Methods, devices, media, and program products for predicting rmpp based on changes in pulmonary microvasculature

Abstract

The present application belongs to the field of intelligent medical treatment, and particularly relates to a method, device, medium and program product for predicting RMPP based on lung microvessel changes. The method comprises the following steps: S101: acquiring CT images of a pediatric MPP patient; S102: extracting the CT images to obtain quantitative PBV parameters, wherein the quantitative PBV parameters comprise BV10%, wherein BV10% refers to the percentage of blood volume in blood vessels with a cross-sectional area greater than 10 mm2 in total lung blood volume; and S103: if BV10% is higher than a first threshold value, determining that the pediatric MPP patient has a high risk of developing into RMPP, otherwise outputting a prediction result that the pediatric MPP patient has a low risk of developing into RMPP. The present application discovers the prediction effect of microvessel changes in CT on the occurrence and development of RMPP by quantitatively measuring the microvessel changes, realizes the clinical value of early diagnosis of RMPP, and thus drives early treatment in the clinic.

Description

Technical Field

[0001] This invention relates to the field of intelligent healthcare, and more specifically, to a method, device, medium, and program product for predicting RMPP based on changes in pulmonary microvessels. Background Technology

[0002] Community-acquired pneumonia (CAP) is a leading cause of hospitalization in children, with Mycoplasma pneumoniae (MP) accounting for 20%–40% of pediatric CAP cases. Although MPP is usually self-limiting, 6.83%–40.84% ​​of cases can progress to refractory MPP (RMPP), significantly increasing the risk of pulmonary and extrapulmonary complications, including pulmonary necrosis and systemic embolic events. RMPP is characterized by persistent fever, and clinical symptoms and radiological findings may worsen even after ≥7 days of appropriate macrolide treatment, often requiring immunomodulatory agents or second-line antibiotics. Early identification and diagnosis of RMPP are crucial to prevent disease progression and minimize associated complications.

[0003] Furthermore, MPP patients are considered to exhibit a hypercoagulable state. Recent studies have found that RMPP patients have higher levels of C-reactive protein (CRP), lactate dehydrogenase (LDH), neutrophil ratio (NR), and D-dimer compared to non-RMPP patients, indicating exacerbated inflammation and a hypercoagulable state. In fact, pulmonary embolism has been reported in RMPP patients, involving lobular, segmental, and even subsegmental or more distal arteries.

[0004] Pathological findings in a mouse model of *M. pulmonale* infection further revealed narrowing of pulmonary arterioles (<500 μm in diameter). However, conventional computed tomography (CT) primarily captures macroscopic parenchymal changes and lacks the resolution to visualize these microvessels. Therefore, it remains unclear whether the pronounced hypercoagulable state and inflammation observed in RMPP also affect the pulmonary microvessels in affected children.

[0005] Recent advances in quantitative angiography have enhanced our understanding of microvascular abnormalities in the lungs. This method utilizes conventional thin-slice CT scans, avoiding additional radiation exposure, which is particularly important for pediatric patients. We hypothesize that similar microvascular abnormalities may also occur in patients with RMPP. Summary of the Invention

[0006] In view of the above problems, this invention provides a method for predicting RMPP based on changes in pulmonary microvessels. Specifically, we propose that CT-derived quantitative PBV parameters—particularly the ratio of BV in small vessels (<5 mm², BV 5%) to large vessels (>10 mm², BV 10%)—may differ between RMPP and non-RMPP patients, reflecting changes in microvessels and hemodynamics. Therefore, this study aims to perform quantitative pulmonary vascular analysis to further explore pulmonary microvascular changes in RMPP patients and evaluate the predictive value of PBV parameters for the occurrence of RMPP.

[0007] This application (first aspect) discloses a method for predicting RMPP based on changes in pulmonary microvessels, comprising:

[0008] S1: Acquire CT images of pediatric MPP patients;

[0009] S2: Extract the CT images to obtain quantitative PBV parameters, the quantitative PBV parameters include BV10%, where BV10% refers to the percentage of blood volume in blood vessels with a cross-sectional area greater than 10 mm² to the total lung blood volume;

[0010] S3: If BV10% is higher than the first threshold, the prediction result of the pediatric MPP patient being at high risk of developing RMPP is determined; otherwise, the prediction result of developing RMPP being at low risk is output.

[0011] Furthermore, S3 is replaced by S3': the quantitative PBV parameters are input into the classifier to obtain the prediction result of the risk level of developing into RMPP;

[0012] Optionally, the pediatric MPP patient is under 18 years of age;

[0013] Optionally, the pediatric MPP patients are 5-10 years old.

[0014] Furthermore, the clinical characteristics of patients with Mycoplasma pneumoniae pneumonia are obtained simultaneously. The clinical characteristics and the quantitative PBV parameters are input into a classifier to obtain the prediction results of whether the MPP patients will develop into RMPP. The clinical characteristics include: duration of azithromycin treatment before admission, body temperature, presence of severe pneumonia, and CRP level.

[0015] Furthermore, the quantitative PBV parameter also includes BV5%, where BV5% refers to the percentage of blood volume in blood vessels with a cross-sectional area of ​​less than 5 mm² relative to the total pulmonary blood volume.

[0016] Optionally, BV5% refers to the percentage of blood volume in blood vessels with a cross-sectional area of ​​1.25 mm²-5 mm² relative to the total lung blood volume.

[0017] Furthermore, the quantitative PBV parameter also includes BV5-10%, where BV5-10% represents the percentage of blood volume in vessels with a cross-sectional area between 5 mm² and 10 mm² relative to the total pulmonary blood volume.

[0018] Furthermore, the first threshold is calculated based on the BV10% of the training set and the label indicating whether it has developed into an RMPP.

[0019] Furthermore, the clinical features include one or more of the following: duration of azithromycin treatment prior to admission, body temperature, presence of severe pneumonia, CRP level, BV 10%, albumin, gamma-glutamyl transferase, platelet distribution width, serum prealbumin, heart rate, creatine kinase, platelet count, hemoglobin, BV 5%, BV 5-10%, adenosine deaminase, monocyte count, whether glucocorticoids were used prior to admission, and mixed infection.

[0020] The second aspect of this application discloses a system for predicting RMPP based on changes in pulmonary microvessels, comprising:

[0021] Acquisition module 201: Used to acquire CT images of pediatric MPP patients;

[0022] Feature extraction module 202: used to extract the CT image to obtain quantitative PBV parameters, the quantitative PBV parameters including BV10%, wherein BV10% refers to the percentage of blood volume in blood vessels with a cross-sectional area greater than 10 mm² to the total lung blood volume;

[0023] Prediction module 203: If BV10% is higher than the first threshold, it determines that the pediatric MPP patient has a high risk of developing RMPP; otherwise, it outputs a prediction result that the patient has a low risk of developing RMPP.

[0024] A third aspect of this application discloses a computer device, the device comprising: a memory and a processor; the memory being used to store program instructions; the processor being used to invoke the program instructions, which, when executed, are used to perform the steps of the method described above.

[0025] The fourth aspect of this application discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0026] The fifth aspect of this application discloses a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0027] This application has the following beneficial effects:

[0028] (1) This application discovered the predictive role of microvascular changes in CT in the occurrence and development of RMPP by quantitatively measuring microvascular changes, realizing the clinical value of early diagnosis of RMPP, thereby driving early clinical treatment;

[0029] (2) Compared with traditional pneumonia volume analysis (such as solid volume), quantitative pulmonary vascular analysis can reveal more subtle pathophysiological changes;

[0030] (3) When integrated into XGBoost-based models, these quantitative PBV parameters significantly improved RMPP prediction in cross-validation cohorts compared to models using only clinical variables. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic diagram of the method flow provided in the first aspect of the present invention;

[0033] Figure 2 This is a schematic diagram of a program product provided in the second aspect of the present invention;

[0034] Figure 3 This is a schematic diagram of a computer device provided in an embodiment of the present invention;

[0035] Figure 4 This is a schematic diagram of the architecture of an exemplary computing device provided in an embodiment of the present invention;

[0036] Figure 5 This is a schematic diagram of the storage medium provided in an embodiment of the present invention;

[0037] Figure 6 This is a distribution diagram of blood vessels with different cross-sectional areas provided in an embodiment of the present invention;

[0038] Figure 7 This is a schematic diagram of the predicted AUC on the cross-validation set and external test set of a clinical-BV fusion model, a clinical feature-only model, and an embodiment of the present invention. Detailed Implementation

[0039] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0040] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as S101, S102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Figure 1 This is a schematic flowchart of a method for predicting RMPP based on assessment of pulmonary microvascular changes provided by an embodiment of the present invention. Specifically, the method includes the following steps:

[0043] S101: Acquire CT images of pediatric MPP patients;

[0044] S102: Extract the CT images to obtain quantitative PBV parameters, the quantitative PBV parameters including BV10%, wherein BV10% refers to the percentage of blood volume in blood vessels with a cross-sectional area greater than 10 mm² to the total lung blood volume;

[0045] S103: If BV10% is higher than the first threshold, the prediction result of the pediatric MPP patient being at high risk of developing RMPP is determined; otherwise, the prediction result of developing RMPP being at low risk is output.

[0046] The scheme in this application is based on the following research.

[0047] 1. Methods and Materials

[0048] The study has been approved by the institutional ethics committees of both hospitals, and no individual consent was required for the retrospective analysis.

[0049] 1.1. Study Design and Patient Selection

[0050] This retrospective study included consecutively diagnosed pediatric patients with MPP from two medical institutions, forming a cross-validation cohort and an external validation cohort.

[0051] The cross-validation cohort included patients admitted to the general inpatient ward of a tertiary pediatric specialist hospital between July 2019 and December 2023. The external validation cohort included patients hospitalized in the pediatric ward of a tertiary general hospital between January 2023 and April 2024.

[0052] Inclusion criteria were: (1) pediatric patients aged > 28 days to ≤ 18 years; (2) clinically diagnosed with MPP; and (3) all cohorts had a baseline chest CT scan during hospitalization, except for the cross-validation cohort which required a follow-up CT scan within 2 months of discharge. Exclusion criteria were: (1) baseline or follow-up CT image quality was insufficient for quantitative analysis; (2) no laboratory test data available; (3) immunodeficiency or documented prior immunosuppressive therapy; and (4) need for mechanical ventilation during hospitalization. Ultimately, the cross-validation and external validation cohorts included 512 and 124 patients, respectively. The diagnostic criteria for MPP can be found in Supplementary Material 1.1.

[0053] 1.2. Data Collection and Chest CT Imaging

[0054] All patients included in this study were diagnosed with mycoplasmal paroxysmal positional pulmonary embolism (MPP). Clinical, etiological, and laboratory data were retrospectively collected from the digital hospital information system. Clinical variables included demographic characteristics, past comorbidities, duration of fever and cough prior to admission, pre-admission treatment, initial vital signs, and oxygen support requirements at admission. Disease severity was classified according to the 2023 Chinese Pediatric MPP Guidelines. Pathogen detection results (including viruses, bacteria, and atypical pathogens) were recorded. Laboratory variables obtained at admission included complete blood count, liver and kidney function tests, inflammatory markers, coagulation tests, and electrolytes.

[0055] Clinical outcomes, complications, and hospitalizations (e.g., glucocorticoids, intravenous immunoglobulin, bronchoscopy) were extracted from discharge records. For outcomes, RMPP was defined as persistent fever, worsening clinical symptoms, progression of lung imaging, or extrapulmonary complications after ≥ 7 days of macrolide therapy. Patients were divided into RMPP and non-RMPP groups.

[0056] Thin-slice (≤1.5 mm), non-contrast chest CT images were acquired and analyzed from image archives and communication systems. The earliest chest CT acquired during hospitalization was selected as the baseline CT. For the cross-validation cohort, baseline and follow-up chest CT scans within 2 months after discharge were retrospectively collected. For the external validation cohort, only the baseline chest CT scan was acquired.

[0057] 1.3. Quantitative pulmonary vascular analysis

[0058] Lung vessels were automatically segmented using a previously validated UV-Net-based segmentation model. The detailed method steps include:

[0059] This study uses the deep learning framework UV-Net for three-dimensional pulmonary vessel segmentation.

[0060] This model incorporates a U-shaped network architecture, including a 2D encoder module and a 3D decoder module, which effectively adapts to data features. To expand the receptive field and incorporate global contextual information, the network includes an Atrous Spatial Pyramid Pooling (ASPP) module, which fuses multi-scale feature maps to preserve spatial and semantic details. For upsampling, the model employs the PixelShuffle technique to restore spatial resolution while maintaining detailed anatomical structures, ensuring continuity and topological consistency in the segmentation of pulmonary vessels and airways.

[0061] The segmentation process first involves the preliminary delineation of lung lobes with well-defined boundaries. Based on the delineated lung lobe boundaries, boundary representation B-rep data is constructed. The B-rep data structure contains multiple topological entities—faces, edges, half-edges, and vertices—as well as the connections between them, and interview parameter surfaces (including vascular surfaces in our study).

[0062] Topology UV-Net uses a face adjacency graph derived from B-rep to model the topology, where vertices V represent faces in B-rep and edges E encode the connectivity between faces.

[0063] Airway segmentation is then performed to preserve the tree-like connectivity of the bronchial structure.

[0064] In addition, the vessel segmentation module generates a complete and connected vessel network, which can accurately quantify vessel volume based on cross-sectional area.

[0065] During internal validation, the algorithm demonstrated high segmentation accuracy, with a Dice coefficient of 92.68% for arteries and 89.96% for veins. These results highlight the robustness and clinical applicability of UV-Net in pulmonary vascular analysis.

[0066] The segmented pulmonary vessels were then divided into three categories based on their cross-sectional area: <5 mm² (BV5), 5–10 mm² (BV5–10), and >10 mm² (BV10). Figure 6 Calculate the blood volume in each category and express it as a percentage of total lung blood volume, denoted as BV5%, BV5–10%, and BV10%, respectively.

[0067] 1.4. Development and Validation of the RMPP Prediction Model

[0068] A predictive model for RMPP was developed using clinical variables and PBV parameters.

[0069] First, candidate predictors collected at admission (including demographics, clinical symptoms, previous treatments, vital signs, laboratory tests, and BV parameters) were screened, and predictors with missing data >40% in the cross-validation cohort or external validation cohort were excluded.

[0070] To develop a predictive model for refractory mycoplasma pneumonia (RMPP), we evaluated five machine learning algorithms: Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Lightweight Gradient Boosting Machine (LightGBM), Logistic Regression (LR), and K-Nearest Neighbors (KNN). XGBoost, officially released in 2016, is an advanced ensemble learning algorithm that constructs multiple classification and regression trees within an optimized distributed gradient boosting framework. Compared to traditional machine learning algorithms, it offers higher complexity and superior performance. LightGBM is a high-performance distributed gradient boosting framework based on decision trees, widely used for ranking, classification, and other machine learning tasks. LR is a widely used linear classification model for evaluating the relationship between a dependent variable and multiple independent variables, particularly suitable for binary classification problems. SVM is a supervised learning algorithm designed to construct an optimal hyperplane to separate positive and negative samples, performing well in binary classification and high-dimensional datasets. KNN is an instance-based nonparametric learning algorithm that classifies samples based on their proximity to labeled instances. It is simple yet effective in handling multi-class problems and problems involving nonlinear decision boundaries.

[0071] For these models, we included 19 variables selected through minimum absolute shrinkage and operator-selective regression, and optimized the hyperparameters using 5-fold cross-validation based on average performance metrics. Subsequently, the final models were retrained on the entire cross-validation cohort using the determined optimal hyperparameters. Receiver Operating Characteristic (ROC) curve analysis was used to comprehensively evaluate model performance, with key metrics including area under the ROC curve (AUC), accuracy, precision, sensitivity, specificity, and F1 score. Evaluations were conducted in both the cross-validation and external validation cohorts. XGBoost performed best on both datasets and was therefore selected as the final analysis algorithm.

[0072] All models were developed in R software (version 4.3.1) using packages such as “caret”, “glmnet”, “knn”, “e1071”, “lightgbm”, and “xgboost”.

[0073] Based on the selected predictors, two models were constructed: (1) a clinical model containing 16 selected clinical variables and (2) a clinical BV model incorporating all 19 variables. For model development, the model hyperparameters were optimized through 5x cross-validation and retrained on the full cross-validation cohort with the determined optimal hyperparameters.

[0074] Model performance was assessed internally and externally using receiver operating characteristic (ROC) curve analysis, calculating the area under the ROC curve (AUC), accuracy, precision, sensitivity, specificity, and F1 score for both cohorts. The DeLong test was used to compare the AUC differences between the clinical and clinical BV models. Shapley additive interpretation (SHAP) values ​​were calculated to quantify the feature importance in the clinical BV model.

[0075] 1.5. Statistical Analysis

[0076] Statistical analysis was performed using R software (version 4.3.1). Chi-square tests were used for comparisons of categorical variables, and Student's t-test (for normally distributed data) or Wilcoxon rank-sum test (for non-normally distributed data) were used for comparisons of continuous variables. Holm's method was used for correction of multiple comparisons. Missing data were imputed using the multiple imputation method in R's "mice" package.

[0077] The association between PBV parameters and RMPP was investigated in a cross-validation cohort. Numerical variables were standardized using z-score standardization, and univariate logistic regression was performed to initially examine the association. Clinically relevant variables with <40% missing data were selected using LASSO regression (“glmnet” package) and included in a multivariate model to independently assess BV parameters. Furthermore, subgroup analyses were performed for the 5–10 year age group based on age distribution, age-related immune differences, and the higher prevalence of MPP in children over 5 years of age in the cross-validation cohort. Pearson correlation analysis further assessed the relationship between significant BV parameters and key laboratory indicators (D-dimer, CRP, LDH) and duration of fever.

[0078] Develop XGBoost models using the "caret" and "xgboost" packages. Perform SHAP analysis using the "SHAPforxgboost" package. Perform ROC analysis using the "pROC" package. A p-value less than 0.05 is considered statistically significant.

[0079] 2. Results

[0080] 2.1. Comparison of clinical and laboratory characteristics between RMPP and non-RMPP

[0081] As shown in Table 1, the cross-validation cohort included 512 patients (256 men and 256 women; median age 6.42 years [IQR, 4.33–8.25]), of whom 265 were classified as non-RMPP and 247 as RMPP. Patients in the RMPP group had significantly longer durations of fever and cough prior to admission, and a higher incidence of severe / critical pneumonia. Furthermore, these patients were more likely to require oxygen support and had elevated temperature, pulse, and respiratory rate (all p ≤ 0.01). In terms of clinical outcomes, compared to the non-RMPP group, the RMPP group had significantly longer hospital stays, prolonged total fever duration, and a higher incidence of pulmonary complications (all p ≤ 0.03).

[0082] Regarding etiology, there were no statistically significant differences among the groups (all p > 0.05). Laboratory results showed that patients with RMPP had significantly higher levels of CRP, erythrocyte sedimentation rate (ESR), neutrophil count, LDH, and D-dimer (all p ≤ 0.03).

[0083] 2.2. PBV parameter analysis at baseline and follow-up

[0084] At baseline, the BV5% in the RMPP group was significantly lower than that in the non-RMPP group (58.50% vs. 60.63%, p = 0.007), while the BV5-10% and BV10% in the RMPP group were both significantly higher than those in the non-RMPP group (median BV5-10%: 15.74% vs. 15.13%, p = 0.03; median BV10%: 20.90% vs. 19.39%, p = 0.004).

[0085] The median interval for follow-up CT scans was 16.00 days [13.00, 23.00]. Follow-up CT scans showed a significant increase in BV5% relative to baseline (p < 0.001). Conversely, both BV5-10% and BV10% values ​​on follow-up CT scans were significantly lower than baseline (all p < 0.01), suggesting that lung blood distribution may have recovered over time. Notably, during the follow-up period, BV5% in the RMPP group remained lower than that in the non-RMPP group (median: 62.42% vs. 63.55%, p = 0.03), while BV10% was higher in the RMPP group (Table 1).

[0086] Table 1. Comparison of quantitative parameters of pulmonary blood volume during follow-up in patients with refractory and non-refractory mycoplasma pneumoniae in cross-validation cohorts.

[0087]

[0088] Table Note: Unless otherwise stated, data are presented in terms of patient numbers, with percentages in parentheses. BV5%, BV5-10%, and BV10% represent the percentage of total lung blood volume contained in vessels with cross-sectional areas less than 5 mm², 5-10 mm², and greater than 10 mm², respectively. * indicates p < 0.05.

[0089] 2.3. Correlation analysis of RMPP and quantitative characteristics of pulmonary blood volume and its association with RMPP-related risk factors.

[0090] As shown in Table 2, in the cross-validation cohort, univariate analysis showed that each PBV parameter was significantly associated with the occurrence of RMPP (all p ≤ 0.03). In the subsequent multivariate logistic regression analysis, after adjusting for clinical covariates, BV5% remained an independent protective factor against RMPP (odds ratio [OR] = 0.70, 95% confidence interval [CI]: 0.54–0.89, p = 0.005), while BV10% became an independent risk factor (OR = 1.49, 95% CI: 1.17–1.92, p = 0.002). Similarly, in patients aged 5–10 years, after adjusting for relevant covariates, BV5% continued to show a protective association with RMPP (OR = 0.68; 95% CI: 0.49–0.83; p = 0.018), while BV10% remained a significant risk factor (OR = 1.58; 95% CI: 1.15–2.19; p = 0.005).

[0091] Table 2. Univariate and multivariate logistic regression analyses of quantitative pulmonary blood volume parameters associated with refractory mycoplasma pneumonia in the cross-validation cohort.

[0092]

[0093] Table Note: The adjusted model for the entire cohort included the following covariates: duration of azithromycin treatment before admission, severe pneumonia, body temperature, platelet distribution width, albumin, serum prealbumin, prothrombin time, thrombin time, serum phosphorus, sodium, uric acid, and cystatin C. For patients aged >5 years and <10 years, the adjusted model included the following covariates: duration of azithromycin treatment before admission, duration of fever before admission, severe pneumonia, body temperature, platelet distribution width, albumin, adenosine deaminase, prothrombin time, serum phosphorus, uric acid, and cystatin C.

