Abstract 15506: Pattern of Use of Anakinra for Patients With Kawasaki Disease versus Multisystem Inflammatory Syndrome in Children Associated With COVID-19
Bibliographic record
Abstract
Background/Aims: Anakinra (interleukin-1 blocker) has emerged as a potential adjunctive therapy for treating resistant acute Kawasaki disease (KD) and those with evolving aneurysms, and has been used as both primary and adjunct therapy for treating Multisystem Inflammatory Syndrome in Children (MIS-C), both in the absence of randomized trials. We sought to determine patterns of use, adverse events and evidence of benefit. Methods: The International KD Registry contemporaneously enrolled 727 patients with KD (site diagnosis confirmed by AHA criteria) and 1476 with MIS-C (site diagnosis confirmed by CDC criteria) from 39 sites in 7 countries from 01/2020 to 01/2023. Data collected included demographics, clinical features and presentation, management, laboratory values, and outcomes. Results: Anakinra was used at 22 (56%) sites for 11 (1.5%) KD patients (median duration 21 days) and 257 (17.4%) MIS-C patients (8 days; 11% discharged on anakinra). For KD, anakinra was used for treatment resistance for 5 sites, evolving aneurysms 3, macrophage activation syndrome (MAS) 1 and not-specified for 2. For MIS-C, anakinra use was determined by protocol for 3 sites, on a case-by-case basis for 10, reserved for treatment failure for 3 and not-specified for 6 sites. Reported indications for use (may be multiple) included routine use for ICU patients for 34% of treated MIS-C patients, worsening/persistent lab abnormalities 28%, critical decompensation (primarily cardiac) 21%, persistent/recurrent fever 21%, treatment failure 9%, coronary artery abnormalities 8%, and cytokine storm/MAS for 4%. Adverse events in treated MIS-C patients were noted for 23 (9%) and included neutropenia in 9, transaminitis 8, injection site pain/rash 4 and one each with anemia and thrombocytopenia. Extreme patient heterogeneity and confounding by indication precluded a formal analysis of impact on outcomes, although sites reported evidence of clinical improvement relevant to reported indications for all but one patient. Conclusions: Greater anakinra use for MIS-C vs KD likely reflects greater perceived indication, primarily greater clinical and inflammatory severity. Subjective evidence of benefit and a low prevalence of adverse events suggest an ongoing role for anakinra.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".