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Predicting coronary artery lesions in Kawasaki disease: a nomogram based on LASSO regression feature selection
Jing Su1,2, Ying Bai1,2, Hua Wang2,3
1Department of Laboratory Medicine, Children's Hospital of Hebei Province, Shijiazhuang, China.
Insights
This study developed a nomogram to predict coronary artery lesions (CALs) in children with Kawasaki disease (KD). The model uses age, clinical type, platelet count, and thrombin time for early risk identification.
Area of Science:
- Pediatric Cardiology
- Rheumatology
- Medical Informatics
Background:
- Coronary artery lesions (CALs) are severe complications of Kawasaki disease (KD).
- Early identification of high-risk children with KD is crucial for timely treatment.
- Predictive models can aid in risk stratification for CALs.
Purpose of the Study:
- To develop and validate a nomogram-based predictive model for CALs in pediatric KD patients.
- To identify key clinical and laboratory factors associated with CAL development in KD.
- To improve early risk assessment for CALs in children with KD.
Main Methods:
- Retrospective analysis of 255 children diagnosed with KD.
- Least absolute shrinkage and selection operator (LASSO) regression for feature selection.
- Multivariable logistic regression, nomogram construction, and internal validation using bootstrap resampling.
- Model performance assessed by ROC curves, calibration plots, Hosmer-Lemeshow test, and decision curve analysis (DCA).
Main Results:
- The incidence of CALs in the cohort was 29.4%.
- Independent risk factors identified: age, clinical type, platelet count (PLT), and thrombin time (TT).
- The nomogram demonstrated good predictive performance (AUC = 0.807) and clinical utility via DCA.
Conclusions:
- A validated nomogram incorporating age, clinical type, PLT, and TT can predict CALs risk in children with KD.
- This tool facilitates early identification of high-risk pediatric patients.
- The model supports clinical decision-making for Kawasaki disease management.
Background:
Coronary artery lesions (CALs) represent the most serious complications of Kawasaki disease (KD), and the early identification of children at high risk for CALs is critical for guiding treatment strategies. This study aimed to develop a nomogram-based model to predict the risk of CALs in children with KD.
Methods:
This retrospective analysis enrolled 255 children diagnosed with KD between January 2024 and May 2025. Feature selection was performed using the least absolute shrinkage and selection operator regression. Variables with non-zero coefficients were subsequently incorporated into multivariable logistic regression to identify risk factors associated with CALs. A nomogram was constructed based on these predictors, and internal validation was performed using the bootstrap resampling method. The performance of the model was assessed using receiver operating characteristic (ROC) curves, calibration curves, the Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA).
Results:
The incidence of CALs in children with KD was 29.4% (75/255). Our results identified age, clinical type, platelet count (PLT), and thrombin time (TT) as independent risk factors for CALs in children with KD. The area under the ROC curve of the model was 0.807 (95% confidence interval [CI] = 0.750-0.860). The calibration curves and the Hosmer-Lemeshow goodness-of-fit test revealed good consistency between the predicted probabilities and observed outcomes, and DCA confirmed that the model had good clinical utility.
Conclusions:
We developed and internally validated a nomogram-based predictive model based on age, clinical type, PLT, and TT that may facilitate the early identification of children with KD at high risk of developing CALs.
