Noninvasive prediction of pulmonary artery pressure and vascular resistance by using cardiac magnetic resonance indices

    Noninvasive Prediction of Pulmonary Artery Pressure and Vascular Resistance by using Cardiac Magnetic Resonance indices Zhang Zhang, Meng Wang, Zhenwen Yang, Fan Yang, Dong Li, Tielian Yu, Ningnannan Zhang PII: DOI: Reference:

S0167-5273(16)33230-2 doi:10.1016/j.ijcard.2016.10.068 IJCA 23811

To appear in:

International Journal of Cardiology

Received date: Revised date: Accepted date:

13 July 2016 23 October 2016 26 October 2016

Please cite this article as: Zhang Zhang, Wang Meng, Yang Zhenwen, Yang Fan, Li Dong, Yu Tielian, Zhang Ningnannan, Noninvasive Prediction of Pulmonary Artery Pressure and Vascular Resistance by using Cardiac Magnetic Resonance indices, International Journal of Cardiology (2016), doi:10.1016/j.ijcard.2016.10.068

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ACCEPTED MANUSCRIPT Noninvasive Prediction of Pulmonary Artery Pressure and Vascular

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Resistance by using Cardiac Magnetic Resonance indices

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Zhang Zhang Ph.Da, Meng Wang MS.a, Zhenwen Yang Ph.D.&MD.b, Fan Yang MS. a,

Department of Radiology, Tianjin Medical University General Hospital,

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Dong Li Ph.D. a, Tielian Yu Ph.D.&MD.a, Ningnannan Zhang Ph.D.a,*

Tianjin 300052, China Department of Cardiology, Tianjin Medical University General Hospital,

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b

Tianjin 300052, China

Corresponding Author and Reprint Address:

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Ningnannan Zhang, Ph.D.

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Running head: Evaluation of PA pressure by CMR

Department of Radiology

Tianjin Medical University General Hospital

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Tianjin 300052, China

Phone: (086) 60362018 Fax: (949) 60362706

E-mail: [email protected]

The first two authors contributed to this work equally.

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ACCEPTED MANUSCRIPT ABSTRACT Background: Cardiac magnetic resonance (CMR) has promise of being able to provide

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frequent cardiac morphology and function evaluations noninvasively for repeated follow-ups

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of pulmonary arterial hypertension (PAH) patients after the initial right heart catheterization

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(RHC) diagnosis. By using the noninvasive CMR indices, the present study aimed to formulate and validate a prediction model of mean pulmonary artery pressure (mPAP) and pulmonary vascular resistance (PVR).

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Methods: Both Derivation Cohort (N=25) and Validation Cohort (N=25) of PAH patients

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underwent CMR and RHC within one week. Fast cine and phase-contrast sequences were used to calculate CMR indices, including ventricular mass index (VMI), interventricular septum curvature ratio (CR), and positive pulmonary arterial flow (QP). The gold standard

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mPAP (mPAPRHC) and PVR (PVRRHC) were measured from RHC. mPAP was calculated using CMR indices (mPAPCMR) from the Derivation Cohort. Multiple linear regression was applied

Results:

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for analysis. The

equation

predicting

mPAP

was

mPAPCMR

=

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28.837VMI-26.479CR-0.201QP+57.021. The equation was then applied to the Validation Cohort to verify the accuracy of the prediction equation. mPAPCMR was correlated linearly with mPAPRHC as mPAPRHC = 0.8055mPAPCMR + 7.9056 (r² = 0.6470, p < 0.001). Moreover, PVR calculated from CMR (PVRCMR) was also correlated with the PVRRHC in both the Derivation Cohort (r² = 0.4092, p < 0.001) and the Validation Cohort (r² = 0.3480, p < 0.001). Conclusion: The application of the mPAPCMR and PVRCMR technique could potentially provide a noninvasive method to evaluate the hemodynamics for PAH patients during follow-ups as well as right ventricle function assessment.

Key Words: Magnetic resonance imaging; Pulmonary arterial hypertension; Pulmonary 2 / 44

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artery pressure; Pulmonary vascular resistance

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ACCEPTED MANUSCRIPT INTRODUCTION Pulmonary arterial hypertension (PAH) is a rapidly progressive disease in the pulmonary

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circulatory system and characterized by elevation of the mean pulmonary artery pressure

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(mPAP) above 25 mmHg when the heart is at rest and measured by right heart catheterization

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(RHC). In PAH, vasoconstriction and vascular remodeling lead to a progressive increase in pulmonary vascular resistance (PVR) and mPAP, which has detrimental effects on the heart, particularly on the right ventricle (RV). The afterload stress rises may result in right heart

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failure and cause hemodynamic (i.e., PVR and mPAP) instability and sudden death. Early accurate diagnosis and timely clinical treatment may slow the pulmonary vascular remodeling

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and delay the onset of the right heart failure, which would prolong patients’ lives [1]. RHC is the current reference standard for the diagnosis and the assessment of the severity of PAH.

