Identification of capillary rarefaction and resultant prognostication in cardiac allograft patients using coronary wave intensity analysis

Authors: Broyd C.J. PhD1,3, Hernández-Pérez F.2, Segovia J.2, Echavarría-Pinto M PhD.1, Quirós-

Carretero A. PhD1, Salas C.2, Gonzalo N. PhD1, Jiménez-Quevedo P.1, Nombela L.1, Salinas P.1, Núñez-

Gil I.1, Del Trigo M.1., Alonso-Pulpón L.2, Fernández-Ortiz A.1, Macaya C.1., Parker K. PhD3, Hughes A.

PhD4, Mayet J. PhD3, Davies J. PhD3, Escaned J. PhD1.

1. Hospital Clínico San Carlos, Madrid

2. Hospital Universitario Puerta de Hierro, Madrid

3. Imperial College London

4. University College London

Author for correspondence:

Dr Javier Escaned

Department of Cardiology

Hospital Clinico San Carlos

Prof Martin Lagos, S/N

Madrid

escaned@secardiologia.es

Abstract word count:

Word count:

References: 24

Figures: 4

Tables: 1

Running title: Capillary density and CAV prognosis with WIA

Abstract

Introduction: Within cardiac allograft recipients, capillary rarefaction appears to reflect one of the

earliest structural changes of allograft vasculopathy(CAV). Measuring such rarefaction using

coronary flow reserve or intracoronary resistance measurements is hampered because of their

relatively non-specific interrogation of the microcirculatory domain. We investigated the potential of

wave intensity analysis (WIA) to document capillary rarefaction and ultimately for predicting CAV in

a group of transplant patients.

Methods: 44 allograft patients with unobstructed coronary arteries and normal LV function

successfully underwent combined LAD pressure and flow measurements at rest and with

intracoronary adenosine. CFR, HMR, IHDVPS, Pzf and WIA were calculated. In a subgroup of 15

patients, simultaneous right ventricular biopsies were obtained and analysed for capillary density. All

patients were followed up with 1-3 yearly screening angiography.

Results: A statistically significant relationship with capillary density was noted with CFR (r=0.52,

p=0.048) and the backward decompression wave (BDW) (r=-0.65, p<0.01), only the latter of which

was maintained with multiple regression analysis (β=-0.53, p=0.02). Over a mean follow-up of

9.3±5.2 years, the BDW was able to predict outcome in terms of CAV-events (p=0.04) as well as the

development (p=0.01), severity (p=0.04) and rate (p=0.09) of angiographic CAV. Similar trends with

prognosis were noted with IHDVPS but no other microvascular indices.

Conclusions: Within cardiac transplant patients, WIA is able to quantify the early histological

changes of CAV, namely capillary rarefaction, and can predict clinical and angiographic outcomes.

This proof-of-concept for WIA also lends weight to its used in the assessment of other disease

processes in which capillary rarefaction is involved.

Key words: vasculopathy, coronary artery, physiology, microcirculation, cardiac transplant

AbbreviationsAOI – Arteriolar Obliteration Index

BDW – Backward Decompression Wave

BMR – Basal Microvascular Resistance

CAV – Cardiac Allograft Vasculopathy

CFR – Coronary Flow Reserve

HMR – Hyperemic Microvascular Resistance

IHDVPS – Instantaneous Hyperemic Diastolic Velocity Pressure Slope

iMR – Index of Microcirculatory Resistance

ISHLT – International Society for Heart and Lung Transplantation

IVUS – IntraVascular UltraSound

WIA – Wave Intensity Analysis

Pzf – Zero Flow Pressure

Introduction

Structural remodelling of the coronary microcirculation occurs in a number of conditions resulting in

microcirculatory dysfunction1. A reduction in capillary density (rarefaction) constitutes a specific

form of microcirculatory remodelling resulting in an impairment of coronary haemodynamics and

influencing long-term outcome2,3. Within cardiac allograft recipients, evidence suggests that the

earliest changes of cardiac allograft vasculopathy (CAV) occur as capillary rarefaction4,5 before the

process is clinically appreciable using angiography and IVUS. However, a sound physiology-based

methodology capable of quantifying early-CAV through capillary rarefaction is missing.

Since capillary density is the main determinant of coronary blood capacity6, a physiological technique

capable of quantifying microcirculatory filling during early diastole might provide valuable clues on

the presence of the subtended capillary density. The Backward Decompression Wave (BDW),

measurable through Wave Intensity Analysis (WIA), originates from the microcirculation and is

thought to quantify the re-expansion of the coronary capillary network in early diastole7. As such, it

may give a relatively-specific measure of capillary density and rarefaction uninfluenced by coexisting

arteriolar obliteration whose influence is noted on flow during mid-to-late diastole8. This is

particularly important since such types of structural remodelling may also occur through disease

processes with an independent effect on microcirculatory haemodynamics and outcome8.

To test this hypothesis, and to validate WIA as a diagnostic tool capable of measuring capillary

rarefaction, we analysed coronary pressure and Doppler flow velocity measurements obtained in a

cohort of consecutive of patients that had previously undergone cardiac transplantation in whom

routine angiography had been scheduled. The study had two parts: firstly, a comparison of WIA and

other physiological indices with capillary density estimated by histomorphometric-examination of

simultaneously-obtained endomyocardial biopsies. Secondly, an assessment of the relationship

between WIA and other physiological indices with long-term outcomes in patients with cardiac

transplantation.

Methods

Study population

Fifty-two consecutive heart transplantation patients scheduled for routine follow-up cardiac

catheterization and endomyocardial biopsy were included in this study; all transplant patients had

angiographically normal coronary arteries at enrolment. The study was approved by the centre’s

ethics committee, and all participants provided informed consent.

Data acquisition

All patients underwent coronary pressure and flow acquisition and the following indices were

constructed: Coronary Flow Reserve (CFR), Basal Microvascular Resistance (BMR), Hyperemic

Microvascular Resistance (HMR), IHDVPS (Instantaneous Hyperaemic Diastolic Velocity Pressure

Slope), Zero Flow Pressure (Pzf) and WIA. A subgroup of 17 patients underwent a simultaneous

histological examination in addition to the above physiology measurements to measure capillary

density and the arteriolar-obliteration index (AOI); 8 control patients were included for comparison

(for details, see the Methods section in the Supplementary Appendix, available at NEJM.org).

Follow up

The Cardiac Allograft Vasculopathy (CAV) end-point was defined as a pre-determined composite of

all CAV-related events incorporating CAV-related death, re-transplantation and re-vascularisation.

Patients underwent 1-3 yearly clinical and angiographic screening to identify evidence of CAV. Once

identified, it was quantified according to standard ISHLT grading9. IVUS findings were stratified

according to the Stanford classification10.

Statistics

Data analyses were performed by STATA 13.1 for Windows (STATA software, TX, USA). Significant

differences between the study subgroups were determined by Student’s t-test. ISHLT data were

analysed using one-way repeated measures analysis of variance with post-hoc Tukey-Kramer

correction. Linear regression analysis was performed to assess univariate relationships between

continuous variables. Multiple linear regression analysis was used to perform statistical adjustments

to the microcirculatory features for capillary density, physiological measures and prognostic

outcome. Survival free of CAV was analysed with the Kaplan-Meier method, using the log rank test

for comparison between groups. A p value of <0.05 was considered statistically significant.

Physiological analysis was performed by an investigator blinded to the clinical and histological

outcome. For convenience, when displayed in bar-charts the backward decompression wave is

shown as positive values. Similarly, when discussing relative wave-intensity sizes, backward

decompression wave magnitude refers to absolute values (i.e. -1 is “less than” -10).

Results

Adequate physiological data was obtained in 44 patients of whom 15 had undergone histological

assessment; these patients are referred to as our study population. Baseline demographics are

demonstrated in Table 1. All patients successfully completed the physiological±histological study

according to the protocol described without complications.

