Development of a Predictive Model for Cardiac Dysfunction in MIS-C Patients Utilizing Laboratory Biomarkers

Authors

• Guliz Erdem
• Brendan Galdo
• Roshini S Abraham
• Allayne Stephans
• Simon Lee
• Jun Yasuhara
• Brent Merryman
• Diego Cruz Vidal
• Nathan M Money
• Jennifer Colgan
• Risa Bochner
• Ron L Kaplan
• Erin Aldag
• Thomas Graf
• Steve Rust

Language

English

Publication Date

2-1-2026

Journal

Children

DOI

10.3390/children13020216

PMID

41749572

PMCID

PMC12940013

PubMedCentral® Posted Date

2-1-2026

PubMedCentral® Full Text Version

Post-print

Abstract

Background and objectives: Early identification of cardiac dysfunction in multi-system inflammatory syndrome in children (MIS-C) is crucial for effective management. Our primary objective was to predict left ventricular systolic dysfunction (LVSD) through a multicenter collaborative assessing admission laboratory data and echocardiogram findings.

Methods: Laboratory and clinical data were collected by retrospective chart review from a cohort of pediatric patients admitted and treated for MIS-C in our institutions. Laboratory data including absolute lymphocyte count, albumin, sedimentation rate, C-reactive protein, procalcitonin, d-dimer, fibrinogen, ferritin, interleukin-6 level, and lymphocyte subsets (T, B and NK quantitation, TBNK) were collected. We built a LASSO logistic regression model to predict which MIS-C patients would have left ventricular systolic dysfunction LVSD using only laboratory data obtained within the first 24 h of admission.

Results: Of the 1474 MIS-C patients evaluated, 297 had LVSD. The linear kinetic analysis found differences in albumin, lymphocyte count, C-reactive proteins and fibrinogen for systolic dysfunction patients, and of these C-reactive proteins, fibrinogen and procalcitonin were more predictive earlier. The best model for coronary artery abnormalities (CAAs) performed poorly, with a mean cross-validated AUC of 0.57. The model performed well with a cross-validated AUC of 0.845.

Conclusions: This model identified widely available biomarkers to successfully predict systolic dysfunction in MIS-C patients. Those at high risk of systolic dysfunction had higher peak laboratory values for C-reactive protein, fibrinogen, and procalcitonin early on. A regularized logistic regression model was validated to provide excellent discrimination for LVSD.

Keywords

MIS-C, Kawasaki disease, left ventricular systolic dysfunction, data analytics

Published Open-Access

yes

Recommended Citation

Erdem, Guliz; Galdo, Brendan; Abraham, Roshini S; et al., "Development of a Predictive Model for Cardiac Dysfunction in MIS-C Patients Utilizing Laboratory Biomarkers" (2026). Faculty, Staff and Student Publications. 4151.
https://digitalcommons.library.tmc.edu/uthmed_docs/4151