Cytokine profiles and laboratory parameters as indicators to distinguish children with PFAPA and bacterial infection.
Periodic fever, aphthous stomatitis, pharyngitis, and cervical adenitis syndrome (PFAPA) is characterized by recurrent febrile episodes associated with one or more of the symptoms described by the acronym, and easily misdiagnosed as other infectious diseases, especially tonsillitis. We aimed to describe the clinical, laboratory parameters and cytokine profiles in patients with PFAPA and to explore indicators to distinguish children with PFAPA from bacterial infection. Patients with PFAPA and bacterial infection, who had cytokine panels performed by Flowcytomix technique during the febrile episodes (prior to any treatments), were retrospectively enrolled from January 2020 to June 2024 in Shenzhen Children's Hospital. Clinical data were collected from inpatient medical records. Serum levels of cytokines and other laboratory parameters were compared between patients with PFAPA and those with identified bacterial infection. Multivariate regression analysis and a receiver operating characteristic (ROC) curve analysis were performed to construct a diagnostic model to assess the potential role of serum cytokines and laboratory parameters in the diagnosis of PFAPA. 67 patients with PFAPA and 160 patients with identified bacterial infection were included in this study. The level of serum IFN-γ, and the IFN-γ/IL-6 ratio in PFAPA patients were significantly higher than those in identified bacterial infection ( < 0.0001). The cutoff value of serum IFN-γ/IL-6 ratio for differentiating PFAPA from bacterial infection was > 0.43, and the area under the receiver operating characteristic curve (AUC) was 0.79, with a sensitivity of 70.15% and a specificity of 71.88%. A diagnosis model combined IL-10, platelet and IFN-γ/IL-6 ratio was built, and the AUC was 0.95, with the sensitivity and specificity as 90.3% and 89.6%, respectively. The IFN-γ/IL-6 ratio during febrile episodes may be useful in the early diagnosis of PFAPA by differentiating this disease from bacterial infection. The combined model based on IFN-γ/IL-6, IL-10, and PLT optimizes the diagnosis efficiency.