Development and Validation of Multivariable Machine-Learning Models for the Prediction of Multisystemic Inflammatory Syndrome Outcomes in Latin American Children.

ABSTRACT:

Aim

We aimed to develop and test machine learning algorithms for the prediction of severe outcomes associated with MIS-C.

Method

An observational ambispective cohort study was conducted including children aged from 1 month to 18 years old in 84 hospitals from the REKAMLATINA (Red de la Enfermedad de Kawasaki en America Latina) network diagnosed with MIS-C from 1st January 2020 to 31st June 2022. Multiple models were developed to predict four main outcomes: paediatric intensive care unit (PICU) admission, need for inotropes, need for mechanical ventilation, and death. Performance measures were accuracy for PICU admission, inotropes use and mechanical ventilation, and the area under the receiver operating characteristic curve (AUROC) for death. Variable contribution was analys

SUBMITTER: Buonsenso D 

PROVIDER: S-EPMC12687682 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature