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

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 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

SUBMITTER: Woo J 

PROVIDER: S-EPMC11994043 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature