Predicting coronary artery abnormalities in Kawasaki disease: Model development and validation
Clinical Snapshot
PICO Framework
| P — Population | Children with Kawasaki disease from two Japanese multicenter registries (PEACOCK n=4,973, Post-RAISE n=2,438) |
| I — Intervention | Predictive models using clinical variables obtained within one week of diagnosis |
| C — Comparator | Simple model using maximum Z score at week 1 versus complex logistic regression and machine learning models |
| O — Outcomes | Coronary artery abnormalities (maximum coronary artery Z score ≥2) at one month after diagnosis |
Bottom Line
This large multicenter study developed and validated predictive models for coronary artery abnormalities in Kawasaki disease using data from over 7,000 Japanese children. Despite testing multiple approaches including machine learning, even the best-performing model (AUC 0.79) failed to safely reduce routine echocardiographic screening at one month post-diagnosis. The simple model using maximum coronary artery Z score at week 1 performed as well as complex multivariable models. With coronary artery abnormality rates of 5-7%, the authors conclude that current standard practice of routine echocardiography should continue until superior predictors are identified. This reinforces the importance of systematic cardiac surveillance in all Kawasaki disease patients regardless of initial clinical presentation.
Key Findings
P Value: Not reported in abstract
Effect Size: AUC 0.79 for simple model using maximum Z score at week 1
Primary Outcome: Coronary artery abnormalities (Z score ≥2) at one month
Nnt Or Sensitivity: CAA incidence 5.5% (PEACOCK) and 6.8% (Post-RAISE); models failed to efficiently reduce screening
Confidence Interval: Not specified in abstract
Clinical Application
Models showed limited clinical utility for reducing routine echocardiography screening Relevant to Australian pediatric cardiology practice; current RACGP and cardiac society guidelines recommend routine echocardiographic surveillance in Kawasaki disease Children with Kawasaki disease requiring coronary artery surveillance
Abstract
Routine echocardiography at one month after diagnosis is the current standard for screening coronary artery abnormalities (CAA), a major complication of Kawasaki disease. The study aimed to develop and validate models to predict CAA and assess whether routine echocardiography could be safely reduced in low-risk patients. Two prospective multicenter Japanese registries were utilized: PEACOCK (development/internal validation) and Post-RAISE (external validation). Variables obtained within one week of diagnosis were used to predict CAA at one month after diagnosis, defined as a maximum coronary artery Z score (Zmax) ≥ 2. The models included simple models using the previous maximum Z score only, logistic regression models, and machine learning models (LightGBM and XGBoost). Discrimination, calibration, and clinical utility were assessed. Among 4,973 PEACOCK and 2,438 Post-RAISE patients, the CAA incidence was 5.5% and 6.8%, respectively. Twenty-two models were developed using 29 variables. For external validation, a simple model using the maximum Z score at week 1 produced an area under the curve (AUC) of 0.79; adding other variables or using more complex models did not increase the AUC by more than 0.02. The models failed to efficiently reduce the number of echocardiographic examinations while minimizing missed cased of CAA.Conclusion: Until superior predictors are identified, routine echocardiography at one month after diagnosis should remain the standard practice.
References
- 1.Wang, Q., Kimura, Y., Oba, J., Ishikawa, T., Ohnishi, T., Akahoshi, S., Iio, K., Morikawa, Y., Sakurada, K., Kobayashi, T., & Miura, M. (2026). Predicting coronary artery abnormalities in Kawasaki disease: Model development and validation. European Journal of Pediatrics. https://doi.org/10.1007/s00246-024-03762-9
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