Effectiveness of early Anakinra on cardiac function in children with multisystem inflammatory syndrome of COVID-19: a systematic review
Bibliographic record
Abstract
BACKGROUND: Multisystem Inflammatory Syndrome in Children (MIS-C) associated with SARS-CoV-2 can lead to severe cardiovascular complications. Anakinra, an interleukin-1 receptor antagonist, is proposed to benefit the hyperinflammatory state of MIS-C, potentially improving cardiac function. This systematic review evaluated the effectiveness of early Anakinra administration on cardiac outcomes in children with MIS-C. METHODS: A comprehensive search across PubMed, Embase, and Web of Science until March 2024 identified studies using Anakinra to treat MIS-C with reported cardiac outcomes. Observational cohorts and clinical trials were included, with data extraction focusing on cardiac function metrics and inflammatory markers. Study quality was assessed using the Newcastle-Ottawa Scale. RESULTS: Six studies met the inclusion criteria, ranging from retrospective cohorts to prospective clinical studies, predominantly from the USA. Anakinra dosages ranged from 2.3 to 10 mg/kg based on disease severity. Several studies showed significant improvements in left ventricular ejection fraction and reductions in inflammatory markers like C-reactive protein, suggesting Anakinra's role in enhancing cardiac function and mitigating inflammation. However, findings on vasoactive support needs were mixed, and some studies did not report significant changes in acute cardiac support requirements. CONCLUSION: Early Anakinra administration shows potential for improving cardiac function and reducing inflammation in children with MIS-C, particularly those with severe manifestations. However, the existing evidence is limited by the observational nature of most studies and lacks randomized controlled trials (RCTs). Further high-quality RCTs are necessary to conclusively determine Anakinra's effectiveness and optimize its use in MIS-C management for better long-term cardiac outcomes and standardized treatment protocols.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".