258 patients with CHD were included, and they were divided into AF group (n=92) and non-AF group (n=1166) according to whether AF was complicated. Logistic regression analysis showed that age, coronary multiple branch lesion, history of heart failure, history of drinking, pulmonary hypertension, left atrial diameter, left ventricular end-diastolic diameter and diabetes mellitus were independent risk factors for the occurrence of AF in CHD patients (P < 0.05). The ROC curve showed that the AUC of this model was 0.956 (95% CI (0.916, 0.995)) and the consistency index was 0.966. The calibration curve of the model is close to the ideal curve. The analysis of decision curve shows that the prediction value of the model is better when the probability threshold of the model is 0.042~0.963.<br/><strong>Conclusion:</strong> The nomogram model established in this study for predicting the risk of AF in patients with CHD has better predictive performance and has certain reference value for clinical identification of high-risk groups prone to AF in patients with CHD.<br/><br/><strong>keywords:</strong> coronary heart disease, atrial fibrillation, risk factors, logistic regression analysis, nomogram model<br/>","PeriodicalId":56009,"journal":{"name":"Risk Management and Healthcare Policy","volume":"1 1","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of a Nomogram That Predicts the Risk of Atrial Fibrillation in Patients with Coronary Heart Disease\",\"authors\":\"Xinfu Cao, Yi Sun, Yuqiao Chen, Chao Tang, Hongwen Yu, Xiaolong Li, Zhenhua Gu\",\"doi\":\"10.2147/rmhp.s466205\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<strong>Objective:</strong> To explore the risk factors of atrial fibrillation (AF) in patients with coronary heart disease (CHD), and to construct a risk prediction model.<br/><strong>Methods:</strong> The participants in this case-control study were from the cardiovascular Department of Changzhou Affiliated Hospital of Nanjing University of Chinese Medicine from June 2016 to June 2023, and they were divided into AF group and non-AF group according to whether AF occurred during hospitalization. The clinical data of the two groups were compared by retrospective analysis. Multivariate Logistic regression analysis was used to investigate the risk factors of AF occurrence in CHD patients. The nomogram model was constructed with R 4.2.6 language “rms” package, and the model’s differentiation, calibration and effectiveness were evaluated by drawing ROC curve, calibration curve and decision curve.<br/><strong>Results:</strong> A total of 1258 patients with CHD were included, and they were divided into AF group (n=92) and non-AF group (n=1166) according to whether AF was complicated. Logistic regression analysis showed that age, coronary multiple branch lesion, history of heart failure, history of drinking, pulmonary hypertension, left atrial diameter, left ventricular end-diastolic diameter and diabetes mellitus were independent risk factors for the occurrence of AF in CHD patients (P < 0.05). The ROC curve showed that the AUC of this model was 0.956 (95% CI (0.916, 0.995)) and the consistency index was 0.966. The calibration curve of the model is close to the ideal curve. The analysis of decision curve shows that the prediction value of the model is better when the probability threshold of the model is 0.042~0.963.<br/><strong>Conclusion:</strong> The nomogram model established in this study for predicting the risk of AF in patients with CHD has better predictive performance and has certain reference value for clinical identification of high-risk groups prone to AF in patients with CHD.<br/><br/><strong>keywords:</strong> coronary heart disease, atrial fibrillation, risk factors, logistic regression analysis, nomogram model<br/>\",\"PeriodicalId\":56009,\"journal\":{\"name\":\"Risk Management and Healthcare Policy\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Risk Management and Healthcare Policy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/rmhp.s466205\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Risk Management and Healthcare Policy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/rmhp.s466205","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Development of a Nomogram That Predicts the Risk of Atrial Fibrillation in Patients with Coronary Heart Disease
Objective: To explore the risk factors of atrial fibrillation (AF) in patients with coronary heart disease (CHD), and to construct a risk prediction model. Methods: The participants in this case-control study were from the cardiovascular Department of Changzhou Affiliated Hospital of Nanjing University of Chinese Medicine from June 2016 to June 2023, and they were divided into AF group and non-AF group according to whether AF occurred during hospitalization. The clinical data of the two groups were compared by retrospective analysis. Multivariate Logistic regression analysis was used to investigate the risk factors of AF occurrence in CHD patients. The nomogram model was constructed with R 4.2.6 language “rms” package, and the model’s differentiation, calibration and effectiveness were evaluated by drawing ROC curve, calibration curve and decision curve. Results: A total of 1258 patients with CHD were included, and they were divided into AF group (n=92) and non-AF group (n=1166) according to whether AF was complicated. Logistic regression analysis showed that age, coronary multiple branch lesion, history of heart failure, history of drinking, pulmonary hypertension, left atrial diameter, left ventricular end-diastolic diameter and diabetes mellitus were independent risk factors for the occurrence of AF in CHD patients (P < 0.05). The ROC curve showed that the AUC of this model was 0.956 (95% CI (0.916, 0.995)) and the consistency index was 0.966. The calibration curve of the model is close to the ideal curve. The analysis of decision curve shows that the prediction value of the model is better when the probability threshold of the model is 0.042~0.963. Conclusion: The nomogram model established in this study for predicting the risk of AF in patients with CHD has better predictive performance and has certain reference value for clinical identification of high-risk groups prone to AF in patients with CHD.
期刊介绍：
Risk Management and Healthcare Policy is an international, peer-reviewed, open access journal focusing on all aspects of public health, policy and preventative measures to promote good health and improve morbidity and mortality in the population. Specific topics covered in the journal include:
Public and community health
Policy and law
Preventative and predictive healthcare
Risk and hazard management
Epidemiology, detection and screening
Lifestyle and diet modification
Vaccination and disease transmission/modification programs
Health and safety and occupational health
Healthcare services provision
Health literacy and education
Advertising and promotion of health issues
Health economic evaluations and resource management
Risk Management and Healthcare Policy focuses on human interventional and observational research. The journal welcomes submitted papers covering original research, clinical and epidemiological studies, reviews and evaluations, guidelines, expert opinion and commentary, and extended reports. Case reports will only be considered if they make a valuable and original contribution to the literature. The journal does not accept study protocols, animal-based or cell line-based studies.
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