ArticleRisk management and healthcare policy2024
Development of a Nomogram That Predicts the Risk of Atrial Fibrillation in Patients with Coronary Heart Disease.
Article in Risk management and healthcare policy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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7 authors.
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Abstract
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.
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