Long-term cardiovascular outcomes and mortality following Kawasaki disease: A systematic review and meta-analysis
Bibliographic record
Abstract
Objectives: To determine if children with Kawasaki disease (KD) are at an increased long-term risk of cardiovascular disease and mortality. Methods: A systematic review and meta-analysis was performed. A systematic search of MEDLINE, EMBASE, CINAHL, Cochrane, and Web of Science databases was performed through 2022. English-language publications, patients 0 to 18 years at KD diagnosis, minimum follow-up >1 year, and ≥10 patients included. Of 5072 articles, 181 were included. Cardiovascular events and mortality were extracted and pooled for analysis. Meta-analyses and meta-regression analyses were performed. The primary outcome of interest was the incidence of specific cardiovascular events (composite of myocardial infarction, heart failure or cardiac arrest) and all-cause mortality. Secondary outcomes included the incidence of occlusive coronary events, myocardial infarction, heart failure, cardiac arrest, non-coronary artery bypass grafting (CABG) coronary revascularization procedures, and CABG. Results: Cardiovascular events occurred in 0.85% of children during study follow-up. The incidence rate of cardiovascular events was 370 per 100,000 person-years. Mortality occurred in 0.24% of children during study follow-up. The incidence rate of mortality was 117 per 100,000 person-years. Conclusions: There is a low incidence of cardiovascular events and mortality following childhood KD diagnosis. Further studies are needed to better define this long-term risk.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".