Serum levels of PDGF-CC as a potential biomarker for the diagnosis of Kawasaki disease

BACKGROUND

Kawasaki disease (KD) is an acute systemic vasculitis of unknown etiology that predominantly affects children, and no specific diagnostic biomarkers for KD are available. Platelet-derived growth factor CC (PDGF-CC) is a peptide with angiogenic properties that has been amply demonstrated to play a critical role in the cardiovascular system. This study aimed to investigate the serum expression of PDGF-CC in children with KD and to evaluate the ability of PDGF-CC to diagnose KD.

METHODS

A total of 96 subjects, including 59 KD patients, 17 febrile controls (FC), and 20 healthy controls (HC), were enrolled. Serum levels of PDGF-CC were measured via enzyme-linked immunosorbent assay. The associations between PDGF-CC and clinical laboratory parameters were investigated by correlation analysis. The diagnostic performance was assessed by receiver operating characteristic (ROC) curve analysis.

RESULTS

Serum PDGF-CC levels in the KD group were significantly higher than in the FC and HC groups. Serum PDGF-CC levels in the KD group were positively correlated with white blood cell counts, percentage of neutrophils, IL-2, IL-12p70, TNF-α, and IL-1β levels, and negatively correlated with the percentage of lymphocytes. In the analysis of ROC curves, the area under the curve was 0.796 (95% confidence interval 0.688-0.880; P < 0.0001) for PDGF-CC and increased to 0.900 (95% confidence interval 0.808-0.957; P < 0.0001) in combination with white blood cell counts and C-reactive protein.

CONCLUSIONS

PDGF-CC is a potential biomarker for KD diagnosis, and the combination with white blood cell counts and C-reactive protein can further improve diagnostic performance.

Prediction of Coronary Artery Lesions in Patients With Recurrent Kawasaki Disease

BACKGROUND

A subset of patients with Kawasaki disease (KD) will suffer recurrence. However, there is still a lack of accurate prediction models for coronary artery lesions (CAL) in recurrent KD patients. It is necessary to establish a new nomogram model for predicting CAL in patients with recurrent KD.

METHODS

Data from patients with recurrent KD between 2015 and 2021 were retrospectively reviewed. After splitting the patients into training and validation cohorts, the least absolute shrinkage and selection operator was used to select the predictors of CAL and multivariate logistic regression was used to construct a nomogram based on the selected predictors. The application of area under the receiver operating characteristic curve (AUC), calibration curves, Hosmer-Lemeshow test, Brier score and decision curve analysis were used to assess the model performance.

RESULTS

A total of 159 recurrent KD patients were enrolled, 66 (41.5%) of whom had CAL. Hemoglobin levels, CAL at the first episode, and intravenous immunoglobulin resistance at recurrence were identified by the least absolute shrinkage and selection operator regression analysis as significant predictors. The model incorporating these predictors showed good discrimination (AUC, 0.777) and calibration capacities (Hosmer-Lemeshow P value, 0.418; Brier score, 0.190) in the training cohort. Application of the model to the validation cohort yielded an AUC of 0.741, a Hosmer-Lemeshow P value of 0.623 and a Brier score of 0.190. The decision curve analysis demonstrated that the nomogram model was clinically useful.

CONCLUSIONS

The proposed nomogram model could help clinicians assess the risk of CAL in patients with recurrent KD.