ICU Admission Prediction for Patients With Kawasaki Disease or MIS-C Using Machine Learning

Background: Multisystem inflammatory syndrome in children (MIS-C) and Kawasaki disease (KD) show a broad spectrum of clinical severity, from a relatively benign clinical course to requiring admission to the intensive care unit (ICU). With either, clinical deterioration may be rapid and unexpected. Objectives: The aim of the study was to develop a machine learning (ML) model to predict future ICU admission for patients with KD or MIS-C to augment clinical decision-making. Methods: We developed a prediction model for ICU admission using 2,539 patients <18 years of age with MIS-C or KD enrolled in the International Kawasaki Disease Registry. Using discrete time-point clinical features and engineered time-series clinical features, we developed predictive snapshot and window ML models with logistic regression, XGBoost, and random forest. Performance was compared between the various iterations of the models. Results: ML models effectively predicted admission to the ICU within the next 48 hours of the time of prediction. The time-series window-XGBoost model outperformed other models with an AUROC of 0.92 and an area under the precision-recall curve of 0.86. The incorporation of engineered time-series features improved the precision and recall independent of the length of the sampling time window. Higher ferritin level, treatment with anticoagulant or unfractionated heparin, higher C-reactive protein level, and lower platelet count were identified as the most predictive features for positive ICU prediction. Conclusions: ML algorithms can effectively predict ICU admission for pediatric patients with MIS-C or KD. These models may prompt physicians to pre-emptively implement supportive measures, possibly mitigating the risk of clinical deterioration.