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Evidence-based cardiovascular magnetic resonance cost-effectiveness calculator for the detection of significant coronary artery disease

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Background Although prior reports have evaluated the clinical and cost impacts of cardiovascular magnetic resonance (CMR) for low-to-intermediate-risk patients with suspected significant coronary artery disease (CAD), the cost-effectiveness of CMR compared to relevant comparators remains poorly understood. We aimed to summarize the cost-effectiveness literature on CMR for CAD and create a cost-effectiveness calculator, useable worldwide, to approximate the cost-per-quality-adjusted-life-year (QALY) of CMR and relevant comparators with context-specific patient-level and system-level inputs. Methods We searched the Tufts Cost-Effectiveness Analysis Registry and PubMed for cost-per-QALY or cost-per-life-year-saved studies of CMR to detect significant CAD. We also developed a linear regression meta-model (CMR Cost-Effectiveness Calculator) based on a larger CMR cost-effectiveness simulation model that can approximate CMR lifetime discount cost, QALY, and cost effectiveness compared to relevant comparators [such as single-photon emission computed tomography (SPECT), coronary computed tomography angiography (CCTA)] or invasive coronary angiography. Results CMR was cost-effective for evaluation of significant CAD (either health-improving and cost saving or having a cost-per-QALY or cost-per-life-year result lower than the cost-effectiveness threshold) versus its relevant comparator in 10 out of 15 studies, with 3 studies reporting uncertain cost effectiveness, and 2 studies showing CCTA was optimal. Our cost-effectiveness calculator showed that CCTA was not cost-effective in the US compared to CMR when the most recent publications on imaging performance were included in the model. Conclusions Based on current world-wide evidence in the literature, CMR usually represents a cost-effective option compared to relevant comparators to assess for significant CAD.
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Pandyaetal.
Journal of Cardiovascular Magnetic Resonance (2022) 24:1
https://doi.org/10.1186/s12968-021-00833-1
RESEARCH
Evidence-based cardiovascular magnetic
resonance cost-eectiveness calculator
forthedetection ofsignicant coronary artery
disease
Ankur Pandya1*, Yuan‑Jui Yu2, Yin Ge3, Eike Nagel4, Raymond Y. Kwong3, Rafidah Abu Bakar5, John D. Grizzard6,
Alexander E. Merkler7, Ntobeko Ntusi8, Steffen E. Petersen9, Nina Rashedi10, Juerg Schwitter11,12,
Joseph B. Selvanayagam13,14, James A. White15, James Carr16, Subha V. Raman17, Orlando P. Simonetti18,
Chiara Bucciarelli‑Ducci19, Lilia M. Sierra‑Galan20, Victor A. Ferrari21, Mona Bhatia22 and Sebastian Kelle23,24
Abstract
Background: Although prior reports have evaluated the clinical and cost impacts of cardiovascular magnetic
resonance (CMR) for low‑to‑intermediate‑risk patients with suspected significant coronary artery disease (CAD), the
cost‑effectiveness of CMR compared to relevant comparators remains poorly understood. We aimed to summarize
the cost‑effectiveness literature on CMR for CAD and create a cost‑effectiveness calculator, useable worldwide, to
approximate the cost‑per‑quality‑adjusted‑life‑year (QALY ) of CMR and relevant comparators with context‑specific
patient‑level and system‑level inputs.
Methods: We searched the Tufts Cost‑Effectiveness Analysis Registry and PubMed for cost‑per‑QALY or cost‑per‑
life‑year‑saved studies of CMR to detect significant CAD. We also developed a linear regression meta‑model (CMR
Cost‑Effectiveness Calculator) based on a larger CMR cost‑effectiveness simulation model that can approximate CMR
lifetime discount cost, QALY, and cost effectiveness compared to relevant comparators [such as single‑photon emis‑
sion computed tomography (SPECT), coronary computed tomography angiography (CCTA)] or invasive coronary
angiography.
Results: CMR was cost‑effective for evaluation of significant CAD (either health‑improving and cost saving or having
a cost‑per‑QALY or cost‑per‑life‑year result lower than the cost‑effectiveness threshold) versus its relevant compara‑
tor in 10 out of 15 studies, with 3 studies reporting uncertain cost effectiveness, and 2 studies showing CCTA was
optimal. Our cost‑effectiveness calculator showed that CCTA was not cost‑effective in the US compared to CMR when
the most recent publications on imaging performance were included in the model.
Conclusions: Based on current world‑wide evidence in the literature, CMR usually represents a cost‑effective option
compared to relevant comparators to assess for significant CAD.
Keywords: Cost‑effectiveness, Cardiovascular magnetic resonance, Coronary artery disease
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Introduction
For patients with suspected coronary artery disease
(CAD), cardiovascular magnetic resonance (CMR) offers
a non-invasive and accurate diagnostic option. However,
Open Access
*Correspondence: anpandya@hsph.harvard.edu
1 Department of Health Policy and Management, Harvard T.H. Chan
School of Public Health, 718 Huntington Ave, 2nd Floor, Boston, MA
02115, USA
Full list of author information is available at the end of the article
Page 2 of 14
Pandyaetal. Journal of Cardiovascular Magnetic Resonance (2022) 24:1
there are other diagnostic alternatives for such patients
at low-to-intermediate risk, including ergometry, single-
photon emission computed tomography (SPECT), or
coronary computed tomography angiography (CCTA).
Even conservative management, i.e. waiting for symp-
tomatic disease to worsen and to manifest by complica-
tions, or more aggressive management, i.e., or immediate
invasive coronary angiography (ICA), could represent
options [1]. Depending on certain factors, such as imag-
ing costs, local availability of imaging modalities and
expertise and prevalence of underlying disease, tradeoffs
may be realized among these options that can influence
clinical decision-making [2].
Cost-effectiveness analysis can be used to quantita-
tively weigh tradeoffs between length of life, quality of
life, and incurred costs, across different diagnostic strat-
egies, allowing payers and physician decision-makers to
choose a higher-value pathways [3, 4]. Most health tech-
nology assessments in high-income countries use cost-
per-quality-adjusted life-year (QALY) to quantify value
from cost-effectiveness analyses, with estimated costs
including both immediate imaging costs and all down-
stream costs (including both additional care stemming
from test results and saved costs from averted coronary
heart disease events, follow-up tests, and procedures) [5].
Although there have been prior publications evaluating
the clinical and cost impacts of CMR-based diagnostic
strategies in patients with suspected CAD, there has been
less clarity on the specific cost-effectiveness profile of
CMR compared to its relevant comparators across both
clinical settings and patient types, particularly as evi-
dence of imaging performance continues to evolve [68].
In a 2-step approach, we first analyzed and summa-
rized the comparative cost effectiveness surrounding
use of CMR imaging for the assessment of patients pre-
senting with stable chest pain syndromes compared to
its relevant comparators based on the existing medical
literature. en we created a unique cost-effectiveness
calculator that could be used globally to estimate life-
time discounted costs and QALYs for CMR versus its
relevant comparator techniques. To facilitate global use
for different international geographic regions or refer-
ral populations, adjustment of context-specific patient
and system-level inputs (such as disease prevalence and
imaging costs) was incorporated into the cost-effective-
ness calculator.
Methods
Search strategy ofexisting literature anddata extraction
We conducted a systematic literature review using the
Tufts Cost-Effectiveness Analysis Registry (CEA Registry,
www. ceare gistry. org) and PubMed for English-language
cost-effectiveness published from 2005 to 2020 [9, 10].
e CEA Registry contains 8000 English-language cost-
per-QALY studies. e CEA Registry uses keywords such
as QALYs, quality adjusted, and cost-utility analysis to
search PubMed for English-language publications. e
reference lists of every identified CMR cost-per-QALY
or relevant review study were searched to identify addi-
tional CMR cost-effectiveness studies missed by our
other methods. We only included studies that used CMR
as an imaging strategy to assess for CAD as the primary
clinical condition. Two reviewers (YY and AP) indepen-
dently reviewed each study to extract relevant data, and
resolved any differences in data extractions at in-person
meetings; a third author (SK) was contacted when con-
sensus could not be reached at the in-person meetings.
Detailed information on the search strategy and data
extraction for the CEA Registry is reported elsewhere
[10, 11].
e search terms used to identify cost-per-QALY stud-
ies were combinations of methodological terms (cost-
effectiveness, QALY, incremental cost-effective ratio)
and clinical terms (cardiac magnetic resonance, CMR,
coronary angiography). Articles other than cost-effec-
tiveness studies that were related to the overall value of
CMR (reviews, meta-analyses, diagnostic performance,
editorials, etc.) were identified but not included in our
systematic review (Appendix Fig. 3). We reviewed the
cost-effectiveness studies that included CMR as a strat-
egy and summarized the following information among
these articles: setting of the analysis, comparators
included, analytic perspective taken, analytic time hori-
zon taken, main conclusion on the cost effectiveness of
CMR (where “cost-effective” was defined as either health-
improving and cost saving [“dominant”] [3] or having a
cost-per-QALY or cost-per-life-year result lower than
the cost-effectiveness threshold [4]), and key drivers of
the results. We performed a sensitivity analysis excluding
papers that reported cost-effectiveness outcomes other
than cost-per-QALY or cost-per-life year (such as cost-
per-case detected).
We used author assessments to determine whether
CMR represented a cost-effective option or cost-inef-
fective option for a given paper, which could depend on
country-specific cost-effectiveness thresholds. In the
United States (US), for example, the American College
of Cardiology (ACC) and the American Heart Asso-
ciation (AHA) issued a joint statement on health care
“value” in 2014 that specified that cost-per-QALY results
below $50,000/QALY indicate high-value care, cost-per-
QALY estimates between $50,000/QALY and $150,000/
QALY indicate intermediate-value care, and cost-per-
QALY estimates greater than $150,000/QALY indicate
low-value care [12]. We also categorized some papers as
showing unclear CMR cost-effectiveness when there was
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Pandyaetal. Journal of Cardiovascular Magnetic Resonance (2022) 24:1
considerable uncertainty around the CMR cost-effective-
ness results.
Development oftheCMR cost eectiveness calculator
(meta‑model)
We built a user-friendly CMR cost effectiveness meta-
model based on a larger CMR Markov (state-transition
cohort) model developed for a prior CMR cost-per-
QALY study (by study co-authors AP, YG, RK) performed
for a US health care system perspective [13]. e model
projects lifetime discounted QALY and cost outputs for
five strategies: (1) no imaging; (2) CMR; (3) SPECT; (4)
CCTA; (5) ICA. Other strategies, such as echocardiog-
raphy, were not included in the larger Markov model.
In this model, patients in the CMR, SPECT, and CCTA
groups underwent ICA only if noninvasive imaging
demonstrated abnormal findings. ose with positive
ICA results (with some having also received FFR) were
assumed to undergo both medical and revascularization
therapies, and this combination led to overall improved
health outcomes (quantified using lifetime discounted
QALYs). Patients with normal findings were presumed
to be free of obstructive CAD and were managed accord-
ingly. In the no imaging strategy patients were initially
managed without any investigations. Assuming escalat-
ing symptoms in 58% of patients with obstructive CAD
who did not receive treatment in their first year after
assessment (i.e., patients with false negative results)
would return within the first year and undergo ICA, lead-
ing to medical and revascularization therapies (i.e., 58%
of false negatives would experience the same outcomes
as true positives within 1year) [14]. e Markov model
had four major health states: no clinical major cardiovas-
cular events [MACE, defined as: cardiovascular death,
acute nonfatal myocardial infarction (MI), hospitaliza-
tion for unstable angina or heart failure], history of one
MACE, history of more than one MACE, and all-cause
death [13]. In the Markov model, QALYs are estimated by
combining the length of life patients spend in each of the
health states with a quality-of-life value (“utility”) rang-
ing between 0 (representing death) and 1 (perfect health),
with no MACE having an average utility value of 0.84 and
MACE having an average utility of 0.78 [15]. Life costs
estimated by the Markov model depend on the cost of the
imaging strategies used, procedures performed, and the
acute and long-term healthcare-related costs associated
with each health state (with MACE states having 1st-year
costs between $11,000–18,000 and subsequent annual
costs of $3400) [13]. Both QALY and cost outcomes
can be discounted at an annual rate (such as 3%, recom-
mended for cost-effectiveness analyses performed for the
US setting) [16].
e linear regression-based meta-model was trained
(i.e., coefficients were estimated) on 100,000 model
input–output combinations from the probabilistic sensi-
tivity analysis (2nd-order Monte Carlo simulation) from
the larger Markov simulation model [17]. Previous stud-
ies have shown that simple linear regression-based meta-
models can approximate larger disease models with high
accuracy (r-squared values that often exceed 0.95). e
meta-model allows users to replicate the larger Markov
model results and change 10–20 key meta-model inputs
to view corresponding meta-model outputs to create
customized results for specific scenarios or populations
of interest [18, 19]. Meta-model variable selection was
based on a 0.05 level of significance for beta coefficients
in the linear regressions. We validated our meta-model
on a separate 1000 model input–output combinations
from the probabilistic sensitivity analysis (2nd-order
Monte Carlo simulation) from the larger Markov simula-
tion model that were not used to train the meta-model
(i.e., the test set) [17]. Meta-model goodness-of-fit was
assessed using adjusted r-squared and percentage devia-
tion (for external validation) metrics for the test set.
e meta-model approximates the lifetime discounted
QALY and cost results for a given imaging strategy based
on user-entered population- and system-level inputs,
such as prevalence of CAD, costs of imaging and proce-
dures, and other model inputs listed in Appendix Table3.
We used the meta-model to explain observed differences
in conclusions from published cost-effectiveness analyses
included in our literature review that were performed in
the US setting. Specifically, we used our meta-model to
replicate the cost-effectiveness results of the US study
it was based on Ge etal. [13], which found CMR to be
cost-effective compared to CCTA, SPECT, ICA, and
no imaging strategies [13]. en we changed key input
parameters of the meta-model (such as imaging perfor-
mance and cost parameters, CAD prevalence, treatment
costs, etc.) to replicate the cost-effectiveness results of
another US study (with a similar model structure and
decision problem) that found CCTA to be cost-effective
compared to CMR [20]. Using the meta-model, we then
changed only the imaging performance inputs from the
values in Genders et al. [20] cost-effectiveness study to
the more recent imaging performance inputs used by Ge
et al. [13] to determine whether these imaging perfor-
mance inputs alone could explain the difference in CMR
cost-effectiveness results.
e larger Markov model was programmed in
TreeAge Pro 2019 software (version 19.2.1; TreeAge
Software, LLC, Williamstown, Massachusetts, USA),
the meta-model was created using RStudio (ver-
sion 1.2.5042; RStudio Software, Boston, Massachu-
setts, USA), and the user-friendly cost-effectiveness
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Pandyaetal. Journal of Cardiovascular Magnetic Resonance (2022) 24:1
calculator is programmed in Microsoft Excel (Micro-
soft Corporation, Redmond, Washington, USA) and is
included as a downloadable spreadsheet in Additional
file1 and as a web-based tool at: https:// docs. google.
com/ sprea dshee ts/d/ 12TMg bIS6s DpXka fSYfN aNw8v
Sx- LCTCE 4K_ yAa7m dyY/ edit? usp= shari ng.
Results
Our search yielded 39 studies of which 15 ultimately met
our inclusion criteria (shown in Table1 and Appendix
Fig.3) [13, 14, 2030]. We excluded 17 studies because
they were not original research (such as perspective arti-
cles or literature reviews), they focused on the cost-effec-
tiveness of CMR for heart failure patients (as opposed to
CAD), or they did not include CMR as a comparator. Of
Table 1 Summary of the cost‑effectiveness literature of CMR for CAD (15 total studies)
Attribute # of studies % of
studies
(%)
Study citations
Setting
US setting 5 33 Moschetti et al. [24], Stojanovic et al. [29], Sharples et al. [28], Genders et al. [20], Ge
et al. [13, 31]
European setting (including the UK) 11 73 Walker et al. [14], Boldt et al. [22], Thom et al. [30], Petrov et al. [25], Pontone et al. [27],
Pletscher et al. [26], Moschetti et al. [24], Sharples et al. [28], Genders et al. [20], Camp‑
bell et al. [23], Walker et al. [31]
Other setting 2 13 Bertoldi et al. [21], Kozor et al. [32]
Comparators included
No imaging 2 13 Genders et al. [20], Ge et al. [13]
Stress echocardiography 4 27 Thom et al. [30], Sharples et al. [28], Genders et al. [20], Bertoldi et al. [21]
Stress electrocardiography 4 27 Walker et al. [14], Pletscher et al. [26], Bertoldi et al. [21], Walker et al. [31]
SPECT 11 73 Walker et al. [14], Boldt et al. [22], Thom et al. [30], Pletscher et al. [26], Sharples et al.