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Hemodynamics assessed by RHC provides important information (such as mPAP for diagnosis, and PVR for prognosis and risk stratification), both at the time of diagnosis and

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during follow-ups. There is a consensus among health professionals that RHC should be performed whenever therapeutic decisions can be expected [2,3]. However, the strategies vary

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from centers to hospitals and from regular invasive hemodynamic assessments to predominantly non-invasive follow-ups. There is a need for more reproducible, accurate and easy-to-use methods to monitor PAH patients. Echocardiography is an important non-invasive follow-up tool to assess RV function and to evaluate mPAP by its own technique, but it often fails to obtain a comprehensive and integrated evaluation of the RV structure and function and the pulmonary vascular system [4]. Cardiac magnetic resonance (CMR) imaging provides high-resolution and three-dimensional images of the heart and is more accurate in assessing RV morphology and function than echocardiography [5-9]. Therefore, CMR can provide complementary and valuable evidence for RV functional assessment during PAH patient follow-ups. The purpose of the present study is to validate the mPAP and PVR quantification 4 / 44

ACCEPTED MANUSCRIPT method following the use of noninvasive CMR indices.

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METHODS

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Study Design

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Two cohorts were recruited in the present study: a Derivation Cohort and a Validation Cohort from retrospective PAH patients. Both cohorts underwent CMR and RHC examinations

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within one week. With the CMR indices and multiple linear regression, we formulated an

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equation of mPAP from the Derivation Cohort. By using the RHC as the gold standard, we applied the equation to the Validation Cohort to validate the mPAP and examine PVR

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prediction accuracy. The study protocol was approved by the Ethics Review Panel of the

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Tianjin Medical University General Hospital. All participants signed the consent forms.

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Patients

The Derivation Cohort consisted of 25 consecutive PAH patients (23 women; mean age 38.56 ± 12.76 years old, mPAP 56.96 ± 17.16 mmHg) recruited from the Tianjin Medical University

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General Hospital between March 2012 and December 2013. The Validation Cohort was enrolled as a separate group consisting of 25 consecutive PAH patients (22 women; mean age 40.78 ± 11.34 years old, mPAP 52.21 ± 11.39 mmHg) recruited from the Tianjin Medical University General Hospital between January 2014 and December 2015. All incident patients met the diagnosis standard (i.e., mPAP ≥ 25mmHg, Pulmonary capillary wedge pressure (PCWP) ≤ 15mmHg，PVR > 3 Wood Units) when measured by RHC. CMR Acquisition CMR was performed on a 1.5 T Twin-speed Infinity with Excite II whole body scanner (GE Healthcare, Milwaukee, WI, USA) with an 8-channel cardiac coil. The scanning was conducted with the patient lying in a supine position. The images were acquired 5 / 44

ACCEPTED MANUSCRIPT from retrospective electrocardiogram (ECG) gating during breath holding periods. Fast Imaging Employing Steady-state Acquisition (FIESTA) Short axis cine images were

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acquired using a cardiac gated FIESTA (20 frames per cardiac cycle, slice thickness = 8 mm,

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FOV = 35cm × 35cm, matrix = 224 × 224, flip angle = 45°, bandwidth = 125 KHz/pixel, TR/TE = min full/min full, NEX = 1). A stack of images in the short axis plane were acquired fully covering both ventricles from the apex to the base.

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Fast Cine phase-contrast (PC) Phase-contrast flow imaging was performed with a

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compensated velocity encoded 2D gradient echo sequence. The plane was placed 1.5~2 cm above the pulmonary valves in the long axis of the main pulmonary artery to achieve an

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orthogonal position of the pulmonary arterial trunk (30 frames per cardiac cycle, FOV = 40cm

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× 40cm, matrix = 256 × 256, flip angle = 20°, bandwidth = 31.25 KHz/pixel, TR/TE = min

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full/min full, NEX = 1, velocity encoding = 150 cm/s). The total time of the FIESTA and the PC imaging was 30 minutes.

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Image Analysis

An Image analysis was performed on a GE Advantage Workstation 4.3 (GE AW 4.3) by two radiologists. They both had rich experience in CMR imaging and diagnosis and were blind to the cohort demographics, clinical information, and RHC parameters. RV and LV Functional Indices Endocardial surfaces were manually drawn from the stack of short-axis cine images. The values of right ventricle (RV) volume, left ventricle (LV) volume, end-diastolic volume (EDV), and end-systolic volume (ESV) were obtained from the CMR workstation software (Report Card 3.7). The ejection fraction (EF), stroke volume (SV), and cardiac index (CI) were calculated from EDV and ESV (EF = ESV/EDV × 100%; SV = EDV - ESV; CI = SV × HR). The epicardial and endocardial borders of RV and LV were outlined at 6 / 44

ACCEPTED MANUSCRIPT the end of each diastolic short axis image. When drawing the region of interest (ROI) for ventricular volume, we included the interventricular septum (IVS) volume into the LV volume

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and the RV outflow tract volume into the RV volume (Figure 1). The myocardial volume for

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each slice was calculated by the area of the ventricle wall multiplying the slice thickness. The

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myocardial mass (MM) of RV was calculated by the volume of the myocardium of the ventricle multiplying its density (1.05 g/cm3). The same procedure applied to the estimate of the MM of the LV. Notably, the volume of the papillary muscles and trabeculare were

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regarded as the ventricular volume rather than the myocardium volume. RV and LV EDV,

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ESV, SV and MM were calibrated by the patient’s body surface area (BSA), namely, RV, LV EDV index (EDVI), ESV index (ESVI), SV index (SVI), and MM index (MMI), respectively. Ventricular mass index (VMI) was then calculated as VMI=RVMM/LVMM. The patient’s

(kg)-0.1529] ×1000[5].

