Abstract
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 mm(2); BV5-10%, representing the percentage of PBV contained in vessels with a cross-sectional area between 5 and 10 mm(2); and BV10%, representing the percentage of PBV contained in vessels with a cross-sectional area greater than 10 mm(2). 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.