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

Area of Science:

• Pediatric Cardiology
• Pediatric Critical Care
• Immunology

Background:

• Multi-system inflammatory syndrome in children (MIS-C) can lead to cardiac dysfunction.
• Early identification of left ventricular systolic dysfunction (LVSD) is critical for managing MIS-C.

Purpose of the Study:

• To predict LVSD in MIS-C patients using admission laboratory data.
• To assess the efficacy of a predictive model for LVSD in MIS-C.

Main Methods:

• Retrospective chart review of 1474 MIS-C patients.
• LASSO logistic regression model developed using laboratory data within 24 hours of admission.
• Analysis included lymphocyte count, albumin, C-reactive protein, fibrinogen, and procalcitonin.

Main Results:

• 297 out of 1474 MIS-C patients had LVSD.
• C-reactive protein, fibrinogen, and procalcitonin were identified as early predictive biomarkers.
• The predictive model achieved a cross-validated AUC of 0.845 for LVSD.

Conclusions:

• Widely available biomarkers can effectively predict systolic dysfunction in MIS-C.
• Elevated C-reactive protein, fibrinogen, and procalcitonin levels indicate high risk for LVSD.
• A validated logistic regression model demonstrates excellent discrimination for LVSD in MIS-C.