Prediction nomogram for coronary artery aneurysms at one month in Kawasaki disease.

BACKGROUND: Coronary status at one month after Kawasaki disease (KD) onset had a great significance. The present study aimed to establish a prediction model for coronary artery aneurysms (CAA) at one month in children with KD. METHODS: Patients with a diagnosis of KD between May 2017 and Dec 2018 were enrolled as the development cohort to build a prediction model. The model was validated by internal and external validation. Patients between Jan 2019 and Dec 2019 were enrolled as the validation cohort. The adaptive least absolute shrinkage and selection operator (LASSO) was used to select the possible predictors. Receiving operating characteristic curve (ROC), calibration plots, and decision curve analysis (DCA) were used to evaluate the performance of the model. The performance of the Son score was also assessed. RESULTS: LASSO regression demonstrated that age, sex, and CALs in the acute stage were predictors for CAA at one month. The area under the ROC (AUC) was 0.946 (95% confidence interval: 0.911-0.980) with a sensitivity of 92.5% and a specificity of 90.5%. The calibration curve and the DCA showed a favorable diagnostic performance. The internal and external validation proved the reliability of the prediction model. The AUC of our model and the Son score were 0.941 and 0.860, respectively (P < 0.001). CONCLUSION: Our prediction model for CAA at one month after disease onset in KD had an excellent predictive utility.

A novel model for predicting intravenous immunoglobulin-resistance in Kawasaki disease: a large cohort study.

Background: Predicting intravenous immunoglobulin (IVIG)-resistant Kawasaki disease (KD) can aid early treatment and prevent coronary artery lesions. A clinically consistent predictive model was developed for IVIG resistance in KD. Methods: In this retrospective cohort study of children diagnosed with KD from January 1, 2016 to December 31, 2021, a scoring system was constructed. A prospective model validation was performed using the dataset of children with KD diagnosed from January 1 to June 2022. The least absolute shrinkage and selection operator (LASSO) regression analysis optimally selected baseline variables. Multivariate logistic regression incorporated predictors from the LASSO regression analysis to construct the model. Using selected variables, a nomogram was developed. The calibration plot, area under the receiver operating characteristic curve (AUC), and clinical impact curve (CIC) were used to evaluate model performance. Results: Of 1975, 1,259 children (1,177 IVIG-sensitive and 82 IVIG-resistant KD) were included in the training set. Lymphocyte percentage; C-reactive protein/albumin ratio (CAR); and aspartate aminotransferase, sodium, and total bilirubin levels, were risk factors for IVIG resistance. The training set AUC was 0.825 (sensitivity, 0.723; specificity, 0.744). CIC indicated good clinical application of the nomogram. Conclusion: The nomogram can well predict IVIG resistance in KD. CAR was an important marker in predicting IVIG resistance in Kawasaki disease.