Next Article in Journal
Alterations in Implantation Genes and Dendritic Cells in Endometrial Samples After Antibiotic Treatment
Previous Article in Journal
Comparative Analysis of Ixekizumab Effectiveness with and Without Induction Therapy in Moderate-to-Severe Psoriasis: A Real-World Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 14
Issue 3
10.3390/jcm14030829
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Systematic Review

Evolution of Coronary Microvascular Dysfunction Prevalence over Time and Across Diagnostic Modalities in Patients with ANOCA: A Systematic Review

by
Aurelia Zimmerli
,
Adil Salihu
,
Panagiotis Antiochos
,
Henri Lu
,
Barbara Pitta Gros
,
Alexandre Berger
,
Olivier Muller
,
David Meier
* and
Stephane Fournier
*
Department of Cardiology, Lausanne University Hospital, University of Lausanne, 1011 Lausanne, Switzerland
*
Authors to whom correspondence should be addressed.
J. Clin. Med. 2025, 14(3), 829; https://doi.org/10.3390/jcm14030829
Submission received: 13 December 2024 / Revised: 20 January 2025 / Accepted: 23 January 2025 / Published: 27 January 2025
(This article belongs to the Section Cardiovascular Medicine)
Browse Figures
Versions Notes

Abstract

:
Background: A considerable number of patients with angina undergo invasive coronary angiography, which might reveal non-obstructive coronary arteries (ANOCA). In this setting, they might have coronary microvascular disease (CMD). Its prevalence significantly varies in the literature. This systematic review aims to document the prevalence of CMD over time according to the diagnostic modalities. Methods: A systematic literature review was conducted using PubMed, the Cochrane Library, and Embase, covering publications from inception to 1 May 2024. Among 1471 identified articles, 297 full-text articles were assessed for eligibility. All studies reporting the prevalence of CMD in ANOCA patients based on invasive coronary artery (ICA), positron emission tomography–computed tomography (PET-CT), transthoracic echocardiography (TTE), or cardiac magnetic resonance (CMR) were included. Results: The review included 53 studies (published between 1998 and 2024), encompassing a total of 16,602 patients. Of these studies, 23 used ICA, 15 used PET-CT, 8 used TTE, and 7 used CMR. A statistically significant increase in CMD prevalence over time was observed across all diagnostic modalities (p < 0.05), except for PET-CT, which showed a consistent and stable prevalence over time. Notably, the prevalence rates from all of the diagnostic methods converged towards the 50% prevalence detected by PET-CT. Conclusions: The prevalence of CMD in patients with ANOCA is subject to debate. However, the current data suggest that regardless of the diagnostic method used, the most recent studies tend to converge towards a prevalence value of 50%, which has been consistently reported by PET-CT from the beginning.

1. Introduction

A considerable proportion of patients experiencing chest pain referred to a catheterization laboratory for suspected coronary artery disease present with non-obstructive coronary artery [1]. The presence of angina and/or ischemia in patients with non-obstructive coronary arteries represents an entity called ANOCA and/or INOCA, respectively. Although obstructive ischemic heart disease remains the leading cause of mortality worldwide [2], the absence of obstructive coronary artery disease is not without consequence. Indeed, the diagnosis of angina/ischemia with non-obstructive coronary artery (ANOCA/INOCA) is associated with increased mortality and major adverse cardiac events (MACEs) [3] and impaired quality of life, and can lead to repeated coronary angiography [4,5,6]. There are different mechanisms implicated such as myocardial bridging, vasospastic angina (VSA), or coronary microvascular dysfunction (CMD).
While the methods and diagnostic criteria for VSA are well established, the same cannot be said for CMD. VSA is assessed invasively using intracoronary angiography (ICA) with the administration of intracoronary acetylcholine and the diagnosis is confirmed by the presence of an epicardial artery spasm of ≥90%, accompanied by ECG repolarization abnormalities and reproduction of the symptoms presented by the patient.
In contrast, evaluating coronary microcirculation is more complex due to the diversity of available diagnostic tools, which include both invasive and non-invasive methods, as well as a lack of standardized diagnostic criteria. The assessment of coronary microcirculation primarily involves measuring the coronary flow reserve (CFR), defined as the ratio of coronary blood flow during hyperemia to that measured at rest. CFR can be assessed through invasive coronary angiography (ICA) [7], considered as the gold standard, as well as through various non-invasive methods, including Doppler transthoracic echocardiography (TTE), positron emission tomography–computed tomography (PET-CT), and cardiovascular magnetic resonance (CMR). Among non-invasive procedures, PET-CT is considered as the gold standard [8,9,10]. Furthermore, variability in the recommended diagnostic cut-off for CFR, ranging from 2.0 to 2.5, depending on the guidelines, adds complexity to the diagnosis. In addition, one invasive method that also allows for the calculation of diagnostic criteria is the index of microvascular resistance (IMR), which can be elevated in the classical form of structural CMD but can be low in the functional form of CMD.
Indeed, the reported prevalence of CMD in the literature varies widely, likely due to differences in diagnostic modalities, each with varying sensitivities and specificities [11]. Additionally, advancements in diagnostic techniques over the years have likely improved diagnostic accuracy, whereas earlier studies may have used less sensitive methods, potentially underestimating the prevalence. Finally, the choice of CFR cut-off value, which can range between 2 and 2.5 depending on the study, also plays a significant role in influencing the diagnosis of CMD.
Understanding the prevalence of CMD in ANOCA/INOCA patients is crucial for better patient management. This systematic review aims to document the prevalence of CMD according to the diagnostic modalities used over time and to investigate how the choice of diagnostic cut-off values for CFR influences the reported prevalence of CMD in the identified studies.

2. Methods

This systematic literature review follows a prespecified research protocol registered on the international prospective register of systematic reviews (PROSPERO) (Evolution of Coronary Microvascular Dysfunction Prevalence Over Time and Across Diagnostic Modalities: A systematic Review; CRD42024579422). This study was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) criteria [12].

2.1. Literature Search and Selection Criteria

The systematic literature review was performed using the online databases PubMed, Embase, and The Cochrane Library, from inception to 1 May 2024. The following search terms were used: ((“coronary microvascular dysfunction”) OR (“coronary microvascular”) OR (“microvascular dysfunction”) OR (“coronary microvascular inflammation”) AND (prevalence)). The reference lists of included studies and relevant reviews were manually searched to identify additional relevant references.

2.2. Eligibility Criteria and Selection Process

Studies included in this review were written in English and had to fulfill the following criteria: (1) they were conducted on human subjects, (2) included participants aged ≥ 18 years, (3) reported the prevalence of CMD, and (4) utilized one of the following diagnostic methods: ICA, PET-CT, ETT, or CMR. Exclusion criteria included the following: (1) study design (conference abstracts, letters to the editor, reviews, meta-analyses, case reports, case series), (2) studies involving patients with obstructive coronary artery disease, (3) asymptomatic patients, and (4) recruitment period exceeding 20 years to minimize the impact of changes in diagnostic methods. In cases where the population overlapped, the study with the largest sample size or the most recent one was selected. Two reviewers independently identified the relevant studies (AZ and AS). In cases of disagreement, a third author (SF) made the final decision.

2.3. Quality Assessment and Data Extraction

The quality of the selected study was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2) [13]. The QUADAS-2 tool is recommended for evaluating diagnostic accuracy in systematic reviews. It allows for the assessment of the risk of bias and the applicability of results in both noncomparative and comparative studies. The risk of bias is evaluated across four domains: patient selection, index test, reference standard, and flow and timing. The two reviewers independently extracted data, including the following information for each study: first author’s name, year of publication, sample size, prevalence of CMD, diagnostic modalities used for assessment, and diagnostic cut-off to define CMD.

