Document Type

Article

Publication Date

3-2025

Identifier

DOI: 10.1016/j.jacadv.2025.101621

Abstract

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 patientsfeatures, 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.

Journal Title

JACC Adv

Volume

4

Issue

3

First Page

101621

Last Page

101621

PubMed ID

40147056

Keywords

Kawasaki disease; clinical deterioration; machine learning; multisystem inflammatory syndrome in children (MIS-C); risk prediction

Comments

This article is available under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed.

Publisher's Link: https://www.sciencedirect.com/science/article/pii/S2772963X25000389?via%3Dihub

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