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body surface area (BSA) was calculated as BSA (m2) = [0.0061×height (cm) + 0.0128×weight

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IVS Indices: Eccentricity Index (EI) and Curvature Ratio (CR) The LV EI measurements were traditionally expressed as the ratio of two LV perpendicular minor-axis diameters. The

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Systolic eccentricity index (sEI) measurements were obtained from the mid-papillary short axis image at the end of the systolic phase. The ratio was calculated with the formula sEI=D1/D2, where D1 was the diameter parallel and D2 was perpendicular to the IVS. The Diastolic eccentricity index (dEI) was calculated from the mid-papillary short axis image at the end of the diastolic phase using the same methods (Figure 2) [10]. Every circumcircle passed through each of the three vertices of a triangle. Thus, when the three points were defined, the radius of the circle could be geometrically determined. Two points were initially positioned at the junctions between the IVS and the LV free wall (FW). An additional point was marked in the middle portion of IVS and FW. This method applied to the calculating of 7 / 44

ACCEPTED MANUSCRIPT the end-diastolic radius of the IVS and FW in the mid-short axis cine images at the phase of maximal septal displacement. IVS curvature (CIVS) and FW curvature (CFW) measured in the

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end diastolic phase were used to calculate the curvature ratio (CR=CIVS/CFW). The

FW) were subsequently calculated (Figure 3) [11].

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end-diastolic CIVS (the reciprocal of the radius of IVS) and CFW (the reciprocal of the radius of

PC Hemodynamic Indices The ROI was manually traced as the inner edge of the main

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pulmonary artery contour in the PC amplitude image. The vessel contours of other cardiac

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cycles were automatically drawn with manual correction. Positive pulmonary arterial flow (QP) (l/min), average pulmonary arterial velocity (AVP) (cm/s), maximal and minimal

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pulmonary arterial areas (AREAmax and AREAmin) were calculated with a Report Card. The

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distensibility was calculated with the equation Distensibility = (AREAmax - AREAmin)/

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AREAmin.

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A Swan-Ganz catheter was placed through a 6F introducer via the femoral approach. The heart rate was monitored continuously and the body surface area was recorded. Zero-pressure calibration was performed at the level of the mid-axillary line for the patient in a supine position. Baseline measurements included mean right atrial pressure (mRAP), mean pulmonary arteria pressure (mPAP), systolic pulmonary arteria pressure (sPAP), diastolic pulmonary arteria pressure (dPAP), pulmonary capillary wedge pressure (PCWP), and pulmonary vascular resistance (PVR). PVR from RHC (PVRRHC) was calculated as PVRRHC = (mPAP – PCWP)/CO. Lastly, Cardiac output (CO) was measured by using a thermodilution wire. All the patients underwent CMR and RHC examinations within one week. 8 / 44

ACCEPTED MANUSCRIPT STATISTICAL ANALYSES SPSS 23.0 was used to perform all statistical analyses. All data were presented as the mean ±

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standard deviation (SD) for normally distributed variables. The Independent-Samples T test

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was used to compare the means between the two cohorts. Multiple linear regression was used to formulate an mPAP prediction equation by entering the CMR indices from the Derivation Cohort. It is noted that the CMR showed univariate significant associations. In order to

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achieve linearity, variable transformations were performed where necessary. In the Validation

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Cohort, the Pearson correlation coefficient was implemented to test the relationships between the CMR-derived mPAP (mPAPCMR) and RHC-derived mPAP (mPAPRHC). The degree of

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agreement between mPAPCMR and mPAPRHC was assessed with the Bland-Altman analysis. A

RESULTS

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p < 0.05 was considered statistically significant.

Table 1 summarized the patient demographics, the RHC hemodynamics and the CMR indices.

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There are no statistically significant differences between the two cohorts in terms of the clinical characteristics and RHC hemodynamics. For the CMR indices, there was only one significant difference on RVCI between the two cohorts (RVCI, p=0.012). The CMR indices, including RVMMI, LVSVI, LVCI, VMI, dEI, CR, and Qp, were correlated with mPAP in the Derivation Cohort (p<0.05). Among all the CMR indices, CR had the highest correlation coefficient with mPAP (r = -0.726, p < 0.001) in the Derivation Cohort (Table 2). mPAP Prediction In the Derivation Cohort, RVMMI, LVSVI, LVCI, VMI, dEI, CR, and Qp were entered into multiple regression and the result shows that VMI, CR and Qp were significant predictors of 9 / 44

ACCEPTED MANUSCRIPT mPAP. The equation was mPAPCMR=28.837VMI-26.479CR-0.201QP+57.021(p < 0.001), where the F values for VMI, CR and QP were 4.554(p=0.005), 3.403(p=0.008), and

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4.112(p=0.006) respectively. The overall prediction effect of VMI, CR and Qp on mPAPCMR

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was 68.60%. In this cohort, mPAPCMR showed a strong correlation with the reference standard mPAPRHC (r² = 0.7257). The equation of the regression line was mPAPRHC = 0.7256 mPAPCMR + 15.374 (p < 0.001). When the mPAP prediction equation was applied to the Validation

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Cohort, mPAPCMR was correlated linearly with mPAPRHC as mPAPRHC = 0.8055 mPAPCMR +

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7.9056 (p < 0.001) with a good correlation coefficient (r² = 0.6470) (Fig. 4). Additionally, in the Bland-Altman plot, the mean differences between the mPAPCMR and mPAPRHC were 0.01

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± 14.70 mmHg in the Derivation Cohort and -2.90 ± 17.28 mmHg in the Validation Cohort.

