Abstract O.49: Factors Associated with Development of Coronary Artery Aneurysms after Kawasaki Disease are Generally Similar for Those Treated Promptly Versus Those with Delayed or No Treatment
Bibliographic record
Abstract
Introduction: While the risk is reduced, patients may develop coronary artery aneurysms (CAA) after Kawasaki disease (KD) despite receiving intravenous immunoglobulin (IVIG) within 10 days of onset of symptoms. Risk factors for CAA may differ compared to those patients with delayed or no treatment. Methods: Patients diagnosed with KD between 1990 and 2013 were included. Patients with maximum coronary artery z-scores >5 were classified as having CAA. Separate multivariable regression models were used to determine factors associated with CAA for those with vs. without prompt treatment. Results: Of 1,358 patients included, 83% were treated with IVIG within 10 days and 5.4% developed CAA. Patients who had delayed (>10 days) or no IVIG treatment were at increased odds of developing CAA (OR: 3.1, p<0.001). From 1990-2013, the proportion of patients treated promptly increased (OR: 1.05/year, p=0.006) while the total duration of fever decreased (EST: -0.10 (0.03) days/year, p=0.001). These trends were associated with a shift such that a greater proportion of the patients who developed CAA actually had been treated promptly (from <25% in 1990 to >70% in 2013, OR: 1.1/year, p=0.01). For patients with prompt treatment with IVIG, factors associated with increased odds of CAA were: longer duration of fever prior to treatment (OR: 1.2/day, p=0.04), age <1 year old (OR 3.9, p=0.001), higher pre-IVIG white blood cell count (OR: 1.05/x10 9 /L, p=0.007), lower hemoglobin (OR: 1.4/g/L, p=0.004) and non-response to the initial IVIG treatment (OR: 2.5, p<0.001). For patients with delayed or no treatment, factors associated with increased odds of CAA were: males (OR: 5.4, p=0.009), age <1 year old (OR: 29.9, p<0.001), lower red blood cell count (OR: 2.5/-0.5 x10 12 /L, p=0.01) and higher platelet count at diagnosis (OR: 1.4/100x10 12 /L, p=0.001). Additionally, delayed treatment with IVIG did not reduce the risk of CAA (OR: 1.9, p=0.28), and total duration of fever was not associated with CAA for this group (OR: 1.04/day, p=0.16). . Conclusions: Factors associated with the development of CAA are generally similar for those treated promptly vs. those with delayed or no treatment. For those with delayed diagnosis, treatment with IVIG does not appear to be effective to prevent CAA.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.008 | 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".