Application of peripheral blood routine parameters in the diagnosis of influenza and Mycoplasma pneumoniae

• Published in: Virology journal Vol. 21; no. 1; p. 162
• Main Authors: Chen, Jingrou, Wang, Yang, Hong, Mengzhi, Wu, Jiahao, Zhang, Zongjun, Li, Runzhao, Ding, Tangdan, Xu, Hongxu, Zhang, Xiaoli, Chen, Peisong
• Format: Journal Article
• Language: English
• Published: London BioMed Central 23.07.2024; BioMed Central Ltd; BMC
• Subjects: Adolescent; Adult; Aged; Algorithms; Area under the curve, AUC; Biomedical and Life Sciences; Biomedicine; Blood; blood platelet count; Child; Diagnosis; Diagnosis, Differential; Female; fluorescence; hospitals; Humans; Influenza; Influenza, Human - blood; Influenza, Human - diagnosis; IV@MP algorithm; lymphocyte count; Male; Medical examination; Medical research; Medicine, Experimental; Middle Aged; monocytes; Mycoplasma pneumonia; Mycoplasma pneumoniae; Mycoplasma pneumoniae - isolation & purification; Peripheral blood routine parameters; Physiological aspects; Pneumonia, Mycoplasma - blood; Pneumonia, Mycoplasma - diagnosis; Random forest; t-test; Virology; world wide web; Young Adult; China; Area under the curve, AUC; Random forest; Peripheral blood routine parameters; Influenza; IV@MP algorithm; Mycoplasma pneumonia
• ISSN: 1743-422X; 1743-422X
• DOI: 10.1186/s12985-024-02429-4

Objectives Influenza and Mycoplasma pneumoniae infections often present concurrent and overlapping symptoms in clinical manifestations, making it crucial to accurately differentiate between the two in clinical practice. Therefore, this study aims to explore the potential of using peripheral blood routine parameters to effectively distinguish between influenza and Mycoplasma pneumoniae infections. Methods This study selected 209 influenza patients (IV group) and 214 Mycoplasma pneumoniae patients (MP group) from September 2023 to January 2024 at Nansha Division, the First Affiliated Hospital of Sun Yat-sen University. We conducted a routine blood-related index test on all research subjects to develop a diagnostic model. For normally distributed parameters, we used the T-test, and for non-normally distributed parameters, we used the Wilcoxon test. Results Based on an area under the curve (AUC) threshold of ≥ 0.7, we selected indices such as Lym# (lymphocyte count), Eos# (eosinophil percentage), Mon% (monocyte percentage), PLT (platelet count), HFC# (high fluorescent cell count), and PLR (platelet to lymphocyte ratio) to construct the model. Based on these indicators, we constructed a diagnostic algorithm named IV@MP using the random forest method. Conclusions The diagnostic algorithm demonstrated excellent diagnostic performance and was validated in a new population, with an AUC of 0.845. In addition, we developed a web tool to facilitate the diagnosis of influenza and Mycoplasma pneumoniae infections. The results of this study provide an effective tool for clinical practice, enabling physicians to accurately diagnose and differentiate between influenza and Mycoplasma pneumoniae infection, thereby offering patients more precise treatment plans.