Abstract 10716: Etanercept as an Adjunct Therapy for Acute Kawasaki Disease: A Long Term Follow Up
Bibliographic record
Abstract
Introduction: Etanercept, a soluble TNF receptor, is beneficial when given as an adjunct to IVIg to children with acute Kawasaki disease (KD) and coronary artery (CA) involvement. A multi-center phase III trial, Etanercept as Adjunctive Treatment for Acute Kawasaki Disease (EATAK), showed that this TNF antagonist prevented progression of CA dilation over 6 weeks after acute KD. Aims: This study is aimed to test the long-term benefit of Etanercept in improving CA outcome. Methods: Patients enrolled in the placebo controlled double blind EATAK trial from 3 institutions (Seattle, Montreal, Bronx) who had CA dilation (z≥2.5) on initial presentation were included in this study ( n =40). The percent reduction in the size of affected vessels from echocardiogram at presentation to mid-year (4-8 months), 1-year (9-23 months) and ≥2 years after diagnosis were analyzed, and two-tailed t test used to compare Etanercept (0.8 mg/kg x3 doses) and placebo ( n =18) groups. Results: Both groups showed mean reduction (20%) in coronary artery diameter over a 2 year period (Figure). However, patients treated with Etanercept had accelerated and significantly greater improvement in diameter of affected vessels in the first 6 months after diagnosis [22% ( n =25) vs. 6.8% ( n =13), p =0.015]. The trend continued at 1-year follow up ([24.1% ( n =35) vs. 17.5% ( n =27), p =0.24], and by 2 years both groups showed similar overall change [19.6% ( n =31) vs. 24.7% ( n =24), p =0. 43]. Conclusions: Etanercept as an adjunct treatment to IVIg in acute KD with CA involvement accelerates regression of CA dilation over at least a 6 month period after acute inflammation. Etanercept is a potent anti-inflammatory agent that has both early and long-term benefit in CA remodeling in KD. Acceleration of CA healing by Etanercept may provide long term coronary artery benefit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".