Physiological data

All patients were in sinus rhythm, with a baseline heart rate of 70±17 bpm and normal left

ventricular function. Table S1 in the Supplementary Appendix shows the mean values of the indices

of microcirculatory function measured. No statistically significant influence of hypertension, diabetes

mellitus, or dyslipidaemia were noted on any of the indices of microcirculatory function.

Histological data

An average of 4 biopsies were obtained in each patient. Arteriolar density was similar in transplant

biopsies (2.00±1.22 arterioles per 1 mm2) and control subjects (2.50±0.75 arterioles per 1 mm2,

p=0.3). Capillary density was significantly lower (623±179 vs 1101±322 capillaries per 1 mm2, p<0.01)

in transplant biopsies than in control subjects (Figure S1 in the Supplementary Appendix).

Univariate regression analysis comparing the physiological indices with capillary density was

performed. The strongest relationship was between capillary density and the backward

decompression wave (r=-0.65, p<0.01). A statistically significant relationship with capillary density

was noted with CFR (r=0.52, p=0.048) and a trend towards significance with IHDVPS (r=0.51,

p=0.055) and HMR (r=-0.47, p=0.08) was noted but none with BMR (r=0.36, p=0.2) or Pzf (r=0.13,

p=0.7). (Figure 1). Only the backward decompression wave remained significant on multiple

regression analysis (β=-0.53, p=0.02).

Mean AOI was 77.1 ± 6.2%. A significant relationship was noted between arteriolar obliteration

index and IHDVPS (r=-0.59, p=0.02), but not with the backward decompression wave (r=0.25, p=0.3)

or any other indices of microvascular function.

Follow-up

Two patients were lost to follow-up. Of the remaining 42, mean follow up was 9.3±5.2 years. During

this period there were 12 composite events (CAV-death 5, re-transplantation 4, re-vascularisation 3).

The backward decompression wave was significantly lower in patients suffering an event than those

event-free (-4.5±2.5 vs -7.1±3.9 x103 Wm-2s-1, p=0.04). A similar trend was noted with IHDVPS

(1.2±0.8 vs 1.8±1.2 cm/s/mmHg, p =0.09). No relationship was noted with the other markers of

microvascular function or other waves in the wave-intensity profile (Figure 2).

21 patients ultimately developed angiographic-CAV. Those who did so had a lower IHDVPS value

(1.2±0.8 vs 2.1±1.2 cm/s/mmHg, p=0.01) and a smaller BDW (-4.8±2.6 vs -7.9±4.1 x103 Wm-2s-1,

p=0.01) .

Furthermore, there was a decrease in the backward decompression wave with increasing ISHLT

grade (-7.6±4.0 vs -5.2±2.9 vs -5.6±3.0 vs 4.2±2.3 x103 Wm-2s-1, p=0.07) and IHDVPS (2.1±1.2 vs

1.6±1.0 vs 1.2±0.2 vs 0.9±0.4 cm/s/mmHg, p=0.02) (Figure S2 in the Supplementary Appendix).

Likewise, those who went into to develop the severest disease at IVUS (Stanford IV) had a smaller

BDW at enrolment than those with Stanford III or less (-5.6±6.1 vs -7.9±1.1 Wm -2s-1, p=0.047). No

other relationship between the development of angiographic- or IVUS-CAV and markers of

microcirculatory function were noted.

As there are no standardised normal ranges for wave-intensity in cardiac allograft patients as yet, we

used a cut-off of the 20th percentile of capillary density as a histological poor prognostic marker.

From a linear regression line, the backward decompression wave at this point was calculated as -

5.63x103 Wm-2s-1. Using this cut-off we examined the rate of development of angiographic CAV

disease. Initially, rates were similar; however, there was a divergence over time with higher rates

occurring in those values below the 20th percentile compared to those above it (p=0.09, Figure 3). No

other markers (including IHDVPS, p=0.4) of microvascular function produced an equivalent trend.

To assess for possible confounders, we examined other known predictors for CAV with angiographic

outcomes. Univariate regression also revealed a relationship with recipient diabetes (p=0.04) but not

with donor age (p=0.5), donor sex (p=0.6), recipient cardiac diagnosis (p=0.8), recipient age at

transplantation (p=0.6), surgical ischemic time (p=0.5) or time from index catheterisation (p=0.2).

Multiple regression analysis showed the backward decompression wave (β=0.31, p=0.03) and

IHDVPS (β=-0.35, p=0.01) remained significant but diabetes did not (β=-0.23, p=0.09).

Discussion

The novel findings of this study are:

1. Coronary wave intensity analysis is an intracoronary physiology technique capable of assessing

capillary density in the subtended myocardium

2. Whilst other markers can also provide a measure of capillary density, WIA is the most accurate

tool for this domain

3. In cardiac transplantation patients, the histological evidence of capillary rarefaction is present

before angiographic disease develops which can therefore be identified using wave-intensity

analysis.

4. In turn, this predicts the likelihood of a clinical CAV-event as well as the presence, rate of

development and resultant severity of angiographic CAV (Figure 4).

Wave intensity analysis and capillary density

Cardiac capillary rarefaction is an important pathogenic process in a number of cardiovascular and

systemic diseases but obtaining this information in vivo remains difficult. We have shown that the

backward decompression wave is an adequate measure of capillary density and is able to identify

capillary rarefaction. Whilst both CFR and IHDVPS are also both predictive of capillary density2,8, the

backward decompression wave is more accurate than any other conventional pressure/flow-derived

measures of microvascular function.

Coronary wave intensity analysis was first performed 10 years ago in humans and since then has

been used in a number of physiology studies to predict outcome11 or delineate mechanistic

information12–14. Six waves are identified within each cardiac cycle and each have been ascribed to

particular temporal cardiac processes7. Despite the wealth of clinical insight WIA has provided, until

now this ascription has been based largely on theoretical concepts.

The most clinically relevant wave is the backward decompression wave. This wave is proposed to

originate from the diastolic re-expansion of the intra-myocardial vessels that have been compressed

during systole, akin to a sponge being squeezed then immersed in water before being released.

Work in normal coronary arteries7 and in severe aortic stenosis13 confirmed an appropriate

relationship between the forward compression wave (the ‘squeezing’ force) and the backward

decompression wave. Furthermore, disruption of this relaxation, such as with left ventricular

hypertrophy, causes an alteration in this relationship7. However, these findings only provide a

relatively indirect support for the origin of this wave.

In this series of patients, who are subject to no other obvious influencers of wave intensity, we have

shown that the backward decompression wave relates very closely with the density of capillaries

permeating the myocardium. This therefore provides the first non-physiology-based direct evidence

to support the theoretical concepts of this coronary wave.

Wave intensity analysis to predict cardiac allograft vasculopathy

Cardiac allograft vasculopathy continues to limit the long-term success of cardiac transplantation. It

is a fibro-proliferative disorder that begins within the microcirculation and ultimately progresses to

cause accelerated coronary artery disease with diffuse and circumferential intimal lesions that

progress to occlude coronary blood flow. Given the asensory nature of transplanted-hearts, CAV

currently necessitates frequent angiographic screening15 which incurs significant clinical implications

as well as cost. However, its rate of development remains unpredictable. Accurate diagnosis of pre-

clinical CAV would allow more accurate risk stratification, prognostication, appropriate aggressive

therapy and improve cost-effectiveness and as such is being sought through a number of modalities.

Although distinct from atherosclerotic heart-disease, CAV shares some features with this

pathological process including involvement of the microcirculation16,17. Biopsy-based studies have

identified structural changes that occur within the microcirculation within the first 1-3 months after

transplantation reflecting the earliest stages of CAV4,18 of which capillary rarefaction is a key process5.

Using histological analysis, we have confirmed here that capillary (but not arteriolar) density is

reduced in cardiac transplant patients compared to controls. Given this finding, we used our novel

physiological marker of rarefaction to predict the development, rate of progression and outcome of

CAV.

After a mean follow-up period of nearly 10 years 50% of patients had developed angiographic

evidence of CAV which is consistent with current contemporary practice19. This feature could be

predicted from both the backward decompression wave and IHDVPS but not through any other

markers of microvascular function. Wave-intensity was also able to anticipate its rate of progression

and resultant IVUS severity. Therefore, it appears that the degree of microcirculatory involvement

demonstrated by WIA reflects both the angiographic severity and rate of occurrence of CAV.

We defined a composite endpoint of adverse CAV-related events which included CAV-death, re-

transplantation and revascularisation. Of our pre-defined markers of microvascular function, only

the backward decompression wave was able to identify patients at risk of these events. Potential

future clinical use of this information is suggested in the Supplementary Appendix, available at

NEJM.org.

Other markers of microvascular function

Several other intracoronary physiology tools can be used in vivo to investigate the microcirculation

including CFR, HMR, the index of Microcirculatory Resistance (iMR), IHDVPS and zero-flow pressure

(Pzf). Some of these markers have already been used to provide clinical and physiological insights

into cardiac allograft patients8,20,21. Our analysis incorporated all contemporary non-thermodilutonal

markers of microvascular function but none performed as well as wave-intensity analysis in

identifying histological and prognostic information in this group of patients.

IHDVPS is an alternative way to integrate pressure and flow responses and provides a measure of

conductance. Previous work has shown that this marker correlates with capillary density8 and again

we found a trend here which may have reached significance with a larger patient population.

IHDVPS was also capable of providing some useful insights into prognosis particularly in delineating

the resultant ISHLT-grade of disease severity. However, because of its dual influence from both the

capillary and arteriolar domain it does not provide such a dedicated insight into capillary density as

WIA. We speculate that mid-to-late diastole is governed by the combined resistance effect of the

arterioles and capillaries which is therefore appreciable with IHDVPS. However, in early diastole the

dominant influence is the capacitance of the re-expanding capillaries. Despite this, we are

encouraged by the supportive information provided by IHDVPS in this cohort of patients reinforcing

our earlier work in this field8. We would highlight that rarely does a disease process solely affect one

component of the microcirculation and alternate modalities (such as IHDVPS in this cohort) may

provide important complimentary data.

Whilst Pzf is also constructed from pressure-flow loops it appears to provide differing clinical

information. It is influenced markedly by LVEDP22 and following myocardial infarction provides

significant prognostic information23,24. However, unlike IHDVPS it does not provide information on

microcirculatory histology or predictive data in this study.

CFR has been shown to convey prognostic information regarding left ventricular systolic function in

cardiac-transplant patients20 and is correlated with capillary density2. It is not therefore surprising

that this index also provided some predictive information regarding capillary density. However, as

CFR may be influenced by both macro- and micro-vascular disease25 as well as other haemodynamic

parameters26 it does not behave in such a dedicated fashion as the BDW.

To our knowledge there have been no studies linking whole-cycle derived measures of resistance

(BMR or HMR) with outcome in cardiac transplant patients. In this study, we found no significant

correlation with these measures. There was a trend towards a relationship between HMR and

capillary density which may echo the previously documented relationship between iMR and

histology2.

Limitations and disadvantages

Whilst this study produced a vast amount of sophisticated physiological data the number of patients

included was only moderate. However, the quality of data involved was high and the follow-up

rigorous ensuring maximum reliability of these data. Despite this we acknowledge that larger studies

are required before these findings can be extrapolated to the entire population.

Biopsies were obtained from the right ventricular approach but wave-intensity provided information

on the left ventricle (as it was assessed in the LAD). However, animal studies have shown a

reasonable correlation between right and left ventricular capillary densities27,28 when left ventricular

hypertrophy is absent therefore we feel the two measurements are comparable. Additionally, our

biopsy samples were obtained from the septum which we believe would provide a reasonable

measure of the LAD territory.

We were forced to exclude 8 (15%) patient from our analysis due to poor quality pressure and/or

flow signals. However, with more modern techniques and equipment, and increasing experience we

envisage this percentage could be markedly reduced if used clinically. There was nothing to suggest

those patients excluded were in any way different from our study cohort.

The majority of the index data for this study was gathered prior to the introduction of the combined

Doppler- and pressure-sensor tipped coronary wires that are used for conventional wave-intensity

analysis. Therefore, the pressure signal was obtained from the tip of the catheter rather than at the

same location as the flow-wire. However, we anticipate these waveforms to be identical in the

absence of any epicardial disease – this remains the fundamental basis behind physiological

coronary stenosis assessment in widespread clinical use.

Conclusions

Wave intensity analysis is an excellent tool for assessing capillary rarefaction and provides a focused

insight into the capillary domain of the microcirculation. In cardiac transplant patients, it can identify

the earliest changes of CAV and is able to predict the rate and severity of disease progression. It

therefore confers prognostic outcome data regarding CAV-endpoints. Wave-intensity analysis has a

strong correlate with histological data providing strength to previous theoretical suggestions

regarding the origin of the backward decompression wave.

Funding Sources

Dr Broyd was supported by a grant from the Fundación Interhospitalaria para la Investigación

Cardiovascular (FIC). Dr JE Davies (FS/05/006) is a British Heart Foundation fellow.

Disclosures

None to report.

Tables

Table 1. Baseline characteristic for donor and recipients

Donor

Age (years) 29.2 ± 9.8

Male (%) 36 (82%)

Cause of death

Intracranial haemorrhage (%) 13 (30%)

Other (%) 31 (70%)

Recipient

Age at assessment (years) 54.1 ± 12.7

Age at transplantation (years) 49.2 ± 13.3

Time from transplantation to assessment (months) 59.6 ± 55.0

Reason for transplantation

Dilated cardiomyopathy 22 (50%)

Ischemic cardiomyopathy 12 (27%)

Restrictive cardiomyopathy 5 (11%)

Hypertrophic cardiomyopathy 1 (2.3%)

Valvular heart disease 2 (4.5%)

Congenital heart disease 1 (2.3%)

Re-transplantation 1 (2.3%)

Hypertension 26 (59%)

Hypercholesterolaemia 34 (77%)

Diabetes 14 (32%)

Smoked since transplantation 1 (2.3%)

Table 1. Baseline characteristic for donor and recipients

Figures

Figure 1. Linear regression analysis of 4 indices of microcirculatory function and capillary density

Figure 2. Comparison of the values of 4 indices of microcirculatory function in patients without

(shaded boxes) and with (white boxes) cardiac-allograft vasculopathy driven events during follow-up

(FU).

Figure 3. Kaplin-Meier curve demonstrating survival free from cardiac allograft vasculopathy

(diagnosed angiographically) according to the backward decompression wave.

Figure 4. Schematic representation of the process of cardiac allograft vasculopathy and the ability of

coronary WIA to predict rate and severity of angiographic and clinical outcomes

Figure 1. Linear regression analysis of 4 indices of microcirculatory function and capillary density

The strongest relationship was found between the backward decompression wave and capillary density. Whilst a trend towards significance was noted with

HMR and IHDVPS and a significant relationship with observed with CFR the BDW was the most predictively strong.

Figure 2. Comparison of the values of 4 indices of microcirculatory function in patients without (shaded boxes) and with (white boxes) cardiac-allograft vasculopathy driven events during follow-up (FU).

CAV-related events (a composite of CAV-related death, re-transplantation and re-vascularisation)

were most accurately predicted with the backward decompression wave and this was the only

physiological tool that reached significance.

Figure 3. Kaplin-Meier curve demonstrating survival free from cardiac allograft vasculopathy (diagnosed angiographically) according to the backward

decompression wave.

The backward decompression wave reflecting the 20th percentile of capillary density was used as a cut-off.

Figure 4. Schematic representation of the process of cardiac allograft vasculopathy and the ability of coronary WIA to predict rate and severity of

angiographic and clinical outcomes

In the first few months following transplantation, more severe capillary rarefection occurs in those patients who ultimately go on to develop

angiographically and clinical severe allograft vasculopathy. This change is quantifiable using wave intensity analysis where the size of the BDW identifies

capillary rarefaction and therefore predicts the rate and severity of angiographic and clinical disease and could guide screening rates.

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