tetano
Editor, Senior Moderator
Virol J
. 2024 Jul 23;21(1):162.
doi: 10.1186/s12985-024-02429-4. Application of peripheral blood routine parameters in the diagnosis of influenza and Mycoplasma pneumoniae
Jingrou Chen[SUP] #[/SUP][SUP] 1 2 [/SUP], Yang Wang[SUP] #[/SUP][SUP] 1 2 [/SUP], Mengzhi Hong[SUP] #[/SUP][SUP] 1 2 [/SUP], Jiahao Wu[SUP] 1 2 [/SUP], Zongjun Zhang[SUP] 3 [/SUP], Runzhao Li[SUP] 1 2 [/SUP], Tangdan Ding[SUP] 1 2 [/SUP], Hongxu Xu[SUP] 1 2 [/SUP], Xiaoli Zhang[SUP] 4 [/SUP], Peisong Chen[SUP] 5 6 [/SUP]
Affiliations
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.
Keywords: Mycoplasma pneumonia; Area under the curve, AUC; IV@MP algorithm; Influenza; Peripheral blood routine parameters; Random forest.
. 2024 Jul 23;21(1):162.
doi: 10.1186/s12985-024-02429-4. Application of peripheral blood routine parameters in the diagnosis of influenza and Mycoplasma pneumoniae
Jingrou Chen[SUP] #[/SUP][SUP] 1 2 [/SUP], Yang Wang[SUP] #[/SUP][SUP] 1 2 [/SUP], Mengzhi Hong[SUP] #[/SUP][SUP] 1 2 [/SUP], Jiahao Wu[SUP] 1 2 [/SUP], Zongjun Zhang[SUP] 3 [/SUP], Runzhao Li[SUP] 1 2 [/SUP], Tangdan Ding[SUP] 1 2 [/SUP], Hongxu Xu[SUP] 1 2 [/SUP], Xiaoli Zhang[SUP] 4 [/SUP], Peisong Chen[SUP] 5 6 [/SUP]
Affiliations
- PMID: 39044252
- 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.
Keywords: Mycoplasma pneumonia; Area under the curve, AUC; IV@MP algorithm; Influenza; Peripheral blood routine parameters; Random forest.