Abstract
Objective: We explored the risk factors for intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) and constructed a prediction model based on machine learning algorithms.Methods: A retrospective study including 1,398 KD patients hospitalized in 7 affiliated hospitals of Chongqing Medical University from January 2015 to August 2020 was conducted. All patients were divided into IVIG-responsive and IVIG-resistant groups, which were randomly divided into training and validation sets. The independent risk factors were determined using logistic regression analysis. Logistic regression nomograms, support vector machine (SVM), XGBoost and LightGBM prediction models were constructed and compared with the previous models.Results: In total, 1,240 out of 1,398 patients were IVIG responders, while 158 were resistant to IVIG. According to the results of logistic regression analysis of the training set, four independent risk factors were identified, including total bilirubin (TBIL) (OR = 1.115, 95% CI 1.067–1.165), procalcitonin (PCT) (OR = 1.511, 95% CI 1.270–1.798), alanine aminotransferase (ALT) (OR = 1.013, 95% CI 1.008–1.018) and platelet count (PLT) (OR = 0.998, 95% CI 0.996–1). Logistic regression nomogram, SVM, XGBoost, and LightGBM prediction models were constructed based on the above independent risk factors. The sensitivity was 0.617, 0.681, 0.638, and 0.702, the specificity was 0.712, 0.841, 0.967, and 0.903, and the area under curve (AUC) was 0.731, 0.814, 0.804, and 0.874, respectively. Among the prediction models, the LightGBM model displayed the best ability for comprehensive prediction, with an AUC of 0.874, which surpassed the previous classic models of Egami (AUC = 0.581), Kobayashi (AUC = 0.524), Sano (AUC = 0.519), Fu (AUC = 0.578), and Formosa (AUC = 0.575).Conclusion: The machine learning LightGBM prediction model for IVIG-resistant KD patients was superior to previous models. Our findings may help to accomplish early identification of the risk of IVIG resistance and improve their outcomes.
Highlights
Kawasaki disease (KD) is an acute vasculitis disease with bilateral conjunctival inflammation and atypical rash as the main clinical features
The main complication of KD patients is coronary artery lesions (CALs), which are the main reason for the increase in the incidence of acquired heart disease in children [2]
A total of 1,398 KD patients were included in this study
Summary
Kawasaki disease (KD) is an acute vasculitis disease with bilateral conjunctival inflammation and atypical rash as the main clinical features. It mainly occurs in infants under 5 years of age [1]. It is of great significance to accurately detect IVIG-resistant KD patients and implement appropriate regimens early. The abovementioned scoring systems have performed well in their respective research populations, due to the existence of genetic susceptibility, the prediction performance of these systems in Chongqing city is not good (Table 1) [11, 12], which precludes wide application in the early prediction of IVIG resistance in Chongqing. It remains a challenge to develop a new prediction model with better predictive performance for children in Chongqing city, one of the largest cities in western China
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