A universal model for predicting coronary artery lesions in subgroups of kawasaki disease in China: based on cluster analysis
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Chuxiong Gong1, Feng Li2, Zhongjian Su1
1Cardiovascular Department, Kunming Children's Hospital, Kunming, Yunnan, China.
Insights
Researchers identified three patient clusters in Kawasaki disease (KD). They developed distinct predictive models for coronary artery lesions (CAL) in KD, showing good performance for personalized treatment and health management.
Area of Science:
- Pediatric Cardiology
- Immunology
- Computational Biology
Background:
- Kawasaki disease (KD) is a leading cause of acquired pediatric heart disease.
- Coronary artery lesions (CAL) are the most severe complication of KD, yet prediction remains challenging.
- Current methods for predicting CAL in KD lack standardization and show variable effectiveness.
Purpose of the Study:
- To establish distinct predictive models for CAL in KD based on patient clustering.
- To improve the accuracy of CAL prediction in diverse KD patient populations.
- To facilitate individualized treatment strategies for KD patients.
Main Methods:
- Principal component clustering analysis was used to stratify 1,795 KD patients into subgroups.
- LASSO analysis identified key factors associated with CAL development within each cluster.
- Predictive models for CAL were constructed and validated for each identified subgroup.
Main Results:
- PCA identified three distinct clusters of KD patients.
- Developed CAL predictive models for each cluster demonstrated good performance (AUCs: 0.789, 0.894, 0.773).
- The models offer reliable prediction of CAL complications across different KD patient profiles.
Conclusions:
- This study successfully clustered KD patients and developed tailored CAL predictive models.
- The models provide a foundation for precision medicine approaches in KD management.
- Findings support enhanced health management of coronary arteries in KD patients through individualized risk assessment.
Objective:
Coronary artery lesions (CAL) represent the most severe complication of Kawasaki disease (KD). Currently, there is no standardized method for predicting CAL in KD, and the predictive effectiveness varies among different KD patients. Therefore, our study aims to establish distinct predictive models for CAL complications based on the characteristics of different clusters.
Methods:
We employed principal component clustering analysis to categorize 1,795 KD patients into different clustered subgroups. We summarized the characteristics of each cluster and compared the occurrence of CAL components within each cluster. Additionally, we utilized LASSO analysis to further screen for factors associated with CAL. We then constructed CAL predictive models for each subgroup using the selected factors and conducted preliminary validation and assessment.
Results:
Through PCA analysis, we identified three clusters in KD. We developed predictive models for each of the three clusters. The AUCs of the three predictive models were 0.789 (95% CI: 0.732-0.845), 0.894 (95% CI: 0.856-0.932), and 0.773 (95% CI: 0.727-0.819), respectively, all demonstrating good predictive performance.
Conclusion:
Our study identified the existence of three clusters among KD patients. We developed KD-related CAL predictive models with good predictive performance for each cluster with distinct characteristics. This provides reference for individualized precision treatment of KD patients and aids in the health management of coronary arteries in KD.
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