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Published on: January 14, 2014
Quantitative analysis of pulmonary vascular alterations in children with refractory Mycoplasma pneumoniae pneumonia
Zhoumeng Ying1,2, Zheming Li3,4,5, Jing Li3,4,5
1Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background:
Mycoplasma pneumoniae pneumonia (MPP) is a major cause of community-acquired pneumonia (CAP) in children, with some cases progressing to refractory MPP (RMPP). RMPP is associated with a hypercoagulable state and pulmonary embolism. This study aimed to investigate pulmonary microvascular changes in RMPP and evaluate the predictive value of pulmonary blood volume (PBV) parameters.
Methods:
A retrospective study using UV-Net-based pulmonary vascular analysis included 512 pediatric MPP patients in a cross-validation cohort and 124 pediatric MPP patients in an external testing cohort. Pulmonary blood vessels were segmented and classified by cross-sectional area into three blood-volume fractions: BV5%, representing the percentage of PBV contained in vessels with a cross-sectional area less than 5 mm2; BV5-10%, representing the percentage of PBV contained in vessels with a cross-sectional area between 5 and 10 mm2; and BV10%, representing the percentage of PBV contained in vessels with a cross-sectional area greater than 10 mm2. Logistic regression and extreme gradient boosting were used to analyze associations and predict RMPP. Model performance was assessed via receiver operating characteristic (ROC) curve analysis.
Results:
Patients with RMPP, compared to patients with non-RMPP, had a significantly lower BV5% (median: 58.50% vs. 60.63%, P=0.007) and higher BV10% (median: 20.90% vs. 19.39%, P=0.004). Multivariate analysis revealed BV5% as a protective predictor [odds ratio (OR) =0.70, P=0.005] and BV10% as a risk factor for RMPP (OR =1.49, P=0.002). Compared with the clinical-only model, the model incorporating these computed tomography (CT)-derived parameters significantly improved performance in the cross-validation cohort, demonstrating superiority in terms of area under the ROC curve (AUC) and other metrics (combined model: AUC =0.91, 95% CI: 0.89-0.94; clinical-only model: AUC =0.88, 95% CI: 0.86-0.91; P<0.001). In the external testing cohort, the combined model consistently outperformed the clinical-only model in accuracy, precision, specificity, and F1 score.
Conclusions:
Quantitative analysis revealed microvascular alterations in patients with RMPP. Integrating CT-derived biomarkers can enhance RMPP prediction and facilitate early intervention.
Insights
Refractory Mycoplasma pneumoniae pneumonia (RMPP) in children involves microvascular changes. Pulmonary blood volume analysis using CT scans can predict RMPP risk, aiding early intervention for this severe pneumonia complication.
Area of Science:
- Pulmonary Medicine
- Pediatric Pneumology
- Radiology
Background:
- Mycoplasma pneumoniae pneumonia (MPP) is a common pediatric community-acquired pneumonia (CAP).
- Refractory MPP (RMPP) is a severe form associated with hypercoagulability and pulmonary embolism.
- Investigating pulmonary microvascular changes in RMPP is crucial for understanding disease progression.
Purpose of the Study:
- To analyze pulmonary microvascular alterations in pediatric RMPP.
- To evaluate the predictive value of pulmonary blood volume (PBV) parameters derived from CT scans for RMPP.
- To develop an enhanced predictive model for RMPP.
Main Methods:
- Retrospective analysis of 512 pediatric MPP patients (cross-validation) and 124 (external testing).
- UV-Net-based pulmonary vascular analysis to segment and classify vessels by cross-sectional area.
- Calculation of blood-volume fractions (BV5%, BV5-10%, BV10%) and logistic regression/extreme gradient boosting for RMPP prediction.
Main Results:
- RMPP patients showed significantly lower BV5% and higher BV10% compared to non-RMPP.
- BV5% was a protective predictor (OR=0.70), while BV10% was a risk factor (OR=1.49) for RMPP.
- The CT-derived parameter model significantly improved RMPP prediction (AUC=0.91) over clinical factors alone (AUC=0.88).
Conclusions:
- Quantitative CT analysis reveals distinct microvascular alterations in RMPP.
- Integrating CT-derived biomarkers enhances RMPP prediction accuracy.
- Early identification of RMPP through advanced imaging biomarkers can facilitate timely intervention.
