A Nomogram Model for Predicting Recurrent Coronary Thrombosis in Kawasaki Disease Patients

用于预测川崎病患者冠状动脉血栓复发的列线图模型

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Abstract

BACKGROUND: Coronary thrombosis is a serious cardiovascular complication of Kawasaki disease (KD), and recurrence of coronary thrombosis increases the short-term risk of myocardial infarction and the long-term risk of coronary artery disease. However, there are currently no studies predicting the recurrence of coronary thrombosis, so the aim of this study was to develop and validate a nomogram to predict recurrent coronary thrombosis in KD patients. METHODS: This was a retrospective study of data from 149 KD patients who had a history of previous coronary disease at the Children's Hospital of Chongqing Medical University from 2013 to 2020. Independent risk factors were identified using univariate and multivariate logistic regression analyses, and a nomogram was constructed to predict recurrent coronary thrombosis. RESULTS: Multivariate analysis showed that large coronary artery aneurysm(CAA) (Odds Ratio [OR] 4.28; 95% Confidence Interval [CI] 1.39-13.12), saccular CAA (OR 5.03; 95% CI 1.55-16.29), first left anterior descending (LAD) thrombosis (OR 3.90; 95% CI 1.20-12.63), and persistent CAA (OR 43.27; 95% CI 12.23-153.12) were independent risk factors for recurrent coronary thrombosis. Based on these variables, a nomogram was constructed. The Area Under the Curve (AUC) of the nomogram was 0.943, and tenfold cross-validation (200 replicates) showed an average AUC of 0.929. Furthermore, the nomogram not only presented a favorable calibration curve but also demonstrated practical clinical utility. CONCLUSION: Large CAA, saccular CAA, first LAD thrombosis and persistent CAA were independent risk factors for recurrent coronary thrombosis. The nomogram can visually show these independent risk factors and predict probabilities.

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