2.4. Statistical Analyses

Univariate linear regression models were used to model the evolution of CMD prevalence over time by classifying the prevalence observed in each study according to the publication year. This analysis was repeated with the 2 different cut-off values for CFR (≤2 vs. ≤2.5) and for each diagnostic modality, individually (ICA, PET-CT, TTE, and CMR). If the slope of the regression was significantly different from zero (p < 0.05), a temporal trend was considered present. Analyses and figures were generated using GraphPad Prism 10.1.2 (GraphPad Software, Inc., La Jolla, CA, USA).

3. Results

3.1. Article Selection

The flowchart is reported in Figure 1. Our initial search identified a total of 1471 articles, including 763 from PubMed, 679 from Embase, and 29 from The Cochrane Library. A total of 350 duplicates were identified. Of the remaining 1121 articles, 824 were excluded based on the title and abstract screening. Full-text analysis was performed on 297 studies, resulting in the identification of 43 articles eligible for inclusion [14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56]. Additionally, after reviewing the references of the included articles, 10 more were included [57,58,59,60,61,62,63,64,65,66]. Eventually, a total of 53 studies were included in this systematic review.

3.2. Quality Assessment

The risk of bias in the 53 included studies, assessed using the QUADAS-2 tool, is detailed in Table S1. Patient selection was the domain with the highest risk of bias, with 32 out of 53 studies identified as having a high risk of selection bias. The flow and timing domain followed, with 11 studies at high risk and 6 studies having unclear risk. In contrast, the index test and reference standard domains showed a low risk of bias across the included studies.

3.3. Study Characteristics and Prevalence of CMD

The 53 studies (published between 1998 and 2024) included a total of 16,602 patients with ANOCA/INOCA. CMD was assessed using ICA in 23 studies, using PET-CT in 15, using TTE in 8 and using CMR in 7. Regarding the studies based on ICA, 10 employed a Doppler guidewire [21,28,38,45,47,48,51,53,55,58], 11 used the bolus thermodilution [12,16,18,20,32,34,37,39,40,43,54], 1 used the continuous thermodilution [15], and 1 combined the use of Doppler guidewire and bolus thermodilution [19]. The sample sizes of the studies ranged from 11 to 2083 patients (Table 1). The reported prevalence of CMD across these studies showed significant variability, ranging from 22% to 100%.
Overall, a statistically significant increase in CMD prevalence over time across all diagnostic modalities was observed (p < 0.05, Figure 2). Depending on the diagnostic method, an increase in prevalence over time was observed for all modalities except for PET-CT, which showed a relatively stable prevalence over time of approximately 50% (p = 0.84). Notably, the prevalence rates from other diagnostic methods have been converging progressively towards the 50% prevalence of PET-CT.

3.4. The Prevalence According to the CFR Cut-Off (≤2.5 vs. ≤2)

Regarding the choice of the cut-off for CFR, 21 studies chose a CFR cut-off of ≤2.5 [14,17,21,23,25,30,37,38,42,44,47,49,50,55,56,57,58,60,61,62,65], 24 studies chose a cut-off of ≤2 [15,16,18,20,22,26,27,29,31,32,33,34,35,36,38,39,43,45,46,48,51,52,54,59], 4 studies used a different CFR cut-off [24,28,53,64], 2 studies used a qualitative cut-off [19,63], and 2 studies used only the index of microvascular resistance (IMR) [41,66].
The studies using a cut-off ≤2.5 were published between 1998 and 2024, whereas the studies using a cut-off ≤2 were published between 2008 and 2024. As expected, a trend toward a higher prevalence with a cut-off of ≤2.5 as compared to ≤2 was observed (50.1% and 45.1%, respectively, p = 0.07). Of interest, both cut-offs exhibited the same or similar trend to the one observed in general, with an increase in the prevalence of CMD over the years (p = 0.08 and p = 0.37, respectively, Figure 3).

4. Discussion

This systematic review revealed a significant increase in the prevalence of CMD reported in the literature from 1998 to 2024 (p < 0.05). When analyzing each modality individually, it becomes evident that the prevalence measured by TTE, CMR, and ICA has shown an upward trend, while for PET-CT, considered for a long time as the gold standard for non-invasive diagnosis, the prevalence has remained stable [67].

4.1. Impact of Diagnostic Modalities on CMD Prevalence over Time

This convergence towards a prevalence of approximately 50% can be attributed to significant advancements in both non-invasive and invasive diagnostic techniques, allowing for more detailed evaluation of coronary microcirculation over the years. For instance, TTE has seen notable improvements in spatial resolution, while CMR has benefited from enhanced spatial and temporal resolution in perfusion techniques as well as the development of protocols for better quantification of myocardial blood flow [68]. Similarly, coronary angiography has also evolved from using Doppler guides to assess blood flow [69] to the development of the thermodilution, initially with a bolus technique [70,71] and more recently with the continuous thermodilution technique, which is gradually being adopted and appears to be more precise [72,73,74,75]. However, in this systematic review, only one study utilized continuous thermodilution. It can be hypothesized that the prevalence of CMD measured using this method might be even higher due to its increased accuracy.
Moreover, the current increased interest in CMD (almost half of the studies in this systematic review were published in the last 4 years) may possibly be linked to a stronger belief that this diagnosis will be present in a patient, leading to particular attention being paid to the performance of the measurements and their interpretations, possibly carried out by expert centers and dedicated operators.

4.2. Comparative Advantage of Diagnostic Modalities

The criteria for CMD diagnosis are well established, relying on objective thresholds such as CFR ≤ 2.5 or elevated IMR values. However, the heterogeneity in CMD pathophysiology complicates its assessment, as structural and functional CMD can exhibit different diagnostic profiles. For instance, IMR values may remain normal in cases of functional CMD, despite significant microvascular dysfunction. Regarding the choice of diagnostic modalities, ICA provides a distinct advantage over other methods, allowing the measurement of microvascular resistance through the IMR, enabling a more detailed classification into CMD endotypes. Furthermore, for patients in whom VSA is also suspected, ICA allows for both conditions to be assessed in a single procedure, avoiding the need for multiple investigations. However, PET-CT has the significant advantage of measuring CFR across all three coronary territories, whereas ICA typically measures flow in only one territory. A recent study demonstrated that measuring flow in all three territories by ICA increases diagnostic accuracy in patients with ANOCA/INOCA compared to single-vessel testing [76].

4.3. Diagnostic Cut-Off Value for CFR

In the literature, it is well established that the cut-off value for CFR ≤ 2.5 is the current gold standard. However, many older studies have used a cut-off of 2. This shift in diagnostic cut-off values may be attributed to evolving recommendations, particularly with the publication of the European Association of Percutaneous Cardiovascular Intervention (EAPCI) expert consensus in 2020, which advocated for adopting a cut-off of ≤2 [1], whereas the recent European Society of Cardiology (ESC) guidelines for the management of chronic coronary syndrome recommend a cut-off of ≤2.5 [77]. When examining the mean prevalence of CMD based on the cut-off used, it is interesting to note that both cut-offs (CFR ≤ 2.5 and CFR ≤ 2.0) exhibit the same general trend, with an increase in the prevalence of CMD over the years (p = 0.08 and p = 0.37, respectively, as shown in Figure 3). While the EAPCI expert consensus advocates for a cut-off of ≤2, it is noteworthy that studies investigating the prognostic value of CFR are not unanimous in their findings. Studies that have shown a prognostic impact using a cut-off of ≤2.5 are primarily those based on invasive evaluation using Doppler guidewire [50]. In contrast, studies that have shown a prognostic impact with a cut-off of ≤2 have employed ICA thermodilution [78,79]. It is important to highlight that these studies not only included INOCA/ANOCA patients but also those who required revascularization. Our systematic literature review indicates that despite the increasingly frequent use of the ≤2 diagnostic cut-off, CMD prevalence reported in the literature has continued to rise over time. One could argue that this trend could be attributed to a potential increase in CMD, likely driven by the rising prevalence of cardiovascular risk factors in the population [80]. However, the stable prevalence reported in studies using PET-CT suggests that advancements in diagnostic methods are more likely responsible for the observed increase in reported prevalence over time. Future research should focus on assessing the prognostic value of these cut-offs, particularly in INOCA/ANOCA patients, using current gold-standard diagnostic methods such as PET-CT for non-invasive assessment or ICA for invasive evaluation.

