Meng Wu,1,* Jinlong Chen,2,* Ya Gao,3,* Hongbing Chen,1 Wei Li4 

1Department of Clinical Laboratory, Children’s Hospital of Nanjing Medical University, Nanjing, People’s Republic of China; 2Department of Cardiology, Children’s Hospital of Nanjing Medical University, Nanjing, People’s Republic of China; 3Department of Emergency/Critical Medicine, Children’s Hospital of Nanjing Medical University, Nanjing, People’s Republic of China; 4Department of Clinical Research, Children’s Hospital of Nanjing Medical University, Nanjing, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Wei Li, Department of Clinical Research, Children’s Hospital of Nanjing Medical University, Guangzhou Road #72, Nanjing, Jiangsu, 210008, People’s Republic of China, Email weili126@126.com Hongbing Chen, Department of Clinical Laboratory, Children’s Hospital of Nanjing Medical University, Guangzhou Road #72, Nanjing, Jiangsu, 210008, People’s Republic of China, Email Chenghb1999@126.com

Objective: Severe Kawasaki disease (SKD) poses numerous risks. Early identification of SKD is crucial for precise pharmacological intervention, which can reduce the incidence of complications. This study introduces a novel machine learning approach for the early prediction of SKD in pediatric populations, utilizing routinely collected laboratory parameters.
Methods: We extracted patients’ age, sex, and 67 standard laboratory markers from the clinical records of 1,466 patients diagnosed with KD at the Children’s Hospital of Nanjing Medical University. Using Lasso regression, we identified 15 critical predictors from the initial 69 candidates, demonstrating a significant impact on the accuracy of predictive outcomes. We forecasted the binary diagnosis of SKD or ordinary Kawasaki Disease (OKD) using 16 machine learning models, with model performance assessed through AUC-ROC, accuracy, F1 score, DCA, and calibration analysis.
Results: Our study included 1,466 patients with KD, categorized into 1,286 cases of OKD and 180 cases of SKD. Both groups predominantly consisted of male. A significantly lower median age was observed in SKD patients (4.29 years) compared to the OKD group. In our comparative analysis of predictive models, the Gradient Boosting model (AUC 0.952) emerged as the most accurate, followed closely by Ada Boost (AUC 0.945), Random Forest (AUC 0.944), CatBoost (AUC 0.957), and Naive Bayes (AUC 0.951). The GBC model achieved a high accuracy of 0.925, with a sensitivity of 0.628, specificity of 0.967, precision of 0.740, and an F1 score of 0.666, underscoring its robustness in distinguishing between the two KD subgroups. Our analysis identified 15 independent predictors, including absolute basophil count and conjugated bilirubin, that significantly enhanced the diagnostic accuracy of SKD.
Conclusion: Our most effective model demonstrates commendable performance in differentiating OKD from SKD. This advancement empowers pediatric clinicians to make swift clinical decisions, facilitating prompt therapeutic intervention and preventing the onset of severe complications.

Keywords: biomarkers, machine learning, Kawasaki disease, diagnosis