Abstract 11811: Cardiac Biomarkers Differentiate Kawasaki Disease From Multisystem Inflammatory Syndrome in Children Associated With Covid-19
Bibliographic record
Abstract
Introduction: Kawasaki disease (KD) and Multisystem Inflammatory Syndrome in Children (MIS-C) associated with COVID-19 show considerable clinical overlap making differentiation challenging. Hypothesis: Cardiac biomarkers can differentiate KD from MIS-C. Methods: The International KD Registry enrolled 2566 contemporaneous KD, MIS-C and acute COVID-19 pediatric patients from 39 sites in 8 countries from January 2020 through January 2022. The study population included MIS-C patients meeting CDC criteria with confirmed or probable COVID-19 infection, and KD patients meeting AHA guideline criteria without COVID-19 infection. Included patients had to have at least one measurement of NTproBNP or troponin I. KD and MIS-C patients were compared, to assess factors associated with cardiac biomarkers and cardiac outcomes. Receiver operating characteristic curves were used to determine cut points differentiating KD from MIS-C. Results: Of 779 patients with KD, 168 had NTproBNP (median 381 ng/L) and 173 had troponin I (median <10 ug/L) assessed at presentation, while of 1207 patients with MIS-C, 427 had NTproBNP (median 1850 ng/L; p<0.001 vs KD) and 522 had troponin I (median 12.7 ug/L; p<0.001) assessed. Baseline NTproBNP and troponin were correlated mildly (r=0.09; p=0.05) and were associated with older age and higher creatinine levels. Lower LV ejection fraction (LVEF) was associated with MIS-C (vs KD), but not with baseline or peak troponin levels after adjusting for age and creatinine levels. Lower LVEF was significantly associated with higher baseline and peak NTproBNP levels after adjusting for diagnosis and age. Higher peak coronary artery Z score was associated with KD vs MIS-C, but neither cardiac biomarker. Baseline troponin I >10 ug/L predicted MIS-C vs KD with a sensitivity of 57% and specificity of 74% (c-statistic 0.61), and baseline NTproBNP >1600 ng/L with a sensitivity of 54% and specificity of 75% (c-statistic 0.69). Conclusions: Higher baseline troponin I and NTproBNP levels are more predictive of MIS-C compared to KD. Lower LVEF, more common with MIS-C, was associated with higher NTproBNP but not troponin I levels, and coronary artery involvement, more common in KD, was not associated with either cardiac biomarker.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".