Background: This study aims to develop Z-Score models to normalize measurements of three coronary arteries and enhance the diagnosis of Kawasaki disease (KD) in children from newborns to 10 years old. Developing a reliable Z-Score model is challenging, as some existing models fail the normality test. Overcoming these challenges is crucial for improving KD diagnosis.
Method: Detailed measurements of the left main coronary artery (LCA), left anterior descending coronary artery (LAD), and right coronary artery (RCA) were collected, along with patient demographics such as age, height, weight, and body surface area (BSA). Several Z-Score models, named the Kuo Z-Score models, were proposed, with separate designs for different coronary arteries and different age groups, resulting in multiple Z-Score models. The Z-Score model for the RCA employs the Box-Cox method for data transformation. Finally, we tested various age group combinations, selecting models that passed the Anderson-Darling normality test and had higher R-square values for robustness and best data fit.
Results: The study included 1180 participants free from coronary or heart diseases. The Kuo Z-Score models were optimized for LCA, LAD, and RCA across the five age groups 0-6 years, 6-7 years, 7-8 years, 8-9 years, and 9-10 years. Only the normality test for the RCA in the 7-8 year age group failed. The proposed model fitted to the normality assumption outperforming the other models.
Conclusion: The Kuo Z-Score models, applicable across a broad age range, provides robust identification of coronary artery dilatation and aneurysm in KD. The models' capability to normalize diverse data sets marks a significant advancement in KD diagnostic sensitivity, aiding in better clinical decision-making and potentially improving patient outcomes.
Keywords: Box-Cox Transformation; Kawasaki disease; Normality Test; Z-Score.
© 2024. The Author(s).