[0094] Abbreviation: CI, confidence interval. BV5%, BV5–10%, and BV10% represent the percentage of total lung blood volume contained in vessels with cross-sectional areas of 1.25–5 mm², 5–10 mm², and greater than 10 mm², respectively. * indicates p < 0.05.

[0095] Furthermore, correlation analysis showed that BV5% was significantly negatively correlated with key inflammatory and coagulation biomarkers, including NR, CRP, and D-dimer (r = –0.12, –0.13, and –0.19, respectively; all p < 0.01). Conversely, BV10% was significantly positively correlated with these biomarkers (r = 0.14, 0.14, and 0.21, respectively; all p < 0.01).

[0096] 2.4. XGBoost-based RMPP prediction enhanced by quantitative PBV parameters

[0097] To improve the predictive performance of RMPP, we incorporated clinical variables and quantitative PBV features into an XGBoost-based model. Feature selection using LASSO regression identified 19 variables, including 16 clinical features and 3 BV-derived features.

[0098] Table 3. Performance metrics of the XGBoost model in the cross-validation queue and external validation queue.

[0099]

[0100] Table Notes: This table presents the performance metrics of the Extreme Gradient Boosting (XGBoost) model in the clinical-blood volume model and the clinical model. The clinical-blood volume model included all 19 variables selected by LASSO regression (16 clinical variables and 3 quantitative features derived from blood volume), while the clinical model used only 16 clinical variables. Metrics include the area under the curve (AUC) and its 95% confidence interval (CI), accuracy, sensitivity, specificity, precision, and F1 score, presented in both the cross-validation and external validation cohorts. p-values ​​were obtained using the DeLong test, which assesses the difference in AUC between the clinical-blood volume model and the clinical model in the cross-validation and external validation cohorts. * indicates p < 0.05.

[0101] As shown in Table 3 and Figure 7 As shown, in the cross-validation cohort, the clinical-BV model had a significantly higher AUC than the clinical model (AUC: 0.91 vs. 0.88, p < 0.001), and also had higher accuracy, precision, sensitivity, specificity, and F1 score.

[0102] SHAP analysis of the clinical-BV model showed that the five most influential features in the cross-validation cohort were the duration of azithromycin treatment before admission, body temperature, presence of severe pneumonia, CRP level, and BV10%, highlighting the importance of inflammatory markers and BV characteristics.

[0103] To assess the generalizability of the model, we evaluated its performance in an independent external validation cohort comprising 124 patients (median age: 7.00 years [IQR: 5.00–9.00], 52% male). In this cohort, the clinical-BV model had a higher AUC than the clinical model, although the difference was not statistically significant (AUC: 0.84 vs. 0.81, p = 0.09), but performed well in terms of accuracy, precision, specificity, and F1 score.

[0104] To further verify the predictive performance of the features selected in this application, Table 4 further verifies the predictive performance of different prediction models:

[0105] Table 4 Comparison of Predicted AUC for Different Indicators

[0106]

[0107] As can be seen from Table 4, although the individual prediction AUC of the quantitative PBV parameter developed in this invention is slightly low, the combination of BV5% and BV10% can achieve a better prediction effect (AUC: 0.78), and the combination of all three can achieve an even better prediction effect (0.81).

[0108] Furthermore, based on the SHAP analysis of the clinical-BV model (including 3 PBV parameters and 16 clinical features), the 5 most influential parameters were obtained. As shown in Table 4, the predictive AUC of the 5 most influential features reached 0.90, which is comparable to the predictive performance of the original clinical-BV model (0.91).

[0109] 3. Discussion

[0110] In this study, patients with RMPP exhibited a consistent pattern of microvascular remodeling on CT scans, characterized by a decrease in BV5% (microvessels < 5 mm²) and an increase in BV10% (macrovessels > 10 mm²) at baseline and follow-up. These changes indicate altered pulmonary vascular volume distribution, a pattern also observed in patients with acute pulmonary microcirculatory disturbances during COVID-19. Furthermore, multivariate analysis revealed that BV5%, negatively correlated with inflammation and coagulation biomarkers, was an independent protective predictor of RMPP. Conversely, BV10% emerged as an independent risk factor and was positively correlated with these same biomarkers. When integrated into an XGBoost-based model, these quantitative PBV parameters significantly improved RMPP prediction in the cross-validation cohort compared to models using only clinical variables. Although the AUC difference was not statistically significant in the external validation cohort, the clinical-BV model still outperformed the clinical model in terms of accuracy, precision, specificity, and F1 score. These findings suggest that incorporating PBV parameters can enhance the predictive performance of RMPP and maintain clinical applicability in independent cohorts.

