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Differentiating Multisystem Inflammatory Syndrome in Children (MIS-C) from Acute COVID-19 Using Biomarkers: Toward a
Carmen Loredana Petrea Cliveți1,2, Diana-Andreea Ciortea1,3, Gabriela Gurău1,2
1Faculty of Medicine and Pharmacy, Research Center in the Medico-Pharmaceutical Field, "Dunarea de Jos" University of Galati, 800008 Galati, Romania.
Insights
Multisystem inflammatory syndrome in children (MIS-C) has a distinct inflammatory signature compared to COVID-19. Routine biomarkers like CRP, NLR, and PLR can help differentiate these conditions in children.
Area of Science:
- Pediatric immunology
- Infectious disease diagnostics
- Biomarker discovery
Background:
- Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children presents with varied clinical symptoms.
- Multisystem inflammatory syndrome in children (MIS-C) is a distinct hyperinflammatory condition.
- Early differentiation between MIS-C and COVID-19 in pediatric patients is crucial.
Purpose of the Study:
- To identify routine laboratory biomarkers for early discrimination between MIS-C and COVID-19 in children.
- To establish a panel of biomarkers with high diagnostic accuracy.
Main Methods:
- Retrospective comparative study of 144 pediatric patients.
- Analysis using classical statistics, FDR correction, penalized regression, and machine learning.
- Focus on routine laboratory tests for biomarker identification.
Main Results:
- MIS-C exhibits a distinct inflammatory profile (elevated CRP, NLR, PLR; lymphopenia; electrolyte disturbances) compared to COVID-19.
- Machine learning models (Random Forest) achieved high discrimination (AUC = 0.95) using a core set of biomarkers.
- Key discriminatory thresholds identified: CRP ~3.7 mg/dL, NLR ~3.3, PLR ~376, potassium ~4.2 mmol/L.
Conclusions:
- MIS-C is characterized by a specific and reproducible immunological signature differentiating it from COVID-19.
- Identified biomarkers form a basis for developing simple clinical algorithms for pediatric triage and risk stratification.
- A simplified scoring tool for emergency settings is a potential application.
Abstract:
Background/Objectives: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children presents with a heterogeneous clinical spectrum, whereas multisystem inflammatory syndrome in children (MIS-C) is a distinct immunological entity characterized by a hyperinflammatory phenotype and a distinct biological architecture. Identifying routine biomarkers with early discriminatory utility is essential for rapid differentiation between MIS-C and coronavirus disease 2019 (COVID-19). Methods: We conducted a retrospective comparative study of 144 pediatric patients with COVID-19 or MIS-C admitted to a single specialized medical center. The analyses integrated classical statistical methods, Benjamini-Hochberg false discovery rate correction (FDR), penalized regression models, and machine learning algorithms to identify biomarkers with discriminative value, using only routine laboratory tests. Results: MIS-C was associated with an intense inflammatory profile, characterized by increases in C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), lymphopenia, and selective electrolyte disturbances, highlighting a coherent biological architecture. In contrast, COVID-19 showed limited associations with traditional inflammatory markers. Predictive models identified a stable core of biomarkers with excellent performance in Random Forest analysis (area under the curve, AUC = 0.95), and reproducible thresholds (CRP ~3.7 mg/dL, NLR ~3.3, PLR ~376; potassium ~4.2 mmol/L). These findings were independently confirmed using penalized Ridge regression, where the reduced model achieved superior discrimination compared to the full 13-variable model (AUC = 0.93 vs. 0.89) and maintained stable performance under internal cross-validation, reinforcing the clinical relevance of this compact biomarker panel. Conclusions: MIS-C is clearly distinguished from COVID-19 by a specific and reproducible immunological signature. The identified biomarkers may represent a potential foundation for the development of simple clinical algorithms for pediatric triage and risk stratification, opening the prospect of a simplified scoring tool applicable in emergency settings.
