Noninvasive assessment of pulmonary vascular resistance: a synergistic approach using computed tomography pulmonary angiography and echocardiography in pulmonary hypertension

Introduction

Pulmonary vascular resistance (PVR), a key marker of right ventricular afterload (1), is central to clinical decision-making and prognostic assessment in patients with pulmonary hypertension (PH) (2). While right heart catheterization (RHC) remains the gold standard for diagnosing and classifying PH (3,4), this invasive procedure is associated with an increased risk of adverse complications and even life-threatening conditions (5). Exploring non-invasive, easily accessible assessment methods could streamline diagnostic workflows and reduce procedural risks.

Transthoracic echocardiography (TTE) is a widely accessible procedure for assessing cardiac function and hemodynamics (6,7). However, persistent uncertainties surround TTE’s accuracy in quantifying PVR, particularly in patients with elevated PVR levels (8,9). In contrast, computed tomography pulmonary angiography (CTPA) has demonstrated significant capability in diagnosing PH etiologies, screening disease severity, and prognosticating outcomes (10-12). Previous studies using CTPA in chronic thromboembolic pulmonary hypertension (CTEPH) identified the Cobb angle—the angle between the spine and interventricular septum—as a morphological marker predictive of PVR (13,14). Clinically, we have observed that this angle correlates with right ventricular dilatation in PH patients, suggesting its potential capability beyond CTEPH. However, whether this quantitative marker of global cardiac torsion holds diagnostic value across other PH subtypes remains undefined.

Additionally, computed tomography (CT) morphological parameters are confounded by body size variables. Hence, indexing these markers based on body size metrics could enhance their clinical applicability (15-20). Consequently, we investigated the correlation between the Cobb angle, Cobb angles adjusted for body size variables, and TTE parameters with RHC-derived hemodynamic parameters in PH, while assessing their prognostic significance. Subsequently, we evaluated the capacity of CTPA alone or in conjunction with TTE-derived functional or flow parameters to predict PVR. We present this article in accordance with the TRIPOD+AI reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-2152/rc).

Methods

Patients

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Ethics Committee of Qilu Hospital of Shandong University (No. KYLL-202204 [ZX]-022-1) and individual consent for this retrospective analysis was waived. This single-center, retrospective study included patients diagnosed with PH via RHC (21) at Qilu Hospital of Shandong University between September 1, 2018, and July 31, 2022. Participants underwent both TTE and CTPA within a 3-month interval preceding or following the RHC, and TTE and RHC were completed during the same hospital admission (22). Exclusion criteria: (I) patients with coexistence of congenital heart disease, chest malformation, or cardiomyopathy; (II) patients with poor image quality.

Imaging technique

TTE

All patients underwent TTE using a GE Vivid-E95 ultrasonograph (General Electric, USA) equipped with a 3.5-MHz phased-array transducer probe (M5S). The examination followed the methods and diagnostic criteria outlined in the Guidelines of Recommendations from the American Society of Echocardiography (6). During the examination, patients were positioned on their left side or supine and instructed to hold their breath. We collected the following variables from the TTE report: tricuspid annular plane systolic excursion (TAPSE), right ventricular myocardial performance index (RVMPI), tricuspid annular systolic peak velocity (S’) and right ventricular outflow tract acceleration time (RVOT-AT). The definitions of the above variables are detailed in Appendix 1. TTE measurements were carried out by cardiologists, having an average of 8 years of experience in adult echocardiography.

CTPA

CTPA was performed using a Siemens Somatom Force CT scanner (SOMATOM Force, Siemens Healthcare, Erlangen, Germany) with retrospective electrocardiogram (ECG)-gating. Four ECG leads were placed on the patient’s chest in standard positions, and ECG data were continuously recorded throughout the scan. The scan range extended from the apex pulmonis to the bottom of the costophrenic angle. All patients initiated the scan after a deep inspiration while holding their breath. Tube voltage and tube current were automatically adjusted using Care Dose 4D and Care KV technologies. The pitch was tailored to the heart rate, with a table speed of 0.25 seconds. A nonionic contrast agent (iopamidol 370 mg/I/mL, Bracco, Milan, Italy) was administered at a dose of 0.8 mL/kg body weight using a double-barrel high-pressure syringe at a rate of 5 mL/s, followed by 40 mL of normal saline at the same rate. Sanning commenced 5 seconds after the CT attenuation value of the pulmonary artery trunk reached 100 Hounsfield units, monitored using group note tracking procedures technology. Reconstructed images had a slice thickness of 0.75 mm with an interval of 0.50 mm. All image data were transferred to a Syngo.via workstation (Siemens Healthcare, USA) for analysis and measurements.

RHC

RHC was performed using standard techniques with a 4-lumen Swan-Ganz catheter (Type 131F7, Edwards Life-sciences, Irvine, CA, USA). The following parameters were measured and recorded: systolic right atria pressure (sRAP), diastolic right atrial pressure (dRAP), mean right atrial pressure (mRAP), systolic right ventricular pressure (sRVP), diastolic right ventricular pressure (dRVP), mean right ventricular pressure (mRVP), systolic pulmonary artery pressure (sPAP), diastolic pulmonary artery pressure (dPAP), mean pulmonary artery pressure (mPAP), pulmonary capillary wedge pressure (PCWP). Cardiac output (CO) was measured by thermodilution method. Cardiac index (CI) was calculated by: CI = CO/body surface area (BSA). PVR [Wood Unit (WU)] was calculated by: PVR = (mPAP − PCWP)/CO. The RHC procedures were performed by a team of board-certified interventional cardiologists, each with a minimum of 10 years of experience in invasive cardiac procedures.

Image analysis

Cobb angle measurement

As shown in Figure 1A, the Cobb angle, an angle between the interventricular septum and the line joining the midpoint of the sternum to the thoracic vertebral spinous process, was measured at diastolic phase on the transverse image with the maximal ventricular area on the CTPA image (13). The hemodynamic parameters are blinded to the measurer. The CT measurements were executed by radiologists with a sub-specialty in cardiovascular imaging and at least 10 years of experience.

Figure 1 Measurements were selected at the level with the largest biventricular area in diastole. (A) Showed the Cobb angle was 70.4°; (B) demonstrated the measurement of the thoracic longitudinal diameter (a) and the thoracic transverse diameter (b); (C) showed the measurement of the pericardial longitudinal diameter (c) and the pericardial transverse diameter (d).

Calculation for the corrected Cobb angle

The Cobb angle is an inherent physiological angle in human anatomy. Considering the potential influence of body size on the Cobb angle, we employed a correction formula to derive adjusted Cobb angles as follows: corrected Cobb angle = Cobb angle/X.

X presents a correction factor. these factors include the thoracic longitudinal diameter (a), thoracic transverse diameter (b), pericardial longitudinal diameter (c), pericardial transverse diameter (d), and BSA (e). The corrected Cobb angles are presented as Cobb angle/a, Cobb angle/b, Cobb angle/c, Cobb angle/d, and Cobb angle/BSA, separately.

Measurements of all diameters were conducted on the same diastolic CT image used for Cobb angle assessment (Figure 1B,1C). The measurement methodology was as follows: a, the thoracic longitudinal diameter was the length from the posterior edge of midpoint of the sternum to the anterior edge of thoracic vertebra; b, the thoracic transverse diameter was the perpendicular bisector of the thoracic longitudinal diameter extends to the external pleura; c, the pericardial longitudinal diameter was the length along the long axis of the heart from the apical epicardium to the epicardium at the base of the heart; d, the pericardial transverse diameter was the perpendicular bisector of the pericardial longitudinal diameter extends to the epicardium; e, BSA = 0.0061 × height (cm) + 0.0124 × weight (kg) − 0.0099 (23).

Follow-up

Follow-up assessments were performed every 6 months starting from the conclusion of case collection, with the follow-up period ending on May 1, 2024. Follow-up data were collected and assessed through telephone interviews with trained researchers who were blinded to clinical information. The primary endpoint of this study was defined as re-hospitalization for heart failure.

Statistical analysis

Measurement data were presented as mean ± standard deviation. Categorical data was expressed in absolute value (n) and relative frequency (%). Interobserver agreement on measurement data was determined by calculating intraclass correlation coefficients (ICC).

Spearman correlation analysis was employed to determine correlations between CTPA and TTE parameters with RHC parameters. Univariate linear regression analysis was used to evaluate the goodness of fit of CTPA and TTE parameters to PVR. The ability of Cobb angle or optimal corrected Cobb angle combined with TTE parameters to predict PVR was assessed using stepwise multiple linear regression analysis. Bland-Altman analysis was used to compare RHC values and predicted values.

Study subjects were categorized as follows: (I) gender grouping: men vs. women; (II) age grouping: below or equal to the median age vs. above the median age; (III) etiology grouping: pulmonary arterial hypertension (PAH) and PH associated with lung diseases, CTEPH, and PH with unclear and/or multifactorial mechanisms; (IV) grouping based on severity of PVR: severe increased PVR (PVR >8 WU) vs. mild increased PVR (PVR ≤8 WU) (24).

Within each subgroup, the optimal prediction model was evaluated by: (I) assessing correlation between predicted values and RHC values using Spearman correlation analysis; (II) observing the consistency of predicted values with RHC values via Bland-Altman analysis; (III) comparing correlation coefficients between subgroups using Fisher’s Z transformation.

X-Tile software was utilized to identify optimal cut-off values (thresholds) for continuous variables and to generate Kaplan-Meier curves for patient comparison using the Log-rank test. Hazard ratios (HRs) with 95% confidence intervals (95% CIs) were calculated using both univariate and multivariate Cox proportional hazard analyses. A P value of <0.05 (two-sided) was considered significant. Statistical analysis was performed using SPSS (version 27.0, SPSS Inc.).

Results

Clinical characteristics of study population

A total of 78 patients with PH were included in the study. During a median follow-up period of 35 months [interquartile range (IQR), 24.75–43.25 months], 24 patients experienced heart failure. Detailed data can be found in Table 1 and Table S1. Interobserver agreement for measurements of Cobb angle and all diameters (a, b, c, d) was excellent (ICC: 0.983–0.996). Cobb angle showed no significant correlation with the duration of disease (time interval from first symptom to diagnosis and current RHC), age, height, weight, BMI, or BSA of the patients (P>0.05).

Correlation of CTPA and TTE parameters with hemodynamic parameters

Correlation analysis revealed that the CTPA-derived Cobb angle exhibited the strongest positive correlation with PVR (r=0.725, P<0.001), followed by TTE-derived RVOT-AT, which showed a significant correlation (r=−0.643, P<0.001) (see Table S2 for details). Among all RHC parameters, corrected Cobb angle showed strong correlations with PVR (r values: 0.701–0.736, P<0.001).

Evaluation of Cobb angle and optimal corrected Cobb angle alone or combined with TTE parameters for assessing PVR

The univariate linear regression analysis elucidating the relationship between TTE and CTPA parameters with PVR is detailed in Table S3.

Within the TTE parameters, a non-linear correlation was observed between the RVOT-AT and PVR. To address this, the RVOT-AT values were subjected to logarithmic transformation, yielding LnRVOT-AT (as depicted in Figure 2), demonstrating the best goodness-of-fit to PVR among the TTE parameters (R²=0.443, P<0.001).

Figure 2 Scatter plot of the relationship between RVOT-AT, LnRVOT-AT and PVR. PVR, pulmonary vascular resistance; RVOT-AT, right ventricular outflow tract acceleration time; WU, Wood Unit.

In comparison with the Cobb angle’s goodness-of-fit to PVR (R²=0.450, P<0.001), the Cobb angle corrected by thoracic longitudinal diameter (a), thoracic transverse diameter (b), pericardial longitudinal diameter (c), and pericardial transverse diameter (d) either did not improve or slightly improved the goodness-of-fit to PVR (R²: 0.361–0.456, P<0.001). However, correcting the Cobb angle by BSA (Cobb angle/BSA) significantly enhanced the goodness-of-fit to PVR (R²=0.510, P<0.001) (as shown in Figure S1).

As depicted in Figure 3, the multiple linear regression analysis revealed that combining the Cobb angle with TTE parameters predicted PVR and yielded the following equation: PVR (Cobb angle + RVOT-AT) = 39.854 + 0.171 × Cobb angle − 9.507 × LnRVOT-AT (r=0.801, R²=0.602, P<0.001). In this equation, the standardized regression coefficients (β) for Cobb angle and LnRVOT-AT were 0.465 and −0.441, respectively.

Figure 3 Cobb angle, Cobb angle/BSA combined with RVOT-AT to evaluate PVR respectively. BSA, body surface area; PVR, pulmonary vascular resistance; RVOT-AT, right ventricular outflow tract acceleration time; WU, Wood Unit.

Furthermore, when the corrected Cobb angle (Cobb angle/BSA) was combined with TTE parameters to predict PVR, the following equation was obtained: PVR (Cobb angle/BSA + RVOT-AT) = 36.580 + 0.297 × Cobb angle/BSA − 8.744 × LnRVOT-AT (r=0.815, R²=0.634, P<0.001). The β for Cobb angle/BSA and LnRVOT-AT were 0.515 and −0.405, respectively.

Bland-Altman analysis

Bland-Altman analysis was employed to assess the consistency of PVR values obtained via RHC with those predicted (as depicted in Figure 4). Compared to the univariate models, multivariate prediction models yielded narrower 95% CI, indicating improved predictive accuracy particularly in patients with significantly elevated PVR, with more data points falling within the 95% CI.

Figure 4 Bland-Altman analysis shows the consistency of PVR obtained from RHC with predicted values. BSA, body surface area; PVR, pulmonary vascular resistance; RHC, right heart catheterization; RVOT-AT, right ventricular outflow tract acceleration time; WU, Wood Unit.

In summary, the multivariate prediction model outperformed the univariate parametric predictions. The optimal predictive model identified was PVR (Cobb angle/BSA + RVOT-AT) = 36.580 + 0.297 × Cobb angle/BSA − 8.744 × LnRVOT-AT.

The optimal prediction model predicted PVR in subgroups

Table 2 illustrates that the optimal prediction model effectively predicted PVR across various subgroups, including gender, age, and etiology (r values: 0.777–0.834, P<0.001). Statistical analysis using Fisher’s Z transformation indicated that the differences in correlation coefficients between these subgroups were not statistically significant (P>0.05). However, the efficacy of the prediction model was diminished in subgroups with both severely and mildly increased PVR (r values: 0.534–0.541, P<0.001).

Follow-up outcome

X-tile software was used to determine optimal prognostic cutoffs for continuous variables. As shown in Figure 5, Kaplan-Meier survival curves revealed associations between incident heart failure and the following variables: Cobb angle (>50.2°), Cobb angle/BSA (>33.35°), TAPSE (≤12 mm), RVOT-AT (≤49 ms), and RVMPI (>0.83).

Figure 5 Kaplan-Meier curves of TTE and CTPA parameters. BSA, body surface area; CTPA, computed tomography pulmonary angiography; RVMPI, right ventricular myocardial performance index; RVOT-AT, right ventricular outflow tract acceleration time; S’, tricuspid annular systolic peak velocity; TAPSE, tricuspid annular plane systolic excursion; TTE, transthoracic echocardiography.

As shown in Table S4. In univariate Cox regression analysis, age demonstrated an association with heart failure, along with Cobb angle, Cobb angle/BSA, TAPSE, RVOT-AT, and RVMPI. After adjusting for gender, age, BMI, and disease duration, the associations between the Cobb angle (HR: 1.057, P<0.001), Cobb angle/BSA (HR: 1.087, P<0.001), TAPSE (HR: 0.878, P=0.014), RVOT-AT (HR: 0.968, P=0.030), and RVMPI (HR: 5.324, P<0.001) with the incidence of heart failure were analyzed. Notably, the Cobb angle, BSA-adjusted Cobb angle, and RVMPI demonstrated the most substantial prognostic value (P<0.001).

Discussion

Our research yielded three key findings: (I) the easily measurable CTPA morphological markers, including the Cobb angle and BSA-adjusted counterpart, along with commonly used TTE functional and hemodynamic parameters, demonstrate significant correlations with various hemodynamic parameters in PH. Notably, PVR showed particularly strong associations with the Cobb angle, Cobb angle/BSA, and RVOT-AT; (II) these indicators were also linked to the incidence of heart failure within three years of PH diagnosis; (III) the integration of CTPA morphological indicators with TTE-derived flow information effectively predicts PVR, yielding a robust multimodal model that surpasses limitations posed by patient etiology, age, or gender.

The Cobb angle, a noninvasive and readily measurable marker from CTPA, has been predominantly studied in patients with CTEPH. However, clinical data supporting its utility across other PH etiologies remain sparse, and comprehensive RHC correlates for comparison are lacking (13,14). This anatomically inherent physiological angle, which reflects global cardiac torsion, may harbor substantial pathophysiological significance. Our findings indicate that the Cobb angle correlates with comprehensive cardiac loading parameters, including both right ventricular and pulmonary arterial systolic or diastolic pressure loads (sRVP, mRVP, sPAP, dPAP, mPAP, PVR) and cardiac volume loads (CI, CO). Longitudinal follow-up data further validate its association with heart failure incidence. The prognostic value of the Cobb angle in cardiac load-related diseases warrants deeper investigation.

CT-derived morphological parameters alone may have limitations in PVR prediction. Therefore, we hypothesized that adding functional or hemodynamic parameters from TTE, which have a better correlation with PVR, could enhance predictive accuracy. Our data demonstrated that the Cobb angle/BSA (a morphological parameter) combined with RVOT-AT (a flow functional parameter) can explain PVR better. This suggests that increased PVR is associated not only with hemodynamic derangements but also with cardiac morphological remodeling.

In the study of TTE using flow information to predict PVR by Abbas et al., the best prediction model [tricuspid regurgitation velocity (TRV)²/time-velocity integral of the right ventricular outflow tract (TVI_RVOT)] for PVR (r=0.79) was still slightly lower than our optimal prediction model (r=0.815). In addition, their study population had a low PVR values (3.47±3.63 WU), which may have led to bias in their results and poor representation of the real population with PH (25). Another study reported a correlation between TRV/TVI_RVOT and PVR (r=0.73), and the 95% CI (−8.38 to 8.38 WU) in the Bland-Altman analysis was too wide, and TRV/TVI_RVOT did not correlate significantly with severe increased PVR (PVR >8 WU) (r=0.17) (26). Another research found that the correlation between pulmonary artery acceleration time/peak pulmonary artery systolic pressure and PVR (r=−0.74) was also lower than our optimal prediction model (r=0.815) (27).

In the cardiac magnetic resonance study, Zhang et al. utilized the advantages of cardiac magnetic resonance for the assessment of cardiac function and morphology to derive a PVR prediction model (R²=0.409) which was significantly lower than our optimal prediction model (R²=0.634) (28). In another study, the PVR prediction model using pulmonary artery average velocity combined with right ventricular ejection fraction (r=0.83) was slightly better than the optimal prediction model (r=0.818), the 95% CI (−4.71 to 4.89 WU) was also smaller than our results (−6.586 to 6.561 WU) in the Bland-Altman analysis, but the prediction model correlated with PVR in patients with severe increased PVR (PVR >8 WU) (r=0.41) was inferior to our optimal prediction model (r=0.541) (24).

Furthermore, the study by Boxhammer et al. measured the ratio of the pulmonary artery to the ascending aorta using CT and stratified it based on systolic PAP values from echocardiography for non-invasive detection of PH in patients with severe aortic valve stenosis. This approach offers a novel framework for understanding the relationship between CT and TTE measurements in PH assessment (29). Subgroup analyses showed that our optimal prediction model remained unaffected by gender, age, or etiology. Notably, its capacity to evaluate patients with severe PVR elevation surpassed that of prior TTE- or CMR-based models, addressing a critical gap in noninvasive assessment for this high-risk subgroup.

In the prognostic analysis, we also found that the biomarkers derived from CTPA—the Cobb angle (HR: 1.057, P<0.001) and Cobb angle/BSA (HR: 1.087, P<0.001)—are associated with the outcomes in confirmed PH. In addition, although the RVMPI measured by TTE does not have a highly correlation with PVR, its prognostic value (HR: 5.324, P<0.001) is much higher than that of other parameters. RVMPI, a geometric assumption-independent measure of global right ventricular function, has been widely validated in prior research (30,31). In a long-term study on predicting survival, RVMPI (HR: 3.421, P<0.001) also had a much higher predictive value than other TTE parameters, which is similar to the results of this study (32). Given the study’s limited sample size, these prognostic associations may be subject to overestimation or underestimation, underscoring the need for validation in larger cohorts.

Limitations

There are several limitations in this study: (I) the findings of this study are based on retrospective data and lack external validation; therefore, future multi-center, prospective validation in a larger population is necessary; (II) this study did not include individuals with normal PVR values (PVR ≤2 WU); (III) since only 5 out of 78 individuals died, the associated risk of death was not statistically significant for prognosis; (IV) the complexity and cost-effectiveness of dual examination with TTE and CTPA may limit clinical utility. There may be specific scenarios in the diagnostic workup of various PH groups where TTE and CTPA remain essential, especially in CTEPH. In other clinical settings, understanding the evolution of PVR is crucial, potentially allowing CTPA and TTE to replace RHC; (V) considering that the hemodynamics of pre-capillary PH and post-capillary PH may have completely opposite effects on CT and ultrasound indicators, and due to the extremely small number of post-capillary PH cases, which makes it impossible to conduct a statistically powerful analysis, we excluded these two patients with post-capillary PH, we hope to expand the sample size of post-capillary PH cases in future research; (VI) patients with suspected PH are routinely referred to specialized PH clinics for TTE evaluation. Consequently, these assessments predominantly focus on right heart parameters, with left heart evaluation restricted to basic measurements. This limitation restricted the meaningful characterization and analysis of left heart parameters in the study.

Conclusions

The Cobb angle adjusted for BSA and the RVOT-AT emerge as pivotal non-invasive hemodynamic imaging biomarkers, demonstrating predictive power for PVR and carrying significant prognostic value. The synergistic integration of CTPA morphological markers with TTE-derived functional and flow information paves the way for a novel, non-invasive approach to assessing the hemodynamics of PH.

Acknowledgments

We are grateful to Jing Liu, a professor at the Department of Biostatistics, Shandong University, for making important contributions to the statistical methodology of this study.

Footnote

Reporting Checklist: The authors have completed the TRIPOD+AI reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-2152/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-24-2152/dss

Funding: This work was supported by the Natural Science Foundation of Shandong Province (grant No. ZR2024ZD23).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-2152/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Ethics Committee of Qilu Hospital of Shandong University (No. KYLL-202204 [ZX]-022-1) and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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