Prediction of Coronary Artery Lesions in Patients With Recurrent Kawasaki Disease

Background: A subset of patients with Kawasaki disease (KD) will suffer recurrence. However, there is still a lack of accurate prediction models for coronary artery lesions (CAL) in recurrent KD patients. It is necessary to establish a new nomogram model for predicting CAL in patients with recurrent KD. Methods: Data from patients with recurrent KD between 2015 and 2021 were retrospectively reviewed. After splitting the patients into training and validation cohorts, the least absolute shrinkage and selection operator was used to select the predictors of CAL and multivariate logistic regression was used to construct a nomogram based on the selected predictors. The application of area under the receiver operating characteristic curve (AUC), calibration curves, Hosmer–Lemeshow test, Brier score and decision curve analysis were used to assess the model performance. Results: A total of 159 recurrent KD patients were enrolled, 66 (41.5%) of whom had CAL. Hemoglobin levels, CAL at the first episode, and intravenous immunoglobulin resistance at recurrence were identified by the least absolute shrinkage and selection operator regression analysis as significant predictors. The model incorporating these predictors showed good discrimination (AUC, 0.777) and calibration capacities (Hosmer–Lemeshow P value, 0.418; Brier score, 0.190) in the training cohort. Application of the model to the validation cohort yielded an AUC of 0.741, a Hosmer–Lemeshow P value of 0.623 and a Brier score of 0.190. The decision curve analysis demonstrated that the nomogram model was clinically useful. Conclusions: The proposed nomogram model could help clinicians assess the risk of CAL in patients with recurrent KD.