Elastic-to-Muscular Pulmonary Artery Area Ratio and Echocardiographic Pulmonary Arterial Systolic Pressure in the Prediction of Pulmonary Hypertension: A Retrospective Cohort Study

Elastic-to-Muscular Pulmonary Artery Area Ratio and Echocardiographic Pulmonary Arterial Systolic Pressure in the Prediction of Pulmonary Hypertension: A Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Elastic-to-Muscular Pulmonary Artery Area Ratio and Echocardiographic Pulmonary Arterial Systolic Pressure in the Prediction of Pulmonary Hypertension: A Retrospective Cohort Study Gen Zhang¹, Jixiang Liang², Zhipeng Ren¹, Huan Wang¹, Guanzheng Cui¹, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8358122/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Objectives To evaluate the diagnostic accuracy of the elastic-to-muscular pulmonary artery area ratio (EM-AR), derived from 3D-printed digital models, for pulmonary hypertension (PH), both independently and in combination with echocardiographically estimated pulmonary arterial systolic pressure (PASP). Methods This retrospective diagnostic study enrolled 80 patients with suspected pulmonary hypertension, using invasive mean pulmonary arterial pressure (mPAP) from right heart catheterization as the reference standard. Cross-sectional areas of the elastic (third-order, right lower lobe) and muscular (sixth-order, right lower lobe) pulmonary arteries were measured from 3D-printed digital models to calculate the elastic-to-muscular artery ratio (EM-AR). A linear regression model integrating the calculated EM-AR and measured echocardiographic PASP was developed to predict mPAP (mPAP predicted ). Results Quantitative analysis revealed significant remodeling of the pulmonary arterial tree in the PH group, characterized by enlargement of elastic arteries ( p < 0.001), reduction in muscular artery area ( P < 0.001), and a consequent elevation in the EM-AR ( P < 0.001). The EM-AR showed the strongest correlation with invasive mPAP (r = 0.73, P < 0.001) compared to its individual components (elastic artery: r = 0.54, P < 0.001; muscular artery: r = -0.52, P < 0.001). The composite mPAP, derived from a multiple linear regression model of EM-AR and PASP, correlated strongly with invasive mPAP (r = 0.82, P < 0.001) and achieved superior diagnostic accuracy for PH (AUC = 0.95). At the optimal cut-off of 23.9 mmHg, it identified PH with 83.1% sensitivity and 95.2% specificity. Conclusions The EM-AR derived from 3D-printed digital models appears to be a promising indicator of pulmonary vascular remodeling. In our cohort, a multivariable model combining EM-AR with echocardiographic PASP demonstrated excellent diagnostic performance for the noninvasive prediction of pulmonary hypertension. Pulmonary Hypertension 3D Printing Elastic-to-Muscular Artery Ratio Echocardiography Noninvasive Diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Pulmonary hypertension (PH) is a chronic and progressive disease, either idiopathic or secondary to other conditions, defined by a mean pulmonary arterial pressure (mPAP) > 20 mmHg, as per the latest World Symposium on Pulmonary Hypertension guidelines[ 1 ]. Its pathological hallmarks include dilation of elastic arteries, intimal hyperplasia, luminal narrowing, and plexiform lesions in muscular arteries[ 2 ]. Given its potential progression to right heart failure, early diagnosis and intervention are critical. However, early detection remains a significant clinical challenge. While right heart catheterization (RHC) is the diagnostic gold standard, its invasive nature and associated risks prevent its routine use for screening. Consequently, non-invasive imaging plays a central role in evaluating suspected PH. Echocardiography serves as the primary screening tool, offering hemodynamic profiling such as pulmonary pressure estimation and prognostic indices like the TAPSE/PASP ratio. Nevertheless, its diagnostic specificity is limited by methodological variability and operator dependence. Similarly, cardiac MRI, despite providing exquisite detail of the vasculature and right ventricle, is hampered by high costs and limited availability[ 3 ]. Computed tomography (CT) has seen substantial progress in PH evaluation, yet established parameters like main pulmonary artery diameter exhibit considerable diagnostic heterogeneity and limited sensitivity[ 4 ]. A fundamental pathophysiological constraint underlies this limitation: the characteristic vascular pathology of PH involves opposing changes—proximal elastic artery dilation concurrent with distal muscular artery narrowing and rarefaction. The superimposition of these divergent processes ultimately compromises the accuracy of individual CT metrics. This understanding suggests that a composite biomarker, capturing both proximal arterial dilation and distal narrowing, could better reflect the bidirectional pathology of PH, potentially enabling earlier and more accurate diagnosis. However, reliable cross-sectional area measurement of distal muscular arteries is technically challenging on conventional CT. This challenge arises from two main factors: first, the inherent difficulty in accurately identifying and tracking specific generations (e.g., 5th or 6th order) of these small vessels within the complex pulmonary arterial tree; second, the finite spatial resolution and partial volume effects that blur vascular margins and cause significant partial-volume averaging. Consequently, manual or semi-automated luminal measurements are highly variable and unreliable for precise quantification. In this context, three-dimensional (3D) printing-based digital modeling offers a promising solution, enabling accurate reconstruction and quantification of these critical distal segments[ 5 , 6 ]. Capitalizing on this advantage, the present study aimed to evaluate the diagnostic accuracy of the elastic-to-muscular pulmonary artery area ratio (EM-AR), derived from 3D-printing digital models, for diagnosing PH, both alone and in combination with echocardiographically estimated right ventricular systolic pressure. Materials and methods Patients The study protocol was approved by the institutional ethics committee of Suzhou Municipal Hospital (K-2023-006-K01), with a waiver for individual informed consent granted due to its retrospective design. The study cohort was selected by screening consecutive patients presenting to the Respiratory Medicine and Cardiovascular Disease Center outpatient clinics between February 2023 and May 2025 with suspected pulmonary hypertension (n = 101). Enrollment required completion of a comprehensive diagnostic workup, including transthoracic echocardiography, computed tomography pulmonary angiography (CTPA), and right heart catheterization (RHC). Patients who lacked any of these three core examinations were excluded. Exclusion criteria included radiologic evidence of chronic thromboembolic pulmonary disease on CTPA, which may reduce or abolish perfusion in distal muscular arteries, potentially confounding vessel assessment; an interval exceeding 30 days between any of the key imaging studies and RHC; and non-diagnostic image quality. Following these criteria, 21 patients were excluded, leaving a final cohort of 80 eligible subjects. Baseline demographic and clinical characteristics of the final cohort are summarized in Table 1 . CTPA acquisition CTPA examinations were performed on a dual-source CT system (Siemens Somatom Force) using a standardized clinical protocol for pulmonary angiography. Scans were acquired with patients in the supine position, triggered at the end-inspiration phase. The imaging protocol employed vendor-specific dose modulation techniques (CARE kV and CARE Dose4D) for automatic tube voltage and current optimization. A 45 mL bolus of Iohexol (350 mg I/mL) was administered intravenously at a rate of 5 mL/s, followed by a 40 mL saline chaser. Scan initiation was triggered automatically upon contrast arrival in the main pulmonary artery. All images were reconstructed using a hybrid iterative algorithm (ADMIRE, strength 3) with thin-slice parameters (0.75 mm slice thickness, 0.5 mm increment). Echocardiography Comprehensive transthoracic echocardiography was performed on all subjects using a high-end ultrasound system (EPIQ CVx, Philips Healthcare) in accordance with established international guidelines. Pulmonary artery systolic pressure (PASP) was estimated using a standardized multi-parameter approach. The peak velocity of the tricuspid regurgitation jet was measured by continuous-wave Doppler, and the corresponding systolic pressure gradient was calculated using the modified Bernoulli equation. Right atrial pressure was assessed based on the diameter and respiratory-phase collapsibility of the inferior vena cava. Echocardiographic PASP was then determined as the sum of the trans-tricuspid gradient and the estimated right atrial pressure. Right heart catheterisation RHC was performed following contemporary clinical standards for hemodynamic assessment. Under local anesthesia, a balloon-tipped pulmonary artery catheter was advanced via the internal jugular or femoral venous approach under fluoroscopic guidance. Stable positioning was achieved in the pulmonary artery for pressure measurements. Hemodynamic parameters obtained included mean right atrial pressure, pulmonary artery systolic and diastolic pressures, and mean pulmonary arterial pressure(mPAP RHC ). Cardiac output was determined using the thermodilution technique, based on triplicate measurements with a variance of < 10%. Pulmonary artery wedge pressure was recorded during balloon inflation at end-expiration. All pressure tracings were obtained at neutral breath-hold and calibrated against the phlebostatic axis. Image Processing and 3D Reconstruction Clinical data, ultrasound-derived PASP, CTPA images, and RHC data were fully anonymized to ensure that observers and analysts could not link any individual data points. CTPA images were analyzed by a radiologist with over five years of experience in cardiovascular imaging. Thin-slice CTPA images (0.75 mm thickness, 0.5 mm increment) in DICOM format were imported into medical image processing software (Mimics Innovation Suite, Materialise NV). The pulmonary arterial tree was semi-automatically segmented using a threshold-based region-growing algorithm, followed by manual refinement to include distal branches up to the sixth order. The segmented mask was then converted into a 3D surface mesh (STL format), smoothed, and anatomically oriented in 3-matic software (Materialise) to generate a patient-specific digital model of the pulmonary vasculature (Fig. 1 A, B). To ensure consistent and reproducible analysis, arterial branches within the right lower lobe (RLL) were systematically identified. The RLL was chosen as the region of interest due to its relatively predictable branching pattern, favorable orientation for perpendicular cross-sectional measurements, and lower anatomical variability compared to other lung regions. After reconstructing the patient-specific 3D model of the pulmonary arterial tree, measurement points were designated along the vessel centerlines within the RLL for subsequent quantitative analysis (Fig. 1 C). Based on these reference points, the cross-sectional area of the third-order (elastic) artery was measured perpendicular to the vessel centerline using integrated digital calipers. For muscular arteries, all identifiable sixth-order branches within the RLL were measured in the same standardized manner (Fig. 1 D), and their cross-sectional areas were averaged to obtain a single representative muscular artery area per patient. The elastic-to-muscular artery area ratio (EM-AR) was then calculated as the quotient of the elastic artery area and the mean muscular artery area. To compare with existing methods using pulmonary arterial trunk volume to predict pulmonary hypertension, the volume of the pulmonary arterial trunk was also measured on the same 3D digital model. For isolated volumetric analysis, the reconstructed pulmonary arterial tree was digitally color-coded by vascular segments. Using Mimics software, we manually delineated and isolated the following segments: the main pulmonary artery (MPA, magenta), right and left main pulmonary arteries (RPA, yellow; LPA, blue), and their successive branches. The lumen volume (mm³) of each segment was then calculated using the built-in "Volume" tool. This segmental approach enabled a comprehensive morphometric analysis of the entire vasculature, facilitating precise assessment of regional vascular remodeling and the distribution of flow capacity under pathological conditions (Fig. 2 ). Statistical analysis Data completeness was assessed for all study variables. The normality of continuous data, including EM-AR, PASP, and invasive mPAP, was confirmed using the Shapiro-Wilk test. Descriptive statistics are presented as mean ± standard deviation or median (interquartile range) for continuous variables, depending on distribution, and as frequencies (percentages) for categorical variables. Differences in baseline characteristics and vascular measurements between the PH group (mPAP > 20 mmHg) and the non-PH group were compared using independent two-sample t-tests or Mann-Whitney U tests, as appropriate. Standardized effect sizes (Cohen’s d for t-tests) were calculated and reported alongside P-values to quantify the magnitude of observed differences, with 95% confidence intervals for the effect size estimates. The chi-squared test was used for categorical variables. The correlation between the novel morphological parameter (EM-AR) and the reference standard (invasive mPAP) was assessed using Pearson’s correlation coefficient. A multiple linear regression analysis with forward stepwise selection (entry criteria: P < 0.05; removal criteria: P ≥ 0.10) was performed to develop a non-invasive predictive model for mPAP. The model included EM-AR and echocardiographic PASP as covariates. Model performance was evaluated by the coefficient of determination (r²) and the standard error of the estimate. The diagnostic performance of EM-AR, PASP, and the composite predicted mPAP (mPAP predicted ) for identifying PH (mPAP > 20 mmHg) was assessed using receiver operating characteristic (ROC) curve analysis. Areas under the ROC curves (AUC) were calculated and compared using the DeLong test. The optimal diagnostic cut-off values were determined by maximizing the Youden index. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were reported, along with their 95% confidence intervals. A two-sided P-value < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS software (Version 26.0, IBM Corp.). Results Study cohort A total of 80 patients with suspected pulmonary hypertension were included in the final analysis, comprising 59 patients (73.8%) with confirmed PH (mPAP > 20 mmHg) and 21 patients (26.2%) without PH. The two groups were comparable in terms of gender distribution and body surface area (both P > 0.05). However, patients in the PH group were significantly older than those in the non-PH group (49.6 ± 10.5 vs. 40.4 ± 12.7 years; P = 0.005, Cohen’s d = 0.83). As expected, all invasive hemodynamic parameters measured by right heart catheterization-including mPAP, systolic and diastolic PAP, PAWP, and PVR-were significantly elevated in the PH group (all P < 0.001), with large effect sizes (Cohen’s d ranging from − 1.09 to -7.54). Similarly, non-invasive assessments showed significant differences, with higher PASP on echocardiography in the PH group ( P < 0.001, Cohen’s d = -1.56) (Table 1 ). Table 1 Clinical characteristics of the study population No PH PH P value Effect Size (Cohen’s d,95%CI) Number of subjects 21 59 - - Female 13 24 0.165 - Age(years) 40.4 ± 12.7 49.6 ± 10.5 0.005 0.83 (0.27, 1.38) Body of surface area(m 2 ) 1.70 ± 0.25 1.72 ± 0.20 0.742 -0.093(-0.51,0.32) RVSP (mmHg) 21.0 ± 9.66 43.4 ± 15.27 < 0.001 -1.56 (-2.20, -0.90) mPAP RHC (mmHg) 14.9 ± 3.3 56.4 ± 16.9 < 0.001 -2.68 (-3.44, -1.92) Systolic PAP RHC (mmHg) 27.0 ± 4.1 69.3 ± 5.9 < 0.001 -7.54 (-9.27, -5.81) Diastolic PAP RHC (mmHg) 11.2 ± 3.5 28.4 ± 9.7 < 0.001 -1.93 (-2.63, -1.23) PAWP RHC (mmHg) 7.4 ± 4.2 13.6 ± 6.0 < 0.001 -1.09 (-1.68, -0.49) PVR RHC (dyn·s·cm⁻⁵) 120.0 ± 53.7 397.8 ± 198.3 < 0.001 -1.54 (-2.17, -0.90) Data are presented as mean ± standard deviation for continuous variables and as number (percentage) for categorical variables.PH, pulmonary hypertension; RVSP, echocardiographically estimated right ventricular systolic pressure; mPAP_RHC, mean pulmonary arterial pressure measured by right heart catheterization (reference standard); PAP, pulmonary arterial pressure; PAWP, pulmonary artery wedge pressure; PVR, pulmonary vascular resistance. Between-group comparisons for continuous variables were performed using Welch’s t-test (assuming unequal variances). Effect sizes are reported as Cohen’s d with 95% confidence intervals (CI), where |d| ≈ 0.2, 0.5, and 0.8 correspond to small, medium, and large effect sizes, respectively. Quantification of Main Pulmonary Artery (MPA) Volume A significant difference in main pulmonary artery (MPA) volume was observed between the PH and non-PH groups. Patients with PH had significantly larger MPA volumes (645.0 ± 243.2 mm³) compared to those without PH (297.0 ± 130.6 mm³), with a highly significant between-group difference ( P < 0.001) and a large effect size (Cohen’s d = -1.51, 95% CI: -2.18 to -0.84). Correlation analysis further revealed a significant positive association between MPA volume and mPAP RHC (r = 0.62, r² = 0.38, P < 0.0001) (Fig. 4 ). Quantification of Elastic-to-Muscular Artery Ratio Based on quantitative analysis of 3D digital models, the study demonstrated significant structural remodeling of the pulmonary vascular tree in patients with pulmonary hypertension (PH). Compared to non-PH subjects (n = 21), PH patients (n = 59) exhibited enlargement of elastic (third-order) pulmonary arteries (508.8 ± 227.5 mm² vs. 284.1 ± 128.7 mm², P < 0.001; Cohen’s d = 1.23, 95% CI: 0.64 to 1.80), reduction in muscular (sixth-order) artery area (3.00 ± 0.90 mm² vs. 3.97 ± 0.51 mm², P < 0.001; Cohen’s d = -1.36, 95% CI: -1.95 to -0.76), and a consequent increase in the EM-AR (318.3 ± 167.9 vs. 150.2 ± 77.4, P < 0.001; Cohen’s d = 1.25, 95% CI: 0.66 to 1.83) (Fig. 3 ). The composite EM-AR showed a strong positive correlation with invasively measured mPAP (r = 0.73, r² = 0.53, P < 0.001), stronger than the correlation with main pulmonary artery (MPA) volume indexed to body surface area (r = 0.62, r² = 0.38, P < 0.0001) (Fig. 4 ). When analyzed separately, the cross-sectional area of the elastic artery showed a moderate positive correlation with mPAP (r = 0.54, r² = 0.30, P < 0.001), while the muscular artery area showed a moderate negative correlation (r = -0.52, r² = 0.26, P < 0.001). Both individual components correlated less strongly with mPAP RHC than the composite EM-AR and showed weaker associations than the MPA volume index. Development and Performance of the Predictive Model The strong individual correlation of EM-AR with mPAP, combined with the established role of echocardiographic PASP in hemodynamic assessment, led to the development of a multivariate predictive model. Using forward stepwise multiple linear regression with mPAP RHC as the dependent variable, both EM-AR and PASP were retained as significant independent predictors (both P < 0.001). The resulting regression equation was: mPAPpredicted (mmHg) = 5.832 + 0.101 × (EM-AR) + 0.413 × (PASP) (r² = 0.67, standard error of estimate = 10.2 mmHg). The mPAP_predicted derived from this model showed an excellent correlation with invasively measured mPAP (r = 0.82, r² = 0.67, P < 0.001; Fig. 5 A). Bland-Altman analysis revealed a mean bias of 0.43 mmHg between predicted and invasive mPAP, with 95% limits of agreement ranging from − 24.45 to 25.31 mmHg (Fig. 5 B). Diagnostic Accuracy for Pulmonary Hypertension Receiver operating characteristic (ROC) curve analysis demonstrated that the composite mPAP predicted model exhibited superior diagnostic performance for identifying pulmonary hypertension, with an area under the curve (AUC) of 0.95 (95% CI: 0.90–0.99). The overall diagnostic accuracy of the composite model was significantly higher than that of the EM-AR alone [AUC = 0.92; difference in AUC = 0.07, P = 0.032 by DeLong test], echocardiographic PASP alone [AUC = 0.90; difference = 0.10, P = 0.008], and the main pulmonary artery (MPA) volume index alone [AUC = 0.89; difference = 0.11, P = 0.005] (Fig. 6 ). Notably, the individual elastic and muscular artery areas were not subjected to separate ROC analysis to avoid collinearity with the composite EM-AR metric, which already incorporates their dimensional information. At the optimal cut-off value of 23.9 mmHg, determined by maximizing the Youden index, the composite mPAP predicted identified PH with a sensitivity of 83.1% (95% CI: 71.0% – 91.6%), a specificity of 95.2% (95% CI: 76.2% – 99.9%), a positive predictive value (PPV) of 98.0% (95% CI: 89.4% – 99.9%), and a negative predictive value (NPV) of 66.7% (95% CI: 46.0% – 83.5%). In comparison, the optimal cut-off for EM-AR was 197.3, yielding a sensitivity of 79.7% (95% CI: 67.2% – 89.0%), a specificity of 85.7% (95% CI: 63.7% – 97.0%), a PPV of 93.9% (95% CI: 84.3% – 98.5%), and an NPV of 62.1% (95% CI: 42.3% – 79.3%). PASP at a cut-off of 27.5 mmHg had a sensitivity of 81.4% (95% CI: 69.1% – 90.3%), a specificity of 81.0% (95% CI: 58.1% – 94.6%), a PPV of 92.3% (95% CI: 82.4% – 97.7%), and an NPV of 63.0% (95% CI: 42.4% – 80.6%). Discussion This study introduces and evaluates a novel morphological parameter—the elastic-to-muscular pulmonary artery area ratio (EM-AR), derived from three-dimensional-printed digital models—for noninvasive assessment of pulmonary hypertension (PH), when integrated with echocardiographic PASP. The key findings of this study can be summarized in three points: First, quantitative 3D analysis confirmed characteristic bidirectional pulmonary vascular remodeling in PH, characterized by proximal elastic artery dilation concurrent with distal muscular artery narrowing[ 7 , 8 ]. Second, the composite EM-AR ratio demonstrated a stronger correlation with invasively measured mean pulmonary arterial pressure (mPAP) than its individual components or conventional CT parameters such as the main pulmonary artery (MPA) volume index, suggesting that it more comprehensively reflects the overall remodeling process. Third, a multivariate model combining EM-AR and echocardiographic PASP showed reasonable diagnostic accuracy, outperforming either parameter alone, while providing a feasible, noninvasive approach for mPAP estimation. These findings introduce a novel methodological dimension to the quantitative imaging assessment of pulmonary vascular disease. The observed remodeling pattern-proximal arterial dilation coupled with distal narrowing-is consistent with the established bidirectional pathology of PH. Histopathological studies have identified medial hypertrophy, intimal hyperplasia, and plexiform lesion formation in muscular arteries as key contributors to increased pulmonary vascular resistance[ 9 , 10 ]. Simultaneously, proximal elastic arteries dilate in response to chronic pressure overload, driven by mechanisms involving endothelial dysfunction and extracellular matrix remodeling[ 11 , 12 ]. Our study offers in vivo morphological confirmation of this process via 3D imaging. The EM-AR metric integrates these opposing structural changes into a single continuous variable, and its correlation with invasive mPAP indicates a relationship with hemodynamic burden. Notably, EM-AR showed a slightly stronger correlation with mPAP than the MPA volume index-a parameter reflecting primarily proximal changes-likely due to its inclusion of distal vascular information. Recent studies using dual-energy CT pulmonary blood volume analysis and magnetic resonance angiography have similarly reported reduced peripheral perfusion and subsegmental vascular pruning in PH[ 13 ]. EM-AR, derived from routine CTPA, could thus complement these functional observations. Reliable and reproducible measurement of distal pulmonary arteries remains challenging with conventional CT evaluation. Traditional metrics are primarily focused on the central vasculature due to inconsistencies in manual vessel tracking, partial-volume effects, and difficulty in obtaining orthogonal cross-sections[ 14 , 15 ]. Our 3D printing-based digital modeling approach addresses these limitations by allowing semi-automated segmentation and reconstruction, enabling standardized centerline extraction and cross-sectional area measurement of specific pulmonary artery generations (third-order elastic and sixth-order muscular arteries in the right lower lobe). This enhances measurement consistency and reproducibility. The right lower lobe was chosen as the region of interest due to its anatomical consistency and reliable visualization in routine CTPA[ 16 ]. This workflow provides a referential framework for distal pulmonary artery quantification, which can be further optimized and automated. The improvement in diagnostic performance observed with the composite model suggests a potential complementarity between morphological and hemodynamic parameters. Echocardiographic RVSP, though widely used for noninvasive screening, is subject to variability due to factors like acoustic windows, technical assumptions, and operator dependence[ 17 , 18 ]. EM-AR reflects structural remodeling of the vascular bed but may also be influenced by factors beyond pressure alone, such as disease duration or etiology. Combining EM-AR with PASP in a multivariate model allows for a more holistic capture of both immediate hemodynamic status and underlying structural adaptation, thereby improving diagnostic robustness. The model’s high specificity in this study suggests that it could serve as a supportive tool for confirming PH, potentially guiding decisions on subsequent invasive assessments. This method builds on two widely available clinical tests-transthoracic echocardiography and CTPA-without requiring additional contrast or radiation exposure, facilitating clinical translation. While the current 3D modeling process requires specialized software and expertise, ongoing advances in image-processing automation and artificial intelligence could help lower implementation barriers[ 19 ]. Compared to other noninvasive modalities, cardiac MRI provides comprehensive right ventricular evaluation but has limited direct pressure-estimation capability and lower accessibility[ 20 ]. Nuclear imaging, while sensitive for thromboembolism, lacks detailed anatomical resolution[ 21 ]. Among CT-based predictors, our model performed comparably well in this cohort, likely due to its focus on hierarchical vascular remodeling. Several limitations should be considered. First, the single-center, retrospective design may introduce selection bias, and the findings need validation in larger, multicenter, prospective cohorts with a broader range of PH etiologies[ 22 ]. Second, patients with chronic thromboembolic pulmonary hypertension (CTEPH) were excluded due to the potential confounding effect of intraluminal thrombi on distal artery measurements, leaving the applicability of the model to this subgroup unaddressed. While the 3D analysis is reproducible, it is not fully automated, and future integration of deep learning-based segmentation could improve efficiency and precision, particularly for large datasets. Additionally, pulmonary vascular remodeling may vary regionally, especially in hypoxic or fibrotic diseases, limiting the generalizability of a single-lobe measurement. Despite using the right lower lobe to reduce anatomical variability, the generalizability to the entire pulmonary vascular system remains uncertain. Although imaging and right heart catheterization (RHC) were performed within 30 days, physiological variability may still influence results. Lastly, the prognostic and therapeutic potential of EM-AR warrants further investigation in longitudinal studies to assess its evolution over time and predictive value for PH management. Conclusion In conclusion, the elastic-to-muscular pulmonary artery area ratio (EM-AR) derived from 3D digital models offers a quantifiable measure of bidirectional vascular remodeling in pulmonary hypertension. When integrated with echocardiographic RVSP in a multivariate model, EM-AR demonstrates promising diagnostic performance for noninvasive estimation of mean pulmonary arterial pressure. Further multicenter validation and technical refinement are needed before this composite approach can be considered for routine clinical application. Abbreviations CT Computed tomography CTPA Computed tomography pulmonary angiography MPA Main pulmonary artery mPAP Mean pulmonary arterial pressure EM-AR Elastic-to-muscular pulmonary artery area ratio mPAP predicted mPAP predicted by linear regression mPAP RHC mPAP measured by right heart catheterisation PASP Transthoracic echocardiographic pulmonary arterial systolic pressure estimate PH Pulmonary hypertension RHC Right heart catheterisation PAWP Pulmonary Artery Wedge Pressure 3D Three-Dimensional RLL Right Lower Lobe Declarations Ethics approval and consent to participate This retrospective study was conducted in accordance with the ethical principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Ethics Committee of Suzhou Municipal Hospital (Approval No. K-2023-006-K01). The requirement for individual informed consent was waived by the aforementioned ethics committee due to the retrospective nature of the study. Consent for publication Not applicable. Competing Interests The authors declare that they have no competing interests. Funding This work was supported by the National Key Research and Development Program of China (Grant Number 2024YFC3406800); the Noncommunicable Chronic Diseases-National Science and Technology Major Project (Grant Number 2025ZD0552104); the Gusu (Suzhou) Health Talent Program (Grant Number GSWS2022065); and the Talent Program of Gusu School, Nanjing Medical University (Grant Number GSRCKY20210101). Author Contribution Gen Zhang and Dianyuan Li conceived and designed the study. Gen Zhang , Jixiang Liang , and Zhipeng Ren conducted the experiments, data collection, and analysis. Gen Zhang drafted the manuscript. Jixiang Liang , Huan Wang , and Guanzheng Cui contributed to software processing and 3D modeling. Xianzhi Wang , Dongsheng He , Xin Li , Zhiqiang Dai , and Shangxuan Li provided critical resources, clinical expertise, and patient data. All authors reviewed and approved the final manuscript. Acknowledgement We gratefully acknowledge Dr. Panguangyu and Dr. Dagong(Peking University People’s Hospital) for serving as independent radiologists in the blinded assessment of the computed tomography pulmonary angiography (CTPA) images. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References N.F. Ruopp, H.W. Farber. The New World Symposium on Pulmonary Hypertension Guidelines: Should Twenty-One Be the New Twenty-Five?, Circulation 140 (2019) 1134–1136. https://doi.org/10.1161/CIRCULATIONAHA.119.040292 . G. Simonneau, D. Montani, D.S. Celermajer, C.P. Denton, M.A. Gatzoulis, M. Krowka, et al. Haemodynamic definitions and updated clinical classification of pulmonary hypertension, Eur Respir J 53 (2019) 1801913. https://doi.org/10.1183/13993003.01913-2018 . M.R. Fisher, P.R. Forfia, E. Chamera, T. Housten-Harris, H.C. Champion, R.E. Girgis, et al. Accuracy of Doppler Echocardiography in the Hemodynamic Assessment of Pulmonary Hypertension, Am J Respir Crit Care Med 179 (2009) 615–621. https://doi.org/10.1164/rccm.200811-1691OC . K. Rezaei-Kalantari, K. Samimi, H. Zomorodian, H. Bakhshandeh, M. Jafari, A.M. Farahmand, et al. Pulmonary Blood Volume Measured by Dual-Energy Computed Tomography Can Help Distinguish Patients With Pulmonary Hypertension, Front. Cardiovasc. Med. 9 (2022) 835655. https://doi.org/10.3389/fcvm.2022.835655 . P.G.M. Knoops, G. Biglino, A.D. Hughes, K.H. Parker, L. Xu, S. Schievano, et al. A Mock Circulatory System Incorporating a Compliant 3D-Printed Anatomical Model to Investigate Pulmonary Hemodynamics, Artificial Organs 41 (2017) 637–646. https://doi.org/10.1111/aor.12809 . A.D. Bordones, M. Leroux, V.O. Kheyfets, Y.-A. Wu, C.-Y. Chen, E.A. Finol. Computational Fluid Dynamics Modeling of the Human Pulmonary Arteries with Experimental Validation, Ann Biomed Eng 46 (2018) 1309–1324. https://doi.org/10.1007/s10439-018-2047-1 . K.R. Stenmark, I.F. McMurtry. Vascular Remodeling Versus Vasoconstriction in Chronic Hypoxic Pulmonary Hypertension: A Time for Reappraisal?, Circulation Research 97 (2005) 95–98. https://doi.org/10.1161/01.RES.00000175934.68087.29 . S. Matsuoka, G.R. Washko, T. Yamashiro, R.S.J. Estepar, A. Diaz, E.K. Silverman, et al. Pulmonary Hypertension and Computed Tomography Measurement of Small Pulmonary Vessels in Severe Emphysema, Am J Respir Crit Care Med 181 (2010) 218–225. https://doi.org/10.1164/rccm.200908-1189OC . Y. Suzuki, K. Yoshimura, T. Uto, J. Sato, S. Imokawa, T. Suda. Morphological changes in small pulmonary vessels are associated with severe acute exacerbation in chronic obstructive pulmonary disease, COPD Volume 11 (2016) 1435–1445. https://doi.org/10.2147/COPD.S107424 . J. Jacob, B.J. Bartholmai, S. Rajagopalan, M. Kokosi, A. Nair, R. Karwoski, et al. Mortality prediction in idiopathic pulmonary fibrosis: evaluation of computer-based CT analysis with conventional severity measures, Eur Respir J 49 (2017) 1601011. https://doi.org/10.1183/13993003.01011-2016 . A.U. Fayyaz, W.D. Edwards, J.J. Maleszewski, E.A. Konik, H.M. DuBrock, B.A. Borlaug, et al. Global Pulmonary Vascular Remodeling in Pulmonary Hypertension Associated With Heart Failure and Preserved or Reduced Ejection Fraction, Circulation 137 (2018) 1796–1810. https://doi.org/10.1161/CIRCULATIONAHA.117.031608 . N. Hopkins, P. McLoughlin. The structural basis of pulmonary hypertension in chronic lung disease: remodelling, rarefaction or angiogenesis? Journal of Anatomy 201 (2002) 335–348. https://doi.org/10.1046/j.1469-7580.2002.00096.x . D.G. Kiely, D.L. Levin, P.M. Hassoun, D. Ivy, P. Jone, J. Bwika, et al. Statement on imaging and pulmonary hypertension from the Pulmonary Vascular Research Institute (PVRI), Pulm. Circ. 9 (2019) 1–32. https://doi.org/10.1177/2045894019841990 . S. Matsuoka, G.R. Washko, T. Yamashiro, R.S.J. Estepar, A. Diaz, E.K. Silverman, et al. Pulmonary Hypertension and Computed Tomography Measurement of Small Pulmonary Vessels in Severe Emphysema, Am J Respir Crit Care Med 181 (2010) 218–225. https://doi.org/10.1164/rccm.200908-1189OC . S. Matsuoka, G.R. Washko, M.T. Dransfield, T. Yamashiro, R. San Jose Estepar, A. Diaz, E.K. Silverman, et al. Quantitative CT Measurement of Cross-sectional Area of Small Pulmonary Vessel in COPD, Academic Radiology 17 (2010) 93–99. https://doi.org/10.1016/j.acra.2009.07.022 . S. Singhal, R. Henderson, K. Horsfield, K. Harding, G. Cumming. Morphometry of the Human Pulmonary Arterial Tree, Circulation Research 33 (1973) 190–197. https://doi.org/10.1161/01.RES.33.2.190 . C. Hammerstingl, R. Schueler, L. Bors, D. Momcilovic, S. Pabst, G. Nickenig, et al. Diagnostic Value of Echocardiography in the Diagnosis of Pulmonary Hypertension, PLoS ONE 7 (2012) e38519. https://doi.org/10.1371/journal.pone.0038519 . M.A. Al-Omari, J. Finstuen, C.P. Appleton, M.E. Barnes, T.S.M. Tsang. Echocardiographic Assessment of Left Ventricular Diastolic Function and Filling Pressure in Atrial Fibrillation, The American Journal of Cardiology 101 (2008) 1759–1765. https://doi.org/10.1016/j.amjcard.2008.02.067 . R. Gharleghi, C.A. Dessalles, R. Lal, S. McCraith, K. Sarathy, N. Jepson, et al. 3D Printing for Cardiovascular Applications: From End-to-End Processes to Emerging Developments, Ann Biomed Eng 49 (2021) 1598–1618. https://doi.org/10.1007/s10439-021-02784-1 . B.A. Maron, S.H. Abman, C.G. Elliott, R.P. Frantz, R.K. Hopper, E.M. Horn, et al. Pulmonary Arterial Hypertension: Diagnosis, Treatment, and Novel Advances, Am J Respir Crit Care Med 203 (2021) 1472–1487. https://doi.org/10.1164/rccm.202012-4317SO . R.W.W. Biederman, M. Doyle, P. Correa-Jaque, G. Rayarao, R.L. Benza. Integrated use of cardiac MRI and the CardioMEMS ™ HF system in PAH: the utility of coincident pressure and volume in RV failure—the NHLBI-VITA trial, Cardiovasc. Diagn. Ther. 9 (2019) 492–501. https://doi.org/10.21037/cdt.2019.09.05 . M.D. Pokharel, A. Garcia-Flores, D. Marciano, M.C. Franco, J.R. Fineman, S. Aggarwal, et al. Mitochondrial network dynamics in pulmonary disease: Bridging the gap between inflammation, oxidative stress, and bioenergetics, Redox Biology 70 (2024) 103049. https://doi.org/10.1016/j.redox.2024.103049 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Feb, 2026 Reviewers agreed at journal 25 Jan, 2026 Reviews received at journal 22 Jan, 2026 Reviewers agreed at journal 20 Jan, 2026 Reviews received at journal 20 Jan, 2026 Reviewers agreed at journal 20 Jan, 2026 Reviewers invited by journal 20 Jan, 2026 Editor assigned by journal 29 Dec, 2025 Submission checks completed at journal 29 Dec, 2025 First submitted to journal 28 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8358122","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577732769,"identity":"9ad78281-b484-4f3f-a96a-d65f327918f5","order_by":0,"name":"Gen Zhang¹","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Gen","middleName":"","lastName":"Zhang¹","suffix":""},{"id":577732770,"identity":"28a4e70f-624c-4fac-9399-36cc193cb04e","order_by":1,"name":"Jixiang Liang²","email":"","orcid":"","institution":"Xi'an Jiaotong 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11:42:02","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":97125,"visible":true,"origin":"","legend":"","description":"","filename":"2a94f3d79686452688358827b19c36321structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8358122/v1/279515d893a1bf781add1088.xml"},{"id":100884164,"identity":"c10b1d6f-8500-4385-b823-13671f03494e","added_by":"auto","created_at":"2026-01-22 11:42:11","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111391,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8358122/v1/81d09e3503f637a5f3b504cd.html"},{"id":100884052,"identity":"d1e7d929-c58b-4567-83e5-c5ccd3fb3d6e","added_by":"auto","created_at":"2026-01-22 11:42:01","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":386596,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative three-dimensional reconstruction and vascular morphometric analysis in a patient with severe pulmonary hypertension. (A) Axial CT slices showing the original cross-sectional imaging data with segmentation of the pulmonary vasculature.(B) 3D volume rendering of the reconstructed pulmonary arterial tree.(C) Surface model with marked measurement points (red dots) along the central axis of major vessels, used for centerline-based analysis.(D) Cross-sectional area measurements at multiple locations (green ellipses), performed sequentially at the main pulmonary artery, first-order pulmonary artery branches, the third-order (elastic) artery within the RLL, and all identifiable sixth-order (muscular) arteries, with annotated values in mm². The inset highlights a representative bifurcation with detailed area quantification. All 3D reconstructions and morphometric analyses were performed using Mimics 19.0 (Materialise), enabling accurate segmentation, visualization, and quantitative evaluation of vascular geometry.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8358122/v1/b9a37980da7213bfa4f1fc63.jpeg"},{"id":100884190,"identity":"e4f2963f-fe78-4a4e-ab64-35d7e099cde0","added_by":"auto","created_at":"2026-01-22 11:42:14","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":236509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSegmentation and volumetric analysis of the pulmonary arterial tree by distinct anatomical segments. The MPA was segmented from the pulmonary valve to the bifurcation; the luminal volume (mm³) was calculated using the built-in \"Volume\" tool in Mimics software.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8358122/v1/8f8541e1bfd909e6f0bc34f8.jpeg"},{"id":100884197,"identity":"ab962ff4-6621-4243-b8ab-4715ed27aab7","added_by":"auto","created_at":"2026-01-22 11:42:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30291,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of elastic artery area, muscular artery area, and EM-AR) between non-PH and PH groups. Bar charts of elastic artery area, muscular artery area, and EM-AR in patients without (non-PH，n=21) and with pulmonary hypertension (PH, n=59). Data are presented as mean ± standard deviation. P values were derived from independent two‑sample t‑tests.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8358122/v1/14cb49fbb7e74e81f4d5e498.png"},{"id":100884151,"identity":"65c86fb3-c1ac-42a7-b62a-560a96aed4d4","added_by":"auto","created_at":"2026-01-22 11:42:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":354484,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of vascular imaging metrics with invasively measured pulmonary artery pressure. Scatter plots illustrating correlations of (A) elastic artery area, (B) muscular artery area, (C) EM‑AR, and (D) main pulmonary artery (MPA) volume index with mPAP\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eRHC\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e. The solid grey line indicates the line of identity (y=x). Pearson correlation coefficients (r) and associated P‑values are indicated for each comparison.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8358122/v1/fd69cc218850558e9cadb85d.png"},{"id":100884167,"identity":"dee9654d-306d-4f03-9059-69acc5ad7e21","added_by":"auto","created_at":"2026-01-22 11:42:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":113366,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation and agreement of predicted and invasive mPAP. (A)\u0026nbsp;Scatter plot of model-predicted mean pulmonary arterial pressure (mPAP\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003epredicted\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e) versus invasive mPAP measured by right heart catheterization (mPAP\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eRHC\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e). The solid grey line indicates the line of identity (y=x) and pearson’s correlation coefficient (r) and P-value are shown. (B)\u0026nbsp;Bland-Altman plot of the difference between predicted and invasive mPAP against their average. The upper and lower dashed horizontal lines represent the 95% limits of agreement.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8358122/v1/7a6fd4d8b7e6f9392701c759.png"},{"id":100884166,"identity":"5ce2e51e-429c-4c27-9c39-50e2fcabf2b2","added_by":"auto","created_at":"2026-01-22 11:42:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":128722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic (ROC) curve analysis for the non-invasive diagnosis of pulmonary hypertension. ROC curves compare the diagnostic performance of the mPAP\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003epredicted\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, the EM-AR, echocardiographically PASP, and the main pulmonary artery (MPA) volume index. The area under the curve (AUC) values for each parameter are presented in the corresponding Results section.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8358122/v1/be5ee687d70c7f5eec4fa652.png"},{"id":100949957,"identity":"c5f7cb83-8696-4353-a4cd-9664cab4626d","added_by":"auto","created_at":"2026-01-23 07:06:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3457805,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8358122/v1/486dfde3-ba3a-4e32-8f36-988db4fddfdf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elastic-to-Muscular Pulmonary Artery Area Ratio and Echocardiographic Pulmonary Arterial Systolic Pressure in the Prediction of Pulmonary Hypertension: A Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePulmonary hypertension (PH) is a chronic and progressive disease, either idiopathic or secondary to other conditions, defined by a mean pulmonary arterial pressure (mPAP)\u0026thinsp;\u0026gt;\u0026thinsp;20 mmHg, as per the latest World Symposium on Pulmonary Hypertension guidelines[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its pathological hallmarks include dilation of elastic arteries, intimal hyperplasia, luminal narrowing, and plexiform lesions in muscular arteries[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Given its potential progression to right heart failure, early diagnosis and intervention are critical. However, early detection remains a significant clinical challenge. While right heart catheterization (RHC) is the diagnostic gold standard, its invasive nature and associated risks prevent its routine use for screening.\u003c/p\u003e \u003cp\u003eConsequently, non-invasive imaging plays a central role in evaluating suspected PH. Echocardiography serves as the primary screening tool, offering hemodynamic profiling such as pulmonary pressure estimation and prognostic indices like the TAPSE/PASP ratio. Nevertheless, its diagnostic specificity is limited by methodological variability and operator dependence. Similarly, cardiac MRI, despite providing exquisite detail of the vasculature and right ventricle, is hampered by high costs and limited availability[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Computed tomography (CT) has seen substantial progress in PH evaluation, yet established parameters like main pulmonary artery diameter exhibit considerable diagnostic heterogeneity and limited sensitivity[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A fundamental pathophysiological constraint underlies this limitation: the characteristic vascular pathology of PH involves opposing changes\u0026mdash;proximal elastic artery dilation concurrent with distal muscular artery narrowing and rarefaction. The superimposition of these divergent processes ultimately compromises the accuracy of individual CT metrics.\u003c/p\u003e \u003cp\u003eThis understanding suggests that a composite biomarker, capturing both proximal arterial dilation and distal narrowing, could better reflect the bidirectional pathology of PH, potentially enabling earlier and more accurate diagnosis. However, reliable cross-sectional area measurement of distal muscular arteries is technically challenging on conventional CT. This challenge arises from two main factors: first, the inherent difficulty in accurately identifying and tracking specific generations (e.g., 5th or 6th order) of these small vessels within the complex pulmonary arterial tree; second, the finite spatial resolution and partial volume effects that blur vascular margins and cause significant partial-volume averaging. Consequently, manual or semi-automated luminal measurements are highly variable and unreliable for precise quantification.\u003c/p\u003e \u003cp\u003eIn this context, three-dimensional (3D) printing-based digital modeling offers a promising solution, enabling accurate reconstruction and quantification of these critical distal segments[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Capitalizing on this advantage, the present study aimed to evaluate the diagnostic accuracy of the elastic-to-muscular pulmonary artery area ratio (EM-AR), derived from 3D-printing digital models, for diagnosing PH, both alone and in combination with echocardiographically estimated right ventricular systolic pressure.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e The study protocol was approved by the institutional ethics committee of Suzhou Municipal Hospital (K-2023-006-K01), with a waiver for individual informed consent granted due to its retrospective design. The study cohort was selected by screening consecutive patients presenting to the Respiratory Medicine and Cardiovascular Disease Center outpatient clinics between February 2023 and May 2025 with suspected pulmonary hypertension (n\u0026thinsp;=\u0026thinsp;101). Enrollment required completion of a comprehensive diagnostic workup, including transthoracic echocardiography, computed tomography pulmonary angiography (CTPA), and right heart catheterization (RHC). Patients who lacked any of these three core examinations were excluded.\u003c/p\u003e \u003cp\u003eExclusion criteria included radiologic evidence of chronic thromboembolic pulmonary disease on CTPA, which may reduce or abolish perfusion in distal muscular arteries, potentially confounding vessel assessment; an interval exceeding 30 days between any of the key imaging studies and RHC; and non-diagnostic image quality. Following these criteria, 21 patients were excluded, leaving a final cohort of 80 eligible subjects. Baseline demographic and clinical characteristics of the final cohort are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCTPA acquisition\u003c/h3\u003e\n\u003cp\u003eCTPA examinations were performed on a dual-source CT system (Siemens Somatom Force) using a standardized clinical protocol for pulmonary angiography. Scans were acquired with patients in the supine position, triggered at the end-inspiration phase. The imaging protocol employed vendor-specific dose modulation techniques (CARE kV and CARE Dose4D) for automatic tube voltage and current optimization. A 45 mL bolus of Iohexol (350 mg I/mL) was administered intravenously at a rate of 5 mL/s, followed by a 40 mL saline chaser. Scan initiation was triggered automatically upon contrast arrival in the main pulmonary artery. All images were reconstructed using a hybrid iterative algorithm (ADMIRE, strength 3) with thin-slice parameters (0.75 mm slice thickness, 0.5 mm increment).\u003c/p\u003e\n\u003ch3\u003eEchocardiography\u003c/h3\u003e\n\u003cp\u003e Comprehensive transthoracic echocardiography was performed on all subjects using a high-end ultrasound system (EPIQ CVx, Philips Healthcare) in accordance with established international guidelines. Pulmonary artery systolic pressure (PASP) was estimated using a standardized multi-parameter approach. The peak velocity of the tricuspid regurgitation jet was measured by continuous-wave Doppler, and the corresponding systolic pressure gradient was calculated using the modified Bernoulli equation. Right atrial pressure was assessed based on the diameter and respiratory-phase collapsibility of the inferior vena cava. Echocardiographic PASP was then determined as the sum of the trans-tricuspid gradient and the estimated right atrial pressure.\u003c/p\u003e\n\u003ch3\u003eRight heart catheterisation\u003c/h3\u003e\n\u003cp\u003eRHC was performed following contemporary clinical standards for hemodynamic assessment. Under local anesthesia, a balloon-tipped pulmonary artery catheter was advanced via the internal jugular or femoral venous approach under fluoroscopic guidance. Stable positioning was achieved in the pulmonary artery for pressure measurements. Hemodynamic parameters obtained included mean right atrial pressure, pulmonary artery systolic and diastolic pressures, and mean pulmonary arterial pressure(mPAP\u003csub\u003eRHC\u003c/sub\u003e). Cardiac output was determined using the thermodilution technique, based on triplicate measurements with a variance of \u0026lt;\u0026thinsp;10%. Pulmonary artery wedge pressure was recorded during balloon inflation at end-expiration. All pressure tracings were obtained at neutral breath-hold and calibrated against the phlebostatic axis.\u003c/p\u003e\n\u003ch3\u003eImage Processing and 3D Reconstruction\u003c/h3\u003e\n\u003cp\u003eClinical data, ultrasound-derived PASP, CTPA images, and RHC data were fully anonymized to ensure that observers and analysts could not link any individual data points. CTPA images were analyzed by a radiologist with over five years of experience in cardiovascular imaging. Thin-slice CTPA images (0.75 mm thickness, 0.5 mm increment) in DICOM format were imported into medical image processing software (Mimics Innovation Suite, Materialise NV). The pulmonary arterial tree was semi-automatically segmented using a threshold-based region-growing algorithm, followed by manual refinement to include distal branches up to the sixth order. The segmented mask was then converted into a 3D surface mesh (STL format), smoothed, and anatomically oriented in 3-matic software (Materialise) to generate a patient-specific digital model of the pulmonary vasculature (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo ensure consistent and reproducible analysis, arterial branches within the right lower lobe (RLL) were systematically identified. The RLL was chosen as the region of interest due to its relatively predictable branching pattern, favorable orientation for perpendicular cross-sectional measurements, and lower anatomical variability compared to other lung regions. After reconstructing the patient-specific 3D model of the pulmonary arterial tree, measurement points were designated along the vessel centerlines within the RLL for subsequent quantitative analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Based on these reference points, the cross-sectional area of the third-order (elastic) artery was measured perpendicular to the vessel centerline using integrated digital calipers. For muscular arteries, all identifiable sixth-order branches within the RLL were measured in the same standardized manner (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), and their cross-sectional areas were averaged to obtain a single representative muscular artery area per patient. The elastic-to-muscular artery area ratio (EM-AR) was then calculated as the quotient of the elastic artery area and the mean muscular artery area.\u003c/p\u003e \u003cp\u003eTo compare with existing methods using pulmonary arterial trunk volume to predict pulmonary hypertension, the volume of the pulmonary arterial trunk was also measured on the same 3D digital model. For isolated volumetric analysis, the reconstructed pulmonary arterial tree was digitally color-coded by vascular segments. Using Mimics software, we manually delineated and isolated the following segments: the main pulmonary artery (MPA, magenta), right and left main pulmonary arteries (RPA, yellow; LPA, blue), and their successive branches. The lumen volume (mm\u0026sup3;) of each segment was then calculated using the built-in \"Volume\" tool. This segmental approach enabled a comprehensive morphometric analysis of the entire vasculature, facilitating precise assessment of regional vascular remodeling and the distribution of flow capacity under pathological conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003eData completeness\u003c/b\u003e was assessed for all study variables. The normality of continuous data, including EM-AR, PASP, and invasive mPAP, was confirmed using the Shapiro-Wilk test. Descriptive statistics are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range) for continuous variables, depending on distribution, and as frequencies (percentages) for categorical variables.\u003c/p\u003e \u003cp\u003eDifferences in baseline characteristics and vascular measurements between the PH group (mPAP\u0026thinsp;\u0026gt;\u0026thinsp;20 mmHg) and the non-PH group were compared using independent two-sample t-tests or Mann-Whitney U tests, as appropriate. Standardized effect sizes (Cohen\u0026rsquo;s d for t-tests) were calculated and reported alongside P-values to quantify the magnitude of observed differences, with 95% confidence intervals for the effect size estimates. The chi-squared test was used for categorical variables.\u003c/p\u003e \u003cp\u003eThe correlation between the novel morphological parameter (EM-AR) and the reference standard (invasive mPAP) was assessed using Pearson\u0026rsquo;s correlation coefficient. A multiple linear regression analysis with forward stepwise selection (entry criteria: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; removal criteria: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.10) was performed to develop a non-invasive predictive model for mPAP. The model included EM-AR and echocardiographic PASP as covariates. Model performance was evaluated by the coefficient of determination (r\u0026sup2;) and the standard error of the estimate.\u003c/p\u003e \u003cp\u003eThe diagnostic performance of EM-AR, PASP, and the composite predicted mPAP (mPAP\u003csub\u003epredicted\u003c/sub\u003e) for identifying PH (mPAP\u0026thinsp;\u0026gt;\u0026thinsp;20 mmHg) was assessed using receiver operating characteristic (ROC) curve analysis. Areas under the ROC curves (AUC) were calculated and compared using the DeLong test. The optimal diagnostic cut-off values were determined by maximizing the Youden index. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were reported, along with their 95% confidence intervals. A two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analyses were performed using SPSS software (Version 26.0, IBM Corp.).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy cohort\u003c/h2\u003e \u003cp\u003eA total of 80 patients with suspected pulmonary hypertension were included in the final analysis, comprising 59 patients (73.8%) with confirmed PH (mPAP\u0026thinsp;\u0026gt;\u0026thinsp;20 mmHg) and 21 patients (26.2%) without PH. The two groups were comparable in terms of gender distribution and body surface area (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, patients in the PH group were significantly older than those in the non-PH group (49.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5 vs. 40.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7 years; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.83).\u003c/p\u003e \u003cp\u003eAs expected, all invasive hemodynamic parameters measured by right heart catheterization-including mPAP, systolic and diastolic PAP, PAWP, and PVR-were significantly elevated in the PH group (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with large effect sizes (Cohen\u0026rsquo;s d ranging from \u0026minus;\u0026thinsp;1.09 to -7.54). Similarly, non-invasive assessments showed significant differences, with higher PASP on echocardiography in the PH group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Cohen\u0026rsquo;s d = -1.56) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo PH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffect Size (Cohen\u0026rsquo;s d,95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of subjects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge(years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e40.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e49.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.83 (0.27, 1.38)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody of surface area(m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.742\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.093(-0.51,0.32)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRVSP (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e43.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-1.56 (-2.20, -0.90)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emPAP\u003c/b\u003e\u003csub\u003e\u003cb\u003eRHC\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e(mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e14.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e56.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-2.68 (-3.44, -1.92)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSystolic PAP\u003c/b\u003e\u003csub\u003e\u003cb\u003eRHC\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e69.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-7.54 (-9.27, -5.81)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiastolic PAP\u003c/b\u003e\u003csub\u003e\u003cb\u003eRHC\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e28.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-1.93 (-2.63, -1.23)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePAWP\u003c/b\u003e\u003csub\u003e\u003cb\u003eRHC\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-1.09 (-1.68, -0.49)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePVR\u003c/b\u003e\u003csub\u003e\u003cb\u003eRHC\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(dyn\u0026middot;s\u0026middot;cm⁻⁵)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e120.0\u0026thinsp;\u0026plusmn;\u0026thinsp;53.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e397.8\u0026thinsp;\u0026plusmn;\u0026thinsp;198.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-1.54 (-2.17, -0.90)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for continuous variables and as number (percentage) for categorical variables.PH, pulmonary hypertension; RVSP, echocardiographically estimated right ventricular systolic pressure; mPAP_RHC, mean pulmonary arterial pressure measured by right heart catheterization (reference standard); PAP, pulmonary arterial pressure; PAWP, pulmonary artery wedge pressure; PVR, pulmonary vascular resistance. Between-group comparisons for continuous variables were performed using Welch\u0026rsquo;s t-test (assuming unequal variances). Effect sizes are reported as Cohen\u0026rsquo;s d with 95% confidence intervals (CI), where |d| \u0026asymp; 0.2, 0.5, and 0.8 correspond to small, medium, and large effect sizes, respectively.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eQuantification of Main Pulmonary Artery (MPA) Volume\u003c/h2\u003e \u003cp\u003eA significant difference in main pulmonary artery (MPA) volume was observed between the PH and non-PH groups. Patients with PH had significantly larger MPA volumes (645.0\u0026thinsp;\u0026plusmn;\u0026thinsp;243.2 mm\u0026sup3;) compared to those without PH (297.0\u0026thinsp;\u0026plusmn;\u0026thinsp;130.6 mm\u0026sup3;), with a highly significant between-group difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a large effect size (Cohen\u0026rsquo;s d = -1.51, 95% CI: -2.18 to -0.84). Correlation analysis further revealed a significant positive association between MPA volume and mPAP\u003csub\u003eRHC\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.62, r\u0026sup2; = 0.38, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eQuantification of Elastic-to-Muscular Artery Ratio\u003c/h2\u003e \u003cp\u003eBased on quantitative analysis of 3D digital models, the study demonstrated significant structural remodeling of the pulmonary vascular tree in patients with pulmonary hypertension (PH). Compared to non-PH subjects (n\u0026thinsp;=\u0026thinsp;21), PH patients (n\u0026thinsp;=\u0026thinsp;59) exhibited enlargement of elastic (third-order) pulmonary arteries (508.8\u0026thinsp;\u0026plusmn;\u0026thinsp;227.5 mm\u0026sup2; vs. 284.1\u0026thinsp;\u0026plusmn;\u0026thinsp;128.7 mm\u0026sup2;, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;1.23, 95% CI: 0.64 to 1.80), reduction in muscular (sixth-order) artery area (3.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90 mm\u0026sup2; vs. 3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51 mm\u0026sup2;, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Cohen\u0026rsquo;s d = -1.36, 95% CI: -1.95 to -0.76), and a consequent increase in the EM-AR (318.3\u0026thinsp;\u0026plusmn;\u0026thinsp;167.9 vs. 150.2\u0026thinsp;\u0026plusmn;\u0026thinsp;77.4, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;1.25, 95% CI: 0.66 to 1.83) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe composite EM-AR showed a strong positive correlation with invasively measured mPAP (r\u0026thinsp;=\u0026thinsp;0.73, r\u0026sup2; = 0.53, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), stronger than the correlation with main pulmonary artery (MPA) volume indexed to body surface area (r\u0026thinsp;=\u0026thinsp;0.62, r\u0026sup2; = 0.38, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). When analyzed separately, the cross-sectional area of the elastic artery showed a moderate positive correlation with mPAP (r\u0026thinsp;=\u0026thinsp;0.54, r\u0026sup2; = 0.30, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the muscular artery area showed a moderate negative correlation (r = -0.52, r\u0026sup2; = 0.26, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Both individual components correlated less strongly with mPAP\u003csub\u003eRHC\u003c/sub\u003e than the composite EM-AR and showed weaker associations than the MPA volume index.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and Performance of the Predictive Model\u003c/h2\u003e \u003cp\u003eThe strong individual correlation of EM-AR with mPAP, combined with the established role of echocardiographic PASP in hemodynamic assessment, led to the development of a multivariate predictive model. Using forward stepwise multiple linear regression with mPAP\u003csub\u003eRHC\u003c/sub\u003e as the dependent variable, both EM-AR and PASP were retained as significant independent predictors (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The resulting regression equation was:\u003c/p\u003e \u003cp\u003emPAPpredicted (mmHg)\u0026thinsp;=\u0026thinsp;5.832\u0026thinsp;+\u0026thinsp;0.101 \u0026times; (EM-AR)\u0026thinsp;+\u0026thinsp;0.413 \u0026times; (PASP) (r\u0026sup2; = 0.67, standard error of estimate\u0026thinsp;=\u0026thinsp;10.2 mmHg).\u003c/p\u003e \u003cp\u003eThe mPAP_predicted derived from this model showed an excellent correlation with invasively measured mPAP (r\u0026thinsp;=\u0026thinsp;0.82, r\u0026sup2; = 0.67, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Bland-Altman analysis revealed a mean bias of 0.43 mmHg between predicted and invasive mPAP, with 95% limits of agreement ranging from \u0026minus;\u0026thinsp;24.45 to 25.31 mmHg (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic Accuracy for Pulmonary Hypertension\u003c/h2\u003e \u003cp\u003eReceiver operating characteristic (ROC) curve analysis demonstrated that the composite mPAP\u003csub\u003epredicted\u003c/sub\u003e model exhibited superior diagnostic performance for identifying pulmonary hypertension, with an area under the curve (AUC) of 0.95 (95% CI: 0.90\u0026ndash;0.99). The overall diagnostic accuracy of the composite model was significantly higher than that of the EM-AR alone [AUC\u0026thinsp;=\u0026thinsp;0.92; difference in AUC\u0026thinsp;=\u0026thinsp;0.07, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032 by DeLong test], echocardiographic PASP alone [AUC\u0026thinsp;=\u0026thinsp;0.90; difference\u0026thinsp;=\u0026thinsp;0.10, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008], and the main pulmonary artery (MPA) volume index alone [AUC\u0026thinsp;=\u0026thinsp;0.89; difference\u0026thinsp;=\u0026thinsp;0.11, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005] (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Notably, the individual elastic and muscular artery areas were not subjected to separate ROC analysis to avoid collinearity with the composite EM-AR metric, which already incorporates their dimensional information.\u003c/p\u003e \u003cp\u003eAt the optimal cut-off value of 23.9 mmHg, determined by maximizing the Youden index, the composite mPAP\u003csub\u003epredicted\u003c/sub\u003e identified PH with a sensitivity of 83.1% (95% CI: 71.0% \u0026ndash; 91.6%), a specificity of 95.2% (95% CI: 76.2% \u0026ndash; 99.9%), a positive predictive value (PPV) of 98.0% (95% CI: 89.4% \u0026ndash; 99.9%), and a negative predictive value (NPV) of 66.7% (95% CI: 46.0% \u0026ndash; 83.5%).\u003c/p\u003e \u003cp\u003eIn comparison, the optimal cut-off for EM-AR was 197.3, yielding a sensitivity of 79.7% (95% CI: 67.2% \u0026ndash; 89.0%), a specificity of 85.7% (95% CI: 63.7% \u0026ndash; 97.0%), a PPV of 93.9% (95% CI: 84.3% \u0026ndash; 98.5%), and an NPV of 62.1% (95% CI: 42.3% \u0026ndash; 79.3%). PASP at a cut-off of 27.5 mmHg had a sensitivity of 81.4% (95% CI: 69.1% \u0026ndash; 90.3%), a specificity of 81.0% (95% CI: 58.1% \u0026ndash; 94.6%), a PPV of 92.3% (95% CI: 82.4% \u0026ndash; 97.7%), and an NPV of 63.0% (95% CI: 42.4% \u0026ndash; 80.6%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study introduces and evaluates a novel morphological parameter\u0026mdash;the elastic-to-muscular pulmonary artery area ratio (EM-AR), derived from three-dimensional-printed digital models\u0026mdash;for noninvasive assessment of pulmonary hypertension (PH), when integrated with echocardiographic PASP. The key findings of this study can be summarized in three points: First, quantitative 3D analysis confirmed characteristic bidirectional pulmonary vascular remodeling in PH, characterized by proximal elastic artery dilation concurrent with distal muscular artery narrowing[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Second, the composite EM-AR ratio demonstrated a stronger correlation with invasively measured mean pulmonary arterial pressure (mPAP) than its individual components or conventional CT parameters such as the main pulmonary artery (MPA) volume index, suggesting that it more comprehensively reflects the overall remodeling process. Third, a multivariate model combining EM-AR and echocardiographic PASP showed reasonable diagnostic accuracy, outperforming either parameter alone, while providing a feasible, noninvasive approach for mPAP estimation. These findings introduce a novel methodological dimension to the quantitative imaging assessment of pulmonary vascular disease.\u003c/p\u003e \u003cp\u003eThe observed remodeling pattern-proximal arterial dilation coupled with distal narrowing-is consistent with the established bidirectional pathology of PH. Histopathological studies have identified medial hypertrophy, intimal hyperplasia, and plexiform lesion formation in muscular arteries as key contributors to increased pulmonary vascular resistance[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Simultaneously, proximal elastic arteries dilate in response to chronic pressure overload, driven by mechanisms involving endothelial dysfunction and extracellular matrix remodeling[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Our study offers in vivo morphological confirmation of this process via 3D imaging. The EM-AR metric integrates these opposing structural changes into a single continuous variable, and its correlation with invasive mPAP indicates a relationship with hemodynamic burden. Notably, EM-AR showed a slightly stronger correlation with mPAP than the MPA volume index-a parameter reflecting primarily proximal changes-likely due to its inclusion of distal vascular information. Recent studies using dual-energy CT pulmonary blood volume analysis and magnetic resonance angiography have similarly reported reduced peripheral perfusion and subsegmental vascular pruning in PH[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. EM-AR, derived from routine CTPA, could thus complement these functional observations.\u003c/p\u003e \u003cp\u003eReliable and reproducible measurement of distal pulmonary arteries remains challenging with conventional CT evaluation. Traditional metrics are primarily focused on the central vasculature due to inconsistencies in manual vessel tracking, partial-volume effects, and difficulty in obtaining orthogonal cross-sections[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Our 3D printing-based digital modeling approach addresses these limitations by allowing semi-automated segmentation and reconstruction, enabling standardized centerline extraction and cross-sectional area measurement of specific pulmonary artery generations (third-order elastic and sixth-order muscular arteries in the right lower lobe). This enhances measurement consistency and reproducibility. The right lower lobe was chosen as the region of interest due to its anatomical consistency and reliable visualization in routine CTPA[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This workflow provides a referential framework for distal pulmonary artery quantification, which can be further optimized and automated.\u003c/p\u003e \u003cp\u003eThe improvement in diagnostic performance observed with the composite model suggests a potential complementarity between morphological and hemodynamic parameters. Echocardiographic RVSP, though widely used for noninvasive screening, is subject to variability due to factors like acoustic windows, technical assumptions, and operator dependence[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. EM-AR reflects structural remodeling of the vascular bed but may also be influenced by factors beyond pressure alone, such as disease duration or etiology. Combining EM-AR with PASP in a multivariate model allows for a more holistic capture of both immediate hemodynamic status and underlying structural adaptation, thereby improving diagnostic robustness. The model\u0026rsquo;s high specificity in this study suggests that it could serve as a supportive tool for confirming PH, potentially guiding decisions on subsequent invasive assessments.\u003c/p\u003e \u003cp\u003eThis method builds on two widely available clinical tests-transthoracic echocardiography and CTPA-without requiring additional contrast or radiation exposure, facilitating clinical translation. While the current 3D modeling process requires specialized software and expertise, ongoing advances in image-processing automation and artificial intelligence could help lower implementation barriers[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Compared to other noninvasive modalities, cardiac MRI provides comprehensive right ventricular evaluation but has limited direct pressure-estimation capability and lower accessibility[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Nuclear imaging, while sensitive for thromboembolism, lacks detailed anatomical resolution[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Among CT-based predictors, our model performed comparably well in this cohort, likely due to its focus on hierarchical vascular remodeling.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered. First, the single-center, retrospective design may introduce selection bias, and the findings need validation in larger, multicenter, prospective cohorts with a broader range of PH etiologies[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Second, patients with chronic thromboembolic pulmonary hypertension (CTEPH) were excluded due to the potential confounding effect of intraluminal thrombi on distal artery measurements, leaving the applicability of the model to this subgroup unaddressed. While the 3D analysis is reproducible, it is not fully automated, and future integration of deep learning-based segmentation could improve efficiency and precision, particularly for large datasets. Additionally, pulmonary vascular remodeling may vary regionally, especially in hypoxic or fibrotic diseases, limiting the generalizability of a single-lobe measurement. Despite using the right lower lobe to reduce anatomical variability, the generalizability to the entire pulmonary vascular system remains uncertain. Although imaging and right heart catheterization (RHC) were performed within 30 days, physiological variability may still influence results. Lastly, the prognostic and therapeutic potential of EM-AR warrants further investigation in longitudinal studies to assess its evolution over time and predictive value for PH management.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the elastic-to-muscular pulmonary artery area ratio (EM-AR) derived from 3D digital models offers a quantifiable measure of bidirectional vascular remodeling in pulmonary hypertension. When integrated with echocardiographic RVSP in a multivariate model, EM-AR demonstrates promising diagnostic performance for noninvasive estimation of mean pulmonary arterial pressure. Further multicenter validation and technical refinement are needed before this composite approach can be considered for routine clinical application.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e \u003cb\u003eCT\u003c/b\u003e Computed tomography\u003c/p\u003e\u003cp\u003e \u003cb\u003eCTPA\u003c/b\u003e Computed tomography pulmonary angiography\u003c/p\u003e\u003cp\u003e \u003cb\u003eMPA\u003c/b\u003e Main pulmonary artery\u003c/p\u003e\u003cp\u003e \u003cb\u003emPAP\u003c/b\u003e Mean pulmonary arterial pressure\u003c/p\u003e\u003cp\u003e \u003cb\u003eEM-AR\u003c/b\u003e Elastic-to-muscular pulmonary artery area ratio\u003c/p\u003e\u003cp\u003e \u003cb\u003emPAP\u003c/b\u003e \u003csub\u003e \u003cb\u003epredicted\u003c/b\u003e \u003c/sub\u003e mPAP predicted by linear regression\u003c/p\u003e\u003cp\u003e \u003cb\u003emPAP\u003c/b\u003e \u003csub\u003e \u003cb\u003eRHC\u003c/b\u003e \u003c/sub\u003e mPAP measured by right heart catheterisation\u003c/p\u003e\u003cp\u003e \u003cb\u003ePASP\u003c/b\u003e Transthoracic echocardiographic pulmonary arterial systolic pressure estimate\u003c/p\u003e\u003cp\u003e \u003cb\u003ePH\u003c/b\u003e Pulmonary hypertension\u003c/p\u003e\u003cp\u003e \u003cb\u003eRHC\u003c/b\u003e Right heart catheterisation\u003c/p\u003e\u003cp\u003e \u003cb\u003ePAWP\u003c/b\u003e Pulmonary Artery Wedge Pressure\u003c/p\u003e\u003cp\u003e \u003cb\u003e3D\u003c/b\u003e Three-Dimensional\u003c/p\u003e\u003cp\u003e \u003cb\u003eRLL\u003c/b\u003e Right Lower Lobe\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003c/p\u003e\u003cp\u003e This retrospective study was conducted in accordance with the ethical principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Ethics Committee of Suzhou Municipal Hospital (Approval No. K-2023-006-K01). The requirement for individual informed consent was waived by the aforementioned ethics committee due to the retrospective nature of the study.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eNot applicable.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Key Research and Development Program of China (Grant Number 2024YFC3406800); the Noncommunicable Chronic Diseases-National Science and Technology Major Project (Grant Number 2025ZD0552104); the Gusu (Suzhou) Health Talent Program (Grant Number GSWS2022065); and the Talent Program of Gusu School, Nanjing Medical University (Grant Number GSRCKY20210101).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eGen Zhang and Dianyuan Li conceived and designed the study. Gen Zhang , Jixiang Liang , and Zhipeng Ren conducted the experiments, data collection, and analysis. Gen Zhang drafted the manuscript. Jixiang Liang , Huan Wang , and Guanzheng Cui contributed to software processing and 3D modeling. Xianzhi Wang , Dongsheng He , Xin Li , Zhiqiang Dai , and Shangxuan Li provided critical resources, clinical expertise, and patient data. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe gratefully acknowledge Dr. Panguangyu and Dr. Dagong(Peking University People’s Hospital) for serving as independent radiologists in the blinded assessment of the computed tomography pulmonary angiography (CTPA) images.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eN.F. Ruopp, H.W. Farber. The New World Symposium on Pulmonary Hypertension Guidelines: Should Twenty-One Be the New Twenty-Five?, Circulation 140 (2019) 1134\u0026ndash;1136. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCULATIONAHA.119.040292\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.119.040292\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG. Simonneau, D. Montani, D.S. Celermajer, C.P. Denton, M.A. Gatzoulis, M. Krowka, et al. Haemodynamic definitions and updated clinical classification of pulmonary hypertension, Eur Respir J 53 (2019) 1801913. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1183/13993003.01913-2018\u003c/span\u003e\u003cspan address=\"10.1183/13993003.01913-2018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM.R. Fisher, P.R. Forfia, E. Chamera, T. Housten-Harris, H.C. Champion, R.E. Girgis, et al. Accuracy of Doppler Echocardiography in the Hemodynamic Assessment of Pulmonary Hypertension, Am J Respir Crit Care Med 179 (2009) 615\u0026ndash;621. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1164/rccm.200811-1691OC\u003c/span\u003e\u003cspan address=\"10.1164/rccm.200811-1691OC\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. Rezaei-Kalantari, K. Samimi, H. Zomorodian, H. Bakhshandeh, M. Jafari, A.M. Farahmand, et al. Pulmonary Blood Volume Measured by Dual-Energy Computed Tomography Can Help Distinguish Patients With Pulmonary Hypertension, Front. Cardiovasc. Med. 9 (2022) 835655. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcvm.2022.835655\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2022.835655\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP.G.M. Knoops, G. Biglino, A.D. Hughes, K.H. Parker, L. Xu, S. Schievano, et al. A Mock Circulatory System Incorporating a Compliant 3D-Printed Anatomical Model to Investigate Pulmonary Hemodynamics, Artificial Organs 41 (2017) 637\u0026ndash;646. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/aor.12809\u003c/span\u003e\u003cspan address=\"10.1111/aor.12809\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA.D. Bordones, M. Leroux, V.O. Kheyfets, Y.-A. Wu, C.-Y. Chen, E.A. Finol. Computational Fluid Dynamics Modeling of the Human Pulmonary Arteries with Experimental Validation, Ann Biomed Eng 46 (2018) 1309\u0026ndash;1324. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10439-018-2047-1\u003c/span\u003e\u003cspan address=\"10.1007/s10439-018-2047-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK.R. Stenmark, I.F. McMurtry. Vascular Remodeling Versus Vasoconstriction in Chronic Hypoxic Pulmonary Hypertension: A Time for Reappraisal?, Circulation Research 97 (2005) 95\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/01.RES.00000175934.68087.29\u003c/span\u003e\u003cspan address=\"10.1161/01.RES.00000175934.68087.29\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Matsuoka, G.R. Washko, T. Yamashiro, R.S.J. Estepar, A. Diaz, E.K. Silverman, et al. Pulmonary Hypertension and Computed Tomography Measurement of Small Pulmonary Vessels in Severe Emphysema, Am J Respir Crit Care Med 181 (2010) 218\u0026ndash;225. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1164/rccm.200908-1189OC\u003c/span\u003e\u003cspan address=\"10.1164/rccm.200908-1189OC\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY. Suzuki, K. Yoshimura, T. Uto, J. Sato, S. Imokawa, T. Suda. Morphological changes in small pulmonary vessels are associated with severe acute exacerbation in chronic obstructive pulmonary disease, COPD Volume 11 (2016) 1435\u0026ndash;1445. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/COPD.S107424\u003c/span\u003e\u003cspan address=\"10.2147/COPD.S107424\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Jacob, B.J. Bartholmai, S. Rajagopalan, M. Kokosi, A. Nair, R. Karwoski, et al. Mortality prediction in idiopathic pulmonary fibrosis: evaluation of computer-based CT analysis with conventional severity measures, Eur Respir J 49 (2017) 1601011. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1183/13993003.01011-2016\u003c/span\u003e\u003cspan address=\"10.1183/13993003.01011-2016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA.U. Fayyaz, W.D. Edwards, J.J. Maleszewski, E.A. Konik, H.M. DuBrock, B.A. Borlaug, et al. Global Pulmonary Vascular Remodeling in Pulmonary Hypertension Associated With Heart Failure and Preserved or Reduced Ejection Fraction, Circulation 137 (2018) 1796\u0026ndash;1810. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCULATIONAHA.117.031608\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.117.031608\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN. Hopkins, P. McLoughlin. The structural basis of pulmonary hypertension in chronic lung disease: remodelling, rarefaction or angiogenesis? Journal of Anatomy 201 (2002) 335\u0026ndash;348. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1469-7580.2002.00096.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1469-7580.2002.00096.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD.G. Kiely, D.L. Levin, P.M. Hassoun, D. Ivy, P. Jone, J. Bwika, et al. Statement on imaging and pulmonary hypertension from the Pulmonary Vascular Research Institute (PVRI), Pulm. Circ. 9 (2019) 1\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2045894019841990\u003c/span\u003e\u003cspan address=\"10.1177/2045894019841990\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Matsuoka, G.R. Washko, T. Yamashiro, R.S.J. Estepar, A. Diaz, E.K. Silverman, et al. Pulmonary Hypertension and Computed Tomography Measurement of Small Pulmonary Vessels in Severe Emphysema, Am J Respir Crit Care Med 181 (2010) 218\u0026ndash;225. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1164/rccm.200908-1189OC\u003c/span\u003e\u003cspan address=\"10.1164/rccm.200908-1189OC\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Matsuoka, G.R. Washko, M.T. Dransfield, T. Yamashiro, R. San Jose Estepar, A. Diaz, E.K. Silverman, et al. Quantitative CT Measurement of Cross-sectional Area of Small Pulmonary Vessel in COPD, Academic Radiology 17 (2010) 93\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.acra.2009.07.022\u003c/span\u003e\u003cspan address=\"10.1016/j.acra.2009.07.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Singhal, R. Henderson, K. Horsfield, K. Harding, G. Cumming. Morphometry of the Human Pulmonary Arterial Tree, Circulation Research 33 (1973) 190\u0026ndash;197. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/01.RES.33.2.190\u003c/span\u003e\u003cspan address=\"10.1161/01.RES.33.2.190\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC. Hammerstingl, R. Schueler, L. Bors, D. Momcilovic, S. Pabst, G. Nickenig, et al. Diagnostic Value of Echocardiography in the Diagnosis of Pulmonary Hypertension, PLoS ONE 7 (2012) e38519. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0038519\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0038519\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM.A. Al-Omari, J. Finstuen, C.P. Appleton, M.E. Barnes, T.S.M. Tsang. Echocardiographic Assessment of Left Ventricular Diastolic Function and Filling Pressure in Atrial Fibrillation, The American Journal of Cardiology 101 (2008) 1759\u0026ndash;1765. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.amjcard.2008.02.067\u003c/span\u003e\u003cspan address=\"10.1016/j.amjcard.2008.02.067\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR. Gharleghi, C.A. Dessalles, R. Lal, S. McCraith, K. Sarathy, N. Jepson, et al. 3D Printing for Cardiovascular Applications: From End-to-End Processes to Emerging Developments, Ann Biomed Eng 49 (2021) 1598\u0026ndash;1618. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10439-021-02784-1\u003c/span\u003e\u003cspan address=\"10.1007/s10439-021-02784-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB.A. Maron, S.H. Abman, C.G. Elliott, R.P. Frantz, R.K. Hopper, E.M. Horn, et al. Pulmonary Arterial Hypertension: Diagnosis, Treatment, and Novel Advances, Am J Respir Crit Care Med 203 (2021) 1472\u0026ndash;1487. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1164/rccm.202012-4317SO\u003c/span\u003e\u003cspan address=\"10.1164/rccm.202012-4317SO\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR.W.W. Biederman, M. Doyle, P. Correa-Jaque, G. Rayarao, R.L. Benza. Integrated use of cardiac MRI and the CardioMEMS\u003csup\u003e\u0026trade;\u003c/sup\u003e HF system in PAH: the utility of coincident pressure and volume in RV failure\u0026mdash;the NHLBI-VITA trial, Cardiovasc. Diagn. Ther. 9 (2019) 492\u0026ndash;501. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21037/cdt.2019.09.05\u003c/span\u003e\u003cspan address=\"10.21037/cdt.2019.09.05\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM.D. Pokharel, A. Garcia-Flores, D. Marciano, M.C. Franco, J.R. Fineman, S. Aggarwal, et al. Mitochondrial network dynamics in pulmonary disease: Bridging the gap between inflammation, oxidative stress, and bioenergetics, Redox Biology 70 (2024) 103049. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.redox.2024.103049\u003c/span\u003e\u003cspan address=\"10.1016/j.redox.2024.103049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pulmonary Hypertension, 3D Printing, Elastic-to-Muscular Artery Ratio, Echocardiography, Noninvasive Diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-8358122/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8358122/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eObjectives\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo evaluate the diagnostic accuracy of the elastic-to-muscular pulmonary artery area ratio (EM-AR), derived from 3D-printed digital models, for pulmonary hypertension (PH), both independently and in combination with echocardiographically estimated pulmonary arterial systolic pressure (PASP).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis retrospective diagnostic study enrolled 80 patients with suspected pulmonary hypertension, using invasive mean pulmonary arterial pressure (mPAP) from right heart catheterization as the reference standard. Cross-sectional areas of the elastic (third-order, right lower lobe) and muscular (sixth-order, right lower lobe) pulmonary arteries were measured from 3D-printed digital models to calculate the elastic-to-muscular artery ratio (EM-AR). A linear regression model integrating the calculated EM-AR and measured echocardiographic PASP was developed to predict mPAP (mPAP\u003csub\u003epredicted\u003c/sub\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eQuantitative analysis revealed significant remodeling of the pulmonary arterial tree in the PH group, characterized by enlargement of elastic arteries (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reduction in muscular artery area (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a consequent elevation in the EM-AR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The EM-AR showed the strongest correlation with invasive mPAP (r\u0026thinsp;=\u0026thinsp;0.73, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to its individual components (elastic artery: r\u0026thinsp;=\u0026thinsp;0.54, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; muscular artery: r = -0.52, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The composite mPAP, derived from a multiple linear regression model of EM-AR and PASP, correlated strongly with invasive mPAP (r\u0026thinsp;=\u0026thinsp;0.82, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and achieved superior diagnostic accuracy for PH (AUC\u0026thinsp;=\u0026thinsp;0.95). At the optimal cut-off of 23.9 mmHg, it identified PH with 83.1% sensitivity and 95.2% specificity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe EM-AR derived from 3D-printed digital models appears to be a promising indicator of pulmonary vascular remodeling. In our cohort, a multivariable model combining EM-AR with echocardiographic PASP demonstrated excellent diagnostic performance for the noninvasive prediction of pulmonary hypertension.\u003c/p\u003e","manuscriptTitle":"Elastic-to-Muscular Pulmonary Artery Area Ratio and Echocardiographic Pulmonary Arterial Systolic Pressure in the Prediction of Pulmonary Hypertension: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 11:40:40","doi":"10.21203/rs.3.rs-8358122/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-10T08:04:23+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"79945330501902320523312857593649386127","date":"2026-01-25T16:20:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-22T14:25:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63167570993090448735204089104620744634","date":"2026-01-20T21:40:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T20:59:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264963165739984243418234708233624564065","date":"2026-01-20T20:23:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-20T16:20:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-29T11:27:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-29T09:19:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2025-12-28T14:39:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8e63d45-8378-4da0-b0ad-f2d4c6f53c13","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T04:38:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 11:40:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8358122","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8358122","identity":"rs-8358122","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}