Prediction of intravenous immunoglobulin retreatment in children with Kawasaki disease using models combining lymphocyte subset and cytokine profile in an East Asian cohort

Objectives: For children with Kawasaki disease (KD) at high risk of developing coronary artery lesions and requiring retreatment with intravenous immunoglobulin (IVIG), the availability of accurate prediction models remains limited because of inconsistent variables and unsatisfactory prediction results. We aimed to construct models to predict patient's probability of IVIG retreatment combining children's individual inflammatory characteristics.

Methods: Clinical manifestations and laboratory examinations of 266 children with KD were retrospectively analysed to build a development cohort data set (DC) and a validation cohort data set (VC). In the DC, binary logistic regression analyses were performed using R language. Nomograms and receiver operating curves were plotted. The concordance index (C index), net reclassification index, integrated discrimination improvement index and confusion matrix were applied to evaluate and validate the models.

Results: Models_5V and _9V were established. Both contained variables including the percentages of CD8⁺ T cells, CD4⁺ T cells, CD3⁺ T cells, levels of interleukin (IL)-2R and CRP. Model_9V additionally included variables for IL-6, TNF-α, NT-proBNP and sex, with a C index of 0.86 (95% CI 0.79-0.92). When model_9V was compared with model_5V, the NRI and IDI were 0.15 (95% CI 0.01-0.30, P < 0.01) and 0.07 (95% CI 0.02-0.12, P < 0.01). In the VC, the sensitivity, specificity and precision of model_9V were 1, 0.875 and 0.667, while those of model_5V were 0.833, 0.875 and 0.625.

Conclusion: Model_9V combined cytokine profiles and lymphocyte subsets with clinical characteristics and was superior to model_5V achieving satisfactory predictive power and providing a novel strategy early to identify patients who needed IVIG retreatment.