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Published on: August 7, 2017
Predictive value of immune-related parameters in severe Mycoplasma pneumoniae pneumonia in children
Chaoyue Jiang1, Siwen Bao1, Weifeng Shen1
1Department of Laboratory Medicine, The First Hospital of Jiaxing, Affiliated Hospital of Jiaxing University, Jiaxing, China.
Background:
The severity of Mycoplasma pneumoniae pneumonia (MPP) is strongly correlated with the extent of the host's immune-inflammatory response. In order to diagnose the severity of MPP early, this study sought to explore the predictive value of immune-related parameters in severe MPP (sMPP) in admitted children.
Methods:
We performed a database analysis consisting of patients diagnosed at our medical centers with MPP between 2021 and 2023. We included pediatric patients and examined the association between complete blood cell count (CBC), lymphocyte subsets and the severity of MPP. Binary logistic regression was performed to identify the independent risk factors of sMPP. Receiver operating characteristic (ROC) curves were used to estimate discriminant ability.
Results:
A total of 245 MPP patients were included in the study, with 131 males and 114 females, median aged 6.0 [interquartile range (IQR), 4.0-8.0] years, predominantly located in 2023, and accounted for 64.5%. Among them, 79 pediatric patients were diagnosed as sMPP. The parameters of CBC including white blood cell (WBC) counts, neutrophil counts, monocyte counts, platelet counts, and neutrophil-to-lymphocyte ratio (NLR), were higher in the sMPP group (all P<0.05). The parameters of lymphocyte subsets including CD3+ T cell ratio (CD3+%) and CD3+CD8+ T cell ratio (CD3+CD8+%), were lower in the sMPP group (all P<0.05). And CD3-CD19+ B cell ratio (CD3-CD19+%) was higher in the sMPP group. Logistic regression analysis showed that age, CD3-CD19+%, and monocyte counts were identified as independent risk factors for the development of sMPP (all P<0.001). The three factors were applied in constructing a prediction model that was tested with 0.715 of the area under the ROC curve (AUC). The AUC of the prediction model for children aged ≤5 years was 0.823 and for children aged >5 years was 0.693.
Conclusions:
The predictive model formulated by age, CD3-CD19+%, and monocyte counts may play an important role in the early diagnosis of sMPP in admitted children, especially in children aged ≤5 years.
Insights
Early diagnosis of severe Mycoplasma pneumoniae pneumonia (MPP) in children is possible using immune-related parameters. A predictive model combining age, B cell ratio, and monocyte counts shows promise for identifying severe MPP cases.
Area of Science:
- Pediatric Infectious Diseases
- Immunology
- Clinical Diagnostics
Background:
- Mycoplasma pneumoniae pneumonia (MPP) severity correlates with the host's immune-inflammatory response.
- Early diagnosis of severe MPP (sMPP) in children is crucial for timely intervention.
- Immune-related parameters may offer predictive value for sMPP.
Purpose of the Study:
- To explore the predictive value of immune-related parameters for diagnosing severe MPP in admitted children.
- To identify independent risk factors associated with the development of sMPP.
- To develop and evaluate a predictive model for sMPP.
Main Methods:
- Database analysis of pediatric patients diagnosed with MPP between 2021-2023.
- Examination of associations between complete blood cell count (CBC), lymphocyte subsets, and MPP severity.
- Binary logistic regression and Receiver Operating Characteristic (ROC) curve analysis to identify risk factors and assess discriminant ability.
Main Results:
- Elevated WBC, neutrophil, monocyte, platelet counts, and NLR were observed in the sMPP group.
- Lower CD3+ T cell and CD3+CD8+ T cell ratios, and higher CD3-CD19+ B cell ratios were found in the sMPP group.
- Age, CD3-CD19+%, and monocyte counts were identified as independent risk factors for sMPP, with an overall prediction model AUC of 0.715.
Conclusions:
- A predictive model incorporating age, CD3-CD19+%, and monocyte counts shows potential for early sMPP diagnosis in children.
- The model demonstrated higher predictive accuracy in younger children (≤5 years, AUC 0.823) compared to older children (>5 years, AUC 0.693).
- This model can aid in the early identification of severe MPP cases requiring prompt medical attention.
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