This retrospective cohort study analyzed 131 children with confirmed Kawasaki disease (KD) to evaluate the predictive value of routine laboratory indicators for differentiating typical KD (TKD) from incomplete KD (IKD) and for predicting coronary artery lesions (CAL). The sample included 95 children in the TKD group and 36 in the IKD group; 39 developed CAL and 92 did not.
For differentiating TKD from IKD, total protein (TP) was the only independent factor identified. A predictive model for TKD yielded an area under the curve (AUC) of 0.762. For predicting CAL, hypoalbuminemia, hyponatremia, and elevated lactate dehydrogenase (LDH) were independent risk factors, with hypoalbuminemia being the strongest predictor (OR = 0.783, P = 0.001). The predictive model for CAL had an AUC of 0.790.
Safety and tolerability were not reported. The study's limitations were not explicitly stated, but as a retrospective analysis, it is subject to potential confounding and selection bias. Follow-up duration was not reported, and the sample size was modest.
These findings suggest that routine lab tests may help clinicians distinguish TKD from IKD and identify children at higher risk for CAL. However, the predictive models require prospective validation before clinical application, particularly in primary care settings.
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BackgroundTo 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.ObjectiveIncomplete 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 practiceMethodsA 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.ResultTotal 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.ConclusionThis 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.