Routine blood parameters as auxiliary diagnostic tools for Mycoplasma pneumoniae infection in children

Abstract

Introduction. Recently, the incidence of Mycoplasma pneumoniae (M. pneumoniae) infection in children has been increasing annually. Early differential diagnosis of M. pneumoniae infection can not only avoid the abuse of antibiotics, but also is essential for early treatment and reduction of transmission. Gap statement. The change of routine blood parameters may have important clinical significance for the diagnosis of M. pneumoniae infection, but it has not been reported so far. Aim. This study aims to establish a predictive model for M. pneumoniae infection and explore the changes and clinical value of routine blood parameters in children with M. pneumoniae infection, serving as auxiliary indicators for the diagnosis and differentiation of clinical M. pneumoniae infection. Methodology. A total of 770 paediatric patients with respiratory tract infections were enrolled in this study, including 360 in the M. pneumoniae group, 40 in the SARS-CoV-2 group, 200 in the influenza A virus group, and 170 in the control group. The differences of routine blood parameters among all groups were compared, and risk factors were analysed using multivariate logistics analysis, and the diagnostic efficacy of differential indicators using ROC curves. Results. This study revealed that Mono% (OR: 3.411; 95% CI: 1.638–7.102; P=0.001) was independent risk factor associated with M. pneumoniae infection, and Mono% (AUC=0.786, the optimal cutoff at 7.8%) had a good discriminative ability between patients with M. pneumoniae infection and healthy individuals. Additionally, Mono% (OR: 0.424; 95% CI: 0.231–0.781; P=0.006) and Lymp% (OR: 0.430; 95% CI: 0.246–0.753; P=0.003) were independent risk factors for distinguishing M. pneumoniae infection from influenza A virus infection, and the Lymp% (AUC=0.786, the optimal cutoff at 22.1%) and Net% (AUC=0.761, the optimal cutoff at 65.2%) had good discriminative abilities between M. pneumoniae infection and influenza A infection. Furthermore, platelet distribution width (OR: 0.680; 95% CI: 0.538–0.858; P=0.001) was independent risk factor for distinguishing M. pneumoniae infection from SARS-CoV-2 infection. Meanwhile, the ROC curve demonstrated that PDW (AUC=0.786, the optimal cutoff at 15%) has a good ability to differentiate between M. pneumoniae infection and SARS-CoV-2 infection. Conclusion. This study demonstrates that routine blood parameters can be used as auxiliary diagnostic indicators for M. pneumoniae infection and provide reference for the diagnosis and differentiation of clinical M. pneumoniae infection.