Noninvasive Assessment of Left Ventricular Pressure-Volume Relations: Inter- and Intraobserver Variability and Assessment Across Heart Failure Subtypes
2022; Elsevier BV; Volume: 184; Linguagem: Inglês
10.1016/j.amjcard.2022.09.001
ISSN1879-1913
AutoresJonathan Edlund, Per M. Arvidsson, Anders Nelsson, J. Gustav Smith, Martin Magnusson, Einar Heiberg, Katarina Steding‐Ehrenborg, Håkan Arheden,
Tópico(s)Advanced MRI Techniques and Applications
ResumoA 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. 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.1Burrage 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.Circulation. 2021; 144: 1664-1678Crossref PubMed Scopus (40) Google Scholar 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.2Suga H. Ventricular energetics.Physiol Rev. 1990; 70: 247-277Crossref PubMed Scopus (670) Google Scholar 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.3Seemann 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.Circ Cardiovasc Imaging. 2019; 12e008493Crossref PubMed Scopus (44) Google Scholar,4Sjöberg P Seemann F Arheden H Heiberg E. Non-invasive quantification of pressure-volume loops from cardiovascular magnetic resonance at rest and during dobutamine stress.Clin Physiol Funct Imaging. 2021; 41: 467-470Crossref PubMed Scopus (6) Google Scholar 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. 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.5von 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.Lancet. 2007; 370: 1453-1457Abstract Full Text Full Text PDF PubMed Scopus (8062) Google Scholar 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)6Bergström 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.Circulation. 2021; 144: 916-929Crossref PubMed Scopus (130) Google Scholar 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)7Holm 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.ESC Heart Fail. 2020; 7: 2948-2955Crossref PubMed Scopus (23) Google Scholar 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 14, septal e' velocity <7 cm/s or lateral e' velocity 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%.9McDonagh 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 ESD2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure.Eur Heart J. 2021; 42 (Published correction appears in Eur Heart J 2021;42:4901): 3599-3726Crossref PubMed Scopus (4584) Google ScholarTable 1Population characteristics, cardiac parameters and medicationsParticipant 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/male9/65/104/115/103/12Height (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 parametersHeart 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]MedicationsBeta blockers01101311ACEi/ARB/ARNi03111114Aldosterone antagonist00232Thiazide diuretics00210Loop diuretics00899Calcium channel blockers01333HFpEF = 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]. p <0.05 compared to healthy controls. Open table in a new tab 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]. 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. 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).10Heiberg E Sjögren J Ugander M Carlsson M Engblom H Arheden H. Design and validation of segment–freely available software for cardiovascular image analysis.BMC Med Imaging. 2010; 10: 1Crossref PubMed Scopus (679) Google Scholar 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.3Seemann 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.Circ Cardiovasc Imaging. 2019; 12e008493Crossref PubMed Scopus (44) Google Scholar,4Sjöberg P Seemann F Arheden H Heiberg E. Non-invasive quantification of pressure-volume loops from cardiovascular magnetic resonance at rest and during dobutamine stress.Clin Physiol Funct Imaging. 2021; 41: 467-470Crossref PubMed Scopus (6) Google Scholar 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.11Senzaki 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.Circulation. 1996; 94: 2497-2506Crossref PubMed Scopus (298) Google Scholar Maximal ventricular pressure is approximated from brachial pressure,12Kelly RP Ting CT Yang TM Liu CP Maughan WL Chang MS DA Kass Effective arterial elastance as index of arterial vascular load in humans.Circulation. 1992; 86: 513-521Crossref PubMed Scopus (657) Google Scholar 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 al3Seemann 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.Circ Cardiovasc Imaging. 2019; 12e008493Crossref PubMed Scopus (44) Google Scholar 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. 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). 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. 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 parametersPressure-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 dysfunctionValues are presented as median [IQR]. p<0.05 compared to healthy controls. Open table in a new tab 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.View Large Image Figure ViewerDownload Hi-res image Download (PPT) 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]. 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.3Seemann 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.Circ Cardiovasc Imaging. 2019; 12e008493Crossref PubMed Scopus (44) Google Scholar,13Ky B French B May Khan A Plappert T Wang A Chirinos JA Fang JC Sweitzer NK Borlaug BA Kass DA St John Sutton M Cappola TP Ventricular-arterial coupling, remodeling, and prognosis in chronic heart failure.J Am Coll Cardiol. 2013; 62: 1165-1172Crossref PubMed Scopus (175) Google Scholar, 14Majid PA Sharma B Taylor SH. Phentolamine for vasodilator treatment of severe heart-failure.Lancet. 1971; 2: 719-724Abstract PubMed Scopus (206) Google Scholar, 15Ross Jr, J Braunwald E. The study of left ventricular function in man by increasing resistance to ventricular ejection with angiotensin.Circulation. 1964; 29: 739-749Crossref PubMed Scopus (187) Google Scholar It has previously been suggested that noninvasive PV loops may be a useful tool to characterize energetic efficiency in patients with HFpEF.3Seemann 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.Circ Cardiovasc Imaging. 2019; 12e008493Crossref PubMed Scopus (44) Google Scholar 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 al16Bastos 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.Eur Heart J. 2020; 41: 1286-1297Crossref PubMed Scopus (107) Google Scholar 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 al17Russell 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.Eur Heart J. 2012; 33: 724-733Crossref PubMed Scopus (458) Google Scholar 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.17Russell 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.Eur Heart J. 2012; 33: 724-733Crossref PubMed Scopus (458) Google Scholar 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.16Bastos 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.Eur Heart J. 2020; 41: 1286-1297Crossref PubMed Scopus (107) Google Scholar This proportional increase was shown by Lam et al.18Lam CS Roger VL Rodeheffer RJ Bursi F Borlaug BA Ommen SR Kass DA Redfield MM. Cardiac structure and ventricular–vascular function in persons with heart failure and preserved ejection fraction From Olmsted County, Minnesota.Circulation. 2007; 115 (Published correction appears in Circulation 2007;115:e535): 1982-1990Crossref PubMed Scopus (443) Google Scholar However, Kawaguchi et al19Kawaguchi M Hay I Fetics B DA Kass Combined ventricular systolic and arterial stiffening in patients with heart failure and preserved ejection fraction: implications for systolic and diastolic reserve limitations.Circulation. 2003; 107 (Published correction appears in Circulation 2020;141:e809): 714-720Crossref PubMed Scopus (786) Google Scholar 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 al20Maurer MS King DL El-Khoury Rumbarger L Packer M Burkhoff D. Left heart failure with a normal ejection fraction: identification of different pathophysiologic mechanisms.J Card Fail. 2005; 11: 177-187Abstract Full Text Full Text PDF PubMed Scopus (165) Google Scholar 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 al21Chan J Edwards NFA Khandheria BK Shiino K Sabapathy S Anderson B Chamberlain R Scalia GM. A new approach to assess myocardial work by non-invasive left ventricular pressure–strain relations in hypertension and dilated cardiomyopathy.Eur Heart J Cardiovasc Imaging. 2019; 20: 31-39Crossref PubMed Scopus (228) Google Scholar 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.22Chantler PD Lakatta EG Najjar SS. Arterial-ventricular coupling: mechanistic insights into cardiovascular performance at rest and during exercise.J Appl Physiol (1985). 2008; 105: 1342-1351Crossref PubMed Scopus (260) Google Scholar 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.23Suga H. Total mechanical energy of a ventricle model and cardiac oxygen consumption.Am J Physiol. 1979; 236: H498-H505PubMed Google Scholar 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. 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. Download .docx (.01 MB) Help with docx files Download .docx (.09 MB) Help with docx files
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