Purpose: Differentiating infectious from non-infectious respiratory syndromes is critical in emergency settings. This study aimed to assess whether nCD64 and mCD169 exhibit specific distributions in patients with respiratory infections (viral, bacterial, or co-infections) and to evaluate their diagnostic accuracy compared to non-infectious conditions.
Methods: A prospective cohort study enrolled 443 consecutive emergency department patients with respiratory syndromes, categorized into four groups: no infection group (NOIG), bacterial infection group (BIG), viral infection group (VIG), and co-infection group (COING). Multinomial logistic regression was used to evaluate nCD64 and mCD169's association with diagnostic groups and estimate their predictive accuracy.
Results: 290 patients were included in VIG, 53 in BIG, 46 in COING, and 54 in NOIG. nCD64 was associated with bacterial infections and co-infections (p = 2.73 × 10- 16 and p = 8.83 × 10- 11, respectively), but not viral infections. mCD169 was associated with viral infections and co-infections (p = < 2 × 10- 16 and p = 2.45 × 10- 13, respectively), but not bacterial infections. The sensitivity and specificity of nCD64 for detecting bacterial infections were 0.75 and 0.84 (AUC = 0.83), respectively, while for mCD169 they were 0.87 and 0.91 (AUC = 0.92), respectively, for diagnosing viral infections. A diagnostic algorithm incorporating fever, nasopharyngeal swabs for the main respiratory virus, C-reactive protein, procalcitonin, and mCD169 reached an accuracy of 0.79 (95% CI 0.72-0.85) in distinguishing among the different groups.
Conclusions: nCD64 and MCD169 seem valuable for distinguishing between bacterial and viral respiratory infections. Integrating these biomarkers into diagnostic algorithms could enhance diagnostic accuracy aiding patient management in emergency settings.
Keywords: Bacterial infection; Biomarkers; Pneumonia; Viral infection; mCD169; nCD64.
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