[0111] In patients with RMPP, we observed a significant decrease in BV5%, while BV5–10% and BV10% increased. Consistent with these CT findings, histopathological analysis in a mouse model of recurrent MP infection revealed thickening of the pulmonary arteriolar walls and narrowing of the lumen, leading to elevated pulmonary artery pressure. Microscopically, pulmonary arterioles were defined as vessels with a diameter less than 500 μm, corresponding to the subset of vessels classified as BV5% on CT. The consistency between microscopic and imaging observations further suggests that patients with RMPP do indeed exhibit a reduction in pulmonary microvascular volume.

[0112] Given that the pathogenesis of MPP involves direct toxicity, immune-mediated damage, and vascular inflammation / thrombosis, we hypothesized that the observed decrease in BV5% (reflecting reduced microvascular volume in RMPP patients) may be related to microvascular endothelial inflammation or thrombosis induced by enhanced coagulation and inflammatory states. The observed increase in BV10% may reflect compensatory dilation of larger vessels due to reduced microvascular volume. This hypothesis is supported by three pieces of indirect evidence. First, RMPP patients in the cross-validation cohort showed significantly elevated D-dimer, CRP, ESR, and neutrophil counts. Second, correlation analysis further confirmed these findings, revealing a negative correlation between BV5% and inflammatory or coagulation markers. Finally, all recorded vascular thrombotic events occurred in the RMPP group. Furthermore, SHAP value analysis using the XGBoost model showed that BV10% was more important than BV5%. This observation suggests that macrovascular dilation is an active compensatory response, not merely a passive consequence.

[0113] Compared to traditional pneumonia volume analysis (such as consolidation volume), quantitative pulmonary vascular analysis can reveal more subtle pathophysiological changes. Traditional volume analysis primarily reflects solid tissue damage but has limited understanding of the underlying pathogenic mechanisms. In contrast, pulmonary vascular analysis, especially by reducing BV by 5%, can effectively capture the reduction in microvessel volume in patients with RMPP. This reduction highlights specific pathophysiological processes in RMPP, where enhanced coagulation and inflammatory states may affect pulmonary microvessels, leading to microvessel volume reduction. Furthermore, incorporating BV parameters into clinical predictive models improves early RMPP identification. Clinical BV models have demonstrated superior performance compared to clinical models alone (AUC: 0.91 vs. 0.88), enabling timely use of immunomodulators or second-line antibiotics and potentially preventing complications such as necrotizing pneumonia. Moreover, these quantitative vascular measurements can be obtained through routine CT scans, requiring no additional examinations or radiation exposure. Furthermore, compared to non-RMPP patients, RMPP patients continued to show a relative decrease in BV5% during follow-up (median duration: 16 days), similar to the vascular recovery pattern observed in post-COVID-19 studies, where 87.4% of patients still had residual vascular abnormalities at 6 months. This further underscores the necessity of continued vascular monitoring after symptom relief.

[0114] 4. Conclusion

[0115] This study demonstrates that pulmonary vascular changes (RMPP) in children are associated with significant pulmonary microvascular alterations, characterized by decreased BV5% and increased BV10%. These quantitative PBV parameters can serve as independent predictors of RMPP, enhancing the performance of XGBoost-based predictive models and can be easily integrated into routine CT workflows. Assessing pulmonary vascular changes via routine CT imaging is a non-invasive method that can help identify high-risk patients and guide early risk stratification and intervention.

[0116] Figure 3 This is a schematic diagram of a computer device provided in an embodiment of the present invention, such as... Figure 3 As shown, the device 2000 may include: one or more processors 2010 and one or more memories 2020; wherein the memories store computer-readable code that, when run by the one or more processors, can perform the methods described above.

[0117] The processor in this embodiment can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, operations, and logic block diagrams disclosed in this embodiment. The general-purpose processor can be a microprocessor or any conventional processor, and can be based on an x86 or ARM architecture.

[0118] In general, the various exemplary embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. When aspects of embodiments of this disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as non-limiting examples in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0119] For example, the method or apparatus according to embodiments of this disclosure can also be used by means of Figure 4 The architecture of the computing device 3000 shown is used for implementation. For example... Figure 4As shown, the computing device 3000 may include a bus 3010, one or more CPUs 3020, a read-only memory (ROM) 3030, a random access memory (RAM) 3040, a communication port 3050 connected to a network, an input / output component 3060, a hard disk 3070, etc. The storage devices in the computing device 3000, such as the ROM 3030 or the hard disk 3070, may store various data or files used for processing and / or communication of the methods provided in this disclosure, as well as program instructions executed by the CPU. The computing device 3000 may also include a user interface 3080. Of course, Figure 4 The architecture shown is merely exemplary and can be omitted as needed when implementing different devices. Figure 4 One or more components in the computing device shown.

[0120] This invention also includes a computer-readable storage medium, such as... Figure 5 The diagram illustrates a storage medium 4000 provided in an embodiment of the present invention. The computer storage medium 4020 stores computer-readable instructions 4010. When the computer-readable instructions 4010 are executed by a processor, the method described above according to embodiments of the present disclosure can be performed. The computer-readable storage medium in the embodiments of the present disclosure may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Synchronous Link Dynamic Random Access Memory (SLDRAM), and Direct Memory Bus Random Access Memory (DR RAM). It should be noted that the memory used in the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0121] This disclosure also provides a computer program product or computer program that, when executed by a processor, implements the steps of the above-described method, such as... Figure 2 As shown, the computer program product or computer program includes:

[0122] Acquisition module 201: Used to acquire CT images of pediatric MPP patients;

[0123] Feature extraction module 202: used to extract the CT image to obtain quantitative PBV parameters, the quantitative PBV parameters including BV10%, wherein BV10% refers to the percentage of blood volume in blood vessels with a cross-sectional area greater than 10 mm² to the total lung blood volume;

[0124] Prediction module 203: If BV10% is higher than the first threshold, it determines that the pediatric MPP patient has a high risk of developing RMPP; otherwise, it outputs a prediction result that the patient has a low risk of developing RMPP.

[0125] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0126] In general, the various exemplary embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. When aspects of embodiments of this disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as non-limiting examples in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0127] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0128] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0129] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0130] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0131] The exemplary embodiments of this disclosure described in detail above are merely illustrative and not restrictive. Those skilled in the art will understand that various modifications and combinations can be made to these embodiments or their features without departing from the principles and spirit of this disclosure, and such modifications should fall within the scope of this disclosure.

Claims

1. A method for predicting RMPP based on changes in pulmonary microvessels, characterized in that, The method includes: S1: Acquire CT images of pediatric MPP patients; S2: Extract the CT images to obtain quantitative PBV parameters, which include BV10%, BV5%, and BV5-10%. BV10% refers to the percentage of blood volume in blood vessels with a cross-sectional area greater than 10 mm² relative to the total lung blood volume; BV5% refers to the percentage of blood volume in blood vessels with a cross-sectional area less than 5 mm² relative to the total lung blood volume; and BV5-10% represents the percentage of blood volume in blood vessels with a cross-sectional area between 5 mm² and 10 mm² relative to the total lung blood volume. S3: Input the quantitative PBV parameters into the classifier to obtain the prediction results of the risk level of developing into RMPP.

2. The method for predicting RMPP based on changes in lung microvessels according to claim 1, characterized in that, The pediatric MPP patients mentioned are under 18 years of age.

3. The method for predicting RMPP based on changes in lung microvessels according to claim 1, characterized in that, The pediatric MPP patients mentioned are aged 5-10 years.

4. The method for predicting RMPP based on changes in pulmonary microvessels according to claim 1, characterized in that, The BV5% refers to the percentage of blood volume in blood vessels with a cross-sectional area of ​​1.25 mm²-5 mm² relative to the total lung blood volume.

5. The method for predicting RMPP based on changes in pulmonary microvessels according to claim 1, characterized in that, Simultaneously, the clinical characteristics of patients with Mycoplasma pneumoniae pneumonia are obtained, and the clinical characteristics and the quantitative PBV parameters are input into a classifier to obtain the prediction result of whether the MPP patient will develop into RMPP.

6. The method for predicting RMPP based on changes in lung microvessels according to claim 5, characterized in that, The clinical features include one or more of the following: duration of azithromycin treatment prior to admission, body temperature, presence of severe pneumonia, CRP level, BV 10%, albumin, gamma-glutamyl transferase, platelet distribution width, serum prealbumin, heart rate, creatine kinase, platelet count, hemoglobin, BV 5%, BV 5-10%, adenosine deaminase, monocyte count, whether glucocorticoids were used prior to admission, and mixed infection.

7. A computer device, characterized in that, The device includes: a memory and a processor; the memory is used to store a computer program; the processor executes the computer program to implement the steps of the method according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1-6.

9. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-6.

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