A New Diagnostic Model to Distinguish Kawasaki Disease From Other Febrile Illnesses in Chongqing: A Retrospective Study on 10,367 Patients

Abstract

Objective: Kawasaki disease (KD) is one of the most prevailing vasculitis among infants and young children, and has become the leading cause of acquired heart disease in childhood. Delayed diagnosis of KD can lead to serious cardiovascular complications. We sought to create a diagnostic model to help distinguish children with KD from children with other febrile illnesses [febrile controls (FCs)] to allow prompt treatment. Methods: Significant independent predictors were identified by applying multivariate logistic regression analyses. A new diagnostic model was constructed and compared with that from diagnostic tests created by other scholars. Results: Data from 10,367 patients were collected. Twelve independent predictors were determined: a lower percentage of monocytes (%MON), phosphorus, uric acid (UA), percentage of lymphocyte (%LYM), prealbumin, serum chloride, lactic dehydrogenase (LDH), aspartate aminotransferase: alanine transaminase (AST: ALT) ratio, higher level of globulin, gamma-glutamyl transpeptidase (GGT), platelet count (PLT), and younger age. The AUC, sensitivity, and specificity of the new model for cross-validation of the KD diagnosis was 0.906 ± 0.006, 86.0 ± 0.9%, and 80.5 ± 1.5%, respectively. An equation was presented to assess the risk of KD, which was further validated using KD (n = 5,642) and incomplete KD (n = 809) cohorts. Conclusions: Children with KD could be distinguished effectively from children with other febrile illnesses by documenting the age and measuring the level of %MON, phosphorus, UA, globulin, %LYM, prealbumin, GGT, AST:ALT ratio, serum chloride, LDH, and PLT. This new diagnostic model could be employed for the accurate diagnosis of KD.

Keywords: children; diagnostic model; febrile illnesses; independent predictors; kawasaki disease.

Figure 1

ROC and AUC of the diagnostic models for KD diagnosis. The AUC of the new KD diagnostic prediction model was 0.906 ± 0.006. Compared with previous KD diagnosis studies, the AUC value of the new model was higher than the methods of Falcini (0.791 ± 0.012), Barone (0.798 ± 0.017), Okada (0.785 ± 0.014), Song (0.793 ± 0.014), and Ling (0.724 ± 0.013). ROC, receiver-operator characteristic curves; AUC, area under the curve.

Figure 2

ROC and AUC of the diagnostic models for incomplete KD diagnosis. The AUC value of the new diagnostic model for incomplete KD diagnosis was 0.816. ROC, receiver-operator characteristic curves; AUC, area under the curve.