5. Limitations

The primary limitation of this systematic review is related to the risk of bias, particularly regarding patient selection. There was a considerable degree of variability in the inclusion and exclusion criteria across the selected studies. Notably, the definition of obstructive coronary artery disease varied significantly between studies. Additionally, five studies [53,54,55,63,65] only included women, which also contributes to a selection bias. Furthermore, the only study that included patients with atrial fibrillation reported a 100% prevalence of CMD, although this study had the smallest sample size (n = 11). Finally, it is important to note that the prognostic implications of CFR extend beyond CMD and include other conditions such as dilative and hypertrophic cardiomyopathies [81].

Future Perspective

This literature review opens several future perspectives. First, determining a gold-standard diagnostic modality for CMD is inherently complex. Currently, several diagnostic modalities—PET-CT, ICA, TTE, and CMR—are used, often being compared against each other. However, a definitive study involving a single cohort of patients undergoing all of these modalities simultaneously is still lacking.
Invasive methods with CFR or IMR measurements are often considered the gold standard due to the theoretical precision allowed by their invasive aspect, but the fact that these methods are invasive limits their applicability in broader clinical practice. Second, regarding the invasive diagnosis of CMD, two methods are currently employed: bolus thermodilution and continuous thermodilution. Further studies are needed to determine which method provides the best sensitivity, specificity, and reproducibility. Third, the impact on prevalence when CMD is investigated in all of the coronary territories compared to a single one needs to be clarified. Additionally, the impact of CMD on morbidity, quality of life, and healthcare costs, as highlighted by a previous study, is significant. Thus, given the high prevalence of CMD in patients with angina and non-obstructive coronary arteries, a systematic evaluation of microcirculation in these patients might be routinely considered. Finally, future research must explore the effectiveness of current state-of-the-art treatments and their impact on coronary microcirculation.

6. Conclusions

The prevalence of CMD in patients with non-obstructive coronary artery disease has been debated for a long time, with reported rates showing significant variation. However, the current data suggest that recent studies increasingly converge towards a prevalence of around 50%, regardless of the diagnostic method used—a figure that has been consistently observed in PET-CT assessments since the earliest studies. Given the well-established association between CMD and an elevated risk of major adverse cardiac events, systematically evaluating coronary physiology in INOCA/ANOCA patients during coronary angiography may lead to improved outcomes. Such an approach could facilitate earlier diagnosis and the implementation of targeted treatments aimed at reducing MACE risk.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/jcm14030829/s1, Table S1: Quality assessment, risk of bias of the studies included.

Author Contributions

Conceptualization, S.F. and A.Z.; methodology, A.Z., D.M. and S.F.; software, A.Z. and S.F.; validation, all; formal analysis, A.Z., A.S., D.M. and S.F; investigation, A.Z., A.S. and S.F.; resources, S.F.; data curation, D.M. and S.F.; writing—original draft preparation, A.Z.; writing—review and editing, all; visualization, all; supervision, S.F.; project administration, S.F.; funding acquisition, S.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Abbreviations

CMDCoronary microvascular dysfunction
ANOCAAngor with non-obstructive coronary arteries
INOCAIschemia with non-obstructive coronary arteries
ICAInvasive coronary angiography
PET-CTPositron emission tomography–computed tomography
TTETransthoracic echocardiography
CMRCardiac magnetic resonance
CFRCoronary flow reserve

References

  1. Kunadian, V.; Chieffo, A.; Camici, P.G.; Berry, C.; Escaned, J.; Maas, A.; Prescott, E.; Karam, N.; Appelman, Y.; Fraccaro, C.; et al. An EAPCI Expert Consensus Document on Ischaemia with Non-Obstructive Coronary Arteries in Collaboration with European Society of Cardiology Working Group on Coronary Pathophysiology & Microcirculation Endorsed by Coronary Vasomotor Disorders International Study Group. Eur. Heart J. 2020, 41, 3504–3520. [Google Scholar] [PubMed]
  2. Khan, M.A.; Hashim, M.J.; Mustafa, H.; Baniyas, M.Y.; Al Suwaidi, S.; AlKatheeri, R.; Alblooshi, F.M.K.; Almatrooshi, M.E.A.H.; Alzaabi, M.E.H.; Al DArmaki, R.S.; et al. Global Epidemiology of Ischemic Heart Disease: Results from the Global Burden of Disease Study. Cureus 2020, 12, e9349. [Google Scholar] [CrossRef] [PubMed]
  3. Gdowski, M.A.; Murthy, V.L.; Doering, M.; Monroy-Gonzalez, A.G.; Slart, R.; Brown, D.L. Association of isolated Coronary Microvascular Dysfunction With Mortality and Major Adverse Cardiac Events: A systematic Review and Meta-Analysis of Aggregate Data. J. Am. Heart Assoc. 2020, 9, e014954. [Google Scholar] [CrossRef] [PubMed]
  4. Masi, S.; Rizzoni, D.; Taddei, S.; Widmer, R.J.; Montezano, A.C.; Luscher, T.F.; Schiffrin, E.L.; Touyz, R.M.; Paneni, F.; Lerman, A.; et al. Assessment and pathophysiology of microvascular disease: Recent progress and clinical implications. Eur. Heart J. 2021, 42, 2590–2604. [Google Scholar] [CrossRef]
  5. Taqueti, V.R.; Di Carli, M.F. Coronary Microvascular Disease Pathogenic Mechanisms and Therapeutic Options: J Am Coll Cardiol State-of-the-Art Review. J. Am. Coll. Cardiol. 2018, 72, 2625–2641. [Google Scholar] [CrossRef]
  6. Feher, A.; Sinusas, A.J. Quantitative Assessment of Coronary Microvascular Function: Dynamic Single-Photon Emission Computed Tomography, Positron Emission Tomography, Ultrasound, Computed Tomography, and Magnetic Resonance Imaging. Circ. Cardiovasc. Imaging 2017, 10, e006427. [Google Scholar] [CrossRef]
  7. Tonet, E.; Pompei, G.; Faragasso, E.; Cossu, A.; Pavasini, R.; Passarini, G.; Tebaldi, M.; Campo, G. Coronary Microvascular Dysfunction: PET, CMR and CT Assessment. J. Clin. Med. 2021, 10, 1848. [Google Scholar] [CrossRef]
  8. Radico, F.; Zimarino, M.; Fulgenzi, F.; Ricci, F.; Di Nicola, M.; Jespersen, L.; Min Chang, S.; Humphries, K.H.; Marzilli, M.; De Catherina, R. Determinants of long-term clinical outcomes in patients with angina but without obstructive coronary artery disease: A systematic review and meta-analysis. Eur. Heart J. 2018, 39, 2135–2146. [Google Scholar] [CrossRef]
  9. Jespersen, L.; Hvelplund, A.; Abildstrom, S.Z.; Pedersen, F.; Galatius, S.; Madsen, J.K.; Jorgensen, E.; Kelbaek, H.; Prescott, E. Stable angina pectoris with no obstructive coronary artery disease is associated with increased risks of major adverse cardiovascular events. Eur. Heart J. 2012, 33, 734–744. [Google Scholar] [CrossRef] [PubMed]
  10. Bugiardini, R.; Bairey Merz, C.N. Angina with “normal” coronary arteries: A changing philosophy. J. Am. Med. Assoc. 2005, 293, 477–484. [Google Scholar] [CrossRef] [PubMed]
  11. Mileva, N.; Nagumo, S.; Mizukami, T.; Sonck, J.; Berry, C.; Gallinoro, E.; Monizzi, G.; Candreva, A.; Munhoz, D.; Vassilev, D.; et al. Prevalence of Coronary Microvascular Disease and Coronary Vasospasm in Patients With Nonobstructive Coronary Artery Disease: Systematic Review and Meta-Analysis. J. Am. Heart Assoc. 2022, 11, e023207. [Google Scholar] [CrossRef] [PubMed]
  12. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Br. Med. J. 2021, 372, n71. [Google Scholar] [CrossRef] [PubMed]
  13. Whiting, P.F.; Rutjes, A.W.; Westwood, M.E.; Mallett, S.; Deeks, J.J.; Reitsma, J.B.; Leeflang, M.M.G.; Sterne, J.A.C.; Bossuyt, P.M.M.; QUADAS-2 Group. QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies. Ann. Intern. Med. 2011, 155, 529–536. [Google Scholar] [CrossRef]
  14. Zornitzki, L.; Shetrit, A.; Freund, O.; Frydman, S.; Banai, A.; Amar Shamir, R.; Ben-Shoshan, J.; Arbel, Y.; Banai, S.; Konigstein, M. Traditional Cardiovascular Risk Factors and Coronary Microvascular Disease in Women and Men—A Single Center Study. Cardiology 2024, 149, 455–462. [Google Scholar] [CrossRef]
  15. Souza, A.; Rosenthal, M.H.; Moura, F.A.; Divakaran, S.; Osborne, M.T.; Hainer, J.; Dorbala, S.; Blankstein Ron Di Carli, M.F.; Taqueti, V.R. Body Composition, Coronary Microvascular Dysfunction, and Future Risk of Cardiovascular Events Including Heart Failure. J. Am. Coll. Cardiol. Cardiovasc. Imaging 2024, 17, 179–191. [Google Scholar] [CrossRef] [PubMed]
  16. Patel, K.K.; Singh, A.; Peri-Okonny, P.A.; Patel, F.S.; Kennedy, K.F.; Sperry, B.W.; Thompson, R.C.; McGhie, A.I.; Spertus, J.A.; Shaw, L.J.; et al. Prevalence and Prognostic Importance of Abnormal Positron Emission Tomography Among Asymptomatic Patients With Diabetes Mellitus. J. Am. Coll. Cardiol. Cardiovasc. Imaging 2024, 17, 301–310. [Google Scholar] [CrossRef] [PubMed]
  17. Paolisso, P.; Gallinoro, E.; Belmonte, M.; Bertolone, D.T.; Bermpeis, K.; De Colle, C.; Shumkova, M.; Leone, A.; Caglioni, S.; Esposito, G.; et al. Coronary Microvascular Dysfunction in Patients With Heart Failure: Characterization of Patterns in HFrEF Versus HFpEF. Circ. Heart Fail. 2024, 17, E010805. [Google Scholar] [CrossRef]
  18. Niewiara, Ł.; Kleczyński, P.; Guzik, B.; Szolc, P.; Baran, J.; Podolec, J.; Diachyshyn, M.; Zmudka, K.; Legutko, J. Impaired coronary flow reserve in patients with poor type 2 diabetes control: Preliminary results from prospective microvascular dysfunction registry. Cardiol. J. 2024, 31, 185–192. [Google Scholar] [CrossRef]
  19. Kong, H.; Cao, J.; Tian, J.; Yong, J.; An, J.; Zhang, L.; Song, X.; He, Y. Coronary microvascular dysfunction: Prevalence and aetiology in patients with suspected myocardial ischaemia. Clin. Radiol. 2024, 79, 386–392. [Google Scholar] [CrossRef]
  20. Zaragoza, P.F.; Viguer, T.C.; de Urbina, L.M.O.; del Viejo, A.P.; Domingo, E.P.; Domingo, F.P. Thermodilution assessment of vasoreactivity and microvascular function in the absence of obstructive coronary artery disease. REC Interv. Cardiol. 2023, 5, 270–278. [Google Scholar]
  21. Vink, C.E.M.; Woudstra, J.; Lee, J.M.; Boerhout, C.K.M.; Cook, C.M.; Hoshino, M.; Mejia-Renteria, H.; Hun Lee, S.; Jung, J.H.; Echavarria-Pinto, M.; et al. Sex differences in prevalence and outcomes of the different endotypes of chronic coronary syndrome in symptomatic patients undergoing invasive coronary angiography: Insights from the global ILIAS invasive coronary physiology registry. Atherosclerosis 2023, 384, 117167. [Google Scholar] [CrossRef]
  22. Vaz Ferreira, V.; Ramos, R.; Castelo, A.; Mendonça, T.; Almeida-Morais, L.; Pereira-da-Silva, T.; Oliveira, E.; Viegas, J.; Garcia Bras, P.; Grazina, A.; et al. Initial single-center experience of a standardized protocol for invasive assessment of ischemia and non-obstructive coronary artery disease. Rev. Port. Cardiol. 2023, 42, 455–465. [Google Scholar] [CrossRef]
  23. Pintea Bentea, G.; Berdaoui, B.; Samyn, S.; Morissens, M.; van de Borne, P.; Castro Rodriguez, J. Particularities of coronary physiology in patients with atrial fibrillation: Insights from combined pressure and flow indices measurements. Front. Cardiovasc. Med. 2023, 10, 1206743. [Google Scholar] [CrossRef] [PubMed]
  24. Kim, S.R.; Kim, M.N.; Cho, D.H.; Kim, H.D.; Bae, S.A.; Kim, H.L.; Kim, M.A.; Hong, K.S.; Shim, W.J.; Park, S.M. Sex differences of sequential changes in coronary blood flow and microvascular function in patients with suspected angina. Clin. Res. Cardiol. 2023, 113, 1638–1649. [Google Scholar] [CrossRef]
  25. Erhardsson, M.; Ljung Faxén, U.; Venkateshvaran, A.; Svedlund, S.; Saraste, A.; Lagerström Fermer, M.; Gan, L.M.; Shah, S.J.; Tromp, J.; Sp Lam, C.; et al. Regional differences and coronary microvascular dysfunction in heart failure with preserved ejection fraction. ESC Heart Fail. 2023, 10, 3729–3734. [Google Scholar] [CrossRef]
  26. Bhandiwad, A.R.; Valenta, I.; Jain, S.; Schindler, T.H. PET-determined prevalence of coronary microvascular dysfunction and different types in a cardio-metabolic risk population. IJC Heart Vasc. 2023, 46, 101206. [Google Scholar] [CrossRef] [PubMed]
  27. Weber, B.; Perez-Chada, L.M.; Divakaran, S.; Brown, J.M.; Taqueti, V.; Dorbala, S.; Blankstein, R.; Liao, K.; Merola, J.F.; Di Carli, M. Coronary microvascular dysfunction in patients with psoriasis. J. Nucl. Cardiol. 2022, 29, 37–42. [Google Scholar] [CrossRef] [PubMed]
  28. Slivnick, J.A.; Zareba, K.M.; Truong, V.T.; Liu, E.; Barnes, A.; Mazur, W.; Binkley, P. Impairment in quantitative microvascular function in non-ischemic cardiomyopathy as demonstrated using cardiovascular magnetic resonance. PLoS ONE 2022, 17, e0264454. [Google Scholar] [CrossRef]
  29. Lopez, D.M.; Divakaran, S.; Gupta, A.; Bajaj, N.S.; Osborne, M.T.; Zhou, W.; Hainer, J.; Bibbo, C.F.; Skali, H.; Dorbala, S.; et al. Role of Exercise Treadmill Testing in the Assessment of Coronary Microvascular Disease. J. Am. Coll. Cardiol. Cardiovasc. Imaging 2022, 15, 312–321. [Google Scholar] [CrossRef]
  30. Lee, S.H.; Shin, D.; Lee, J.M.; van de Hoef, T.P.; Hong, D.; Choi, K.H.; Hwang, D.; Boerhout, C.K.M.; de Waard, G.A.; Jung, J.H.; et al. Clinical Relevance of Ischemia with Nonobstructive Coronary Arteries According to Coronary Microvascular Dysfunction. J. Am. Heart Assoc. 2022, 11, e025171. [Google Scholar] [CrossRef] [PubMed]
  31. Arnold, J.R.; Kanagala, P.; Budgeon, C.A.; Jerosch-Herold, M.; Gulsin, G.S.; Singh, A.; Khan, J.N.; Squire, I.B.; Ng, L.L.; McCann, G.P. Prevalence and Prognostic Significance of Microvascular Dysfunction in Heart Failure With Preserved Ejection Fraction. J. Am. Coll. Cardiol. Cardiovasc. Imaging 2022, 15, 1001–1011. [Google Scholar] [CrossRef] [PubMed]
  32. Weber, B.N.; Stevens, E.; Barrett, L.; Bay, C.; Sinnette, C.; Brown, J.M.; Divakaran, S.; Bibbo, C.; Hainer, J.; Dorbala, S.; et al. Coronary microvascular dysfunction in systemic lupus erythematosus. J. Am. Heart Assoc. 2021, 10, e018555. [Google Scholar] [CrossRef]
  33. Schumann, C.L.; Mathew, R.C.; Dean, J.H.L.; Yang, Y.; Balfour, P.C.; Shaw, P.W.; Robinson, A.A.; Salerno, M.; Kramer, C.M.; Bourque, J.M. Functional and Economic Impact of INOCA and Influence of Coronary Microvascular Dysfunction. J. Am. Coll. Cardiol. Cardiovasc. Imaging 2021, 14, 1369–1379. [Google Scholar] [CrossRef] [PubMed]
  34. Ozcan, C.; Allan, T.; Besser, S.A.; de la Pena, A.; Blair, J. The relationship between coronary microvascular dysfunction, atrial fibrillation and heart failure with preserved ejection fraction. Am. J. Cardiovasc. Dis. 2021, 11, 29–38. [Google Scholar] [PubMed]
  35. Liao, K.P.; Huang, J.; He, Z.; Cremone, G.; Lam, E.; Hainer, J.M.; Morgan, V.; Bibbo, C.; Di Carli, M. Coronary Microvascular Dysfunction in Rheumatoid Arthritis Compared to Diabetes Mellitus and Association With All-Cause Mortality. Arthritis Care Res. 2021, 73, 159–165. [Google Scholar] [CrossRef] [PubMed]
  36. Jansen, T.P.J.; Elias-Smale, S.E.; van den Oord, S.; Gehlmann, H.; Dimitiriu-Leen, A.; Maas, A.H.E.M.; Konst, R.E.; van Royen, N.; Damman, P. Sex Differences in Coronary Function Test Results in Patient With Angina and Nonobstructive Disease. Front. Cardiovasc. Med. 2021, 8, 750071. [Google Scholar] [CrossRef]
  37. Safdar, B.; D’Onofrio, G.; Dziura, J.; Russell, R.R.; Johnson, C.; Sinusas, A.J. Prevalence and characteristics of coronary microvascular dysfunction among chest pain patients in the emergency department. Eur. Heart J. Acute Cardiovasc. Care 2020, 9, 5–13. [Google Scholar] [CrossRef]
  38. Vita, T.; Murphy, D.J.; Osborne, M.T.; Bajaj, N.S.; Keraliya, A.; Jacob, S.; Diaz Martinez, A.J.; Nodoushani, A.; Bravo, P.; Hainer, J.; et al. Association between nonalcoholic fatty liver disease at CT and coronary microvascular dysfunction at myocardial perfusion PET/CT. Radiology 2019, 291, 330–337. [Google Scholar] [CrossRef]
  39. Suda, A.; Takahashi, J.; Hao, K.; Kikuchi, Y.; Shindo, T.; Ikeda, S.; Sato, K.; Sugisawa, J.; Matsumoto, Y.; Miyata, S.; et al. Coronary Functional Abnormalities in Patients With Angina and Nonobstructive Coronary Artery Disease. J. Am. Coll. Cardiol. 2019, 74, 2350–2360. [Google Scholar] [CrossRef] [PubMed]
  40. Sara, J.D.; Taher, R.; Kolluri, N.; Vella, A.; Lerman, L.O.; Lerman, A. Coronary microvascular dysfunction is associated with poor glycemic control amongst female diabetics with chest pain and non-obstructive coronary artery disease. Cardiovasc. Diabetol. 2019, 18, 22. [Google Scholar] [CrossRef]
  41. Pargaonkar, V.S.; Kobayashi, Y.; Kimura, T.; Schnittger, I.; Chow, E.K.H.; Froelicher, V.F.; Rogers, I.S.; Lee, D.P.; Fearon, W.F.; Yeung, A.C.; et al. Accuracy of non-invasive stress testing in women and men with angina in the absence of obstructive coronary artery disease. Int. J. Cardiol. 2019, 282, 7–15. [Google Scholar] [CrossRef] [PubMed]
  42. Anderson, R.D.; Petersen, J.W.; Mehta, P.K.; Wei, J.; Johnson, B.D.; Handberg, E.M.; Kar, S.; Samuels, B.; Azarbal, B.; Kothawade, K.; et al. Prevalence of Coronary Endothelial and Microvascular Dysfunction in Women with Symptoms of Ischemia and No Obstructive Coronary Artery Disease Is Confirmed by a New Cohort: The NHLBI-Sponsored Women’s Ischemia Syndrome Evaluation-Coronary Vascular Dysfunction (WISE-CVD). J. Interv. Cardiol. 2019, 2019, 7169275. [Google Scholar] [PubMed]
  43. Taqueti, V.R.; Solomon, S.D.; Shah, A.M.; Desai, A.S.; Groarke, J.D.; Osborne, M.T.; Hainer, J.; Bibbo, C.F.; Dorbala, S.; Blankstein, R.; et al. Coronary microvascular dysfunction and future risk of heart failure with preserved ejection fraction. Eur. Heart J. 2018, 39, 840–849. [Google Scholar] [CrossRef] [PubMed]
  44. Shah, S.J.; Lam, C.S.P.; Svedlund, S.; Saraste, A.; Hage, C.; Tan, R.S.; Beussink-Nelson, L.; Ljung Faxen, U.; Lagerstrom Fermer, M.; Broberg, M.A.; et al. Prevalence and correlates of coronary microvascular dysfunction in heart failure with preserved ejection fraction: PROMIS-HFpEF. Eur. Heart J. 2018, 39, 3439–3450. [Google Scholar] [CrossRef] [PubMed]
  45. Ford, T.J.; Stanley, B.; Good, R.; Rocchiccioli, P.; McEntegart, M.; Watkins, S.; Eteiba, H.; Shaukat, A.; Lindsay, M.; Robertson, K.; et al. Stratified Medical Therapy Using Invasive Coronary Function Testing in Angina: The CorMicA Trial. J. Am. Coll. Cardiol. 2018, 72, 2841–2855. [Google Scholar] [CrossRef]
  46. Nel, K.; Nam, M.C.Y.; Anstey, C.; Boos, C.J.; Carlton, E.; Senior, R.; Kaski, J.C.; Khattab, A.; Shamley, D.; Byrne, C.D.; et al. Myocardial blood flow reserve is impaired in patients with aortic valve calcification and unobstructed epicardial coronary arteries. Int. J. Cardiol. 2017, 248, 427–432. [Google Scholar] [CrossRef]
  47. Sara, J.D.; Lennon, R.J.; Ackerman, M.J.; Friedman, P.A.; Noseworthy, P.A.; Lerman, A. Coronary microvascular dysfunction is associated with baseline QTc prolongation amongst patients with chest pain and non-obstructive coronary artery disease. J. Electrocardiol. 2016, 49, 87–93. [Google Scholar] [CrossRef]
  48. Mygind, N.D.; Michelsen, M.M.; Pena, A.; Frestad, D.; Dose, N.; Aziz, A.; Faber, R.; Host, N.; Gustafsson, I.; Hansen, P.R.; et al. Coronary Microvascular Function and Cardiovascular Risk Factors in Women With Angina Pectoris and No Obstructive Coronary Artery Disease: The iPOWER Study. J. Am. Heart Assoc. 2016, 5, e003064. [Google Scholar] [CrossRef] [PubMed]
  49. Valenzuela-Garcia, L.F.; Matsuzawa, Y.; Sara, J.D.S.; Kwon, T.G.; Lennon, R.J.; Lerman, L.O.; Ruiz-Salmeron, R.J.; Lerman, A. Lack of correlation between the optimal glycaemic control and coronary micro vascular dysfunction in patients with diabetes mellitus: A cross sectional study. Cardiovasc. Diabetol. 2015, 14, 106. [Google Scholar] [CrossRef] [PubMed]
  50. Sara, J.D.; Widmer, R.J.; Matsuzawa, Y.; Lennon, R.J.; Lerman, L.O.; Lerman, A. Prevalence of Coronary Microvascular Dysfunction Among Patients With Chest Pain and Nonobstructive Coronary Artery Disease. J. Am. Coll. Cardiol. Cardiovasc. Interv. 2015, 8, 1445–1453. [Google Scholar] [CrossRef] [PubMed]
  51. Taqueti, V.R.; Everett, B.M.; Murthy, V.L.; Gaber, M.; Foster, C.R.; Hainer, J.; Blankstein, R.; Dorbala, S.; Di Carli, M.F. Interaction of impaired coronary flow reserve and cardiomyocyte injury on adverse cardiovascular outcomes in patients without overt coronary artery disease. Circulation 2015, 131, 528–535. [Google Scholar] [CrossRef]
  52. Murthy, V.L.; Naya, M.; Taqueti, V.R.; Foster, C.R.; Gaber, M.; Hainer, J.; Dorbala, S.; Blankstein, R.; Rimoldi, O.; Camici, P.G.; et al. Effects of sex on coronary microvascular dysfunction and cardiac outcomes. Circulation 2014, 129, 2518–2527. [Google Scholar] [CrossRef] [PubMed]
  53. Pepine, C.J.; Anderson, R.D.; Sharaf, B.L.; Reis, S.E.; Smith, K.M.; Handberg, E.M.; Jonhson, B.D.; Sopko, G.; Merz, C.N.B. Coronary microvascular reactivity to adenosine predicts adverse outcome in women evaluated for suspected ischemia results from the National Heart, Lung and Blood Institute WISE (Women’s Ischemia Syndrome Evaluation) study. J. Am. Coll. Cardiol. 2010, 55, 2825–2832. [Google Scholar] [CrossRef]
  54. Sade, L.E.; Eroglu, S.; Bozbaş, H.; Ozbiçer, S.; Hayran, M.; Haberal, A.; Muderrisoglu, H. Relation between epicardial fat thickness and coronary flow reserve in women with chest pain and angiographically normal coronary arteries. Atherosclerosis 2009, 204, 580–585. [Google Scholar] [CrossRef]
  55. Reis, S.E.; Holubkov, R.; Smith, A.J.C.; Kelsey, S.F.; Sharaf, B.L.; Reichek, N.; Rogers, W.J.; Merz, C.N.; Sopko, G.; Pepine, C.J. Coronary microvascular dysfunction is highly prevalent in women with chest pain in the absence of coronary artery disease: Results from the NHLBI WISE study. Am. Heart J. 2001, 141, 735–741. [Google Scholar] [CrossRef]
  56. Kumar, S.; Mehta, P.K.; Eshtehardi, P.; Hung, O.Y.; Koh, J.S.; Kumar, A.; Al-Badri, A.; Rabah, R.; D’Souza, M.; Gupta, S.; et al. Functional coronary angiography in symptomatic patients with no obstructive coronary artery disease. Catheter. Cardiovasc. Interv. 2021, 98, 827–835. [Google Scholar] [CrossRef] [PubMed]
  57. Rahman, H.; Ryan, M.; Lumley, M.; Modi, B.; McConkey, H.; Ellis, H.; Scannell, C.; Clapp, B.; Marber, M.; Webb, A.; et al. Coronary Microvascular Dysfunction Is Associated With Myocardial Ischemia and Abnormal Coronary Perfusion During Exercise. Circulation 2019, 140, 1805–1816. [Google Scholar] [CrossRef]
  58. Kato, S.; Saito, N.; Kirigaya, H.; Gyotoku, D.; Iinuma, N.; Kusakawa, Y.; Iguchi, K.; Nakachi, T.; Fukui, K.; Futaki, M.; et al. Impairment of Coronary Flow Reserve Evaluated by Phase Contrast Cine-Magnetic Resonance Imaging in Patients With Heart Failure With Preserved Ejection Fraction. J. Am. Heart Assoc. 2016, 5, e002649. [Google Scholar] [CrossRef] [PubMed]
  59. Srivaratharajah, K.; Coutinho, T.; deKemp, R.; Liu, P.; Haddad, H.; Stadnick, E.; Davies, R.A.; Chih, S.; Dwivedi, G.; Guo, A.; et al. Reduced Myocardial Flow in Heart Failure Patients With Preserved Ejection Fraction. Circ. Heart Fail. 2016, 9, e002562. [Google Scholar] [CrossRef]
  60. Hasdai, D.; Holmes, D.R.; Jr Higano, S.T.; Burnett, J.C.; Jr Lerman, A. Prevalence of coronary blood flow reserve abnormalities among patients with nonobstructive coronary artery disease and chest pain. Mayo Clin. Proc. 1998, 73, 1133–1140. [Google Scholar] [CrossRef]
  61. Graf, S.; Khorsand, A.; Gwechenberger, M.; Novotny, C.; Kletter, K.; Sochor, H.; Pirich, C.; Maurer, G.; Porenta, G.; Zehetgruber, M. Typical chest pain and normal coronary angiogram: Cardiac risk factor analysis versus PET for detection of microvascular disease. J. Nucl. Med. 2007, 48, 175–181. [Google Scholar]
  62. Sicari, R.; Rigo, F.; Cortigiani, L.; Gherardi, S.; Galderisi, M.; Picano, E. Additive prognostic value of coronary flow reserve in patients with chest pain syndrome and normal or near-normal coronary arteries. Am. J. Cardiol. 2009, 103, 626–631. [Google Scholar] [CrossRef] [PubMed]
  63. Ishimori, M.L.; Martin, R.; Berman, D.S.; Goykhman, P.; Shaw, L.J.; Shufelt, C.; Slomka, P.J.; Thomson, L.E.J.; Schapira, J.; Yang, Y.; et al. Myocardial ischemia in the absence of obstructive coronary artery disease in systemic lupus erythematosus. J. Am. Coll. Cardiol. Cardiovasc. Imaging 2011, 4, 27–33. [Google Scholar] [CrossRef] [PubMed]
  64. Sakamoto, N.; Iwaya, S.; Owada, T.; Nakamura, Y.; Yamauchi, H.; Hoshino, Y.; Mizukami, H.; Sugimoto, K.; Yamaki, T.; Kunii, H.; et al. A reduction of coronary flow reserve is associated with chronic kidney disease and long-term cardio-cerebrovascular events in patients with non-obstructive coronary artery disease and vasospasm. Fukushima J. Med. Sci. 2012, 58, 136–143. [Google Scholar] [CrossRef] [PubMed]
  65. Schroder, J.; Zethner-Moller, R.; Bove, K.B.; Mygind, N.D.; Hasbak, P.; Michelsen, M.M.; Gustafsson, I.; Kastrup, J.; Prescott, E. Protein biomarkers and coronary microvascular dilatation assessed by rubidium-82 PET in women with angina pectoris and no obstructive coronary artery disease. Atherosclerosis 2018, 275, 319–327. [Google Scholar] [CrossRef] [PubMed]
  66. Kotecha, T.; Martinez-Naharro, A.; Boldrini, M.; Knight, D.; Hawkins, P.; Kalra, S.; Patel, D.; Coghlan, G.; Moon, J.; Plein, S.; et al. Automated Pixel-Wise Quantitative Myocardial Perfusion Mapping by CMR to Detect Obstructive Coronary Artery Disease and Coronary Microvascular Dysfunction: Validation Against Invasive Coronary Physiology. J. Am. Coll. Cardiol. Cardiovasc. Imaging 2019, 12, 1958–1969. [Google Scholar] [CrossRef]
  67. Schindler, T.H.; Schelbert, H.R.; Quercioli, A.; Dilsizian, V. Cardiac PET imaging for the detection and monitoring of coronary artery disease and microvascular health. J. Am. Coll. Cardiol. Cardiovasc. Imaging 2010, 3, 623–640. [Google Scholar] [CrossRef]
  68. Quinaglia, T.; Jerosch-Herold, M.; Coelho-Filho, R.O. State-of-the-Art Quantitative Assessment of Myocardial Ischemia By Stress Perfusion Cardiac Magnetic Resonance. Magn. Reson. Imaging Clin. N. Am. 2019, 27, 491–505. [Google Scholar] [CrossRef]
  69. Doucette, J.W.; Corl, P.D.; Payne, H.M.; Flynn, A.E.; Goto, M.; Nassi, M.; Segal, J. Validation of a Doppler guide wire for intravascular measurement of coronary artery flow velocity. Circulation 1992, 85, 1899–1911. [Google Scholar] [CrossRef]
  70. De Bruyne, B.; Pijls, N.H.; Smith, L.; Wievegg, M.; Heyndrickx, G.R. Coronary thermodilution to assess flow reserve: Experimental validation. Circulation 2001, 104, 2003–2006. [Google Scholar] [CrossRef]
  71. Pijls, N.H.; De Bruyne, B.; Smith, L.; Aarnoudse, W.; Barbato, E.; Bartunek, J.; Bech, G.J.W.; Van De Vosse, F. Coronary thermodilution to assess flow reserve: Validation in humans. Circulation 2002, 105, 2482–2486. [Google Scholar] [CrossRef]
  72. Xaplanteris, P.; Fournier, S.; Keulards, D.C.J.; Adjedj, J.; Ciccarelli, G.; Milkas, A.; Pellicano, M.; Van’t Veer, M.; Barbato, E.; Pijls, N.H.J.; et al. Catheter-Based Measurements of Absolute Coronary Blood Flow and Microvascular Resistance: Feasibility, Safety, and Reproducibility in Humans. Circ. Cardiovasc. Interv. 2018, 11, e006194. [Google Scholar] [CrossRef] [PubMed]
  73. Fournier, S.; Keulards, D.C.J.; van ’t Veer, M.; Colaiori, I.; Di Gioia, G.; Zimmermann, F.M.; Mizukami, T.; Nagumo, S.; Kodeboina, M.; El Farissi, M.; et al. Normal values of thermodilution-derived absolute coronary blood flow and microvascular resistance in humans. EuroIntervention 2021, 17, e309–e316. [Google Scholar] [CrossRef] [PubMed]
  74. Gallinoro, E.; Candreva, A.; Colaiori, I.; Kodeboina, M.; Fournier, S.; Nelis, O.; Di Gioia, G.; Sonck, J.; van’t Veer, M.; Pijls, N.H.J.; et al. Thermodilution-derived volumetric resting coronary blood flow measurement in humans. EuroIntervention 2021, 17, e672–e679. [Google Scholar] [CrossRef]
  75. Sakai, K.; Storozhenko, T.; Mizukami, T.; Ohashi, H.; Bouisset, F.; Tajima, A.; van Hoe, L.; Gallinoro, E.; Botti, G.; Mahendiran, T.; et al. Impact of vessel volume on thermodilution measurements in patients with coronary microvascular dysfunction. Catheter. Cardiovasc. Interv. 2024, 103, 885–896. [Google Scholar] [CrossRef] [PubMed]
  76. Rehan, R.; Wong, C.C.Y.; Waever, J.; Chan, W.; Tremmel, J.A.; Fearon, W.F.; Ng, M.K.C.; Yong, A.S.C. Multivessel Coronary Function Testing Increase Diagnostic Yield in Patients With Angina and Nonobstructive Coronary Arteries. JACC Cardiovasc. Interv. 2024, 17, 1091–1102. [Google Scholar] [CrossRef] [PubMed]
  77. Vrints, C.; Andreotti, F.; Koskinas, K.C.; Rossello, X.; Adamo, M.; Ainslie, J.; Banning, A.P.; Budaj, A.; Buechel, R.R.; Chiariello, G.A.; et al. 2024 ESC guidelines for the management of chronic coronary syndromes. Eur. Heart J. 2024, 44, 3415–3537. [Google Scholar]
  78. Lee, J.M.; Jung, J.H.; Hwang, D.; Park, J.; Fan, Y.; Na, S.H.; Doh, J.H.; Nam, C.W.; Shin, E.S.; Koo, B.K. Coronary flow reserve and microcirculatory resistance in patients with intermediate coronary stenosis. J. Am. Coll. Cardiol. 2016, 67, 1158–1169. [Google Scholar] [CrossRef] [PubMed]
  79. Usui, E.; Murai, T.; Kanaji, Y.; Hoshino, M.; Yamaguchi, M.; Hada, M.; Hamaya, R.; Kanno, Y.; Lee, T.; Yonetsu, T.; et al. Clinical significance of concordance or discordance between fractional flow reserve and coonary flow reserve for coronary physiological indices, microvascular resistance, and prognosis after elective percutaneous coronary intervention. EuroIntervention 2018, 14, 798–805. [Google Scholar] [CrossRef]
  80. Mahendiran, T.; Hoepli, A.; Foster-Witassek, F.; Rickli, H.; Roffi, M.; Eberli, F.; Pedrazzini, G.; Jeger, R.; Radovanovic, D.; Fournier, S. Twnety-year trends in the prevalence of midiable cardiovascular risk factors in young acute coronary syndrome patients hospitalized in Switzerland. Eur. J. Prev. Cardiol. 2023, 30, 1504–1512. [Google Scholar] [CrossRef] [PubMed]
  81. Rigo, F.; Gherardi, S.; Galderisi, M.; Pratali, L.; Cortigiani, L.; Sicari, R.; Picano, E. The prognostic impact of coronary flow reserve assessed by Echo-Doppler in non-ischemic dilated cardiomyopathy. Eur. Heart J. 2006, 27, 1319–1323. [Google Scholar] [CrossRef] [PubMed]
Jcm 14 00829 g001
Figure 1. Flowchart diagram according to PRISMA criteria.
Figure 1. Flowchart diagram according to PRISMA criteria.
Jcm 14 00829 g001
Jcm 14 00829 g002
Figure 2. (A) Coronary microvascular resistance (CMD) prevalence over time with all diagnostic modalities. (B) CMD prevalence over time with positron emission tomography–computed tomography (PET-CT). (C) CMD prevalence with transthoracic echocardiogram (TTE). (D) CMD prevalence with cardiovascular magnetic resonance (CMR). (E) CMD prevalence with invasive coronary angiography (ICA).
Figure 2. (A) Coronary microvascular resistance (CMD) prevalence over time with all diagnostic modalities. (B) CMD prevalence over time with positron emission tomography–computed tomography (PET-CT). (C) CMD prevalence with transthoracic echocardiogram (TTE). (D) CMD prevalence with cardiovascular magnetic resonance (CMR). (E) CMD prevalence with invasive coronary angiography (ICA).
Jcm 14 00829 g002
Jcm 14 00829 g003
Figure 3. (A) Coronary microvascular resistance (CMD) prevalence with a cut-off ≤2.5. (B) CMD prevalence with a cut-off ≤2. (C) The prevalence according to the mean coronary flow reserve (CFR) cut-off.
Figure 3. (A) Coronary microvascular resistance (CMD) prevalence with a cut-off ≤2.5. (B) CMD prevalence with a cut-off ≤2. (C) The prevalence according to the mean coronary flow reserve (CFR) cut-off.
Jcm 14 00829 g003
Table 1. Sample Size, prevalence, number of positive patients, diagnostic methods used, and cut-off diagnostic used for the selected study.
Table 1. Sample Size, prevalence, number of positive patients, diagnostic methods used, and cut-off diagnostic used for the selected study.
StudySample SizePrevalence CMD (%)No. PositiveDiagnostic ModalityCut-Off
Zornitzki [14], 202424557.5141ICA (themodilution bolus)CFR < 2.5
Souza [15], 202440049196PET-CTCFR < 2
Patel [16], 2024142548.6692PET-CTMBFR < 2
Paolisso [17], 2024565229ICA (thermodilution continuous)CFR < 2.5
Niewiara [18], 202410131.732ICA (themodilution bolus)CFR < 2
Kong [19], 2024917972CMRPerfusion defect
Zaragoza [20], 2023606036ICA (themodilution bolus)CFR < 2
Vink [21], 2023100744443ICA (Doppler guidewire and thermodilution bolus)CFR ≤ 2.5
Vaz Ferreira [22], 202320357ICA (themodilution bolus)CFR ≤ 2
Pintea Bentea [23], 20231110011ICA (Doppler guidewire)CFR < 2.5
Kim [24], 20232024284Doppler TTECFvR < 2.3
Erhardsson [25], 202320275151Doppler TTECFR < 2.5
Bhandiwad [26], 202323954130PET-CTMFR < 2
Weber [27], 20221744781PET-CTMFR < 2
Slivnick [28], 20229934.334CMRMPRI < 1.51
Lopez [29], 20222493998PET-CTCFR < 2
Lee [30], 202228745,3130ICA (Doppler guidewire)CFR ≤ 2.5
Arnold [31], 20211017070CMRMPR < 2
Weber [32], 202111142.347PET-CTMFR < 2
Schumann [33], 20216624.216CMRMPR < 2
Ozcan [34], 20218042.534ICA (thermodilution bolus)CFR < 2
Liao [35], 202151462.5321PET-CTCFR < 2
Jansen [36], 202125242107ICA (thermodilution bolus)CFR < 2
Kumar [56], 202016365.6107ICA (thermodilution bolus)CFR < 2.5
Rahman [57], 20198552.945ICA (Doppler guidewire)CFR < 2.5
Vita [38], 201988646.4411PET-CTCFR < 2
Suda [39], 201918758.3109ICA (thermodilution bolus)CFR < 2
Sara [40], 20191293849ICA (Doppler guidewire)CFR < 2.5
Kotecha [66], 20192369.616CMRIMR ≥ 25
Pargaonkar [41], 201915021.332ICA (thermodilution bolus)IMR ≥ 25
Anderson [42], 20192224089ICA (thermodilution bolus)CFR ≤ 2.5
Safdar [37], 20181246581PET-CTCFR < 2.5
Taqueti [43], 201820154108PET-CTCFR < 2
Shah [44], 201820274.8151Doppler TTECFR < 2.5
Schroder [65], 2018973231PET-CTMBFR < 2.5
Ford [45], 201815172.2109ICA (thermodilution bolus)CFR < 2
Nel [46], 20171833870Doppler TTEMBFR < 2
Sara [47], 201692630281ICA (Doppler guidewire)CFR ≤ 2.5
Kato [58], 2016564123CMRCFR < 2.5
Mygind [48], 201691926.2241Doppler TTECFVR < 2
Valenzuela-Garcia [49], 201531429.693ICA (Doppler guidewire)CFR < 2.5
Sara [50], 2015143969998ICA (Doppler guidewire)CFR ≤ 2.5
Taqueti [51], 201576146349PET-CTCFR < 2
Murthy [52], 2014121853641PET-CTCFR < 2
Sakamoto [64], 20127316.412Doppler TTECFR ≤ 2.8
Srivaratharajah [59], 201237624,291PET-CTMBFR < 2
Ishimori [63], 201118448CMRPerfusion defect
Pepine [53], 201015336,656ICA (Doppler guidewire)CFR < 2.32
Sicari [61], 20093942287Doppler TTECFR < 2.5
Sade [54], 2008654026Doppler TTECFR < 2
Graf [61], 20068957.351PET-CTCFR < 2.5
Reis [55], 20011594774ICA (Doppler guidewire)CFR < 2.5
Hasdai [60], 199820329.359ICA (Doppler guidewire)CFR < 2.5
ICA: invasive coronary angiography; CFR: coronary flow reserve; PET-CT: positron emission tomography–computed tomography; MBFR: myocardial blood flow reserve; CMR: cardiac magnetic resonance; TTE: transthoracic echocardiography; CFvR: coronary flow velocity reserve.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zimmerli, A.; Salihu, A.; Antiochos, P.; Lu, H.; Pitta Gros, B.; Berger, A.; Muller, O.; Meier, D.; Fournier, S. Evolution of Coronary Microvascular Dysfunction Prevalence over Time and Across Diagnostic Modalities in Patients with ANOCA: A Systematic Review. J. Clin. Med. 2025, 14, 829. https://doi.org/10.3390/jcm14030829

AMA Style

Zimmerli A, Salihu A, Antiochos P, Lu H, Pitta Gros B, Berger A, Muller O, Meier D, Fournier S. Evolution of Coronary Microvascular Dysfunction Prevalence over Time and Across Diagnostic Modalities in Patients with ANOCA: A Systematic Review. Journal of Clinical Medicine. 2025; 14(3):829. https://doi.org/10.3390/jcm14030829

Chicago/Turabian Style

Zimmerli, Aurelia, Adil Salihu, Panagiotis Antiochos, Henri Lu, Barbara Pitta Gros, Alexandre Berger, Olivier Muller, David Meier, and Stephane Fournier. 2025. "Evolution of Coronary Microvascular Dysfunction Prevalence over Time and Across Diagnostic Modalities in Patients with ANOCA: A Systematic Review" Journal of Clinical Medicine 14, no. 3: 829. https://doi.org/10.3390/jcm14030829

APA Style

Zimmerli, A., Salihu, A., Antiochos, P., Lu, H., Pitta Gros, B., Berger, A., Muller, O., Meier, D., & Fournier, S. (2025). Evolution of Coronary Microvascular Dysfunction Prevalence over Time and Across Diagnostic Modalities in Patients with ANOCA: A Systematic Review. Journal of Clinical Medicine, 14(3), 829. https://doi.org/10.3390/jcm14030829

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop