Development and validation of a nomogram-based prognostic model to predict coronary artery lesions in Kawasaki disease from 6847 children in China

Abstract

Background and Objective

Predicting potential risk factors for the occurrence of coronary artery lesions (CAL) in children with Kawasaki disease (KD) is critical for subsequent treatment. The aim of our study was to establish and validate a nomograph-based model for identifying children with KD at risk for CAL.

Methods

Hospitalized children with KD attending Wuhan Children's Hospital from Jan 2011 to Dec 2023 were included in the study and were grouped into a training set (4793 cases) and a validation set (2054 cases) using a simple random sampling method in a 7:3 ratio. The analysis was performed using RStudio software, which first used LASSO regression analysis to screen for the best predictors, and then analyzed the screened predictors using logistic regression analysis to derive independent predictors and construct a nomogram model to predict CAL risk. The receiver operating characteristic (ROC) and calibration curves were employed to evaluate the discrimination and calibration of the model. Finally, decision curve analysis (DCA) was utilized to validate the clinical applicability of the models assessed in the data.

Results

Of the 6847 eligible children with KD included, 845 (12 %) were ultimately diagnosed with CAL, of whom 619 were boys (73 %) with a median age of 1.81 (0.74, 3.51) years. Six significant independent predictors were identified, including sex, intravenous immunoglobulin nonresponse, peripheral blood hemoglobin, platelet distribution width, platelet count, and serum albumin. Our model has acceptable discriminative power, with areas under the curve at 0.671 and 0.703 in the training and validation sets, respectively. DCA analysis showed that the prediction model had great clinical utility when the threshold probability interval was between 0.1 and 0.5.

Conclusions

We constructed and internally validated a nomograph-based predictive model based on six variables consisting of sex, intravenous immunoglobulin nonresponse, peripheral blood hemoglobin, platelet distribution width, platelet count, and serum albumin, which may be useful for earlier identification of children with KD who may have CAL.