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Structure equation model and neural network analyses to predict coronary artery lesions in Kawasaki disease: a
Junji Azuma1, Takehisa Yamamoto2, Motoaki Nitta1
1Department of Paediatrics, Minoh City Hospital, 5-7-1 Kayano, Minoh, Osaka, 562-0014, Japan.
Insights
A new statistical model accurately predicts coronary artery lesions (CALs) in Kawasaki disease (KD). This method aids clinicians in selecting timely therapies to prevent serious complications in children with KD.
Area of Science:
- Pediatric Cardiology
- Rheumatology
- Biostatistics
Background:
- Kawasaki disease (KD) is a critical pediatric illness.
- Coronary artery lesions (CALs) are a major complication of KD.
- Early prediction and intervention are vital for managing KD.
Purpose of the Study:
- To develop and validate a predictive model for CALs in KD patients.
- To utilize statistical methods for identifying risk factors associated with CALs.
- To provide a tool for optimizing therapeutic strategies in KD management.
Main Methods:
- A cohort of 314 children diagnosed with KD was analyzed.
- Coronary artery lesions (CALs) were defined by a maximum z score ≥ 3.0.
- A mean structure equation model (SEM) and neural networks (Nnet) were employed for prediction.
Main Results:
- The SEM demonstrated a perfect fit (R² = 1.000) and excellent discrimination (SS = 2.0).
- Neural network analysis achieved 73% sensitivity, 99% specificity, and a 0.86 c-index for CAL prediction.
- Key predictors included age, sex, treatment resistance, and inflammatory markers.
Conclusions:
- A robust and simple statistical model effectively predicts CALs in KD.
- The model integrates common clinical parameters for practical application.
- This tool can guide therapeutic decisions to prevent CALs in Japanese KD patients.
Abstract:
A new method to predict coronary artery lesions (CALs) in Kawasaki disease (KD) was developed using a mean structure equation model (SEM) and neural networks (Nnet). There were 314 admitted children with KD who met at least four of the six diagnostic criteria for KD. We defined CALs as the presence of a maximum z score of ≥ 3.0. The SEM using age, sex, intravenous immunoglobulin resistance, number of steroid pulse therapy sessions, C-reactive protein level, and urinary β2-microglobulin (u-β2MG/Cr) values revealed a perfect fit based on the root mean square error of approximation with an R2 value of 1.000 and the excellent discrimination of CALs with a sample score (SS) of 2.0 for a latent variable. The Nnet analysis enabled us to predict CALs with a sensitivity, specificity and c-index of 73%, 99% and 0.86, respectively. This good and simple statistical model that uses common parameters in clinical medicine is useful in deciding the appropriate therapy to prevent CALs in Japanese KD patients.
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