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There was no statistically significant difference from zero, implying there was no bias

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PVR Prediction

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between the two methods for mPAP measurements.

CMR-derived PVR (PVRCMR) was calculated with the equation: PVRCMR = mPAPCMR/RVCI, where mPAPCMR was obtained from the multiple linear regression and RVCI was noninvasively measured by using CMR images. Figure 5 shows that PVRCMR was correlated linearly with the PVRRHC both in the Derivation Cohort (PVRRHC = 0.5038 PVRCMR + 9.7608, r² = 0.4092, p < 0.001) and in the Validation Cohort (PVRRHC = 0.6004 PVRCMR + 6.5207, r² = 0.3480, p < 0.001). The mean differences between the two methods of PVR measurements were 2.13 ± 10.74 WU and 0.52 ± 11.33 WU in the Derivation Cohort and in the Validation Cohort respectively. 10 / 44

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DISCUSSION

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In this study, we used the CMR variables from the Derivation Cohort and derived a regression

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equation for the purpose of predicting mPAP. When the regression equation was applied to the

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Validation Cohort, both mPAPCMR showed good correlations with the reference standard mPAPRHC. Additionally, the Bland-Altman analysis also shows that there was no significant

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bias between the CMR-derived hemodynamics and RHC measurements. Clearly both mPAPCMR and PVRCMR can be estimated by using the CMR indices noninvasively. Martinson

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et al. [12] suggested that PAP-guided management of heart failure should be cost-effective. Our findings seem to help achieve this. Given the increasing ageing population and heart

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failure patients, the findings of this study will be of great importance because the

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CMR-derived pressure measurement technique may help reduce hospitalization days and

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follow-up times for PAH patients.

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mPAP Prediction Model

The pulmonary hemodynamic condition is one of the key indicators for PAH diagnosis and follow-up evaluations. However, the ability to estimate mPAP noninvasively and consistently is still a challenge for CMR. Previous studies show that the CMR indices were correlated with mPAP [13-18]. Alunni et al. [19] reported strong correlations between mPAP and several CMR morphology indices including RVEF, RVESV, RVEDV, and RV area changes. Shehata et al. [7,20] found that the RV longitudinal strain had a strong correlation with mPAP in PAH patients. Gan et al. [21] reported that the relative area changes of the pulmonary artery had a curvilinear relationship with mPAP. Recently, researches tried to find a noninvasive method to

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ACCEPTED MANUSCRIPT directly predict mPAP from CMR [22-24]. Swift et al. [5] summarized a series of studies predicting mPAP. The indices of RVMMI, VMI, RV area changes, average velocity, diastolic

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area are generally used to estimate mPAP. The present study used noninvasive CMR indices

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and found that mPAP. VMI, CR, and QP were significant predictors of mPAP. The linear regression equation, which has been validated by the Validation cohort, is mPAPCMR = 28.837VMI -26.479CR -0.201QP +57.021). The following sections will explain the significant

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predictors VMI, CR, and QP in detail.

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VMI, which is defined as the ratio of RV and LV mass, can indicate the degree of RV myocardium hypertrophy. Roeleveld et al. [25,26] first found a moderate degree of correlation

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between VMI and mPAP and concluded that the noninvasive methods could not totally

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replace RHC. Saba et al. [27] reported that VMI measured from CMR can provide an accurate

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and practical method to estimate mPAP in patients with primary and secondary PAH. In addition, Swift et al. [5,28,29] listed a series of indices of cardiac morphology and RV

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function along with the corresponding mPAP and found that both VMI and RVMMI show linear relationships with mPAP. Consistently with these studies, the present study found VMI was correlated with mPAP (r = 0.533, p = 0.006) and has a strong effect on the latter in the linear regression equation (β = 28.837, p = 0.005). CR Cardioradiologists have now paid increasing attention to the published CMR IVS functional indices of sEI, dEI, and CR. RV and LV share IVS as both have the same pericardial sac leading to the interventricular dependence in structure and function. Thus, IVS plays an important functional role for the two ventricles. Generally, LV pressure is higher than RV pressure during cardiac cycles, so a positive left-to-right trans-septal pressure gradient 12 / 44

ACCEPTED MANUSCRIPT exists throughout a cardiac cycle. When pulmonary pressure and RV pressure increase, IVS will be pressed leftward and deform LV in a “D” shape. Consequently, the deformation of LV

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will result in altered LV filling dynamics and function in PAH patients [10,30]. Previous

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research shows that patients with RV pressure overload have systolic flattening of the septum while patients with RV volume overload have diastolic flattening of the septum [4]. The transeptal pressure gradient is a key determinant of abnormal septal motion and flattening in

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PAH patients [31]. Beyar et al. [32] proposed a theory of ventricular interaction and septal

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deformation model and applied it to the study of CR. Roeleveld et al. [25,26] pointed out that a negative curvature of IVS can be noticed when systolic pressure is higher than 67 mmHg,

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which enables a direct visible diagnosis of PAH. They also found a strong correlation between

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CR and sPAP and claimed that CR can be used as an indicator of PAP for PAH patients.

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Pandya et al. [10] stated that the change in septal curvature metrics is moderately correlated with absolute change in mPAP. Swift et al. [8] claimed that mPAP can be accurately estimated

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by using multivariate regression analysis of MRI indices. The equation was mPAPCMR = –4.6 + (interventricular septal angle × 0.23) + (VMI ×16.3). This equation clearly shows that VMI and the angle of the interventricular septum will determine estimation of mPAP. The angle of the interventricular septum, which has a similar index as CR, can indicate the relationship between LV and RV pressure. The multiple linear regression equation in the present study also included VMI and CR as two important CMR indices. When the multiple linear regression equation was applied to the Validation Cohort, mPAPCMR showed a strong linear correlation with mPAPRHC (r² = 0.6470). This is consistent with Dellegrottaglie et al. [11], who proposed a similar idea using CR and cuff-measured systolic blood pressure to estimate sPAP 13 / 44

ACCEPTED MANUSCRIPT noninvasively. Different degrees of systolic septal flattening can lead to varied morphological and functional

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changes. Mild systolic septal flattening is an abnormal condition and used as one of the

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earliest signs of PAH diagnosis. It is generally described as a small degree of flattening of the IVS at end-systole. Moderate septal flattening is associated with more obvious flattening of the septum throughout systole and commonly leads to an LV “D-shaped” configuration

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[4,13,33,34]. Severe septal flattening generally coincides with a reversal of the septal

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curvature during systole and the septum will temporarily bend toward LV. Therefore, sEI, dEI, and the curvature ratio are correlated with mPAP, sPAP, and dPAP in PAH patients and are

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important indicators to detect mPAP evaluation (mPAP >50 mmHg) [11,13,35,36]. The

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reasons for the strong relationship between CR and PAP could be summarized as (1) Septal

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stretch from volume and/or the pressure loading impairs fiber shortening; (2) Systolic flattening of the septum due to delay in the time to peak RV contraction leads to a mechanical

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RV/LV asynchrony; (3) PAH is associated with interventricular dys-synchrony as manifested by accelerated RV free wall and septal activation times. Therefore, the mechanism of RV dysfunction can be better documented. RV pressure and volume overload may increase RV wall tension and prolong RV myocardial shortening. The morphological change may lead to left-to-right delay in the peak shortening or even LV septal bowing [14,30,37]. This will cause LV filling and pumping dysfunction, low adaptability of whole heart, and finally lead to SV reduction. QP Sanz et al.’s [38] study shows a variety of flow measurements in the pulmonary trunk from PC were correlated with the degree of PAP-determined hemodynamic disturbance and 14 / 44

ACCEPTED MANUSCRIPT the average blood velocity throughout the cardiac cycle from PC was strongly correlated with PAP and PVR. QP, as an important parameter from PC sequence, represents blood flow in the

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pulmonary artery trunk and can be used to noninvasively detect presence or absence of

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PAH[39]. PVR Prediction

With the growing availability of an effective technique, early detection of mild elevations in

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PVR will be of great importance. The major determinant of PVR is the condition of small arteriolar (known as resistance arterioles) tone and the pre-capillary arterioles [40]. PVR has

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important implications for management and prognosis in patients with PAH and chronic heart failure. Therefore, previous studies attempted to evaluate PVR by using the noninvasive CMR.

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Sanz et al. [41] used average velocity and diastolic area to estimate PVR. Mousseaux et al.

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[42] studied the acceleration volume to calculate the maximum change in the flow rate in

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order to estimate PVR. Roldán-Alzate et al. [43] used a 4D flow CMR to assess the cardiac function and PVR in a canine model noninvasively. In a study of 171 patients, Aschauer et al. [44] found RVEF was correlated with PVR (r =0.19, p =0.029), which is consistent with our

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findings (i.e., PVR = -0.2099RVEF + 20.609, r = 0.348, p=0.089 in the Derivation Cohort, and PVR = -0.2815RVEF + 24.717, r= 0.523, p=0.009 in the Validation Cohort). CMR emerges as a technique of choice in assessment of RV structure and function for its good spatial and temporal resolution and its high reproducibility. Moreover, RV function evaluation via CMR provides a useful tool for risk stratification and is associated with low mortality and high life quality among PAH patients. The present study also tried to predict PVRCMR by using the mPAPCMR results from the regression equation and CI from CMR measurement. In both cohorts, PVRCMR was correlated with PVRRHC (r² = 0.4092 in the Derivation Cohort, and r² = 0.3480 in the Validation Cohort). However, comparing to the gold standard PVRRHC, the current mPAPCMR did not include the 15 / 44

ACCEPTED MANUSCRIPT PCWP in the calculation and may have overestimated the PVR. Franssen et al. [45] made an important suggestion for PAH patients with normal PCWP. They pointed out that these

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patients may have a dilated left atrium and need to undergo invasive exercise stress testing or

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a volume change before they can be classified as a pre-capillary group. Swift et al. [8] used

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left atrial volume and PC flow measurements as surrogates of left-sided cardiac pressures and cardiac output to calculate PVR, and claimed a new method to evaluate PWCP by using the left atrium volume. Thus, PVRCMR could be used as a supplement for CMR-derived RV

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function to predict prognosis and risk stratification in clinics.

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Limitations

Firstly, the sample size of this study was small, and male PAH patients were especially in

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short. Secondly, real relationships between the CMR indices and mPAP may be more

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complicated such as being multilinear not linear (for instance the RCR Windkessel model [23])

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or vary at different stages when PAH progresses. Thirdly, PWCP was not included in PVR evaluation in the present study and this may cause a diagnostic bias. The future studies should

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recruit a larger sample size of PAH patients with a gender balanced ratio and try different calculation techniques (e.g., not just multiple regression) to archive a more accurate pulmonary hemodynamic evaluation.

CONCLUSIONS The present study demonstrates that mPAP could be more accurately estimated by using the CMR indices. PVR could also be calculated by using mPAPCMR and RVCI. The hemodynamics prediction technique could potentially provide complementary and valuable evidence to support CMR derived from RV functional assessment and reduce follow-up times 16 / 44

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Conflict of interest: The authors do not have conflict of interest.

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for PAH patients.

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Acknowledgements: N/A

Funding: This work was supported by China National Natural Science Foundation grant 81301217 and Tianjin Research Program of Application Foundation and Advanced

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Technology 14JCZDJC57000.

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hypertension of the european society of cardiology (esc) and the european respiratory society (ers): Endorsed by: Association for european paediatric and congenital cardiology (aepc),

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Condliffe, J. M. Wild, D. G. Kiely, Prognostic value of cardiovascular magnetic resonance imaging measurements corrected for age and sex in idiopathic pulmonary arterial hypertension, Circ Cardiovasc Imaging 7 (2014) 100-106. [3] K. Addetia, N. M. Bhave, C. E. Tabit, M. Gomberg-Maitland, B. H. Freed, K. E. Dill, R. M. Lang, V. Mor-Avi, A. R. Patel, Sample size and cost analysis for pulmonary arterial hypertension drug trials using various imaging modalities to assess right ventricular size and function end points, Circ Cardiovasc Imaging 7 (2014) 115-124. [4] F. Haddad, J. Guihaire, M. Skhiri, A. Y. Denault, O. Mercier, S. Al-Halabi, B. Vrtovec, E. Fadel, R. T. Zamanian, I. Schnittger, Septal curvature is marker of hemodynamic, anatomical, and electromechanical ventricular interdependence in patients with pulmonary arterial 18 / 44

ACCEPTED MANUSCRIPT hypertension, Echocardiography 31 (2014) 699-707. [5] A. J. Swift, J. M. Wild, S. K. Nagle, A. Roldan-Alzate, C. J. Francois, S. Fain, K. Johnson,

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enhancement in pulmonary hypertension: Pulmonary hemodynamics, right ventricular function, and remodeling, AJR Am J Roentgenol 196 (2011) 87-94.

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[21] C. Gan, J. W. Lankhaar, J. T. Marcus, N. Westerhof, K. M. Marques, J. G. Bronzwaer, A. Boonstra, P. E. Postmus, A. Vonk-Noordegraaf, Impaired left ventricular filling due to

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right-to-left ventricular interaction in patients with pulmonary arterial hypertension, Am J Physiol Heart Circ Physiol 290 (2006) H1528-1533. [22] M. Terada, Y. Takehara, H. Isoda, T. Uto, M. Matsunaga, M. Alley, Low wss and high osi measured by 3d cine pc mri reflect high pulmonary artery pressures in suspected secondary pulmonary arterial hypertension, Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine 15 (2016) 193-202. [23] R. Badagliacca, R. Poscia, B. Pezzuto, M. Nocioni, M. Mezzapesa, M. Francone, E. Giannetta, S. Papa, C. Gambardella, S. Sciomer, M. Volterrani, F. Fedele, C. Dario Vizza, Right ventricular remodeling in idiopathic pulmonary arterial hypertension: Adaptive versus maladaptive morphology, J Heart Lung Transplant 34 (2015) 395-403. 21 / 44

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Vonk-Noordegraaf, Interventricular septal configuration at mr imaging and pulmonary arterial pressure in pulmonary hypertension, Radiology 234 (2005) 710-717. [26] R. J. Roeleveld, J. T. Marcus, A. Boonstra, P. E. Postmus, K. M. Marques, J. G.

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estimating pulmonary artery pressure in pulmonary hypertension, J Magn Reson Imaging 22 (2005) 67-72.

[27] T. S. Saba, J. Foster, M. Cockburn, M. Cowan, A. J. Peacock, Ventricular mass index

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using magnetic resonance imaging accurately estimates pulmonary artery pressure, Eur Respir

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[28] A. J. Swift, S. Rajaram, D. Capener, C. Elliot, R. Condliffe, J. M. Wild, D. G. Kiely, Lge patterns in pulmonary hypertension do not impact overall mortality, JACC Cardiovasc

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[29]A. J. Swift, S. Rajaram, R. Condliffe, D. Capener, J. Hurdman, C. Elliot, D. G. Kiely, J. M. Wild, Pulmonary artery relative area change detects mild elevations in pulmonary vascular resistance and predicts adverse outcome in pulmonary hypertension, Invest Radiol 47 (2012) 571-577. [30] S. Rain, M. L. Handoko, P. Trip, C. T. Gan, N. Westerhof, G. J. Stienen, W. J. Paulus, C. A. Ottenheijm, J. T. Marcus, P. Dorfmuller, C. Guignabert, M. Humbert, P. Macdonald, C. Dos Remedios, P. E. Postmus, C. Saripalli, C. G. Hidalgo, H. L. Granzier, A. Vonk-Noordegraaf, J. van der Velden, F. S. de Man, Right ventricular diastolic impairment in patients with pulmonary arterial hypertension, Circulation 128 (2013) 2016-2025, 2011-2010. 22 / 44

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septal deformation: A model compared with experimental data, Am J Physiol 265 (1993) H2044-2056.

[33]U. Truong, S. Patel, V. Kheyfets, J. Dunning, B. Fonseca, A. J. Barker, D. Ivy, R. Shandas,

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K. Hunter, Non-invasive determination by cardiovascular magnetic resonance of right

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ventricular-vascular coupling in children and adolescents with pulmonary hypertension, J Cardiovasc Magn Reson 17 (2015) 81.

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Taylor, S. G. Haworth, I. Schulze-Neick, V. Muthurangu, Prognostic significance of cardiac magnetic resonance imaging in children with pulmonary hypertension, Circ Cardiovasc

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Imaging 6 (2013) 407-414.

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evaluation by magnetic resonance imaging, Case Rep Radiol 2015 (2015) 946920. [36] M. Kanski, H. Arheden, D. M. Wuttge, G. Bozovic, R. Hesselstrand, M. Ugander, Pulmonary blood volume indexed to lung volume is reduced in newly diagnosed systemic sclerosis compared to normals--a prospective clinical cardiovascular magnetic resonance study addressing pulmonary vascular changes, J Cardiovasc Magn Reson 15 (2013) 86. [37] T. E. Raymond, J. E. Khabbaza, R. Yadav, A. R. Tonelli, Significance of main pulmonary artery dilation on imaging studies, Ann Am Thorac Soc 11 (2014) 1623-1632. [38] J. Sanz, P. Kuschnir, T. Rius, R. Salguero, R. Sulica, A. J. Einstein, S. Dellegrottaglie, V. Fuster, S. Rajagopalan, M. Poon, Pulmonary arterial hypertension: Noninvasive detection with phase-contrast mr imaging, Radiology 243 (2007) 70-79. 23 / 44

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Magn Reson 15 (2013) 81.

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[40] O. Fabregat-Andres, J. Estornell-Erill, F. Ridocci-Soriano, P. Garcia-Gonzalez, B. Bochard-Villanueva, A. Cubillos-Arango, L. Espriella-Juan Rde, L. Facila, S. Morell, J. Cortijo, Prognostic value of pulmonary vascular resistance estimated by cardiac magnetic

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resonance in patients with chronic heart failure, Eur Heart J Cardiovasc Imaging 15 (2014)

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cardiac magnetic resonance, JACC Cardiovasc Imaging 2 (2009) 286-295. [42] E. Mousseaux, J. P. Tasu, O. Jolivet, G. Simonneau, J. Bittoun, J. C. Gaux, Pulmonary

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arterial resistance: Noninvasive measurement with indexes of pulmonary flow estimated at velocity-encoded mr imaging--preliminary experience, Radiology 212 (1999) 896-902.

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[43] A. Roldan-Alzate, A. Frydrychowicz, K. M. Johnson, H. Kellihan, N. C. Chesler, O. Wieben, C. J. Francois, Non-invasive assessment of cardiac function and pulmonary vascular resistance in an canine model of acute thromboembolic pulmonary hypertension using 4d flow cardiovascular magnetic resonance, J Cardiovasc Magn Reson 16 (2014) 23. [44] S. Aschauer, A. A. Kammerlander, C. Zotter-Tufaro, R. Ristl, S. Pfaffenberger, A. Bachmann, F. Duca, B. A. Marzluf, D. Bonderman, and J. Mascherbauer, 'The Right Heart in Heart Failure with Preserved Ejection Fraction: Insights from Cardiac Magnetic Resonance Imaging and Invasive Haemodynamics', Eur J Heart Fail 18 (2016) 71-80. [45]C. Franssen, and W. J. Paulus, 'Normal Resting Pulmonary Artery Wedge Pressure: A Diagnostic Trap for Heart Failure with Preserved Ejection Fraction', Eur J Heart Fai, 17 24 / 44

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The FIESTA short-axis images show endocardial and epicardial contours. A-B: end-systolic

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phase, C-D: end-diastolic phase. (RV: right ventricle, LV: left ventricle) Figure 2. Eccentricity index measurement and calculation

The FIESTA mid-papillary short axis images of the end-systolic phase and end-diastolic phase

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show eccentricity index calculation. (A: end-diastolic phase; B: end-systolic phase)

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Figure 3. Curvature measurement and calculation

In the FIESTA mid-papillary short axis images of the end-diastolic phase, two points (A:

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junction 1,B: junction2) were initially positioned at the junctions between the interventricular

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septal (IVS) and left ventricle (LV) free wall (FW). Two additional points were then marked

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in the middle portion of IVS (M1) and the FW (M2). By considering A, B, and M1, the radius of the IVS (RIVS) was derived by applying the three-point circle method. A, B, and M2 were

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used similarly to calculate the radius of the FW (RFW). Figure 4.mPAP prediction compared with RHC (Derivation Cohort: A and B) A linear regression analysis (A) and the Bland-Altman analysis (B) of CMR derived mPAP (mPAPCMR) and RHC measured mPAP (mPAPRHC)

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Derivation Cohort are shown. The solid line represents the regression line (mPAPRHC = 0.7256 mPAPCMR + 15.374; r² = 0.7257, p< 0.001). The mean difference between the mPAPCMR and mPAPRHC is 0.01±14.70 mmHg in the Derivation Cohort. (Derivation Cohort: C and D)In the Validation Cohort, mPAPCMR showed a strong correlation with the reference standard mPAPRHC (mPAPRHC = 0.8055 mPAPCMR + 7.9056; r² = 0.6470, 26 / 44

ACCEPTED MANUSCRIPT p< 0.001) (C).In the Bland-Altman plot (D), the mean difference between the mPAPCMR and mPAPRHCis2.13±10.74 mmHg.

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Figure 5.PVR prediction compared with RHC

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(Derivation Cohort: A and B) A linear regression analysis (A) shows that CMR derived PCR (PVRCMR) correlated well with the RHC measured PVR (PVRRHC) in the Derivation Cohort (PVRRHC = 0.5038 PVRCMR + 9.7608, r² = 0.4092, p<0.001). The mean difference between

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the two methods of PVR measurements is -2.19±18.27 WU in the Derivation Cohort (B).

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(Derivation Cohort: C and D) PVRCMR correlated well with the gold standard PVRRHC as (PVRRHC = 0.6004 PVRCMR + 6.5207, r² = 0.3480, p<0.001) in the Validation Cohort(C).

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ACCEPTED MANUSCRIPT TABLE 1. Comparison between the Derivation Cohort and Validation Cohort Derivation Cohort

Validation Cohort p (N=25)

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(N=25)

38.56±12.76

40.78±11.34

0.265

Male / Female

2/23

3/22

0.895

HR

81.72±9.31

81.33±10.05

0.777

7

6

0.865

12

13

0.972

5

6

0.789

mPAP (mmHg)

56.96±17.16

52.21±11.39

0.263

sPAP(mmHg)

91.24±29.55

85.88±22.13

0.194

dPAP(mmHg)

33.20±12.24

34.75±13.51

0.383

mRAP(mmHg)

7.32±4.52

9.08±5.62

0.208

PCWP(mmHg)

10.08±3.70

10.29±3.98

0.529

PVR(WU)

18.97±17.01

15.02±6.32

0.071

RVEDVI (ml/m2)

113.88±32.27

121.58±54.82

0.063

RVESVI (ml/m2)

74.27±30.26

95.35±46.02

0.510

RVMMI (g/cm5)

31.90±8.86

36.34±14.72

0.071

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Age (years old)

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Demographics

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Etiology of PAH Idiopathic

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Congenital heart disease

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Connective tissue disease

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RHC

CMR-LV&RV

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ACCEPTED MANUSCRIPT 39.61±10.85

46.23±21.40

0.119

RVCI(ml/min∙m2)

3.22±0.89

3.79±1.84

0.012*

RVEF (%)

37.01±13.35

34.47±11.75

0.703

LVEDVI(ml/m2)

60.08±16.41

LVESVI(ml/m2)

20.96±9.40

LVMMI(g/cm5)

42.10±11.20

LVSVI(ml/m2)

39.12±9.39

LVCI(ml/min∙m2)

3.19±0.83

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RVSVI(ml/m2)

0.166 0.086

52.35±17.84

0.408

45.97±15.49

0.086

3.76±1.47

0.141

65.84±7.73

64.35±9.77

0.322

0.79±0.24

0.71±0.25

0.704

1.34±0.21

1.36±0.33

0.091

dEI (%)

1.60±0.42

1.70±0.64

0.115

CR (%)

0.58±0.20

0.59±0.26

0.439

AVP(cm/s)

60.26±13.81

62.88±12.69

0.627

Qp(ml/min)

42.63±20.23

42.57±15.57

0.650

AERAmax (cm2)

14.00±5.13

12.30±3.64

0.127

AERAmin (cm2)

12.21±4.84

10.50±3.85

0.198

Distensibility (%)

16.95±11.77

17.05±10.89

0.642

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LVEF (%)

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LVESV=Left ventricular end-systolic volume index

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LVMMI=Left ventricular myocardial mass index LVSVI=Left ventricular stroke volume index

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ACCEPTED MANUSCRIPT TABLE 2. Correlation between CMR indices and the mPAP in the Derivation Cohort Derivation Cohort

Correlations With mPAP r

RVEDVI (ml/m2)

-0.081

0.699

RVESVI (ml/m2)

-0.090

0.668

0.686

0.000*

0.009

0.965

0.054

0.799

0.282

0.172

0.533

0.006*

-0.206

0. 100

LVESVI(ml/m2)

-0.269

0. 108

LVMMI(g/cm5)

-0.027

0.899

LVSVI(ml/m2)

-0.414

0.040*

LVCI(ml/min∙m2)

-0.472

0.017*

LVEF (%)

0.219

0.294

sEI (%)

0.367

0.071

dEI (%)

0.459

0.021*

p

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RVMMI (g/cm5) RVSVI(ml/m2)

CMR-LV

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LVEDVI(ml/m2)

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-0.726

0.000*

AVP(cm/s)

-0.336

0.100

Qp(ml/min)

-0.433

AREAmax (cm2)

0.233

0.262

AREAmin (cm2)

0.287

0.164

Distensibility (%)

-0.384

0.058

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0.031*