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List of contributors
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Sergio García-Blas
Hospital Clínico Universitario de Valencia, Spain
Santiago Jiménez-Valero
Hospital Clínico Universitario de Valencia, Spain
Clara Bonanad
Hospital Clínico Universitario de Valencia, Spain
Juan Sanchis
Hospital Clínico Universitario de Valencia, Spain
Vicente Bodí
Hospital Clínico Universitario de Valencia, Spain
Joana Delgado Silva
Coimbra’s Hospital and University Centre—General Hospital, Portugal
Marco Costa
Coimbra’s Hospital and University Centre—General Hospital, Portugal
Lino Gonçalves
Coimbra’s Hospital and University Centre—General Hospital, Portugal
Nenad Filipovic
Faculty of Engineering, University of Kragujevac, Serbia
Arindam Bit
National Institute of Technology, Raipur, India
Himadri Chattopadhyay
Jadavpur University, India
Ignacio Labarride
National University of Central Buenos Aires, Argentina
Ehab Essa
Swansea University, UK
Xianghua Xie
Swansea University, UK
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Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
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Huaizhong Zhang
Edge Hill University, UK
James Cotton
Royal Wolverhampton NHS Trust, UK
Dave Smith
ABMU, UK
Manya V Afonso
Wageningen University and Research, Netherlands
J Miguel Sanches
Institute for Systems and Robotics, Portugal
E Gerardo Mendizabal-Ruiz
University of Guadalajara, Mexico
M’hamed Bentourkia
University of Sherbrooke, Canada
Ioannis A Kakadiaris
University of Houston, TX, USA
Timur Aksoy
Sabanci University, Turkey
Gozde Unal
Istanbul Technical University, Turkey
Franjo Pernus
University of Ljubljana, Slovenia
Ziga Spiclin
University of Ljubljana, Slovenia
Su-Lin Lee
Imperial College London, UK
Angelos Karlas
Technical University of Munich, Germany
Alessio Dore
Imperial College London, UK
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Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
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Luca Saba
University of Cagliari, Italy
Sumit K Banchhor
National Institute of Technology, India
Harman S Suri
Monitoring and Diagnostic Division, AtheroPoint™, USA
Narendra D Londhe
National Institute of Technology, India
Tadashi Araki
Toho University Medical Center Omori Hospital, Japan
Nobutaka Ikeda
National Center for Global Health and Medicine, Japan
Klaudija Viskovic
University Hospital for Infectious Disease, Croatia
Shoaib Shafique
CorVasc MDs PC, IN, USA
John R Laird
St Helena Hospital, CA, USA
Ajay Gupta
Weill Cornell Medical College, NY, USA
Andrew Nicolaides
Vascular Screening and Diagnostic Centre, UK
Pankaj K Jain
Indian Institute of Technology Varanasi (BHU), India
Ayman El-Baz
University of Louisville, KY, USA
Vimal K Shrivastava
Kalinga Institute of Industrial Technology, India
Shoaib Shafique
CorVasc Vascular Laboratory, IN, USA
xxii
Section I
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Vascular and intravascular clinical analysis
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IOP Publishing
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Vascular and Intravascular Imaging Trends, Analysis, and
Challenges, Volume 1
Stent applications
Petia Radeva and Jasjit S Suri
Chapter 1
OCT in the evaluation of late stent pathology:
restenosis, neoatherosclerosis and late
malapposition
Sergio García-Blas, Santiago Jiménez-Valero, Clara Bonanad, Juan Sanchis and
Vicent Bodi
1.1 Stent evolution and late stent pathology
Coronary stents were developed to overcome the limitations of percutaneous
transluminal coronary angioplasty (PTCA), improving acute success, facilitating
the management of complications (mainly coronary dissection and acute vessel
closure) and lowering restenosis rates [1, 2]. However, PTCA and subsequent stent
deployment are an aggression to the arterial wall and leave a metallic material that
must be incorporated into the vascular structure through a complex restoration
process called neointimal coverage or re-endothelialization.
