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Development and validation of an early predictive model for coronary artery lesions in incomplete Kawasaki disease
Yongmao Xu1, Shuhui Wang2, Chi Zhang1
1Department of Cardiology, The Affiliated Xuzhou Children's Hospital of Xuzhou Medical University, Xuzhou, China.
Insights
This study identified key risk factors for coronary artery lesions (CALs) in children with incomplete Kawasaki disease (IKD). A predictive model using these factors can help identify high-risk patients early.
Area of Science:
- Pediatric Cardiology
- Rheumatology
- Epidemiology
Background:
- Incomplete Kawasaki disease (IKD) incidence is rising.
- IKD is linked to increased risk of coronary artery lesions (CALs).
- Predictors for CAL in IKD remain unclear.
Purpose of the Study:
- Identify risk factors for CAL in pediatric IKD.
- Develop a predictive model for CAL occurrence in IKD patients.
Main Methods:
- Retrospective analysis of IKD patient data (2021-2023).
- Least Absolute Shrinkage and Selection Operator (LASSO)-logistic regression to identify predictors.
- Nomogram construction for a predictive model and external validation.
Main Results:
- Eight variables identified as predictors: gender, fever duration, conjunctival injection, cervical lymphadenopathy, ESR, Neu%, ALT, and AST.
- The nomogram model showed good predictive accuracy (AUC 0.817 in training, 0.888 in testing).
- The model demonstrated high sensitivity and specificity in both cohorts.
Conclusions:
- A predictive model integrating clinical and laboratory variables can reliably assess CAL risk in pediatric IKD.
- This model aids in early identification of IKD patients at risk for CAL.
Background:
The incidence of incomplete Kawasaki disease (IKD) has been rising, and it is associated with a higher risk of coronary artery lesions (CALs); however, the underlying reasons remain unclear. This study conducted a comparative analysis of the clinical data of children in the IKD and complete Kawasaki disease (CKD) groups, and aimed to determine risk factors associated with CAL in children diagnosed with IKD through least absolute shrinkage and selection operator (LASSO)-logistic regression, and to develop a predictive model for CAL occurrence in this population.
Methods:
Clinical records of IKD patients admitted to Xuzhou Children's Hospital between January 2021 and December 2023 were retrospectively analyzed. Based on diagnostic criteria, subjects were classified into CAL and non-CAL groups, forming the training dataset. Predictive variables were identified using LASSO regression with cross-validation. A nomogram was constructed to visualize the prediction model. Data from IKD patients hospitalized between January and June 2024 were utilized as an external validation cohort (test dataset) to assess the model's predictive accuracy.
Results:
Eight variables were retained as predictors through LASSO regression: gender, fever duration, conjunctival injection, cervical lymphadenopathy, erythrocyte sedimentation rate (ESR), neutrophil percentage (Neu%), alanine aminotransferase (ALT), and aspartate aminotransferase (AST). The nomogram-based model yielded an area under the curve (AUC) of 0.817 [95% confidence interval (CI): 0.757-0.878], with sensitivity and specificity of 83.1% and 71.6%, respectively. When applied to the test cohort, the model demonstrated an AUC of 0.888 (95% CI: 0.720-0.975), with corresponding sensitivity of 75.0% and specificity of 88.0%.
Conclusions:
The model integrating gender, fever duration, conjunctival injection, cervical lymphadenopathy, ESR, Neu%, ALT, and AST, offers a reliable approach for predicting CAL risk in pediatric IKD cases.
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