Chong Hu ¹ Xiao Yan ^2* Henglian Song ^3* Qin Dong ^1* Changying Yi ^1* Xin Lv ^1* Jianzhi Li ^1*

• ¹ Jinan Children's Hospital, Jinan, Shandong Province, China
• ² Qingdao Municipal Hospital, Qingdao, Shandong Province, China
• ³ Jining Second People's Hospital, Jining, China

The nomogram is a powerful and robust tool in disease risk prediction, summarizing complex variables into a visual model interpretable with quantified risk probability. In the current study, the nomogram was developed to predict the occurrence of CAL among KD patients. This is especially valuable in the early identification of the risk of CAL, which will lead to proper diagnosis and treatment to reduce associated complications.Retrospective clinical data of 677 children diagnosed with KD who were treated in the Children's Hospital Affiliated to Shandong University were analyzed. According to the result of coronary echocardiography, all subjects were divided into CAL group and N-CAL group . Lasso regression was applied for the identification of the most informative predictors of CAL. Based on this, a nomogram was developed for accurate risk estimation.The data were divided into a training set and a validation set. Receiver operating characteristic analysis, calibration curves, and decision curve analysis all supported the high accuracy and clinical utility of this model. Lasso regression highlighted five key predictors: sodium, hemoglobin, platelet count, D-dimer, and Cystatin C. A nomogram based on these predictors was established and successfully validated in both datasets. In the training set, the AUC was 0.819 and in the validation set it was 0.844. The C-index of the calibration curve in the training set was 0.820, while in the validation set it was 0.844. In decision curve analysis, the predictive benefit of the model was greater than zero when the threshold probability was below 95% in the training set and below 92% in the validation set.The predictive factors identified through the Lasso regression approach and the development of the nomogram are important contributions in this respect. This model have high predictive accuracy and reliability for identifying high-risk children in the very early stage of disease with remarkable precision, hence laying the foundation for personalized treatment strategies and targeted treatment; thus, providing a strong scientific basis for precise therapeutic intervention.