A Diagnostic Prediction Model to Distinguish Multisystem Inflammatory Syndrome in Children.

ABSTRACT:

Objective

Features of multisystem inflammatory syndrome in children (MIS-C) overlap with other syndromes, making the diagnosis difficult for clinicians. We aimed to compare clinical differences between patients with and without clinical MIS-C diagnosis and develop a diagnostic prediction model to assist clinicians in identification of patients with MIS-C within the first 24 hours of hospital presentation.

Methods

A cohort of 127 patients (<21 years) were admitted to an academic children's hospital and evaluated for MIS-C. The primary outcome measure was MIS-C diagnosis at Vanderbilt University Medical Center. Clinical, laboratory, and cardiac features were extracted from the medical record, compared among groups, and selected a priori to identify candidate predictors. Final

SUBMITTER: Clark MT 

PROVIDER: S-EPMC9746665 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature