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A predictive model and nomogram for coronary artery injury in Kawasaki disease based on laboratory indicators: a
Yanyan Li1, Zhiqing Chen1, Xiaoyan Wang1
1Department of Pediatrics, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Insights
Routine laboratory tests can help differentiate Kawasaki disease (KD) types and predict coronary artery lesions (CAL). Hypoalbuminemia is a key predictor of CAL in KD patients, aiding early diagnosis and risk stratification.
Area of Science:
- Pediatric Cardiology
- Clinical Laboratory Science
- Diagnostic Biomarkers
Background:
- Kawasaki disease (KD) diagnosis can be challenging, especially for incomplete KD (IKD).
- Coronary artery lesions (CAL) are a severe complication of KD, necessitating early detection.
- Routine laboratory parameters offer potential for early KD assessment and complication prediction.
Purpose of the Study:
- To investigate laboratory parameter changes in children with KD.
- To analyze correlations between lab markers, KD phenotypes (typical vs. incomplete), and CAL development.
- To develop predictive models and nomograms for KD diagnosis and CAL risk stratification.
Main Methods:
- Retrospective analysis of 131 children with KD.
- Logistic regression to identify independent predictors for KD types and CAL.
- Evaluation of predictive models using ROC curves, calibration plots, and decision curve analysis.
- Internal validation via bootstrap resampling and construction of visual nomograms.
Main Results:
- Total protein (TP) distinguished typical KD from IKD.
- Hypoalbuminemia, hyponatremia, and elevated lactate dehydrogenase (LDH) predicted CAL in KD.
- Hypoalbuminemia was the strongest CAL predictor (OR=0.783, P=0.001).
- Predictive models for typical KD and CAL achieved AUCs of 0.762 and 0.790, respectively, with good calibration.
Conclusions:
- Routine laboratory indicators are valuable for KD phenotypic differentiation and CAL risk prediction.
- Developed predictive models and nomograms offer convenient, quantitative tools for early KD diagnosis and CAL risk management.
- These tools are particularly beneficial for primary care settings with limited diagnostic resources.
Background:
To explore the changes in various laboratory parameters in children with Kawasaki disease, analyze their correlations with complete and incomplete Kawasaki disease as well as coronary artery lesions and non-coronary artery lesions, and establish prediction models and nomograms.
Objective:
Incomplete Kawasaki disease (IKD) is prone to misdiagnosis, and coronary artery lesion (CAL) represents its severe complication. This study aimed to explore the predictive value of routine laboratory indicators for typical/incomplete KD and CAL, and to construct reliable predictive models and nomograms, thereby providing quantitative tools for early screening and risk stratification in primary clinical practice.
Methods:
A total of 131 children with confirmed Kawasaki disease (KD) from January 2023 to June 2025 were retrospectively enrolled, who were assigned to the TKD group (n = 95) and IKD group (n = 36), as well as the coronary artery lesion (CAL) group (n = 39) and non-CAL (NCAL) group (n = 92). Univariate and multivariate logistic regression analyses were applied to screen out independent influencing factors. The performance of the predictive models was evaluated using receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA). Bootstrap resampling was adopted for internal validation of the models, and visual nomograms were constructed accordingly.
Result:
Total protein (TP) was the only independent factor for differentiating typical from IKD; hypoalbuminemia, hyponatremia, and elevated lactate dehydrogenase (LDH) were identified as independent risk factors for KD complicated with CAL, with hypoalbuminemia being the strongest predictor (OR = 0.783, P = 0.001). The area under the curve (AUC) of the predictive model for TKD was 0.762, and that of the CAL predictive model was 0.790. Both models showed good calibration and positive clinical net benefit, and the corresponding nomograms enabled rapid individualized quantitative risk prediction.
Conclusion:
This study confirmed the clinical value of routine laboratory indicators in phenotypic differentiation of KD and risk prediction of CAL. The constructed predictive models and visual nomograms feature convenient detection and simple operation, which can provide practical references for the early precise diagnosis and treatment of KD and the prevention and control of CAL risk, and are particularly suitable for primary medical institutions with limited diagnostic resources.
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