Establishment and validation of a predictive model for coronary artery lesions in children with KDSS

Kawasaki Disease Shock Syndrome (KDSS) represents a severe manifestation of Kawasaki Disease (KD). In recent years, logistic regression prediction models have gained widespread application in forecasting the occurrence probabilities of various diseases. The objective of this study is to explore the clinical characteristics of pediatric patients with KDSS complicated by coronary artery lesions (CALs) and to develop and validate a logistic regression model for predicting the likelihood of CALs in children with KDSS.

Our study enrolled 102 pediatric patients diagnosed with KDSS at the Cardiology Department of our hospital between January 2020 and March 2024, all of whom had comprehensive medical histories and physical examination results. Logistic regression analysis was employed to identify the most predictive variables. Utilizing a training set (n = 72), we constructed a logistic regression model to predict CALs in children with KDSS. The model's predictive capabilities were further assessed using logistic regression. The Receiver Operating Characteristic (ROC) curve served as a tool to evaluate the performance of the logistic regression model. Additionally, a nomogram model was developed through the visualization of the calibration curve using a 1000-bootstrap resampling method. The efficacy of these results was validated in an independent validation set (n = 30).

Univariate analysis revealed nine variables that exhibited significant differences between the CAL and normal coronary artery groups. Further logistic regression analysis identified fever duration, low hemoglobin levels, and low serum phosphorus as independent predictors of CALs in KDSS. The training set demonstrated an area under the ROC curve of 0.837, with a sensitivity of 83.3% and a specificity of 81.2%. The calibration curve indicated a strong agreement between the predicted values of the logistic regression model and the actual observed values in both the training and validation sets.

We have successfully established a feasible and highly accurate logistic regression model for predicting CALs in patients with KDSS. This model holds potential for early prediction of CALs and possesses significant clinical implications.