[28], Genders et al. [20], Stojanovic et al. [29], Sharples et al. [28], Ge et al. [13], Walker
et al. [31], Kozor et al. [32], Walker et al. [31]
CC TA 5 33 Pontone et al. [27], Genders et al. [20], Bertoldi et al. [21], Ge et al. [13]
Immediate coronary angiography 10 67 Walker et al. [14], Boldt et al. [22], Thom et al. [30], Petrov et al. [25], Pletscher et al. [26],
Moschetti et al. [24], Sharples et al. [28], Ge et al. [13], Walker et al. [31], Kozor et al. [32]
Main conclusion on CMR value
CMR cost‑effective 10 67 Walker et al. [14], Boldt et al. [22], Petrov et al. [25], Pontone et al. [27], Pletscher et al.
[26], Moschetti et al. [24], Stojanovic et al. [29], Ge et al. [13], Walker et al. [31], Kozor
et al. [32]
CMR not cost‑effective 2 13 Genders et al. [20], Bertoldi et al. [21]
Unclear cost‑effectiveness 3 20 Thom et al. [30], Sharples et al. [28], Campbell et al. [23]
Key drivers of cost‑effectiveness results
Underlying CAD prevalence 9 60 Walker et al. [14], Boldt et al. [22], Thom et al. [30], Pletscher et al. [26], Moschetti et al.
[24], Stojanovic et al. [29], Genders et al. [20], Ge et al. [13], Kozor et al. [32]
Test costs 4 27 Pletscher et al. [26], Moschetti et al. [24], Sharples et al. [28], Bertoldi et al. [21]
Perspective taken
Societal 1 8 Genders et al. [20]
Healthcare system or payer 10 67 Walker et al. [14], Boldt et al. [22], Pletscher et al. [26], Moschetti et al. [24], Stojanovic
et al. [29], Bertoldi et al. [21], Campbell et al. [23], Ge et al. [13], Walker et al. [31], Kozor
et al. [32]
Hospital 1 7 Stojanovic et al. [29]
Not clearly stated 4 27 Thom et al. [30], Petrov et al. [25], Pontone et al. [27], Sharples et al. [28]
Model time horizon
Lifetime 7 47 Walker et al. [14], Pletscher et al. [26], Genders et al. [20], Bertoldi et al. [21], Campbell
et al. [23], Ge et al. [13], Kozor et al. [32]
10–30 years 2 13 Boldt et al. [22], Petrov et al. [25]
3–10 years 1 7 Thom et al. [30]
< 3 years 4 27 Pontone et al. [27], Stojanovic et al. [29], Sharples et al. [28], Walker et al. [31]
Not stated 1 7 Moschetti et al. [24]
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Pandyaetal. Journal of Cardiovascular Magnetic Resonance (2022) 24:1
the remaining studies, an additional seven were excluded
for not being a cost-effectiveness analysis (such as stud-
ies evaluating only costs or clinical effectiveness). Fur-
ther two studies were excluded in our sensitivity analysis
restricting our analysis to cost-per-QALY or cost-per-
life-year studies [24, 29].
Summary ofmethods used forCMR cost‑eectiveness
studies
Table1 shows that most of the 15 studies that met our
inclusion criteria were performed in a US (five studies)
[13, 20, 24, 28, 29] or European setting (seven included the
United Kingdom [14, 20, 23, 24, 28, 30, 31], three included
Germany [22, 24, 25]). One study (Bertoldi etal.) was per-
formed for the Brazilian public health system [21] and
another (Kozer etal.) was performed for the Australian
health care system [32]. In 11 of the 15 studies CMR was
compared to SPECT strategies, in 10/15 immediate ICA,
in 5/15 to CCTA, and 4/15 to stress electrocardiography.
Simulation models (such as decision trees or state transi-
tion models) were used for 10/15 studies [13, 14, 2024,
26, 29, 32], and seven of these ten studies extrapolated
outcomes for a lifetime time horizon; the five studies not
using simulation models relied on empirical data [25, 27,
28, 30, 31] resulting in time horizons of less than 10years.
Summary ofresults ofCMR cost‑eectiveness studies
CMR was found to be cost-effective versus its relevant
comparator in 10/15 studies [13, 14, 22, 2427, 29, 31,
32]. Among these ten studies, the most common compar-
ators to CMR were strategies that used ICA [13, 14, 22,
2426, 31, 32] or SPECT [13, 14, 22, 26, 29, 31, 32], while
three studies compared CMR versus strategies that used
CCTA [13, 27, 31], and one directly compared CMR to a
no imaging strategy [13].
Two studies concluded that CCTA was more cost-
effective (Bertoldi et al. and Genders et al.) compared
to CMR [20, 21]. In sensitivity analyses, Bertoldi et al.
found that a cost reduction of 79% was required for the
CMR strategy to be cost-effective versus the CCTA from
the Brazilian public health system perspective [21]. is
result was driven by only slightly higher QALYs from the
CMR strategy compared to the CCTA strategy based on
sensitivity and specificity inputs from meta analyses pub-
lished in 2010 [33] (for CMR) and 2008 [34] (for CCTA).
Genders etal. focused on a low-to-moderate risk popula-
tion (performing separate analyses for men and women)
and found that CCTA as a first-line test (combined with
baseline echocardiography in all patients and additional
invasive diagnostic work-up in patients with positive
CCTA) almost always dominated CMR in the three set-
tings analyzed (the United States, the Netherlands, and
the United Kingdom) [20]. ese results were primarily
driven by assuming superior accuracy for CCTA (sensi-
tivity of 0.98 and specificity of 0.89) [3436] compared
to CMR (0.89 and 0.76, respectively) [37]. Model-based
cost-effectiveness studies by Walker etal. and Ge et al.
used similar model structures as applied by Genders
etal., but with CMR operating characteristics based on
more recent trials and meta-analyses [7, 8, 38]. With this
approach CMR was cost-effective compared to other
imaging strategies such as ICA [13, 14], SPECT [13, 14],
and CCTA (with CT-derived fractional flow reserve) [13].
ree studies found unclear cost-effectiveness among
the imaging strategies analyzed [23, 28, 30], including
two studies that were cost-effectiveness analyses con-
ducted alongside a randomized controlled trial, which
were not powered to show statistically significant differ-
ences in cost-effectiveness outcomes [28, 30]. Campbell
etal. developed a simulation model that included posi-
tron emission tomography (PET) for the United Kingdom
health care payer perspective and found in probabilistic
sensitivity analyses that there were similar probabilities
of CMR or PET being optimal in the relevant cost-effec-
tiveness threshold ranges for the United Kingdom [23].
Across the 15 studies included in our review, nine
found that underlying prevalence of CAD was a key
driver of the cost-effectiveness findings [13, 14, 20, 22,
24, 26, 29, 30, 32], and four found that these results were
sensitive to changes in imaging prices [21, 24, 26, 28]. In
our sensitivity analysis restricting inclusion to cost-per-
QALY or cost-per-life-year studies, 8/13 found CMR to
be cost-effective [13, 14, 22, 2527, 31, 32], 3/13 showed
uncertain cost-effectiveness rankings [23, 28, 30], and
2/13 concluded that CCTA was superior [20, 21]. We did
not find evidence that CMR was more or less likely to be
cost-effective across settings; among the 5 studies that
were performed for the US setting [13, 20, 24, 28, 29],
3 found that CMR was cost-effective [13, 24, 29], which
was similar to the proportion of all studies that found
CMR to be cost-effective (10 out of 15).
CMR cost eectiveness calculator (meta‑model) results
e fitted coefficients for the lifetime discounted QALY
and cost results for ‘No Imaging’ strategy, and incre-
mental QALY and cost results for the ‘CMR’, ‘CCTA,
‘SPECT’, and ‘Immediate ICA’ strategies are shown in
Appendix Table3. Validation using the test sets showed
good fits for each model, with r-squared values ranging
from 0.846 to 0.999 across the ten meta-models, with
percentage deviation varying from 0.117% to 0.028%,
which are also shown in Appendix Table 3. Table 2
shows the cost-effectiveness analysis results of our
meta-model compared to: (1) the model originally used
to derive it (i.e., a replication of the Ge etal. cost-effec-
tiveness results using our meta-model); (2) the Genders
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Pandyaetal. Journal of Cardiovascular Magnetic Resonance (2022) 24:1
etal. study that concluded that CCTA was more cost-
effective than CMR in the US (i.e., a replication of
the Genders etal. cost-effectiveness results using our
meta-model); (3) and meta-model results using key
input values from the Genders et al. paper (such as
CMR and CCTA sensitivity and specificity, CMR and
CCTA costs, underlying prevalence of CAD, and other
selected inputs) with Ge etal. model inputs for all other
inputs (see Appendix Table4 for full list) [13, 20].
We were able to use the meta-model to closely replicate
the incremental cost-effectiveness results for CCTA com-
pared to a ‘No Imaging’ strategy and to CMR (Table2);
when the Genders etal. model inputs values were used,
CCTA dominated CMR (i.e. CCTA had greater QALYs
and lower costs) [20]. When the Ge et al. model input
values were used, CMR dominated CCTA, replicating the
cost-effectiveness comparisons from the Ge etal. analy-
sis [13, 20]. When the meta-model was fed with Genders
etal. inputs except for the updated sensitivity and speci-
ficity values as used by Ge etal., CMR dominated CCTA
(Table2, Figs.1 and 2). CMR would have to cost 182% of
our base-case estimate ($807 to $1465) before the CCTA
strategy would be considered more cost-effective (using
a willingness-to-pay of $100,000 per QALY). Appendix
Table5 shows other threshold values for imaging perfor-
mance, prevalence, and age inputs for CMR and the other
imaging strategies we evaluated using the meta-model.
Discussion
Our systematic literature review found that most (62%)
studies formally evaluating the cost-effectiveness of CMR
compared to other relevant imaging options for patients
with suspected CAD world-wide concluded that:
CMR-based diagnostic strategies produced health at
reasonable value compared to setting-specific cost-
effectiveness thresholds.
When CMR is not available, CCTA represents a
cost-effective alternative compared to a no imaging
strategy or immediate ICA strategy, which is consist-
ent with current recommendations of major interna-
tional cardiac societies [39].
ese cost-effectiveness outcomes depend on the oper-
ating characteristics of the imaging modality of interest
and the underlying prevalence of CAD in the population
of interest. It is therefore of importance for readers of
such cost-effectiveness analyses to scrutinize the sources
of these key variables when interpreting the resulting
cost-effectiveness outcomes. Publications which con-
cluded that CCTA dominated CMR used inputs in the
cost-effectiveness models which were outside the range
of the majority of reported results. We also found that
relying on a single data source to estimate the cost-effec-
tiveness of CMR, such as a cost-effectiveness analysis
conducted alongside a clinical trial, could lead to uncer-
tain results (e.g. when such trials are underpowered for
cost and QALY outcomes).
Considering uncertain and setting-specific inputs that
could drive cost-effectiveness results, we developed a
CMR cost-effectiveness calculator that end-users (phy-
sicians, hospital decision-makers, guidelines writers,
Table 2 Published and meta‑model lifetime discount quality‑
adjusted life year, costs, and cost‑effectiveness results for
selected imaging strategies and scenarios
A CMR sensitivity = 0.89, CMR specicity = 0.87, CMR cost = $807, CCTA
sensitivity = 0.90, CCTA specicity = 0.71, CCTA cost (includes FFR for some
patients) = $981, age = 62.5years, coronary angiography cost = $3941,
CABG cost = $38,979, PCI cost = $36,556, prevalence of coronary artery
disease = 32.4%, propor tion male = 53%
B CMR sensitivity = 0.89, CMR specicity = 0.76, CMR cost = $621, CCTA
sensitivity = 0.98, CCTA specicity = 0.89, CCTA cost = $372, age = 60years,
coronary angiograph cost = $2989, CABG cost = $38,217, PCI cost = $6529,
prevalence of coronary artery disease = 30.0
C CMR sensitivity = 0.89, CMR specicity = 0.87, CMR cost = $621, CCTA
sensitivity = 0.90, CCTA specicity = 0.71, CCTA cost = $372, age = 60years,
coronary angiograph cost = $2989, CABG cost = $38,217, PCI cost = $6529,
prevalence of coronary artery disease = 30.0
Strategy Costs QALYs ICER
Published Ge et al. [13] results
No imaging $16,936 12.721 Reference
CMR $19,273 12.765 $52,000/QALY
CC TA $19,886 12.765 Dominated by CMR
Meta‑model with Ge et al. [13] inputsA (replication of Ge et al. [13])
No imaging $19,223 12.160 Reference
CMR $21,962 12.204 $63,000/QALY
CC TA $22,578 12.203 Dominated by CMR
Published Genders et al. [20] results for men
No imaging $6827 11.062 Reference
CC TA $13,177 11.840 $29,000/QALY
CMR $14,172 11.840 Dominated by CCTA
Published Genders et al. [20] results for women
No imaging $7506 12.110 Reference
CC TA $14,109 12.340 $29,000/QALY
CMR $15,198 12.330 Dominated by CCTA
Meta‑model with selected Genders et al. [20] inputsB (replication of
Genders et al. [20])
No imaging $12,549 12.844 Reference
CC TA $14,487 12.889 $42,000/QALY
CMR $14,289 12.884 Dominated by CCTA
Meta‑model with selected Genders et al. [20] inputs except Ge et al.
sensitivity/specificityC
No imaging $12,549 12.844 Reference
CMR $13,997 12.885 $34,000/QALY
CC TA $14,823 12.885 Dominated by CMR
Page 7 of 14
Pandyaetal. Journal of Cardiovascular Magnetic Resonance (2022) 24:1
payers, health economist researchers) can use to approxi-
mate setting-specific cost-effectiveness results. e abil-
ity to iteratively generate these estimates is crucial given
the importance of weighting potential tradeoffs between
length of life, quality of life, and local costs across imaging
modalities available to physicians aiming to diagnosis and
Fig. 1 Meta‑model lifetime discounted incremental net monetary benefit results (compared to ‘No Imaging’); higher incremental net monetary
benefit indicates better cost effectiveness profile. Net Monetary Benefit (NMB) is a single metric that monetizes QALYs (using willingness‑to‑pay
of $100,000/QALY) and subtracts costs. CMR cardiovascular magnetic resonance, CCT coronary computed tomography angiography, ICA invasive
coronary angiography; Genders et al. [20]; Ge et al. [13]
Fig. 2 Meta‑model lifetime discounted quality‑adjusted life year and cost results (compared to ‘No Imaging’). Squares indicate CMR, circles indicate
CCT, colors indicate meta‑model input sources, arrows represent comparisons of CMR vs. CCT for a given inputs source (strategies to the bottom
and right to their comparators have higher quality adjusted life years (QALYs) and lower costs, i.e. they are dominant strategies), dotted lines
represent cost‑effectiveness thresholds (strategies below cost‑effectiveness thresholds are good value compared to ‘No Imaging’)
Page 8 of 14
Pandyaetal. Journal of Cardiovascular Magnetic Resonance (2022) 24:1
treat ischemic heart disease, and the setting-specific and
evolving nature of these inputs. e calculator also allows
to determine strategies, which allow to achieve diagnosis
at the lowest possible cost, or to treat significant CAD with
the lowest cumulative cost of care. As expected, changing
the sensitivity and specificity of CMR or CCTA changes
the relative cost effectiveness of each of these modalities.
Our cost-effectiveness calculator allows users to update
these inputs as newer evidence and meta-analyses are
published. Our replication and adaptation of the Gen-
ders etal. study showed this explicitly. Other key drivers
of CMR cost-effectiveness, such as disease prevalence and
imaging or treatment costs, can also be setting-specific,
and we found there are many settings (almost all non-US/
non-European countries, with the exceptions of one study
for Brazil and one study for Australia) without any formal
cost-effectiveness analyses published. Users can use our
tool world-wide to better align their local understanding of
model inputs to cost-effectiveness results.
Our study focused on cost-per-QALY or cost-per-life-
year-saved studies, but there are other types of economic
evaluation studies beyond formal cost-effectiveness analy-
ses (such as cost-minimization analyses) that we identi-
fied comparing CMR to other imaging strategies used to
diagnose CAD [40]. Data from the European CMR registry
[41], which contains data from 59 medical centers across 18
countries, were used for two such analyses that compared
CMR to ICA-based strategies; these studies both found
that the CMR-based strategy would result in cost savings
compared to inpatient ICA, driven by the costs differences
between strategies and reduced revascularization proce-
dures in the CMR-based strategies [42, 43]. A randomized
controlled trial assigning 109 patients to either a CMR
observation unit arm or usual inpatient care arm found
that the CMR-based care arm reduced cardiac-related
costs during the hospitalization and over the first year post-
discharge [44]. ese studies were not included in our lit-
erature review as they did not include a QALY or life-year
effectiveness measure, but they add to our overall study
conclusion that CMR can represent high- or intermediate-
value care, or even produce cost-savings, depending on the
imaging strategy CMR is being compared to. Future clini-
cal studies providing sex-specific inputs (on imaging per-
formance, for instance) could also help reveal whether the
cost-effectiveness of CMR differs for men and women.
Limitations
Our study has limitations that should be noted. For our
systematic literature review, we were limited to the exist-
ing published literature. ere is publication bias in terms
of what settings cost-effectiveness of CMR studies are per-
formed for (with US, Germany, and the United Kingdom
relatively overrepresented), and there is the possibility of
financial or non-financial bias in the study authors that
could affect which model inputs they choose (thus affect-
ing model results). Our cost-effectiveness calculator tool
can somewhat mitigate this bias, if users have non-biased
inputs they believe would better reflect the current state of
the evidence or their local populations or settings. e cost-
effectiveness calculator is based on a meta-model of a larger
Markov model, and therefore the calculator contains many
model limitations of the larger model in addition to its own
imperfect ability to replicate the model results. To overcome
this limitation, we performed a model validation analysis on
the meta-model using data that were specifically not used to
generate the model. Our meta-model was also limited to the
strategies evaluated in the larger Markov model [13], which is
why echocardiography is not included as a comparator in the
meta-model. Due to data limitations, we also did not model
potential side effects from contrast agents used for CCTA or
CMR or radiation exposure from CCTA, which would have
amplified our lifetime discount QALY meta-model results
comparing CCTA to CMR [45]. Finally, our review and
cost calculator focus on cost-effectiveness outcomes, which
might not capture all dimension that are relevant for health
policy or clinical decision-making, missing elements such as
equity and patients’ preference considerations.
Despite those limitations, cost-effectiveness analysis
represents the field’s best attempt to systematically and
fairly quantify the value of health care interventions, a
notion supported by the 2014 ACC-AHA policy state-
ment that uses cost-effectiveness metrics to differentiate
high-value care (health gains are worth their costs) from
low-value care (prices should be lowered to be in line
with the health gains that are produced by the interven-
tion of interest) [12].
Conclusions
e majority of cost-effectiveness evidence evaluating
CMR-based diagnostic strategies for patients with sus-
pected CAD identify CMR to be a cost-effective imag-
ing strategy, delivering high-value care in many settings.
When comparing diagnostic techniques, the optimal strat-
egy depends on factors that change over time and across
clinical settings, such as imaging performance, imaging
costs, and disease prevalence. erefore, decision-makers
should contextualize existing literature with additional
information, whether from their local data when available
or through use of a tool like the cost-effectiveness calcula-
tor we present here. Such tools can assist in obtaining the
most realistic estimates of overall value from options avail-
able to diagnose and treat significant CAD.
Appendix
See Fig.3, Tables3, 4, 5.
Page 9 of 14
Pandyaetal. Journal of Cardiovascular Magnetic Resonance (2022) 24:1
Fig. 3 Search strategy and paper selection process
Page 10 of 14
Pandyaetal. Journal of Cardiovascular Magnetic Resonance (2022) 24:1
Table 3 Meta‑model inputs, regression coefficients, and performance metrics
Variable Meta‑model coecient
No imaging QALY No imaging cost Incremental
QALY CMR,
SPECT, CCTA
a
Incremental cost
CMR, SPECTaIncremental
cost CCTA
bIncremental
QALY immediate
ICAa
Incremental cost
immediate ICAa
Intercept 32.5468 1124.0687 0.0108 930.0 1257.9 0.0246 832.2
Age in years 0.2343 239.1547 0.0002 12.8481 13.0193 0.0001 14.6191
Probability of
patient having
CAD
4.0808 50,086.6 0.1366 4335.6 3826.9 n/a 1643.7
Sensitivity of CMR n/a n/a 0.0484 1749.6 n/a n/a n/a
Specificity of CMR n/a n/a 0.0055 2649.1 n/a n/a n/a
Sensitivity of CCTA n/a n/a n/a n/a 1797.7 n/a n/a
Specificity of CCTA n/a n/a n/a n/a 2655.3 n/a n/a
Probability patient
with false negative
result returns to
get coronary angi‑
ography within
1 year
0.0958 4034.8 0.0963 3562.4 3595.5 0.1082 4014.4
Annual rate of hav‑
ing a new MACE
event for patients
without CAD
75.8 287,992.6 n/a n/a n/a 0.0768 n/a
Post‑1st year
rate of having a
MACE event for
patients with CAD
who received
revascularization
procedure
13.8625 29,804.1 0.2165 16,366.8 16,579.2 0.2434 18,378.7
Hazard rate ratio
for patients who
received medical
therapy and
revascularization
procedure (natural
log)
0.2203 528.3 0.1499 577.3 587.7 0.1685 645.2
Probability of
dying during PCI
n/a n/a 0.2742 n/a n/a 0.3074 n/a
Probability of
dying during CABG
n/a n/a 0.1148 n/a n/a 0.1263 n/a
Cost of CMR (or
SPECT)
n/a n/a n/a 0.9987 n/a n/a n/a
Cost of CCTA n/a n/a n/a n/a 1.000 n/a n/a
Cost of FFR‑CT
(added to CCTA for
some patients)
n/a n/a n/a n/a 0.4068 n/a n/a
Cost of immediate
coronary angiogra‑
phy with FFR
n/a n/a n/a 0.0534 0.0982 n/a 0.2967
Cost of immediate
coronary angiogra‑
phy without FFR
n/a n/a n/a 0.0775 0.1416 n/a 0.4279
Cost of CABG n/a 0.0851 n/a 0.0131 0.0133 n/a 0.0147
Cost of PCI n/a 0.1901 n/a 0.0294 0.0300 n/a 0.0331
Acute (1st‑year)
cost of non‑fatal
MACE
n/a 0.1817 n/a 0.0034 0.0038 n/a 0.0045
Acute (1st‑year)
cost of fatal MACE
n/a 0.1233 n/a 0.0024 0.0021 n/a 0.0032
Page 11 of 14
Pandyaetal. Journal of Cardiovascular Magnetic Resonance (2022) 24:1
n/a = not included (not beta coecient not signicant at an alpha = 0.05 level)
a Incremental compared to ‘No Imaging’
b Incremental compared to ‘No Imaging’; the same coecients were used for incremental CMR, CTTA, and SPECT QALYs, given the exact same model structure and
type of model inputs
c Incremental compared to ‘No Imaging’; this meta-model (costs for CCTA) had dierent model inputs due to some patients receiving FFR (not the case for CMR or
SPECT costs)
Table 3 (continued)
Variable Meta‑model coecient
No imaging QALY No imaging cost Incremental
QALY CMR,
SPECT, CCTA
a
Incremental cost
CMR, SPECTaIncremental
cost CCTA
bIncremental
QALY immediate
ICAa
Incremental cost
immediate ICAa
Utility of coronary
heart disease
health state
1.0242 n/a 0.0277 n/a n/a 0.0312 n/a
Annual discount
rate for costs and
QA LYs
126.8 82,131.3 0.0108 5653.8 5623.4 0.4461 6349.3
Meta model
performance No imaging
QA LY No imaging
Cost Incremental
QALY CMRbIncremental
cost CMRcIncremental
cost CCTA
aIncremental
QALY
immediate ICAa
Incremental
cost immediate
ICAa
External valida‑
tion r‑squared 0.9757 0.9133 0.9757 0.9982 0.9861 0.9999 0.9858
External valida‑
tion deviation
compared to
Markov model
0.046% 0.025% 0.048% to
0.064%
0.031% to
0.018% 0.007% 0.001% 0.040%
Table 4 Comparison of selected model inputs from Ge et al. [13] and Genders et al. [20]
AHRQ Agency for Healthcare Research and Quality, CABG coronary artery bypass grafting, CAD coronary artery disease, CCTA coronary computed tomographic
angiography, CMR cardiovascular magnetic resonance, CPT current procedural terminology, ICA invasive coronary angiography, FFR fractional ow reserve, ICER
incremental cost-eectiveness ratio, MACE major adverse cardiovascular event(s), MI myocardial infarction, PCI percutaneous coronary intervention, QALY quality-
adjusted life year, SPECT single-photon emission computed tomography, XCA x-ray coronary angiography
Variable Genders 2015 value [20] Genders source(s) Ge 2020 value [13] Ge source(s)
CMR sensitivity 0.89 Jaarsma et al. [37] 0.89 Knuuti et al. [8]
CMR specificity 0.76 Jaarsma et al. [37] 0.87 Knuuti et al. [8]
CMR cost $621 CPT 75563 and 93015 $807 CMS
CCTA sensitivity 0.98 Mowatt et al. [34], Scheutz
et al. [36], von Ballmoos et al.
[35]
0.90 Danad et al. [7]
CCTA specificity 0.89 Mowatt et al. [34], Scheutz
et al. [36], von Ballmoos et al.
[35]
0.71 Danad et al. [7]
CCTA cost (includes FFR for
some patients) $372 CPT 75574 $981 CMS
Age (years) 60 Assumption 62.5 Kwong et al. [46]
ICA cost $2989 CPT 93454 $3941 CMS
CABG cost $38,217 AHRQ 2011 $38,797 O’Sullivan et al. [47]
PCI cost $6529 CPT 92980 $36,556 O’Sullivan et al. [47]
Prevalence of CAD 0.30 Nieman et al. [48] 32.4% Kwong et al. [46]
Proportion male Sex‑specific analyses Not applicable 53% Kwong et al. [46]
Page 12 of 14
Pandyaetal. Journal of Cardiovascular Magnetic Resonance (2022) 24:1
Abbreviations
ACC : American College of Cardiology; AHA: American Heart Association;
AHRQ: Agency for Healthcare Research and Quality; CABG: Coronary artery
bypass grafting; CAD: Coronary artery disease; CCTA : Coronary computed
tomographic angiography; CMR: Cardiovascular magnetic resonance; CPT:
Current procedural terminology; ESC: European Society of Cardiology; FFR:
Fractional flow reserve; ICA: Invasive coronary angiography; ICER: Incremental
cost‑effectiveness ratio; MACE: Major adverse cardiovascular event(s); MI:
Myocardial infarction; PCI: Percutaneous coronary intervention; PET: Positron
emission tomography; QALY: Quality‑adjusted life year; SPECT: Single‑photon
emission computed tomography; XCA: X‑ray coronary angiography.
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s12968‑ 021‑ 00833‑1.
Additional le1. Users can enter model inputs that relate to their popu‑
lation of interest (e.g., age, probability of patient having treatable CAD),
imaging strategies (sensitivity, specificity, cost), treatments (e.g., effective‑
ness, risks, and costs of revascularization procedures), and other variables
(e.g., willingness‑to‑pay for health, discount rate), to then see how these
collection of inputs translates to lifetime per‑person net monetary benefit
(i.e., an overall metric of value using cost‑effectiveness analysis), lifetime
discounted quality‑adjusted life years (i.e., the main measure of effective‑
ness), and lifetime discounted costs. Default input values for the US are
provided in the model file.
Acknowledgements
CBD is in part supported by the NIHR Biomedical Research Centre at University
Hospitals Bristol NHS Foundation Trust and the University of Bristol. The views
expressed in this publication are those of the author(s) and not necessarily
those of the NHS, the National Institute for Health Research or the Department
of Health and Social Care.
Authors’ contributions
AP and SK designed the study. AP and YY conducted the literature review.
AP, YY, EN, JS, OPS, CD, LMS, and SK interpreted the results from the literature
review. AP, YG, and RK constructed the simulation model used to create the
meta‑model. AP programmed the meta‑model. AP, YY, and SK drafted the
manuscript. All authors revised the final manuscript. All authors read and
approved the final manuscript.
Funding
This work was funded in part by a research grant provided by Novartis to S.
Kelle at the German Heart Institute Berlin, Germany.
Availability of data and materials
The data used to calculate all meta‑model results is contained in the “CMR CEA
Calculator_Demo” Excel file, which is included as a Additional file for this paper
and at: https:// docs. google. com/ sprea dshee ts/d/ 12TMg bIS6s DpXka fSYfN
aNw8v Sx‑ LCTCE 4K_ yAa7m dyY/ edit? usp= shari ng.
Declarations
Ethics approval and consent to participate
Not needed as this research only involved data from published sources and
simulation modeling of hypothetical populations (i.e., no human subjects
research).
Consent for publication
Not applicable.
Competing interests
S. Kelle receives research funding by Novartis and Philips Healthcare. S. E.
Petersen provides consultancy to and is a shareholder of Circle Cardiovascu‑
lar Imaging Inc, Calgary, Canada. J. Schwitter and E. Nagel receive research
funding by Bayer Healthcare. J. White is a shareholder of Cohesic Inc. S. Kelle
and E. Nagel are supported by the DZHK (German Centre for Cardiovascular
Research) and by the BMBF (German Ministry of Education and Research).
CBD is the Chief Executive Officer (part time) of the Society for Cardiovascular
Magnetic Resonance (SCMR). JC was president of the Society for Cardiovascu‑
lar Magnetic Resonance (SCMR) at the time this work was performed, receives
research support from Bayer, Siemens, Guerbet, advisory board/lectures for
Bayer, Siemens, Bracco. The other co‑authors have no disclosures.
Author details
1 Department of Health Policy and Management, Harvard T.H. Chan School
of Public Health, 718 Huntington Ave, 2nd Floor, Boston, MA 02115, USA.
2 National Taiwan University Hospital, Taipei, Taiwan. 3 Cardiovascular Division
of the Department of Medicine, Brigham and Women’s Hospital, Boston, MA,
USA. 4 Institute for Experimental and Translational Cardiovascular Imaging,
DZHK (German Centre for Cardiovascular Research) Centre for Cardiovascular
Imaging, Partner Site RheinMain, University Hospital Frankfurt/Main, Frankfurt
am Main, Germany. 5 Department of Cardiology, National Heart Institute,
Kuala Lumpur, Malaysia. 6 Department of Radiology, Virginia Commonwealth
University Medical Center, Main Hospital, Richmond, VA, USA. 7 Department
of Neurology, Weill Cornell Medicine/NewYork‑Presbyterian Hospital, New
Table 5 Threshold values for selected meta‑model inputs where the net monetary benefit (i.e., overall value quantified as monetized
QALYs at $100,000/QALY minus costs) of the CMR strategy is equal to a competing strategy (Ge 2020 value represents base‑case model
input value)
CAD coronary artery disease, CCTA coronary computed tomographic angiography, CMR cardiovascular magnetic resonance, SPECT single-photon emission computed
tomography, QALY quality-adjusted life year, XCA x-ray coronary angiography
a Threshold value for this model input parameter that sets net monetary benet (i.e., value) of CMR strategy equal to this strategy. “None” indicates not threshold value
result exists for this parameter (i.e., CMR strategy is optimal over full range of possible values holding all other model inputs constant). Parentheses indicate percent of
base-case value used be Ge etal. [13]
Variable Base‑case values in meta‑model Calculated threshold values to be equivalent to CMR strategy
Ge 2020 value
[13]Ge source(s) No imagingaCCTA
aSPECTaXCAa
CMR sensitivity 0.89 Knuuti et al. [8] 0.37 (42%) 0.68 (76%) 0.55 (62%) 0.34 (38%)
CMR specificity 0.87 Knuuti et al. [8] 0.37 (43%) 0.66 (76%) 0.55 (63%) 0.34 (39%)
CMR cost $807 CMS $2420 (300%) $1465 (182%) $1840 (228%) $2522 (313%)
Age (years) 62.5 Kwong et al. [46] None None None None
Prevalence of CAD 32.4% Kwong et al. [46] 15% (47%) None None 66% (204%)
Page 13 of 14
Pandyaetal. Journal of Cardiovascular Magnetic Resonance (2022) 24:1
York, NY, USA. 8 Department of Medicine, University of Cape Town & Groote
Schuur Hospital, Cape Town, South Africa. 9 William Harvey Research Institute,
NIHR Barts Biomedical Research Centre, Queen Mary University of London,
London, UK. 10 Department of Cardiovascular Medicine, Mayo Clinic, Rochester,
MN, USA. 11 Division of Cardiology, Cardiovascular Department, CMR Center
University Hospital, Lausanne, Switzerland. 12 Faculty of Biology and Medicine,
University of Lausanne, UniL, Lausanne, Switzerland. 13 Department of Medi‑
cine, School of Medicine and Public Health, Flinders University, Adelaide,
Australia. 14 Department of Hear t Health, South Australian Health and Medical
Research Institute, Adelaide, Australia. 15 Division of Cardiology, Department
of Cardiac Sciences, Stephenson Cardiac Imaging Centre, University of Calgary,
Calgary, Canada. 16 Department of Radiology, Feinberg School of Medicine,
Northwestern University, Chicago, IL, USA. 17 Krannert Institute of Cardiology,
Indiana University School of Medicine, Indianapolis, IN, USA. 18 Departments
of Internal Medicine and Radiology, The Ohio State University, Columbus,
OH, USA. 19 Bristol Heart Institute, Bristol National Institute of Health Research
(NIHR) Biomedical Research Centre, University Hospitals Bristol NHS Trust
and University of Bristol, Bristol, UK. 20 Cardiovascular Division, Department
of Cardiology, American British Cowdray Medical Center, Mexico City, Mexico.
21 Cardiovascular Division and Penn Cardiovascular Institute, Perelman School
of Medicine, University of Pennsylvania Medical Center, Philadelphia, PA, USA.
22 Department of Imaging, Fortis Escorts Heart Institute, New Delhi, India.
23 Department of Internal Medicine and Cardiology, Charité – Universitäts‑
medizin Berlin, Campus Virchow Klinikum, Berlin, Germany. 24 Department
of Internal Medicine and Cardiology, DZHK (German Centre for Cardiovascular
Research), Partner Site Berlin, German Heart Institute Berlin (DHZB), Berlin,
Germany.
Received: 12 July 2021 Accepted: 30 November 2021
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... In volume 24 of the Journal of Cardiovascular Magnetic Resonance there were 3 articles [1][2][3] with an incorrect affiliation for Chiara Bucciarelli-Ducci. The 3 incorrect and correct affiliations are listed below: The original articles have been updated. ...
Article
(1) Background: The impact of imaging-derived ischemia is still under debate and the role of stress perfusion cardiac magnetic resonance (spCMR) in non-high-risk patient still needs to be clarified. The aim of this study was to evaluate the impact of spCMR in a case series of stable long-standing chronic coronary syndrome (CCS) patients with ischemia and no other risk factor. (2) Methods: This is a historical prospective study including 35 patients with history of long-standing CCS who underwent coronary CT angiography (CCTA) and additional adenosine spCMR. Clinical and imaging findings were included in the analysis. Primary outcomes were HF (heart failure) and all major cardiac events (MACE) including death from cardiovascular causes, myocardial infarction, or hospitalization for unstable angina, or resuscitated cardiac arrest. (3) Results: Mean follow-up was 3.7 years (IQR: from 1 to 6). Mean ejection fraction was 61 ± 8%. Twelve patients (31%) referred primary outcomes. Probability of experiencing primary outcomes based on symptoms was 62% and increased to 67% and 91% when multivessel disease and ischemia, respectively, were considered. Higher ischemic burden was predictive of disease progression (OR: 1.59, 95%CI: 1.18-2.14; p-value = 0.002). spCMR model resulted non inferior to the model comprising all variables (4) Conclusions: In vivo spCMR-modeling including perfusion and strain anomalies could represent a powerful tool in long-standing CCS, even when conventional imaging predictors are missing.
Article
Full-text available
Objective To assess the cost-effectiveness of management strategies for patients presenting with chest pain and suspected coronary heart disease (CHD): (1) cardiovascular magnetic resonance (CMR); (2) myocardial perfusion scintigraphy (MPS); and (3) UK National Institute for Health and Care Excellence (NICE) guideline-guided care. Methods Using UK data for 1202 patients from the Clinical Evaluation of Magnetic Resonance Imaging in Coronary Heart Disease 2 trial, we conducted an economic evaluation to assess the cost-effectiveness of CMR, MPS and NICE guidelines. Health outcomes were expressed as quality-adjusted life-years (QALYs), and costs reflected UK pound sterling in 2016–2017. Cost-effectiveness results were presented as incremental cost-effectiveness ratios and incremental net health benefits overall and for low, medium and high pretest likelihood of CHD subgroups. Results CMR had the highest estimated QALY gain overall (2.21 (95% credible interval 2.15, 2.26) compared with 2.07 (1.92, 2.20) for NICE and 2.11 (2.01, 2.22) for MPS) and incurred comparable costs (overall £1625 (£1431, £1824) compared with £1753 (£1473, £2032) for NICE and £1768 (£1572, £1989) for MPS). Overall, CMR was the cost-effective strategy, being the dominant strategy (more effective, less costly) with incremental net health benefits per patient of 0.146 QALYs (−0.18, 0.406) compared with NICE guidelines at a cost-effectiveness threshold of £15 000 per QALY (93% probability of cost-effectiveness). Results were similar in the pretest likelihood subgroups. Conclusions CMR-guided care is cost-effective overall and across all pretest likelihood subgroups, compared with MPS and NICE guidelines.
Article
Full-text available
Metamodels can be used to reduce the computational burden associated with computationally demanding analyses of simulation models, though applications within health economics are still scarce. Besides a lack of awareness of their potential within health economics, the absence of guidance on the conceivably complex and time-consuming process of developing and validating metamodels may contribute to their limited uptake. To address these issues, this paper introduces metamodeling to the wider health economic audience and presents a process for applying metamodeling in this context, including suitable methods and directions for their selection and use. General (i.e., non-health economic specific) metamodeling literature, clinical prediction modeling literature, and a previously published literature review were exploited to consolidate a process and to identify candidate metamodeling methods. Methods were considered applicable to health economics if they are able to account for mixed (i.e., continuous and discrete) input parameters and continuous outcomes. Six steps were identified as relevant for applying metamodeling methods within health economics, i.e. 1) the identification of a suitable metamodeling technique, 2) simulation of datasets according to a design of experiments, 3) fitting of the metamodel, 4) assessment of metamodel performance, 5) conduct the required analysis using the metamodel, and 6) verification of the results. Different methods are discussed to support each step, including their characteristics, directions for use, key references, and relevant R and Python packages. To address challenges regarding metamodeling methods selection, a first guide was developed towards using metamodels to reduce the computational burden of analyses of health economic models. This guidance may increase applications of metamodeling in health economics, enabling increased use of state-of-the-art analyses, e.g. value of information analysis, with computationally burdensome simulation models.
Article
Full-text available
Background: Stress cardiac magnetic resonance imaging (CMR) has demonstrated excellent diagnostic and prognostic value in single-center studies. Objectives: This study sought to investigate the prognostic value of stress CMR and downstream costs from subsequent cardiac testing in a retrospective multicenter study in the United States. Methods: In this retrospective study, consecutive patients from 13 centers across 11 states who presented with a chest pain syndrome and were referred for stress CMR were followed for a target period of 4 years. The authors associated CMR findings with a primary outcome of cardiovascular death or nonfatal myocardial infarction using competing risk-adjusted regression models and downstream costs of ischemia testing using published Medicare national payment rates. Results: In this study, 2,349 patients (63 ± 11 years of age, 47% female) were followed for a median of 5.4 years. Patients with no ischemia or late gadolinium enhancement (LGE) by CMR, observed in 1,583 patients (67%), experienced low annualized rates of primary outcome (<1%) and coronary revascularization (1% to 3%), across all years of study follow-up. In contrast, patients with ischemia+/LGE+ experienced a >4-fold higher annual primary outcome rate and a >10-fold higher rate of coronary revascularization during the first year after CMR. Patients with ischemia and LGE both negative had low average annual cost spent on ischemia testing across all years of follow-up, and this pattern was similar across the 4 practice environments of the participating centers. Conclusions: In a multicenter U.S. cohort with stable chest pain syndromes, stress CMR performed at experienced centers offers effective cardiac prognostication. Patients without CMR ischemia or LGE experienced a low incidence of cardiac events, little need for coronary revascularization, and low spending on subsequent ischemia testing. (Stress CMR Perfusion Imaging in the United States [SPINS]: A Society for Cardiovascular Resonance Registry Study; NCT03192891).
Article
Full-text available
Background: Coronary computed tomography angiography (CCTA) is now widely used in the diagnosis of coronary artery disease since it is a rapid, minimally invasive test with a diagnostic accuracy comparable to coronary angiography. However, to meet demands for increasing spatial and temporal resolution, higher x-ray radiation doses are required to circumvent the resulting increase in image noise. Exposure to high doses of ionizing radiation with CT imaging is a major health concern due to the potential risk of radiation-associated malignancy. Given its increasing use, a number of dose saving algorithms have been implemented to CCTA to minimize radiation exposure to "as low as reasonably achievable (ALARA)" without compromising diagnostic image quality. Objective: The purpose of this review is to outline the most recent advances and current status of dose saving techniques in CCTA. Method: PubMed, Medline, EMBASE and Scholar databases were searched to identify feasibility studies, clinical trials, and technology guidelines on the technical advances in CT scanner hardware and reconstruction software. Results: Sub-millisievert (mSv) radiation doses have been reported for CCTA due to a combination of strategies such as prospective electrocardiogram-gating, high-pitch helical acquisition, tube current modulation, tube voltage reduction, heart rate reduction, and the most recent novel adaptive iterative reconstruction algorithms. Conclusion: Advances in radiation dose reduction without loss of image quality justify the use of CCTA as a non-invasive alternative to coronary catheterization in the diagnosis of coronary artery disease.
Article
Full-text available
Aims: The purpose of this study is to assess the economic cost differences and the associated treatment resource changes between the developing coronary artery disease (CAD) diagnostic tool fast strain-encoded cardiac imaging (Fast-SENC) and the current commonly used stress test single-photon emission computed tomography (SPECT). Materials and methods: We first created a ‘payer perspective’ model consisting of long-term and short-term components that used a hypothetical cohort of patients of average age (60.8 years) presenting with chest pain and suspected CAD to assess cost-impact. We then built a cost impact model that assessed likely savings from a ‘hospital perspective’ from substituting Fast-SENC for a portion of SPECTs assuming an average number of annual SPECT tests performed in U.S. hospitals. Results: In the payer model, using Fast-SENC followed by coronary angiography (CA) and percutaneous coronary intervention (PCI) treatment when necessary is less costly than the SPECT method when considering both direct and indirect costs of testing. Expected costs of the Fast-SENC were between $2,510 and $2,632 per correct diagnosis, while expected costs for the SPECT were between $3,157 and $4,078. Fast-SENC reduced false positives by 50% and false negatives by 86%, generating additional cost savings. The hospital model showed total costs per CAD patient visit of $825 for SPECT and $376 for Fast-SENC. Limitations: Limitations of this study are that clinical data are sourced from other published clinical trials on how CAD diagnostic strategies impact clinical outcome, and that necessary assumptions were made which impact health outcomes. Conclusion: The lower cost, higher sensitivity and specificity rates, and faster, less burdensome process for detecting CAD patients make Fast-SENC a more capable and economically beneficial stress test than SPECT. The payer model and hospital model demonstrate an alignment between payer and provider economics as Fast-SENC provides monetary savings for patients and resource benefits for hospitals.
Article
Background Coronary artery disease (CAD) remains a major public health problem in Australia and globally. A variety of imaging techniques allow for both anatomical and functional assessment of CAD and selection of the optimal investigation pathway is challenging. Cardiovascular magnetic resonance (CMR) is not widely used in Australia, partly due to perceived cost and lack of Federal Government reimbursement compared to the alternative techniques. The aim of this study was to estimate the cost-effectiveness of different diagnostic strategies in identifying significant CAD in patients with chest pain suggestive of angina using the evidence gathered in the Clinical Evaluation of Magnetic Resonance Imaging in Coronary Heart Disease 2 (CE-MARC trial), analysed from the Australian health care perspective. Methods A decision analytic model coupled with three distinct Markov models allowed eight potential clinical investigation strategies to be considered; combinations of exercise electrocardiogram stress testing (EST), single-photon emission computed tomography (SPECT), stress CMR, and invasive coronary angiography (ICA). Costs were from the Australian health care system in Australian dollars, and outcomes were measured in terms of quality-adjusted life-years. Parameter estimates were derived from the CE-MARC and EUropean trial on Reduction Of cardiac events with Perindopril in patients with stable coronary Artery disease (EUROPA) trials, and from reviews of the published literature. Results The most cost-effective diagnostic strategy, based on a cost-effectiveness threshold of $45,000 to $75,000 per QALY gained, was EST, followed by stress CMR if the EST was positive or inconclusive, followed by ICA if the stress CMR was positive or inconclusive; this held true in the base case and the majority of scenario analyses. Conclusions This economic evaluation shows that an investigative strategy of stress CMR if EST is inconclusive or positive is the most cost-effective approach for diagnosing significant coronary disease in chest pain patients within the Australian health care system.
Article
Objectives The aim of this study was to compare, using results from the multicenter SPINS (Stress CMR Perfusion Imaging in the United States) study, the incremental cost-effectiveness of a stress cardiovascular magnetic resonance (CMR)–first strategy against 4 other clinical strategies for patients with stable symptoms suspicious for myocardial ischemia: 1) immediate x-ray coronary angiography (XCA) with selective fractional flow reserve for all patients; 2) single-photon emission computed tomography; 3) coronary computed tomographic angiography with selective computed tomographic fractional flow reserve; and 4) no imaging. Background Stress CMR perfusion imaging has established excellent diagnostic utility and prognostic value in coronary artery disease (CAD), but its cost-effectiveness in current clinical practice has not been well studied in the United States. Methods A decision analytic model was developed to project health care costs and lifetime quality-adjusted life years (QALYs) for symptomatic patients at presentation with a 32.4% prevalence of obstructive CAD. Rates of clinical events, costs, and quality-of-life values were estimated from SPINS and other published research. The analysis was conducted from a U.S. health care system perspective, with health and cost outcomes discounted annually at 3%. Results Using hard cardiovascular events (cardiovascular death or acute myocardial infarction) as the endpoint, total costs per person were lowest for the no-imaging strategy ($16,936) and highest for the immediate XCA strategy ($20,929). Lifetime QALYs were lowest for the no-imaging strategy (12.72050) and highest for the immediate XCA strategy (12.76535). The incremental cost-effectiveness ratio for the CMR-based strategy compared with the no-imaging strategy was $52,000/QALY, whereas the incremental cost-effectiveness ratio for the immediate XCA strategy was $12 million/QALY compared with CMR. Results were sensitive to variations in model inputs for prevalence of disease, hazard rate ratio for treatment of CAD, and annual discount rate. Conclusions Prior to invasive XCA, stress CMR can be a cost-effective gatekeeping tool in patients at risk for obstructive CAD in the United States. (Stress CMR Perfusion Imaging in the United States [SPINS] Study; NCT03192891