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Noninvasive Assessment of Left Ventricular Pressure-Volume Relations: Inter- and Intraobserver Variability and Assessment Across Heart Failure Subtypes

Open AccessPublished:September 30, 2022DOI:https://doi.org/10.1016/j.amjcard.2022.09.001
      A novel method to derive pressure-volume (PV) loops noninvasively from cardiac magnetic resonance images has recently been developed. The aim of this study was to evaluate inter- and intraobserver variability of hemodynamic parameters obtained from noninvasive PV loops in healthy controls, subclinical diastolic dysfunction (SDD), and patients with heart failure with preserved ejection fraction, mildly reduced ejection fraction, and reduced ejection fraction. We included 75 subjects, of whom 15 were healthy controls, 15 subjects with SDD (defined as fulfilling 1 to 2 echocardiographic criteria for diastolic dysfunction), and 15 patients with preserved ejection fraction, 15 with mildly reduced ejection fraction, and 15 with reduced ejection fraction. PV loops were computed using time-resolved left ventricular volumes from cardiac magnetic resonance images and a brachial blood pressure. Inter- and intraobserver variability and intergroup differences of PV loop-derived hemodynamic parameters were assessed. Bias was low and limits of agreement were narrow for all hemodynamic parameters in the inter- and intraobserver comparisons. Interobserver difference for stroke work was 2 ± 9%, potential energy was 4 ± 11%, and maximal ventricular elastance was −4 ± 7%. Intraobserver for stroke work was −1 ± 7%, potential energy was 3 ± 4%, and maximal ventricular elastance was 1 ± 5%. In conclusion, this study presents a fully noninvasive left ventricular PV loop analysis across healthy controls, subjects with SDD, and patients with heart failure with preserved or impaired systolic function. In conclusion, the method for PV loop computation from clinical-standard manual left ventricular segmentation was rapid and robust, bridging the gap between clinical and research settings.
      There is currently a lack of clinically available, safe, and reliable diagnostic tools with sufficient granularity to meaningfully investigate the hemodynamics of patients under suspicion of heart failure.
      • Burrage MK
      • Hundertmark M
      • Valkovič L
      • Watson WD
      • Rayner J
      • Sabharwal N
      • Ferreira VM
      • Neubauer S
      • Miller JJ
      • Rider OJ
      • Lewis AJM.
      Energetic basis for exercise-induced pulmonary congestion in heart failure with preserved ejection fraction.
      Left ventricular (LV) pressure-volume (PV) loop analysis provides unique physiologic insight into hemodynamic parameters and may support clinical decision making based on ventricular function, energy consumption, and stroke work.
      • Suga H.
      Ventricular energetics.
      A newly developed and validated method for calculating PV loops noninvasively using cardiac magnetic resonance (CMR) images and brachial blood pressure makes PV loop analysis more clinically available and safer than invasive methods.
      • Seemann F
      • Arvidsson P
      • Nordlund D
      • Kopic S
      • Carlsson M
      • Arheden H
      • Heiberg E.
      Noninvasive quantification of pressure-volume loops from brachial pressure and cardiovascular magnetic resonance.
      ,
      • Sjöberg P
      • Seemann F
      • Arheden H
      • Heiberg E.
      Non-invasive quantification of pressure-volume loops from cardiovascular magnetic resonance at rest and during dobutamine stress.
      The aim of this study was to evaluate inter- and intraobserver variability of noninvasive PV loops in healthy controls, subclinical diastolic dysfunction (SDD), and patients with heart failure with preserved ejection fraction (HFpEF), mildly reduced ejection fraction (HFmrEF), and reduced ejection fraction (HFrEF). In addition, we aimed to evaluate a new method designed to accelerate the workflow of computing PV loops.

      Methods

      This study was approved by the regional ethical review board in Lund, Sweden (permit 2005/269 and 2013/891) and follows the Declaration of Helsinki. Written informed consent was obtained from all research participants before data acquisition. All examinations were performed in accordance with current guidelines and regulations at Skåne University Hospital Lund, Sweden. The study is reported following the Strengthening the Reporting of Observational Studies in Epidemiology criteria for observational cohort studies.
      • von Elm E
      • Altman DG
      • Egger M
      • Pocock SJ
      • Gøtzsche PC
      • Vandenbroucke JP
      • Initiative STROBE
      The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.
      We prospectively recruited 75 participants from 1 of 5 groups: healthy controls (n = 15) and participants with SDD (n = 15) from the population-based Swedish CArdioPulmonary bioImage Study (SCAPIS)
      • Bergström G
      • Persson M
      • Adiels M
      • Björnson E
      • Bonander C
      • Ahlström H
      • Alfredsson J
      • Angerås O
      • Berglund G
      • Blomberg A
      • Brandberg J
      • Börjesson M
      • Cederlund K
      • de Faire U
      • Duvernoy O
      • Ekblom Ö
      • Engström G
      • Engvall JE
      • Fagman E
      • Eriksson M
      • Erlinge D
      • Fagerberg B
      • Flinck A
      • Gonçalves I
      • Hagström E
      • Hjelmgren O
      • Lind L
      • Lindberg E
      • Lindqvist P
      • Ljungberg J
      • Magnusson M
      • Mannila M
      • Markstad H
      • Mohammad MA
      • Nystrom FH
      • Ostenfeld E
      • Persson A
      • Rosengren A
      • Sandström A
      • Själander A
      • Sköld MC
      • Sundström J
      • Swahn E
      • Söderberg S
      • Torén K
      • Östgren CJ
      • Jernberg T.
      Prevalence of subclinical coronary artery atherosclerosis in the general population.
      and patients with heart failure (HFpEF n = 15, HFmrEF n = 15, HFrEF n = 15) from clinical referrals or the prospective HeARt and brain failure inVESTigation (HARVEST)
      • Holm H
      • Bachus E
      • Jujic A
      • Nilsson ED
      • Wadström B
      • Molvin J
      • Minthon L
      • Fedorowski A
      • Nägga K
      • Magnusson M.
      Cognitive test results are associated with mortality and rehospitalization in heart failure: Swedish prospective cohort study.
      study of hospitalized patients with heart failure. Participant characteristics are presented in Table 1. Healthy controls had no history of cardiovascular disease, were nonsmokers, and had blood pressure <140/90 mm Hg. Participants identified with SDD were nonsmokers, had blood pressure <140/90 mm Hg, and had no abnormal cardiac findings on CMR or coronary computed tomography angiography. However, upon examination with echocardiography, they fulfilled either 1 or 2 echocardiographic criterium clinically used to diagnose diastolic dysfunction:
      • Pieske B
      • Tschöpe C
      • de Boer RA
      • Fraser AG
      • Anker SD
      • Donal E
      • Edelmann F
      • Fu M
      • Guazzi M
      • Lam CSP
      • Lancellotti P
      • Melenovsky V
      • Morris DA
      • Nagel E
      • Pieske-Kraigher E
      • Ponikowski P
      • Solomon SD
      • Vasan RS
      • Rutten FH
      • Voors AA
      • Ruschitzka F
      • Paulus WJ
      • Seferovic P
      • Filippatos G
      How to diagnose heart failure with preserved ejection fraction: the HFA–PEFF diagnostic algorithm: a consensus recommendation from the Heart Failure Association (HFA) of the European Society of Cardiology (ESC).
      average E/e′ >14, septal e’ velocity <7 cm/s or lateral e’ velocity <10 cm/s, left atrial maximum volume index >34 ml/m2, or tricuspid regurgitation >2.8 m/s. Thus, the SDD group did not meet criteria to be diagnosed as diastolic dysfunction, which requires >50% of the 4 criteria to be fulfilled. Patients with heart failure all had a clinical diagnosis of heart failure made by a cardiologist and were divided into subgroups, depending on the LV ejection fraction (EF) as determined from CMR. HFpEF was defined as EF ≥50%, HFmrEF as EF 41% to 49%, and HFrEF as EF ≤40%.
      • McDonagh TA
      • Metra M
      • Adamo M
      • Gardner RS
      • Baumbach A
      • Böhm M
      • Burri H
      • Butler J
      • Čelutkienė J
      • Chioncel O
      • Cleland JGF
      • Coats AJS
      • Crespo-Leiro MG
      • Farmakis D
      • Gilard M
      • Heymans S
      • Hoes AW
      • Jaarsma T
      • Jankowska EA
      • Lainscak M
      • Lam CSP
      • Lyon AR
      • McMurray JJV
      • Mebazaa A
      • Mindham R
      • Muneretto C
      • Francesco Piepoli M
      • Price S
      • Rosano GMC
      • Ruschitzka F
      • Kathrine Skibelund A
      Group ESD
      2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure.
      Table 1Population characteristics, cardiac parameters and medications
      Participant characteristicsHealthy controls (n=15)SDD (n=15)HFpEF (n=15)HFmrEF (n=15)HFrEF (n=15)
      Age (years)62 [60 – 65]63 [60 – 64]71 [59 – 81]65 [56 – 75]66 [60 – 70]
      Female/male9/65/104/115/103/12
      Height (cm)169 [163 – 180]177 [167 – 187]175 [170 – 179]174 [166 – 180]174 [167 – 179]
      Weight (kg)69 [65 – 97]87 [68 – 94]90 [73 – 104]94 [81 – 98]78 [71 – 93]
      BSA (m2)1.76 [1.72 – 2.09]2.06 [1.74 – 2.21]2.07 [1.84 – 2.26]2.13 [1.94 – 2.21]1.99 [1.79 – 2.11]
      Cardiac parameters
      Heart rate (beats/minute)63 [54 – 74]69 [64 – 77]61 [54 – 73]64 [59 – 80]68 [60 – 71]
      Systolic blood pressure (mmHg)121 [109 – 132]126 [114 – 132]130 [118 – 153]126 [105 – 143]123 [102 – 130]
      Diastolic blood pressure (mmHg)74 [67 – 82]77 [70 – 85]69 [65 – 84]70 [59 – 86]75 [70 – 80]
      Left ventricular mass (g)85 [73 – 134]106 [80 – 133]140 [104 – 155]147 [109 – 193]
      p <0.05 compared to healthy controls.
      147 [122 – 209]
      p <0.05 compared to healthy controls.
      End-diastolic volume (ml)147 [135 – 186]160 [125 – 186]204 [179 – 216]216 [181 – 245]
      p <0.05 compared to healthy controls.
      295[256 – 401]
      p <0.05 compared to healthy controls.
      End-systolic volume (ml)59 [46 – 90]74 [49 – 81]86 [75 – 102]115 [98 – 138]
      p <0.05 compared to healthy controls.
      217 [176 – 291]
      p <0.05 compared to healthy controls.
      Stroke volume (ml)91 [88 – 99]96 [76 – 103]108 [99 – 120]91 [86 – 110]82 [69 – 110]
      Ejection fraction (%)60 [55 – 66]57 [56 – 61]54 [51 – 60]45 [42 – 48]
      p <0.05 compared to healthy controls.
      27 [19 – 34]
      p <0.05 compared to healthy controls.
      Cardiac output (L/min)6.0 [5.7 – 6.7]6.3 [5.1 – 7.2]7.0 [6.2 – 7.3]6.5 [5.1 – 8.0]5.5 [4.5 – 6.6]
      Medications
      Beta blockers01101311
      ACEi/ARB/ARNi03111114
      Aldosterone antagonist00232
      Thiazide diuretics00210
      Loop diuretics00899
      Calcium channel blockers01333
      HFpEF = heart failure with preserved ejection fraction; HFmrEF = heart failure with mildly reduced ejection fraction; HFrEF = heart failure with reduced ejection fraction; SDD = subclinical diastolic dysfunction; ACEi = angiotensin-converting enzyme inhibitor; ARB = angiotensin receptor blocker; ARNI = angiotensin receptor-neprilysin inhibitor.
      Values are presented as median [IQR].
      low asterisk p <0.05 compared to healthy controls.
      Cardiovascular magnetic resonance imaging was performed using a 1.5T scanner (Siemens Aera, Siemens Healthcare, Erlangen, Germany). Balanced steady-state free precession cine images were acquired in the 2-, 3-, and 4-chamber views, as well as the short-axis view covering the left ventricle. Typical short-axis imaging parameters: slice thickness 8 mm, in-plane spatial resolution 1.0 × 1.0 mm, no slice gap, flip angle 70°, TE/TR 1.1/41 ms. Retrospective electrocardiogram-gating was used, and data were reconstructed to 25 timeframes per cardiac cycle. Brachial blood pressure was measured by an automatic brachial cuff in conjunction with CMR image acquisition.
      We developed and validated a semiautomatic LV segmentation technique using spline interpolation for time-resolved segmentation. This technique was first evaluated in 12 datasets chosen at random from healthy controls and patients with heart failure. For this initial evaluation, the LV endocardial border was segmented manually over the entire cardiac cycle by 2 observers with 3 (observer 1, JE) and 10 (observer 2, PA) years of experience in CMR research. Next, we used a spline interpolation approach (Figure 1) for LV segmentation, based solely on end-systolic, end-diastolic, and mid-diastasis (when applicable) timeframes, segmented manually by the more experienced observer. The spline interpolation method deforms the segmentation in each short-axis slice separately, allowing corrections if desirable. For this evaluation, we used only minor manual corrections in the most basal slices. Interobserver variability between observers 1 and 2, as well as intraobserver variability between manual and spline interpolation segmentation performed by observer 2, was assessed for hemodynamic parameters derived from the PV loop analysis, based on this initial dataset.
      Figure 1
      Figure 1Overview of interpolation method for left ventricular segmentation covering one cardiac cycle. Left panel: first, the left ventricle was manually segmented in 2 or 3 timeframes; ED, ES, and diastasis in cases where present. Right panel: spline interpolation was used to fill in the missing timeframes in each slice separately, resulting in a volume-time curve for the entire cardiac cycle. ED = end-diastole; ES = end-systole.
      Bias and limits of agreement were lower comparing manual with spline interpolation than interobserver variability (Supplementary Figure 1, Supplementary Table 1). Thus, we chose the spline interpolation method for segmenting the remaining datasets of the study. An observer (observer 1, JE) performed interpolation based on manual delineations in end-systolic and end-diastolic timeframes by 2 observers with 10 (observer 2, PA) and 15 (observer 3, KSE) years of CMR experience, respectively. All image and PV loop analysis was performed using Segment 3.3 R9405e (http://segment.heiberg.se).
      • Heiberg E
      • Sjögren J
      • Ugander M
      • Carlsson M
      • Engblom H
      • Arheden H.
      Design and validation of segment–freely available software for cardiovascular image analysis.
      PV loop parameters were computed using a plug-in for Segment, as described in a previous work, where the method was validated against invasively obtained parameters.
      • Seemann F
      • Arvidsson P
      • Nordlund D
      • Kopic S
      • Carlsson M
      • Arheden H
      • Heiberg E.
      Noninvasive quantification of pressure-volume loops from brachial pressure and cardiovascular magnetic resonance.
      ,
      • Sjöberg P
      • Seemann F
      • Arheden H
      • Heiberg E.
      Non-invasive quantification of pressure-volume loops from cardiovascular magnetic resonance at rest and during dobutamine stress.
      Briefly, heart rate and time-resolved volumetric data from cardiovascular magnetic resonance imaging, as well as brachial blood pressure, were used as model input. These data are used to scale a time-varying elastance model to calculate ventricular pressure over the cardiac cycle.
      • Senzaki H
      • Chen CH
      • DA Kass
      Single-beat estimation of end-systolic pressure-volume relation in humans. A new method with the potential for noninvasive application.
      Maximal ventricular pressure is approximated from brachial pressure,
      • Kelly RP
      • Ting CT
      • Yang TM
      • Liu CP
      • Maughan WL
      • Chang MS
      • DA Kass
      Effective arterial elastance as index of arterial vascular load in humans.
      whereas the user is prompted to estimate the end-diastolic pressure used to scale the time-varying elastance curve. For this study, we used a fixed value of 7.5 mm Hg, which is likely an underestimation of the true filling pressures in some of the patients. However, a sensitivity analysis performed by Seemann et al
      • Seemann F
      • Arvidsson P
      • Nordlund D
      • Kopic S
      • Carlsson M
      • Arheden H
      • Heiberg E.
      Noninvasive quantification of pressure-volume loops from brachial pressure and cardiovascular magnetic resonance.
      showed minimal impact on derived hemodynamic parameters when varying peak diastolic LV pressure between 0 and 15 mm Hg, and later, validation using invasive PV experiments confirmed that values between 3 and 30 mm Hg have a small impact on the loop parameters (unpublished data). Figure 2 shows an example of a PV loop indicating the analyzed parameters: stroke work, potential energy, PV area (PVA), ventricular efficiency, energy per ejected volume, mean external power, maximal ventricular elastance (Emax), and effective arterial elastance (EA). Ventricular-arterial (VA) coupling was calculated as EA/Emax.
      Figure 2
      Figure 2Example of a noninvasive pressure-volume loop derived from cardiovascular magnetic resonance imaging and brachial cuff blood pressure, with hemodynamic parameters indicated. Additional pressure-volume loop derived hemodynamic parameters include ventricular efficiency (VE) defined as SW/(SW+PE), PVA as SW+PE, energy per ejected volume as PVA/(EDV-ESV), mean external power as SW*(heart rate/60), and ventricular-arterial coupling as EA/Emax.. ESV = end-systolic volume; EDV = end-diastolic volume; PE= potential energy; SW = stroke work.
      Continuous data are presented as median and interquartile range, unless otherwise stated. Intergroup differences were evaluated using Kruskal-Wallis H test with Dunn post hoc test, with significance assigned at p <0.05. Interobserver and intraobserver variability was evaluated using Bland-Altman plots and Pearson correlation coefficients. Statistical analysis was conducted using GraphPad Prism 8.4.1 (GraphPad Software, San Diego, California) and SPSS Statistics 27.0 (IBM Corp, Armonk, New York).

      Results

      Participant biometric characteristics, basic cardiac parameters, and medication are presented in Table 1. Interobserver variability of LV volumes and interobserver and intraobserver variability of hemodynamic parameters derived from PV loop analysis is presented Figure 3, Supplementary Table 1. Bias was low and limits of agreement were narrow for all hemodynamic parameters in the interobserver and intraobserver comparisons.
      Figure 3
      Figure 3Comparison of manual left ventricular segmentation and spline interpolation for PV loop derived hemodynamics. Pearson correlation (left column) and Bland-Altman plots for the inter- and intraobserver evaluation when comparing manual left ventricular segmentation and spline interpolation. Bias was lower and limits of agreement narrower when comparing manual to interpolated (right column) compared to the interobserver evaluation (center column). In the scatter plots, the solid line is a line of identity. In the Bland-Altman plots, the dashed line denotes the bias and the dotted lines the limits of agreement (defined as bias ± 1.96 SD).
      Hemodynamic parameters derived from PV loop analysis for all groups are presented in Table 2. Figure 4 shows noninvasive PV loop analysis of ventricular energetics and Figure 5 shows noninvasive PV loop analysis of ventricular and arterial elastance.
      Table 2Pressure-volume loop derived hemodynamic parameters
      Pressure-volume loop analysisHealthy controls (n=15)SDD (n=15)HFpEF (n=15)HFmrEF (n=15)HFrEF (n=15)
      Stroke work (J)1.1 [0.9 – 1.3]1.2 [0.9 – 1.4]1.5 [1.2 – 1.6]1.1 [0.9 – 1.4]0.8 [0.6 – 1.0]
      Potential energy (J)0.4 [0.3 – 0.6]0.5 [0.4 – 0.6]0.7 [0.6 – 0.8]0.9 [0.7 – 1.2]
      p<0.05 compared to healthy controls.
      1.7 [1.2 – 1.9]
      p<0.05 compared to healthy controls.
      Pressure-volume area (J)1.5 [1.2 – 1.8]1.7 [1.2 – 2.0]2.1 [1.8 – 2.4]2.0 [1.6 – 2.3]2.5 [2.0 – 2.9]
      p<0.05 compared to healthy controls.
      Mean external power (J/s)1.2 [1.0 – 1.4]1.4 [1.1 – 1.6]1.4 [1.2 – 1.8]1.2 [0.9 – 1.6]0.9 [0.6 – 1.1]
      Energy per ejected volume (J/L)17 [14 – 18]18 [16 – 20]18 [17 – 24]21 [18 – 25]30 [27 – 36]
      p<0.05 compared to healthy controls.
      Ventricular efficiency (%)73 [67 – 79]70 [67 – 74]67 [63 – 73]55 [55 – 58]
      p<0.05 compared to healthy controls.
      34 [24 – 42]
      p<0.05 compared to healthy controls.
      Arterial elastance, EA (mmHg/ml)1.1 [0.9 – 1.3]1.2 [1.0 – 1.5]1.3 [0.9 – 1.3]1.2 [1.0 – 1.4]1.5 [1.0 – 1.8]
      Maximal ventricular elastance, Emax (mmHg/ml)1.4 [1.3 – 1.7]1.5 [1.2 – 1.8]1.2 [1.1 – 1.5]0.9 [0.8 – 1.1]
      p<0.05 compared to healthy controls.
      0.5 [0.3 – 0.6]
      p<0.05 compared to healthy controls.
      Ventricular-arterial coupling (EA/Emax)0.7 [0.6 – 0.9]0.8 [0.7 – 0.9]0.9 [0.7 – 1.1]1.3 [1.1 – 1.5]
      p<0.05 compared to healthy controls.
      2.9 [2.1 – 4.4]
      p<0.05 compared to healthy controls.
      HFpEF = heart failure with preserved ejection fraction; HFmrEF = heart failure with mildly reduced ejection fraction; HFrEF = heart failure with reduced ejection fraction; SDD = subclinical diastolic dysfunction
      Values are presented as median [IQR].
      low asterisk p<0.05 compared to healthy controls.
      Figure 4
      Figure 4Noninvasive pressure-volume loop analysis of ventricular energetics. (A) No difference was found for stroke work and (E) external power. (B) Significant differences were seen in pressure-volume, (C) PVA, (D) ventricular efficiency and (F) energy per ejected volume. Horizontal bars denote median values.
      CTL = healthy controls; SDD = subclinical diastolic dysfunction.
      Figure 5
      Figure 5Noninvasive pressure-volume loop analysis of ventricular and arterial elastance. (A) Significant differences were seen in maximal ventricular efficiency and (C) ventricular-arterial coupling. (B) No difference was found for arterial elastance. CTL = healthy controls; SDD = subclinical diastolic dysfunction.

      Discussion

      In this study, we present the first experience with a fully noninvasive LV PV loop analysis across healthy controls, subjects with SDD, and patients with heart failure with preserved or impaired systolic function. Bias was low and limits of agreement were narrow for all hemodynamic parameters in the inter- and intraobserver comparisons. Furthermore, the presented new method for PV loop analysis based on the interpolation of clinical routine LV segmentation shows results comparable to completely manual segmentation but with vastly reduced workload. This makes noninvasive PV loops derived from CMR feasible to implement clinically.
      Our finding that systolic heart failure groups display differences in PV parameters affected by impaired contractility and larger cardiac volumes is in line with previous results from invasive PV loop studies.
      • Seemann F
      • Arvidsson P
      • Nordlund D
      • Kopic S
      • Carlsson M
      • Arheden H
      • Heiberg E.
      Noninvasive quantification of pressure-volume loops from brachial pressure and cardiovascular magnetic resonance.
      ,
      • Ky B
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      • Sweitzer NK
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      • Cappola TP
      Ventricular-arterial coupling, remodeling, and prognosis in chronic heart failure.
      • Majid PA
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      • Taylor SH.
      Phentolamine for vasodilator treatment of severe heart-failure.
      • Ross Jr, J
      • Braunwald E.
      The study of left ventricular function in man by increasing resistance to ventricular ejection with angiotensin.
      It has previously been suggested that noninvasive PV loops may be a useful tool to characterize energetic efficiency in patients with HFpEF.
      • Seemann F
      • Arvidsson P
      • Nordlund D
      • Kopic S
      • Carlsson M
      • Arheden H
      • Heiberg E.
      Noninvasive quantification of pressure-volume loops from brachial pressure and cardiovascular magnetic resonance.
      Although there were no statistical differences between healthy controls and patients with HFpEF for any of the variables in the present study, noninvasive PV loops at rest may add incremental information when following up patients over time to detect subtle changes in function. Bastos et al
      • Bastos MB
      • Burkhoff D
      • Maly J
      • Daemen J
      • den Uil CA
      • Ameloot K
      • Lenzen M
      • Mahfoud F
      • Zijlstra F
      • Schreuder JJ
      • Van Mieghem NM.
      Invasive left ventricle pressure–volume analysis: overview and practical clinical implications.
      suggested that assessment of PV loops at rest and exercise can help diagnose HFpEF. Because brachial blood pressure and cardiac volumes can be assessed during exercise using exercise CMR, PV loops during exercise can be obtained and potentially unmask early symptoms of heart failure, both systolic and diastolic. This was, however, beyond the scope of this study.
      The described method of using CMR to calculate PV loops is not the first noninvasive approach of assessing myocardial energetics. Pressure-strain loops derived from echocardiography have previously been shown by Russel et al
      • Russell K
      • Eriksen M
      • Aaberge L
      • Wilhelmsen N
      • Skulstad H
      • Remme EW
      • Haugaa KH
      • Opdahl A
      • Fjeld JG
      • Gjesdal O
      • Edvardsen T
      • Smiseth OA.
      A novel clinical method for quantification of regional left ventricular pressure–strain loop area: a non-invasive index of myocardial work.
      to provide a noninvasive index of myocardial work. There are, however, several key differences between the methods. Using echocardiography, isovolumetric contraction, ejection, and isovolumetric relaxation are normalized by stretching or compressing the curve to the same duration; whereas in CMR, volumetric data, time-resolved through spline interpolation, provide empiric and non-normalized volumes and durations. In echocardiography, in the place of volumetric data, regional wall longitudinal 2-dimensional strain data are adjusted to the timings of the cardiac events.
      • Russell K
      • Eriksen M
      • Aaberge L
      • Wilhelmsen N
      • Skulstad H
      • Remme EW
      • Haugaa KH
      • Opdahl A
      • Fjeld JG
      • Gjesdal O
      • Edvardsen T
      • Smiseth OA.
      A novel clinical method for quantification of regional left ventricular pressure–strain loop area: a non-invasive index of myocardial work.
      This is different from CMR, where the actual time-resolved volumetric data are used. Pressure-strain analysis provides the added benefit of assessing regional LV function and information regarding constructive versus wasted work, but a limitation of the pressure-strain index lies in the size of the ventricle affecting strain estimation, where a dilated ventricle results in an underestimation of strain. Although the CMR method would minimize such errors using non-normalized volumetric data, no regional information is provided.
      VA coupling (EA/Emax) in the present study differed between controls and systolic heart failure. However, there was no difference between controls and SDD or HFpEF. Although these findings can be explained by similar EA between groups and lower Emax only in HFmrEF and HFrEF, previous studies show conflicting results regarding VA coupling in HFpEF. If there is a proportional increase in both Ees and EA, there will be no difference in the VA coupling.
      • Bastos MB
      • Burkhoff D
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      • Daemen J
      • den Uil CA
      • Ameloot K
      • Lenzen M
      • Mahfoud F
      • Zijlstra F
      • Schreuder JJ
      • Van Mieghem NM.
      Invasive left ventricle pressure–volume analysis: overview and practical clinical implications.
      This proportional increase was shown by Lam et al.
      • Lam CS
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      Cardiac structure and ventricular–vascular function in persons with heart failure and preserved ejection fraction From Olmsted County, Minnesota.
      However, Kawaguchi et al
      • Kawaguchi M
      • Hay I
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      Combined ventricular systolic and arterial stiffening in patients with heart failure and preserved ejection fraction: implications for systolic and diastolic reserve limitations.
      found VA coupling to be lower in HFpEF, which is explained by a disproportionate increase in end-systolic elastance (Ees) compared with EA. Furthermore, Maurer et al
      • Maurer MS
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      • El-Khoury Rumbarger L
      • Packer M
      • Burkhoff D.
      Left heart failure with a normal ejection fraction: identification of different pathophysiologic mechanisms.
      showed both Ees and EA in normotensive HFpEF to not differ from healthy controls who are normotensive, which is similar to the findings in the present study, where blood pressure did not differ between groups. However, Chan et al
      • Chan J
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      A new approach to assess myocardial work by non-invasive left ventricular pressure–strain relations in hypertension and dilated cardiomyopathy.
      showed an increase in LV work measured by pressure-strain echocardiography in patients with hypertension. Discrepancies in the findings regarding hemodynamic parameters in HFpEF could thus result from differences in control group characteristics or the heterogeneity of patients with HFpEF, such as presence or absence of hypertension.
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      • Lakatta EG
      • Najjar SS.
      Arterial-ventricular coupling: mechanistic insights into cardiovascular performance at rest and during exercise.
      In addition and not limited to the HFpEF group, heart failure medication could affect hemodynamic parameters. For example, contractility (Emax) could be decreased by β-blockers, or EA could be decreased by a lower heart rate or a decreased systemic vascular resistance caused by β-blockers or blood pressure medication. Finally, ventricular energetics could be affected by changes in ventricular loading conditions resulting from the aforementioned medications. The differences between groups in this study could thus potentially be underestimated, owing to the high degree of medication of all 3 heart failure groups.
      A potential benefit of using PV analysis compared with evaluation of EF and blood pressure separately is the added information regarding ventricular energetics. For example, PVA is proportional to cardiac oxygen consumption.
      • Suga H.
      Total mechanical energy of a ventricle model and cardiac oxygen consumption.
      In our study, HFrEF had increased potential energy and PVA, and although we did not find a statistically significant difference comparing HFpEF with healthy controls, a visual trend was seen toward increased stroke work and PVA in this group. This suggests both systolic and diastolic heart failure to increase cardiac oxygen consumption but through differing mechanisms. Thus, PV analysis could provide unique hemodynamic insights into the stages between the very basal metabolism of the myocytes and the end-product of cardiac output, moving a step closer to phenotyping cardiac metabolism on an individual basis.
      Further PV loop analysis of heart failure is needed to assess the prognostic importance and potential role in guiding specific therapies. Furthermore, as described previously, PV loop analysis during physical exercise may unmask hemodynamic irregularities not evident at rest. Finally, noninvasive PV analysis may be useful in longitudinal studies looking at the individual patient rather than comparing between heterogeneous groups of heart failure.
      In conclusion, to the best of our knowledge, this study is the first experience with a fully noninvasive LV PV loop analysis across healthy controls, subjects with SDD, and patients with heart failure with preserved or impaired systolic function. Bias was low and limits of agreement were narrow for all hemodynamic parameters in the inter- and intraobserver comparisons. The proposed new method for PV loop computation from clinical-standard manual LV segmentation was rapid and robust, bridging the gap between clinical and research settings.

      Disclosures

      Katarina Steding-Ehrenborg reports a relation with Bayer Medical that includes speaking and lecture fees. Einar Heiberg is the founder of Medviso AB, Lund, Sweden, which sells a commercial version of Segment. The other authors have no conflicts of interest to declare.

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