A new scoring in differential diagnosis: multisystem inflammatory syndrome or adenovirus infection?

Abstract

Background/aim: Differentiating multisystem inflammatory syndrome in children (MIS-C) from adenovirus infection (AI) can be challenging due to similar clinical and laboratory findings. This study aimed to identify distinguishing characteristics and develop a scoring system to facilitate accurate diagnosis.

Materials and methods: A comprehensive review of medical records was undertaken for 108 MIS-C patients and 259 patients with confirmed AI. A comparison of laboratory data and clinical findings was conducted across the patient groups using appropriate statistical tests.

Results: The MIS-C patients were significantly older than the AI patients (p < 0.001). Diarrhea, rash, abdominal pain, vomiting, nonexudative conjunctivitis, lymphadenopathy tachycardia, bradycardia, hypotension, hypoxia seizures, agitation, headache, and altered consciousness symptoms were more frequently associated with MIS-C (p < 0.001), while cough and runny nose were significantly more common in AI (p < 0.001). Lymphopenia and thrombocytopenia were more prevalent in the MIS-C patients (p < 0.001). AI and MIS-C were scored by giving one point each to the parameters that created the difference. For AI, being ≤60 months of age, the presence of cough, runny nose and absence of diarrhea, rash, abdominal pain, vomiting, nonexudative conjunctivitis, lymphadenopathy, tachycardia, bradycardia, hypotension, hypoxia seizures, agitation, headache, and altered consciousness, lymphopenia, thrombocytopenia, and C-reactive protein value <124.5 mg/L were determined as each parameter plus one point. A total score above 14 could predict AI with a high degree of accuracy, with sensitivity at around 97.5% and specificity at 92.6%.

Conclusion: The proposed inpatient scoring system, when used in conjunction with polymerase chain reaction testing, may improve the early differentiation of AI and MIS-C. This approach could help reduce unnecessary testing and optimize resource allocation. Further research with larger samples should investigate this novel scoring system to establish its reliability and generalizability.