Cardiac Manifestations and Emerging Biomarkers in Multisystem Inflammatory Syndrome in Children (MIS-C): A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Cardiac involvement is a key prognostic factor in multisystem inflammatory syndrome in children (MIS-C), a rare but serious inflammatory condition that typically occurs 2-6 weeks after SARS-CoV-2 infection and is characterized by fever, systemic inflammation, and multiorgan involvement. Biomarkers may aid in early detection, severity assessment, and treatment stratification. OBJECTIVE: To evaluate the diagnostic utility of established and emerging serum biomarkers in MIS-C, with an emphasis on cardiac dysfunction and disease severity. METHODS: A systematic search was conducted in PubMed, Scopus, and Web of Science up to April 2025. Eligible studies included pediatric MIS-C cases with reported serum biomarkers. Meta-analyses were performed for NT-proBNP and troponin using random-effects models. Descriptive profiling was applied to emerging biomarkers. Subgroup comparisons were explored between severe and moderate MIS-C. Quality assessment followed the Newcastle-Ottawa Scale, and publication bias was assessed via funnel plots and Egger's test. RESULTS: A total of 67 studies were included, comprising >4000 pediatric MIS-C cases. NT-proBNP and troponin were consistently elevated (pooled means: 9697 pg/mL and 0.384 ng/mL, respectively), with a low risk of publication bias. Emerging biomarkers such as CXCL9, angiopoietin-2, and vitamin D revealed high inter-study variability but potential prognostic value. Subgroup analyses for selected studies (n = 5) suggested higher biomarker levels in severe MIS-C. CONCLUSIONS: NT-proBNP and troponin are robust indicators of cardiac injury in MIS-C. Emerging biomarkers show promise but require validation. Future studies should include copeptin and adopt standardized reporting to refine biomarker-guided management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".