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Updated: Jun 19, 2025
High-throughput Detection Method for Influenza Virus
Published on: February 4, 2012
Application of peripheral blood routine parameters in the diagnosis of influenza and Mycoplasma pneumoniae
Jingrou Chen1,2, Yang Wang1,2, Mengzhi Hong1,2
1Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China.
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.
Insights
This study developed a diagnostic model using routine blood parameters to differentiate between influenza and Mycoplasma pneumoniae infections. The IV@MP algorithm achieved an AUC of 0.845, aiding clinical diagnosis.
Area of Science:
- Clinical diagnostics
- Infectious disease research
- Hematology
Background:
- Influenza and Mycoplasma pneumoniae infections share overlapping symptoms, complicating clinical differentiation.
- Accurate diagnosis is essential for effective patient treatment and management.
Purpose of the Study:
- To explore the utility of peripheral blood routine parameters in distinguishing influenza from Mycoplasma pneumoniae infections.
- To develop a diagnostic model for differentiating these two common respiratory infections.
Main Methods:
- A cohort of 209 influenza and 214 Mycoplasma pneumoniae patients was analyzed.
- Routine blood indices were tested, and statistical tests (T-test, Wilcoxon test) were applied.
- A diagnostic algorithm, IV@MP, was constructed using the random forest method based on selected indices (Lym#, Eos#, Mon%, PLT, HFC#, PLR).
Main Results:
- Key indices including lymphocyte count (Lym#) and platelet count (PLT) were identified for model construction.
- The developed IV@MP algorithm demonstrated strong diagnostic performance with an Area Under the Curve (AUC) of 0.845.
- The algorithm was successfully validated in a separate patient cohort.
Conclusions:
- The IV@MP algorithm provides an effective tool for differentiating influenza and Mycoplasma pneumoniae infections in clinical settings.
- A web tool was developed to facilitate the practical application of this diagnostic algorithm.
- Accurate differentiation enables more precise treatment plans for patients.
