A simple scoring model based on machine learning predicts intravenous immunoglobulin resistance in Kawasaki disease.

ABSTRACT:

Introduction

In Kawasaki disease (KD), accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial to reduce a risk for developing coronary artery lesions.

Objective

To establish a simple scoring model predicting IVIG resistance in KD patients based on the machine learning model.

Methods

A retrospective cohort study of 1002 KD patients diagnosed at 12 facilities for 10 years, in which 22.7% were resistant to initial IVIG treatment. We performed machine learning with diverse models using 30 clinical variables at diagnosis in 801 and 201 cases for training and test datasets, respectively. SHAP was applied to identify the variables that influenced the prediction model. A scoring model was designed using the influential clinical variables based on the S

SUBMITTER: Sunaga Y 

PROVIDER: S-EPMC9832252 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature