Non-invasive assessment of ventriculo-arterial coupling using aortic wave intensity analysis combining central blood pressure and phase-contrast cardiovascular magnetic resonance

Abstract

Background

Wave intensity analysis (WIA) in the aorta offers important clinical and mechanistic insight into ventriculo-arterial coupling, but is difficult to measure non-invasively. We performed WIA by combining standard cardiovascular magnetic resonance (CMR) flow-velocity and non-invasive central blood pressure (cBP) waveforms.

Methods and results

Two hundred and six healthy volunteers (age range 21–73 years, 47% male) underwent sequential phase contrast CMR (Siemens Aera 1.5 T, 1.97 × 1.77 mm², 9.2 ms temporal resolution) and supra-systolic oscillometric cBP measurement (200 Hz). Velocity (U) and central pressure (P) waveforms were aligned using the waveform foot, and local wave speed was calculated both from the PU-loop (c) and the sum of squares method (cSS). These were compared with CMR transit time derived aortic arch pulse wave velocity (PWV_tt). Associations were examined using multivariable regression. The peak intensity of the initial compression wave, backward compression wave, and forward decompression wave were 69.5 ± 28, −6.6 ± 4.2, and 6.2 ± 2.5 × 10⁴ W/m²/cycle², respectively; reflection index was 0.10 ± 0.06. PWV_tt correlated with c or cSS (r = 0.60 and 0.68, respectively, P < 0.01 for both). Increasing age decade and female sex were independently associated with decreased forward compression wave (−8.6 and −20.7 W/m²/cycle², respectively, P < 0.01) and greater wave reflection index (0.02 and 0.03, respectively, P < 0.001).

Conclusion

This novel non-invasive technique permits straightforward measurement of wave intensity at scale. Local wave speed showed good agreement with PWV_tt, and correlation was stronger using the cSS than the PU-loop. Ageing and female sex were associated with poorer ventriculo-arterial coupling in healthy individuals.

Introduction

An integrated assessment of the cardiovascular system is clinically and mechanistically important, yet ventricular and arterial function are often considered in isolation. Wave intensity analysis (WIA) is a technique that characterizes flow generated by the heart and the afterload imposed by the vasculature in terms of wave propagation.^1,2 It also calculates wave reflection and wave speed which predict coronary and cardiac events, independently of conventional cardiovascular risk factors.^3–8

Waves transmit energy and arise in the circulation as a result of cardiac contraction and relaxation, or reflection. Because reflection occurs from circulatory sites of impedance mismatching, WIA describes the efficiency of energy transfer in the cardiovascular system. The magnitude of energy transferred by a wave is quantified as the product of changing pressure and flow-velocity at the same location.⁹ Waves are further characterized by their direction of travel (forwards or backwards), and the pressure gradient across them (compression or decompression waves). Both these characteristics determine their impact on pressure and flow [e.g. a forward compression wave (FCW) increases pressure and accelerates flow whereas a forward decompression decreases pressure and decelerates flow]. In addition to measuring the timing and intensity of waves, WIA can quantify local wave speed, a measure of arterial stiffness.^9–12 The relationship with the reference standard of regional pulse wave velocity measured from transit time (PWV_tt) has not been established.

Traditionally, WIA has been derived invasively using simultaneous catheter measures of pressure and flow or velocity.¹³ It has offered insights into a range of diseases but because of feasibility, understanding of healthy ageing and sex differences in the aorta has been limited.^14–17 Phase-contrast cardiovascular magnetic resonance (CMR) imaging allows non-invasive assessment of aortic flow, and CMR is the gold-standard for anatomically standardized cross-sectional measurements. CMR distensibility has successfully been used as a central pressure surrogate to perform WIA, but the method does not provide a direct measure of wave energy and can be technically challenging.^14,18 Cuff-based devices simplify the acquisition of central blood pressure (cBP) waveform data and show good agreement with invasive measures.¹⁹

The aims of this study were¹ to use non-invasive direct measures of the cBP and velocity waveforms to perform WIA,² to compare measures of local wave speed with a reference of conventionally calculated PWV_tt, and³ to evaluate associations between aortic WIA and age and sex in healthy individuals.

Methods

Study population

Two hundred and thirty-seven healthy participants were recruited from the pre-training assessment of the Marathon Study. This is an observational study recruiting healthy volunteers to investigate the effects of first-time marathon training on cardiovascular function.²⁰ Acquisition of data for WIA did not add extra time to the standard tests performed. Inclusion criteria were age over 18 years, no past significant medical history, no previous marathon-running experience, and current participation in running for <2 h per week. All procedures were in accordance with the principles of the Helsinki declaration, all participants gave written informed consent and the study was approved by the London Queen Square National Research Ethics Service Committee (15/LO/0086).

A total of 211 participants underwent paired phase-contrast CMR and cBP waveform recording (Supplementary data online, Figure S1). Five participants were excluded due to noisy BP profiles, leaving a total 206 participants.

cBP and heart rate estimation

Supra-systolic oscillometric brachial BP was measured over 10 s with a sampling frequency of 200 Hz in duplicate after a period of rest in the semi-supine position. (Cardioscope II BP+, 6, Sydney, Australia). A single ensemble averaged central pressure estimate (P) was derived from the second 10 s measurement of the brachial supra-systolic arterial waveform, as previously described.²¹ This has been shown to yield highly correlated central systolic BPs and pressure waveforms with invasive catheter assessment, no bias, and good intra- and re-test reliability.^21,22 Heart rate (HR) was taken as the average of the HR during the recording.

CMR acquisition and analysis

After BP acquisition, CMR was performed at 1.5 T (Magnetom Aera, Siemens AG Healthcare, Erlangen, Germany). Participants were supine for approximately half an hour of scanning before the sequence acquisitions used for this analysis. Single-shot electrocardiogram (ECG)-gated white blood sagittal aortic (‘candy cane’) views were acquired first, to allow 3D aortic arch length measurement and standardized cross-sectional imaging. This was used to pilot axial aortic blood flow-velocity maps at the level of the pulmonary artery bifurcation. The spoiled gradient echo phase-contrast sequence used was free-breathing, ECG-gated and segmented, with the following parameters: acquired temporal resolution 9.2 ms (reconstructed to 100 cardiac phases per RR interval); spatial resolution 1.97 × 1.77 mm²; slice thickness 6 mm; through-plane velocity encoding 150 cm/s; field of view 192 × 108 mm; flip angle 20°.

Images were analysed using validated software to obtain velocity-time profiles for the ascending and descending aorta (ArtFun, University Pierre Marie Curie–INSERM).^23,24 The only user interaction was to select the centre and border of the lumen on the modulus imaging. A circular cross-sectional aortic lumen region of interest (ROI) was then contoured automatically and propagated to each velocity-encoded phase; automatic contours were checked and modified manually if necessary. Mean aortic velocity within each ROI was then calculated for every phase to plot a velocity-time profile. Ascending aortic velocity-time profiles (U) were combined with BP waveforms for WIA (Figure 1). Figure 2 provides a flow chart of data acquisition and analysis.

Figure 1.

Aortic wave intensity analysis. Top: Foot-to-foot alignment of scaled pressure (blue) and velocity (red) waveforms. Bottom: Wave intensity analysis example showing initial compression (FCW), backward compression (BCW), and protodiastolic decompression (FDW) waves.

Figure 2.

Analysis of blood pressure and CMR-derived velocity data. (A) After a period of rest the patient underwent oscillometric brachial blood pressure on two occasions immediately prior to MRI. A Pulsecor BP+ device acquired 10 s of brachial waveforms at 200 Hz. After, phase-contrast MRI was acquired at the level of the pulmonary artery using a free-breathing ECG-gated sequence, acquired at c.100 Hz at 60 bpm. (B) A single ensemble averaged central pressure (P) was estimated and velocity (U) measured at each time point. (C) Data were aligned by waveform foot to foot. (D) Wave speed measured in early systole using the pressure-velocity loop and sum of squares method after the application of a Savitzky–Golay filter. (E) Wave intensity calculated using the derivatives of pressure and velocity. $\rho$, density of blood (1050 kg/m³); c, P-U derived wave speed; cSS, sum of squares estimated c.

Pulse wave velocity calculation using transit time

Aortic arch PWV was calculated from the 3D distance between the ascending and descending aortic locations of the phase-contrast imaging and the transit time between velocity profiles:

$$\mathrm{Aortic}\mathrm{ }\mathrm{arch}\mathrm{ }{\mathrm{PWV}}_{\mathrm{tt}}=\frac{3\mathrm{D} \mathrm{distance} }{\mathrm{transit} \mathrm{time}}.$$

Distance travelled was measured in a 3D coordinate system combining the sagittal and axial imaging using at least 14 markers placed in the centreline of the aorta. The transit time was calculated using the least squares estimate between the systolic upslopes, which has shown to be most accurate and reproducible.^25,26 Measurements were repeated by another observer in 11 cases and showed excellent intra- and inter-observer reproducibility (intraclass correlation coefficient 0.99 and 0.95, respectively).

Local wave speed estimation

The central pressure waveform (P) was linearly interpolated to the same sample frequency as the ascending aorta velocity data (U), and waveforms were aligned using the foot and early part of the systolic upstroke in pressure and flow velocity (Figure 1). Alignment and analysis of physiological signals was performed using custom written software in Matlab R2016a (The MathWorks, Inc., Natick, MA, USA). Based on the conservation of mass and momentum, wave speed, c, is a function of the change in pressure and velocity described by the water-hammer equation:¹⁰

$${\mathit{dP}}_{\pm }=\pm \rho \mathrm{c}\mathrm{ }{\mathit{dU}}_{\pm }$$

where + refers to waves moving away from the heart, − to waves moving towards the heart, and $\rho$ is the density of blood (1050 kg/m³).

It is assumed that reflected waves are absent in early systole, c was therefore estimated as the gradient of the PU-loop at this time, Figure 2:

$$c=\frac{1}{\rho }\mathrm{ }\frac{\mathrm{dP}}{\mathrm{dU}}.$$

Wave speed can also be estimated by assuming that net wave energies are minimized over a complete cardiac cycle. This is known as the sum of squares method (cSS)¹¹ and was calculated as:

$$c\mathrm{SS}=\mathrm{ }\frac{1}{\rho \mathrm{ }}\sqrt{\frac{{\sum \mathit{dP}}^2}{{\sum \mathit{dU}}^2}}.$$

Wave intensity analysis

For WIA and cSS, the P and U waveforms were filtered using a standard 7 point, second order polynomial Savitzky–Golay filter to smooth data and calculate derivatives.²⁷ Net wave intensity was calculated as the product of the derivative of pressure (dP) and velocity (dU) over the cardiac cycle:⁹

$$\mathit{dI}=\mathit{dP}·\mathit{dU}.$$

When wave speed is known (c), forward and backward wave intensity can be solved using the water-hammer equation:

$${\mathrm{WI}}_{+}={\frac{1}{4\rho c}\left(\mathit{dP}+\rho c·\mathit{dU}\right)}^2$$

$${\mathrm{WI}}_{-}={-\frac{1}{4\rho c}\left(\mathit{dP}-\rho c·\mathit{dU}\right)}^2$$

where ${\mathrm{WI}}_{+}\mathrm{ }\mathrm{is}\mathrm{ }\mathrm{the}\mathrm{ }\mathrm{f}$orward wave intensity, ${\mathrm{WI}}_{-}\mathrm{ }\mathrm{is}$ the backward wave intensity, and c is wave speed estimated using the PU-loop method.

Wave intensity was quantified using the magnitude and timing of the peak of three waves:²⁸ the initial FCW, backward compression wave (BCW), and forward protodiastolic decompression (expansion) (FDW) wave (Figure 1). To enable comparisons between subjects, the sample period was normalized by the duration of the cardiac cycle,¹⁵ but can be converted into W/m²/s² simply by multiplying by the HR per second. The reflection index was taken as the ratio of BCW/FCW.¹⁷ Wave energy was calculated as the area under each wave. For comparison, wave separation analysis was performed to calculate the reflection magnitude, taken as the ratio of the peak backward to the peak forward pressure amplitudes.²

Anthropomorphic and other assessments

Height was recorded using a standard stadiometer. Weight and body fat percentage were measured using digital bioimpedance scales (BC-418, Tanita, USA). Body surface area was calculated using the Mosteller formula. Maximal oxygen consumption (peak VO₂) was estimated by a cardio-pulmonary exercise test on a semi-supine ergometer (Ergoselect1200, Ergoline, Germany) using an incremental protocol standardized by bodyweight and gender, as previously described.²⁰

Statistics

Data were analysed in R (R foundation, Vienna, Austria) using RStudio Server version 0.98 (Boston, MA, USA). All continuous variables are expressed as mean ± SD or median (interquartile range) for skewed data. Normality was checked using the Shapiro–Wilk test. Categorical variables are expressed as percentages. Characteristics are stratified by age decile and gender. Groups were compared using independent-samples Student’s t-tests for normally distributed continuous variables or Mann–Whitney U tests and χ² tests for non-normally distributed and categorical variables, respectively. For trends over age deciles, the non-parametric Mann–Kendall monotonic trend test was used. Pearson’s correlation coefficient (r) and Bland–Altman limits of agreement (LoA) were used to assess correlation and agreement, respectively. Multivariable linear regression models for the association between age and WIA parameters were adjusted for covariates that a priori could confound the relationship; these were sex, HR, and height; similarly, sex was adjusted for age, HR, and height. Mean arterial pressure was not included as it may be dependent on wave generation rather than the converse.²⁹ Regression diagnostics were performed and data were log-transformed if appropriate. All tests were two tailed, and P < 0.05 was considered statistically significant.

Results

Baseline characteristics

In 206 healthy volunteers, the median age was 37 years (range 21–73 years), 189 (92%) were normotensive (<140/90 mmHg) on assessment; and mean aortic arch PWV_tt was 4.7 ± 1.5 m/s, Table 1. The peak intensity of the initial compression wave, BCW, and FDW were 69.5 ± 28, −6.6 ± 4.2, and 6.2 ± 2.5 × 10⁴ W/m²/cycle², respectively; reflection index was 0.10 ± 0.06.

Table 1.

Study participant characteristics stratified by sex and age decile

Participant characteristics	Males					Females
	20–30	30–40	40–50	50–60	60+	20–30	30–40	40–50	50–60	60+
n	31	31	22	9	3	33	35	30	9	3
Age (years)	26 ± 2	32 ± 2	44 ± 3	54 ± 2	66 ± 4	26 ± 2	33 ± 3	45 ± 3	54 ± 2	67 ± 6
Height (cm)	181 ± 7	181 ± 7	180 ± 6	177 ± 9	172 ± 4	167 ± 5	167 ± 5	167 ± 6	169 ± 5	160 ± 5
Weight (kg)	78 ± 8	82 ± 15	85 ± 9	81 ± 15	76 ± 7	63 ± 9	69 ± 11	69 ± 15	71 ± 13	70 ± 16
BMI (kg/m2)	24 ± 3	25 ± 4	26 ± 3	26 ± 4	26 ± 2	22 ± 3	25 ± 4	25 ± 5	25 ± 5	27 ± 5
BSA (m2)	2.0 ± 0.1	2.0 ± 0.2	2.0 ± 0.1	2.0 ± 0.2	1.9 ± 0.1	1.7 ± 0.1	1.8 ± 0.1	1.8 ± 0.2	1.8 ± 0.1	1.7 ± 0.2
Body fat (%)	15 ± 5	20 ± 6	23 ± 5	22 ± 6	23 ± 2	28 ± 6	32 ± 7	32 ± 8	33 ± 8	35 ± 7
Peak VO2 (mL/kg/min)	43 ± 6	38 ± 6	33 ± 6	34 ± 7	25 ± 3	35 ± 4	32 ± 6	29 ± 7	24 ± 4	24 ± 3
Resting heart rate (bpm)	70 ± 15	70 ± 15	72 ± 14	67 ± 14	64 ± 4	74 ± 15	71 ± 10	69 ± 12	67 ± 12	67 ± 11
Brachial SBP (mmHg)	124 ± 11	124 ± 12	128 ± 10	133 ± 18	146 ± 19	113 ± 8	113 ± 10	117 ± 13	127 ± 20	136 ± 33
Brachial DBP (mmHg)	75 ± 4	75 ± 6	79 ± 5	79 ± 7	79 ± 10	72 ± 5	73 ± 6	74 ± 7	80 ± 10	76 ± 18
Aortic SBP (mmHg)	113 ± 10	113 ± 10	117 ± 9	124 ± 19	136 ± 23	104 ± 8	106 ± 10	109 ± 11	122 ± 19	129 ± 31

BMI, body mass index; BSA, body surface area; DBP, diastolic blood pressure; MAP, mean arterial pressure; SBP, systolic blood pressure; Peak VO₂, maximal oxygen consumption.

Local wave speed compared to PWV_tt

There was a moderate correlation between PWV_tt and c, and this was stronger with cSS (r = 0.60 and 0.68, respectively, P < 0.01 for both). PWV_tt was greater than c, and this difference was reduced for cSS [difference: −1.3 (LoA: −3.8 to 1.2) vs. −0.64 (LoA: −3.0 to 1.7) m/s, respectively; Figure 3].

Figure 3.

Correlation (top row) and Bland–Altman analysis (bottom row) of wave speed and pulse wave velocity (PWV) measured by transit time. Left: wave speed calculated from the pressure-velocity slope during early systole (c). Right: wave speed calculated from the sum of squares method (cSS). The dotted line in the upper two panels indicates the line of unity. In the lower two panels the dotted lines indicate the mean difference and the limits of agreement.

Wave speed and wave intensity by age decade

Both c and cSS increased from youngest (20–30 year old) to oldest (≥60 year old) age decade (Figure 4), although cSS tended to be higher than c. Table 2 displays all WIA measures stratified by age decade and sex.

Figure 4.

Influence of age on wave speed and wave intensity indices. (A) Wave speed (c) measured by PU-loop. (B) Wave speed measured by sum of squares method (cSS). (C) Forward compression wave. (D) Backward compression wave. (E) Forward decompression wave. (F) Reflection index. ns: P > 0.05, *P < 0.05, ****P < 0.0001.

Table 2.

Wave intensity analysis stratified by sex and age decile

Wave intensity measures	Males					Females
	20–30	30–40	40–50	50–60	60+	20–30	30–40	40–50	50–60	60+
Wave speed (ms−1)
c	3.4 ± 0.9	3.3 ± 1.1	3.9 ± 1.2	4.9 ± 2.0	5.7 ± 1.8	2.7 ± 0.6	2.7 ± 0.8	3.3 ± 1.0	4.0 ± 1.0	5.2 ± 2.9
cSS	3.4 ± 0.9	3.6 ± 0.9	4.6 ± 1.2	5.8 ± 2.3	7.3 ± 2.4	3.0 ± 0.7	3.5 ± 0.8	4.3 ± 1.1	5.5 ± 2.0	7.3 ± 4.2
Pulse wave velocity (m/s)	4 ± 0.7	4 ± 0.7	5 ± 1.1	7 ± 1.8	9 ± 3.4	4 ± 0.6	4 ± 0.6	5 ± 0.9	8 ± 2.1	7 ± 1.6
Wave peak (104 W/m2/cycle2)
FCW	99 ± 35	84 ± 27	61 ± 23	62 ± 17	71 ± 1	67 ± 22	61 ± 24	52 ± 14	47 ± 9	68 ± 19
BCW	7.0 ± 4	5.9 ± 3	6.1 ± 4	8 ± 5	11.6 ± 6	5.3 ± 5	6.0 ± 4	7.4 ± 5	7.7 ± 5	10.5 ± 3
FDW	8.4 ± 3	6.7 ± 2	6.2 ± 2	7.5 ± 3	10.3 ± 3	5.2 ± 2	4.8 ± 2	5.2 ± 2	6.5 ± 1	6.6 ± 2
Wave timing (ms)
FCW	50 ± 5	50 ± 7	52 ± 4	53 ± 3	56 ± 3	49 ± 5	48 ± 4	51 ± 5	53 ± 2	53 ± 2
BCW	152 ± 32	163 ± 24	169 ± 14	164 ± 11	162 ± 8	158 ± 19	154 ± 28	157 ± 32	167 ± 8	164 ± 6
FDW	258 ± 38	268 ± 40	275 ± 26	303 ± 48	303 ± 19	272 ± 40	272 ± 45	296 ± 27	311 ± 25	306 ± 33
Reflection index	0.06 ± 0.03	0.07 ± 0.02	0.10 ± 0.04	0.12 ± 0.05	0.16 ± 0.08	0.07 ± 0.05	0.10 ± 0.07	0.14 ± 0.07	0.17 ± 0.11	0.16 ± 0.03
Reflection magnitude	0.54 ± 0.1	0.55 ± 0.1	0.58 ± 0.1	0.56 ± 0.1	0.59 ± 0.1	0.55 ± 0.1	0.57 ± 0.1	0.61 ± 0.1	0.59 ± 0.1	0.61 ± 0.1
Wave energy (J)
Forward wave	5.6 ± 1.6	4.6 ± 1.4	3.6 ± 1.0	3.5 ± 0.7	4.6 ± 0.5	3.5 ± 1.1	3.5 ± 1.2	3.0 ± 0.6	2.9 ± 0.4	4.3 ± 1.0
Backward wave	0.74 ± 0.4	0.67 ± 0.2	0.65 ± 0.3	0.70 ± 0.4	1.05 ± 0.4	0.58 ± 0.4	0.67 ± 0.3	0.71 ± 0.4	0.73 ± 0.3	1.13 ± 0.4

Pulse wave velocity was measured conventionally in the aortic arch from transit time.

c, wave speed measured by the pressure-velocity loop; cSS, wave speed measured by the sum of squares method; BCW, backward compression wave; FCW, forward compression wave; FDW, forward decompression wave; J, joules.

FCW decreased progressively with age up to 50–60 year old, but rose in ≥60 year old. The BCW increased steadily from youngest to oldest age decade. This resulted in a steady increase in reflection index with age decade. There was no convincing trend in FDW with age. Age-related trends were not modified by sex, so data for both sexes are pooled in Figure 4.

Associations of wave intensity after adjustment for potential confounders

In multivariable analysis including age, sex, HR, and height as covariates, older age was associated with a smaller FCW, a larger BCW, and a larger reflection index. Male sex was associated with a higher FCW, no difference in BCW and consequently a lower reflection index, and a higher FDW compared with females. Higher HR was associated with a lower FCW, a lower BCW, a lower reflection index, and a lower FDW. Height was not associated with any WIA parameter in adjusted models. Associations between WIA and peak VO₂, and body fat are detailed in Supplementary data online, Tables S1 and S2.

Discussion

This is the first study to determine wave intensity and local wave speed by combining direct non-invasive measures of cBP and velocity data from phase contrast CMR. This straightforward method allows aortic WIA to be performed at scale, here in the largest cohort reported to date. This technique was validated by showing good agreement between local wave speed (calculated from combined non-invasive pressure and phase contrast CMR-derived flow data) and conventionally measured pulse wave velocity using transit time. WIA patterns and magnitude appeared similar to invasive data,^13,30,31 but here the non-invasive nature of testing permitted exploration of healthy ventriculo-arterial coupling. The resolution was sufficient to detect that ageing and female sex were independently associated with decreased FCW energy and an increased proportion of wave reflection, suggesting a less energy efficient cardiovascular system.

How can non-invasive aortic WIA be used?

A similar pattern of wave intensity was found in all individuals, which can help to improve our understanding of pulsatile haemodynamics. We observed a dominant FCW in the early phase of LV ejection, which has been associated with myocardial contractility, and a smaller FDW, associated with the time constant of LV relaxation.³² A BCW was also observed which is thought to relate to reflected waves originating from distal sites of impedance mismatch.³¹ These patterns and wave timings are similar to invasive studies, but an earlier BCW compared to other non-invasive data may reflect population characteristics or quantification differences (wave foot vs. peak).^14,30,33

Describing pressure and flow changes together, WIA has advanced the way we understand ventricular function and arterial coupling in different pathologies. Patients with chronic heart failure have an impaired ability to generate a systolic FCW, but the FCW undergoes more reflection, increasing afterload.³⁴ Wave intensity can predict left ventricular ejection fraction recovery or quality of life improvement in patients undergoing valvular surgery, and help to identify sub-clinical systolic and diastolic dysfunction in children with heart failure and preserved ejection fraction.^35–37 Increased wave reflection is associated with outcomes in systemic hypertension,⁴ and in the pulmonary circulation is an early and specific marker in the development of pulmonary hypertension.^15,38,39 Due to reliance on invasive measures, most of these previous insights have been derived from studies of small patient cohorts. Non-invasive approaches have the potential to improve our understanding of cardiovascular haemodynamics in health and disease, and can be applied to longitudinal studies.

WIA at scale using central measures

Whilst central haemodynamics have most impact on the ventricle, previous non-invasive studies of WIA have typically been in peripheral arteries because they are easier to interrogate using Doppler ultrasound and arterial tonometry.^4,32 The approach developed in this study uses measures of central pressure and velocity rather than surrogates such as distensibility, which are dependent on aortic stiffness. Like other methods,¹⁶ these data were acquired sequentially, but simultaneous acquisition is feasible in future studies using longer tubing for the cBP cuff measurement. This would enable the oscillometric device to be situated in the MRI control room for pressure measurement, whilst the patient has flow measured in the MRI scanner.

Local wave speed estimation

The validity of two single-point methods of wave speed were tested by comparing with a conventional regional estimate extending across the aortic arch (PWV_tt).¹⁴ We used two previously described single-point methods, one based on the pressure-velocity loop¹⁰ and the other using the sum-of-squares method.¹¹ Both showed acceptable agreement with the transit time-based method which was assumed to be the reference, although agreement was slightly better for cSS which is consistent with the findings of a previous in vitro study.⁴⁰ Because wave speed increases distally, both measures of ascending aortic local wave speed were expectedly lower than regional PWV_tt which extends to the aortic arch.

Associations between sex or age and aortic WIA

These data show that females have a greater wave reflection index in the aorta and lower FDW magnitude, which has not been reported previously, to our knowledge. Consistent with Li et al.¹⁴, females also demonstrated a smaller FCW. Borlotti et al.¹⁷ found a sex difference in the reflection index in carotid but not femoral arteries, however, wave reflection in the aorta is different to that seen in the carotid. Differences in wave reflection may provide a substrate for the development of heart failure.⁴¹

The increase in wave speed and arterial stiffness with age are well recognized, however, age-related changes in aortic WIA measures have only previously been described in one study, which used diameter rather than pressure measurements to derive an alternative index of wave intensity.¹⁴ The study also reported a decrease in FCW, BCW and an increase in reflection index but reported a decrease in FDW rather than the lack of change seen in this cohort. Differences with these data may be due to different study populations, or the use of diameter as a surrogate measure of pressure in the previous study, which itself is inversely related to wave speed. The proportion of reflection increased with age whether measured by wave separation or WIA. The contribution of higher intensity waves appears more pronounced at older ages and higher degrees of overall reflection. This suggests that the greater reflection that occurs with healthy ageing presents a more adverse load on the heart.⁴²

Study limitations

Because data were acquired over several cardiac cycles and ensemble averaged, the average cycle is truncated leading to a slight shortening of the duration of diastole; however, since wave intensity in end-diastole is negligibly small this is unlikely to affect our findings. Participants were recruited based on their intention to participate in a first marathon, and while they were not engaged in training at the time of study it is unlikely that they are representative of the general population. Older participants were relatively under represented and are probably biased through selective recruitment of more healthy individuals. Similarly, patients were excluded with any known significant medical problems including hypertension or diabetes mellitus. We used a free-breathing phase-contrast CMR sequence which provides sufficient spatio-temporal resolution for the velocity profile. A similar sequence has also been used to measure CMR distensibility as a surrogate for central pressure, but because it is free-breathing this may compromise accuracy for measures of compliance due to through-plane motion.³³ Breath-held sequences are possible using an accelerated spiral sequence but can be difficult to analyse due to respiratory artifact or lower signal to noise.⁴³ Haematocrit differences between sexes were not accounted for, although this is unlikely to affect blood density significantly.

Conclusion

This article describes a novel non-invasive method for WIA, using cBP and CMR velocity data. Local wave speed measured by this technique showed good agreement with regional PWV and the method has straightforward application for large sample sizes. In healthy individuals, women had a smaller FCW and poorer overall ventriculo-arterial coupling than men. In both sexes, older age was associated with higher wave speed and poorer ventriculo-arterial coupling as assessed by WIA.

Supplementary data

Supplementary data are available at European Heart Journal - Cardiovascular Imaging online.