The first devices used were bare metal stents (BMSs). Pathological studies have
suggested that re-endothelialization occurs with the development of neointimal
tissue within the luminal area of stents, reaching an intimal hyperplasia peak from 6
to 12 months after BMS deployment [3]. After BMS deployment, in-stent restenosis
(ISR) was still a relevant issue with rates up to 20%–30%, due to excessive
neointimal proliferation [1, 2]. Initial reports showed excellent late performance of
these stents after the initial phase of higher risk ISR, with clinical stability of the
stented site at 8–10 years after BMS implantation [4]. Therefore, ISR is generally
considered to be a stable process, with an early peak in intimal hyperplasia followed
by a quiescent period. However, there is emerging histological and clinical evidence
of late de novo in-stent neoatherosclerosis [5–7]. These studies indicate that in some
patients there is degenerative evolution of neointima into an unstable atherosclerotic
lesion and that ISR should not always be regarded as a benign entity.
doi:10.1088/2053-2563/ab01fach1 1-1 ª IOP Publishing Ltd 2019
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
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Drug-eluting stents (DESs) were designed to inhibit smooth muscle cell proliferation
which, together with the extracellular matrix, is the main component of neointimal
tissue [8, 9]. A DES comprises a metallic stent frame, antiproliferative drugs and
eventually a polymer (although recently polymer-free stents have been developed). They
have shown reduced rates of angiographic restenosis and improved clinical outcomes
[10, 11] and have become the main agent for treating coronary artery disease. However,
some concerns arose some years after the widespread use of DESs was established,
mainly due to the emergence of very late stent thrombosis (VLST). First-generation
DES-treated patients showed a rate of late stent thrombosis (LST) of 0.53% per year
which steadily increased to 3% over 4 years [12, 13]. Proposed mechanisms for this LST
include insufficient strut coverage, late-acquired malapposition, or the development of
neoatherosclerotic changes within the neointimal layer. New generation stents with
other drugs, thinner struts and biocompatible polymers, among other innovations, seem
to have overcome the challenge of LST [14]. Despite better outcomes with technological
developments, late stent pathology is still an issue and the increasing number of
coronary stents being used means that a pathology that affects a small percentage of the
population at risk will translate into a significant number of patients. Better understanding is needed of the healing process after stent deployment and the development
and evolution of late stent pathology. Furthermore, the clinical manifestations of these
entities are frequently severe and have a poor prognosis.
Bioresorbable vascular scaffolds (BVSs) have been developed recently to maintain the initial advantages of stents but disappear after some years, thus leaving no
foreign material in the arterial wall. Initial reports of extended follow-up beyond
three years suggest a favorable vascular late response after resorption, with late
luminal gain due to plaque and vessel remodeling, and potential ‘sealing’ of plaques
by creation of a superficial fibrous tissue layer. This may avoid late complications,
however, further evaluation and follow-up of these devices is needed.
1.2 OCT characterization of late stent pathology
Optical coherence tomography (OCT) has a resolution about ten times higher than
that of intravascular ultrasound (IVUS)—10–20 μ m and 80–120 μm, respectively). It
is able to identify stent struts and accurately define and quantify their relationship
with the vessel wall. Previous studies have shown the superiority of OCT over IVUS
in evaluating strut coverage, apposition and neointimal hyperplasia. OCT can also
identify neoatherosclerotic changes. The second generation of OCT imaging
systems, i.e. optical frequency domain imaging (OFDI), has provided a substantial
advance due to a markedly increased speed of image acquisition, improving the
feasibility of this technique and reducing complications and patient discomfort
during image acquisition. All these features make OCT a unique tool for the
evaluation of acute and late stent performance.
1.2.1 Stent coverage: re-endothelialization
The high spatial resolution of OCT (10–20 μm axial) enables detailed in vivo
assessment of individual stent strut coverage. Preclinical studies have established the
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accuracy and reproducibility of OCT in detecting DES endothelialization. Suzuki
et al analyzed the performance of OCT and IVUS to evaluate neointimal coverage
compared to histology in a swine model. They found that OCT had a high
correlation with histology measurements (r = 0.980, p < 0.001, for the lumen
area; r = 0.978, p < 0.001, for the stent area; and r = 0.961, p < 0.001, for the
neointimal area) and a good diagnostic accuracy for detecting a small degree of
neointima (AUC = 0.967, 95% CI 0.914–1.019). Conversely, IVUS showed a poorer
correlation with histology (AUC = 0.781, 95% CI 0.621–838) [15]. Also, in a
pathological study in a porcine coronary model, Murata et al demonstrated a high
correlation between OCT and histology regarding neointimal thickness, and
neointimal and luminal area. Interestingly, OCT and histology detected a similar
proportion (1.16% and 1.84%) of uncovered struts. OCT seems to correlate
appropriately with histology in either the absence (<20 μm) or the presence
(>100 μm) of robust neointima; however, the correlation does not seem to be very
linear between these values (the proportions of struts displaying neointimal thicknesses ranging from 20–80 μm differed significantly) [16]. Prati et al proved an
adequate linear correlation between neointimal measurements obtained by OCT and
histological measures, with a correlation coefficient of 0.726 (p <0.0001), in a rabbit
model. Furthermore, intra- and inter-observer reproducibility were 0.9 and 0.8,
respectively [17]. Initial reports also correlated neointima visualized by OCT with
histopathological findings in humans [18]. Nakano et al found a good correlation
between OCT and histological analysis for strut coverage in 14 human stented
coronary segments from autopsy specimens, with a sensitivity of 79%, a specificity of
97% and good inter-observer reproducibility [19].
The second generation of OCT imaging systems, i.e. OFDI, have also shown
excellent agreement with histological analysis regarding neointimal thickness
(r = 0.90, p < 0.01) and strut coverage (r = 0.96, p < 0.01). Moreover, optical
density measurements revealed a significant difference between fibrin- and neointima-covered coronary stent struts, suggesting that differences in optical density
provide information on the type of stent strut coverage (see figure 1.1). Namely, the
pixel intensity (optical density) of stent strut coverage in OFDI images, normalized
Figure 1.1. Complete versus incomplete strut coverage. (A) A complete homogeneous layer of neoendothelium. (B) Incomplete coverage, struts from 11 to 3 (arrows) are not covered, probably due to slight incomplete
apposition.
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for optical density of the stent struts, revealed an excellent diagnostic accuracy
(AUC = 0.859) for differentiating fibrin versus neointimal stent strut coverage.
Therefore, densitometric analysis may represent a promising tool to obtain further
information on the type of stent strut coverage [20].
Its accuracy in detecting and quantifying neointima makes OCT a unique
technique to evaluate the process of endothelialization in vivo. OCT assessment of
stent strut coverage is not standardized, and a quantitative or qualitative approach
can be used. In the first methodology, strut coverage is evaluated through quantification of tissue coverage area: the operator manually traces the stent and lumen area,
deriving the tissue coverage area. Using this type of approach, good intra- and
inter-observer agreement has been reported for neointima thickness measurements
[17, 21, 22]. Volume measurements can be performed using Simpson’s rule, where the
areas of each cross section in a pullback multiplied by the slice thickness (frame–frame
spacing) are added over the segmented volume of interest [23, 24]. Data can also be
quantified at strut-level, measuring the thickness of the covering tissue in each strut,
namely the distance between the luminal surface of the covering tissue and the luminal
surface of the strut [23, 24]. The second and most used approach to evaluate strut
coverage is a visual qualitative classification of strut coverage as a binary variable
(covered or not covered). This qualitative evaluation of strut coverage by OCT has
proven to have good inter- and intra-observer agreement. The zoom setting is an
important bias and the range of intra-observer agreement according to the zoom used
is very broad—the same strut can have a 0%–25% probability of being considered as
uncovered depending on the zoom used [25]. Qualitative analysis is generally
expressed as the percentage of uncovered struts, and other measurements have been
proposed, such as the linear distribution of strut coverage [24]. OCT assessment of
strut coverage is limited by its axial resolution and by blooming artifacts (intense
signal generated by the reflection of light against the metallic struts) which may cause
one to overestimate the number of uncovered struts.
Different studies have employed different arbitrary cut-off values for the ratio of
uncovered struts which might be considered clinically relevant. For example a rate of
>10% uncovered struts was used in the optical coherence tomography in acute
myocardial infarction (OCTAMI) trial 28, whereas rates of >5% and >10% uncovered
struts were employed in the LEADERS trial. However, histological data showed a
higher prevalence of thrombus in stents with a ratio of uncovered struts >30% [26].
Won et al analyzed OCT imaging at a median of 851 days after DES implantation in
535 lesions treated with DESs, and found that the best cut-off value for the percentage
of uncovered struts for predicting major events was 5.9% [27]. However, to date there
is not enough evidence to establish a definitive cut-off value of the percentage of
uncovered struts or malapposed struts that is clinically relevant. Thus, a larger OCT
study is warranted to evaluate the reliability of the degree of incomplete coverage for
identifying patients at a clinically relevant increased risk of LST.
1.2.1.1 Stent coverage according to clinical presentation
Pathological observations suggest that the ratio of uncovered to total stent struts is
increased in patients with acute coronary syndrome after DES implantation [28].
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OCT evaluation of DES implanted in patients with ST-elevation myocardial
infarction (STEMI) revealed a higher frequency of uncovered stent struts at a
median follow-up of 9 months when compared with patients with stable coronary
disease (93.8% versus 67.7%, p = 0.048). Data from the same study showed that DES
implantation in STEMI was the only independent predictor for both the presence of
uncovered struts and incomplete stent apposition (ISA) at follow-up [29]. This may
be one of the underlying factors that make acute coronary syndrome a patientrelated factor for stent thrombosis (see further in section 1.2.5).
1.2.1.2 Stent coverage depending on the stent type
Analysis of coverage at strut-level using OCT is the most common surrogate
endpoint in OCT studies, providing a measurable variable for comparison between
different stents and also being an important parameter for the approval of new drugeluting stents by regulatory agencies.
• First-generation DESs. These include the sirolimus-eluting stent (SES) and
paclitaxel-eluting stent (PES).
Takano et al compared 3 month and 2 year OCT findings in patients who
had received a first-generation SES. The neointimal coverage had advanced
during the follow-up, as shown by a greater thickness of neointima, a lower
frequency of uncovered struts and a lower prevalence of patients with a cross
section of uncovered strut ratio >0.3 in the 2 year evaluation. However, the
prevalence of patients who had any cross-sections with an uncovered strut
ratio >0.3 was still significant (38%). Therefore, despite the evolving neointimal coverage, a few stent struts might persist as uncovered struts longterm [30]. Other OCT studies agreed with the conclusion that first-generation
SESs showed a higher frequency of incomplete strut coverage compared to
BMSs [31, 32].
Ishigami et al evaluated 60 patients classified into three groups according
to the time elapsed since SES implantation. The follow-up time was
associated with a significant increase in mean neointimal area and neointimal
thickness, and a significant decrease in the number of uncovered stent struts.
However, even at the later follow-up, only 17.6% of implanted SESs were
completely covered by neointima [ 33].
In a comparative study between two first-generation stents, OCT examination at 6 months showed that compared to SESs, PESs have a non-uniform
and larger neointimal thickness with fewer uncovered struts, and more peristrut low-density areas, probably related to inflammatory areas and fibrin
deposits [34]
• Second-generation DESs.
Second-generation stents have demonstrated a better clinical and angiographic performance compared to first-generation stents [35], and a lower rate
of stent thrombosis [14, 36–38]. OCT evaluation of strut coverage has
provided insight into the possible mechanisms underlying these better
outcomes.
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