Risk Model Development and Validation for Prediction of Coronary Artery Aneurysms in Kawasaki Disease in a North American Population

Abstract

Background

Accurate prediction of coronary artery aneurysms ( CAAs) in patients with Kawasaki disease remains challenging in North American cohorts. We sought to develop and validate a risk model for CAA prediction.

Methods and Results

A binary outcome of CAA was defined as left anterior descending or right coronary artery Z score ≥2.5 at 2 to 8 weeks after fever onset in a development cohort (n=903) and a validation cohort (n=185) of patients with Kawasaki disease. Associations of baseline clinical, laboratory, and echocardiographic variables with later CAA were assessed in the development cohort using logistic regression. Discrimination (c statistic) and calibration (Hosmer‐Lemeshow) of the final model were evaluated. A practical risk score assigning points to each variable in the final model was created based on model coefficients from the development cohort. Predictors of CAAs at 2 to 8 weeks were baseline Z score of left anterior descending or right coronary artery ≥2.0, age <6 months, Asian race, and C‐reactive protein ≥13 mg/ dL (c=0.82 in the development cohort, c=0.93 in the validation cohort). The CAA risk score assigned 2 points for baseline Z score of left anterior descending or right coronary artery ≥2.0 and 1 point for each of the other variables, with creation of low‐ (0–1), moderate‐ (2), and high‐ (3–5) risk groups. The odds of CAAs were 16‐fold greater in the high‐ versus the low‐risk groups in the development cohort (odds ratio, 16.4; 95% CI, 9.71–27.7 [ P<0.001]), and >40‐fold greater in the validation cohort (odds ratio, 44.0; 95% CI, 10.8–180 [ P<0.001]).

Conclusions

Our risk model for CAA in Kawasaki disease consisting of baseline demographic, laboratory, and echocardiographic variables had excellent predictive utility and should undergo prospective testing.