Coronary artery disease (CAD) refers to the atherosclerotic narrowing of coronary arteries that is often asymptomatic early in the disease course but may lead to stable or unstable angina or myocardial infarction with progressive thickening or plaque rupture.1–4 Cardiovascular disease is the leading global cause of mortality and morbidity.5–8 Common risk factors for CAD include dyslipidemia, tobacco use, hypertension, family history of CAD, diabetes mellitus, and obesity.9,10 Complications include acute coronary syndromes (ACS), ST-elevation myocardial infarction (STEMI), acute heart failure, arrhythmias, and sudden death.1–4 There is general agreement about approaches to secondary prevention of CAD and its complications,1–4 and proper management requires a proper assessment of the patients’ condition.
Angiography and coronary computed tomographic angiography can be used to determine the presence of gross stenosis and hypoperfusion, but they cannot provide a correct assessment of the general perfusion of the hearts.11,12 In addition, because of the need for arterial catheterization (which carries risks like hemorrhage and pseudoaneurysms), digital subtraction angiography is an invasive procedure,13–16 and it is ill-suited for repeated examinations in a follow-up context and is generally performed when patients report symptom recurrence. Functional perfusion tests can be used to determine epicardial perfusion. These tests include positron emission tomography (PET), single-photon emission computed tomography (SPECT), and cardiovascular magnetic resonance (CMR).17–21 All are accurate for detecting epicardial CAD; by measuring tissue blood flow, they capture microvascular disease, which is an advantage over CTA and angiography for determining the status of the whole myocardial circulation.17–21
Indeed, chronic coronary syndrome involves macrovascular epicardial CAD and microvascular dysfunction, both of which result in reduced coronary blood flow (ie, the circulation of the blood in the coronary blood vessels), leading to reduced myocardial blood flow (MBF, ie, the volume of blood transiting through tissue at a certain rate) and adverse outcomes.22–25 Transplanted hearts are susceptible to cardiac allograft vasculopathy and CAD, which are major factors limiting survival after heart transplantation because of limited coronary blood flow.26,27 Patients with normal coronary but with chest pain have an increased risk of major adverse cardiovascular events (MACEs) because of the possible presence of microvascular angina, which also leads to decreased MBF.28–30 Still, chronic coronary syndrome is amenable to medical and interventional therapies.22–25 When using PET, the absolute quantification of MBF and the ratio of stress MBF to rest MBF, known as the myocardial perfusion reserve (MPR) or coronary flow reserve (CFR), can be obtained. An alternative to PET that does not use ionizing radiation is CMR.20 Unlike PET perfusion data, CMR provides qualitative data because of the complexity and time needed for computation. Nevertheless, the development of new quantitative CMR techniques allows “perfusion mapping,” where, in addition to conventional images, perfusion maps are generated automatically on the scanner with each image pixel encoding MBF (mL/g/min).31,32
Previous studies showed that stress MBF is a reliable predictor of outcomes, independent of the presence of significant stenosis.33–37 Nevertheless, Valenta et al33 highlighted that evidence was still lacking for the routine use of MBF for the management of patients with CAD. Herzog et al34 and Murthy et al36 proposed the concept of normal/abnormal perfusion, but the application of such dichotomized variables also needs validation. MPR has been validated to be a better predictor of cardiac death than left ventricular ejection fraction.37
Still, whether stress MBF can predict MACE during long-term follow-up is unknown. Therefore, we conducted this systematic review and meta-analysis to investigate the prognostic value of MBF for MACE. The results could provide some data to back or not the use of MBF during follow-up of patients with CAD.
MATERIALS AND METHODS
These systematic review and meta-analysis were performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.38 The PICOS principle was used to build the search strategy.39 PubMed, Embase, Cochrane, CNKI, and WANFANG were queried for papers published up to January 2021, followed by screening based on the inclusion and exclusion criteria. The search was made using the MeSH terms “Myocardial blood flow,” “positron emission tomography,” “cardiovascular magnetic resonance,” and “major adverse cardiac events,” as well as relevant key words. The reference lists of the retrieved papers were screened for potentially relevant papers.
The eligibility criteria are described here. The exposure had to be the incremental unit of stress MBF (mL/g/min), but due to the small number of studies on the subject, the dichotomization as low versus high MBF was allowed. The imaging methods had to be either PET/CT or CMR. The outcome had to be the occurrence of MACE during follow-up. Finally, only articles in English were included. This might leave out important data, but the choice was made to allow an international readership to be able to consult each included study.
Study characteristics (first author, year of publication, country, and study design), protocol characteristics (imaging method, radionuclide for PET, and follow-up duration), patient characteristics (sex, sample size, baseline rest MBF, baseline stress MBF, and incidence of MACE), and the outcome (MACE, with hazard ratios [HRs], and 95% confidence intervals [CI]) were extracted by 2 different investigators (Changjie Pan and Ruohan Yin), independently, according to a prespecified protocol and forms. Any discrepancy in the 2 assessments was discussed until a consensus was reached.
Quality of the Evidence
The quality of the evidence of all included articles was assessed independently by 2 authors (Changjie Pan and Chunhong Hu) according to the Newcastle-Ottawa Scale (NOS) criteria for quality assessment of cohort studies.40 As earlier, discrepancies were resolved through discussion.
All statistical analyses were performed using STATA SE 14.0 (StataCorp, College Station, TX). The time-to-event data were summarized as HRs and their corresponding 95% CIs. Statistical heterogeneity among studies was estimated using Cochran’s Q-test and the I2 index. High heterogeneity was indicated by an I2 > 50% and a Q-test P < 0.10, in which case the random-effects model was used; otherwise, the fixed-effects model was used. Two-sided P values less than 0.05 were considered statistically different. The potential publication bias was first planned to be examined using funnel plots and Egger’s test, but the final number of included studies in each analysis was less than 10, in which case these tests can yield misleading results.41
Selection of the Studies
Figure 1 presents the study selection process. The initial search yielded 343 records. After removing the duplicates (n = 109), 234 records were screened, and 84 were excluded. Then, 150 abstracts or full-text articles were assessed for eligibility, and 144 were excluded (study aim/design, n = 115; outcomes, n = 29). Finally, 6 studies were included.
Table 1 presents the characteristics of the 6 included studies.42–47 There were 3 prospective cohort studies42–44 and 3 retrospective cohort studies,45–47 for a total of 2326 patients and 300 MACEs. Three studies were from Europe,42,43,47 1 was from Japan,45 1 from North America,46 and 1 international.44 Table S1 (https://links.lww.com/CIR/A38) presents the quality of the evidence. One study scored 5 stars,47 1 scored 7 stars,45 3 scored 8 stars,8,42,46 and 1 scored 9 stars.43
TABLE 1. -
Literature Search and Characteristics of the Included Studies
||Sex, Male (N)
||Rest MBF (mL/min/g)
||Stress MBF (mL/min/g)
||Outcomes (n events)
|Farhad et al
|Knott et al
|Meinel et al
||USA, UK, Germany, Italy, China, Korea
|Hamaya et al
|Harms et al
|Monroy-Gonzalez et al
CMRI indicates cardiac magnetic resonance imaging; CTMPI, computed tomography myocardial perfusion imaging; MBF, myocardial blood flow
; PET, positron emission tomography
Association of MACEs With Increments of Stress MBF Units
Four studies presented stress MBF data by unit increments.42,43,46,47 The pooled HR with its 95% confidence interval suggested that an increase in stress MBF by 1 mL/g/min is a protective factor for MACE (HR = 0.32; 95% CI, 0.18–0.57; I2 = 62.9%, Pheterogeneity = 0.044) (Fig. 2).
Association of MACEs With High/Low Stress MBF
Two studies reported stress MBF as high/low.44,45 The results showed that a high-stress MBF was protective against MACEs (HR = 0.43; 95% CI, 0.24–0.78; I2 = 39.5%, Pheterogeneity = 0.199) (Fig. 3).
MBF is a reliable predictor of outcomes of CAD,22–25 but whether stress MBF can predict MACEs during long-term follow-up is unknown. Therefore, this meta-analysis aimed to investigate the prognostic value of MBF for MACE. The results suggest that quantification of stress MBF using PET/CT and CMR (either as unit increments or high/low) might have incremental predictive value for future MACEs in a population at intermediate to high cardiovascular risk. This is supported by a previous meta-analysis that applied different inclusion criteria than the present meta-analysis. Still, the results will require validation in large prospective randomized controlled trials.48
CAD is caused by a gross obstruction of the coronary blood flow that leads to myocardial ischemia and necrosis.1–4 Still, even in the absence of a gross obstruction with overt ischemia, impaired MBF can lead to general poor oxygenation of the cardiac muscle and poor myocardial outcomes that can eventually develop into MACEs.22–25 At rest, oxygenation extraction by the myocardium is maximal, and myocardial oxygenation is therefore dependent upon MBF, which is tightly regulated to prevent ischemia. This same regulation plays a role under stress conditions to increase blood flow in the heart to provide enough oxygen to the myocardium.49,50 Still, the MBF can be impaired under stress in the presence of endothelial dysfunction, external compression, or arteriole rarefaction.49,50 Under severe heart conditions, the MBF can even be impaired at rest.49 All these factors contribute to a low-level ischemic state that is conducive to MACE development and poor cardiovascular outcomes.49,50
Imaging methods are available to determine the MBF, including PET, SPECT, and CMR.17–21 PET uses radionuclides but allows the absolute quantification of MBF and MPR (or CFR). CMR involves no radiations at all20 but, until recently, could only provide a qualitative assessment of MBF.31,32 The present meta-analysis supports the concept that higher MBF, whether quantitatively or qualitatively, is associated with a lower risk of MACEs. This result is not surprising since all 6 included studies42–47 were positive studies that reported lower MACE occurrence with higher MBF. Publication bias could not be analyzed because of the small number of included studies,41 but a publication bias cannot be ruled out since all included studies reported positive associations. Of note, heterogeneity was significant in all analyses. The proportion of females was variable, from 22%46 to 75%,47 and the mean age also varies from 51 to 65 years. Since age and male sex are important risk factors for CAD,9,10 there might be variable eligibility criteria among the studies that could limit the generalizability of the included studies and result in heterogeneity. The differences in patient populations, local practices, initial invasive management, exercise, medical therapy intensity, and stenosis location all contribute to heterogeneity. These factors can also influence the accuracy of MBF for the prediction of MACEs. In addition, 3 studies used radionuclides methods,42,46,47 and 3 used non-nuclear methods.43–45 Finally, the median follow-up was variable, from 144 to 847 years. Shorter follow-up might underestimate the incidence of MACEs. Nevertheless, MBF is a measure of tissue perfusion, but ischemia is not only dependent upon perfusion (ie, oxygen supply) but also upon oxygen consumption. Therefore, an adequate measurement should also consider oxygen demand during a stress test. Future studies should examine that.
Still, despite the possible publication bias and the high heterogeneity, the results are globally supported by previous studies that did not enter the present meta-analysis.33–35,51–54 Herzog et al34 that CFR determined13N-ammonia-PET was a strong predictor of MACE over 3 years. A previous meta-analysis showed that coronary microvascular dysfunction was associated with a 5-fold risk of MACEs.55 Green et al48 showed that in patients with known/suspected CAD, impaired CFR was associated with MACEs. It has been shown that obesity had a detrimental impact on CFR, while leptin levels could improve CFR,35 suggesting possible avenues to improve CFR. Murthy et al36 showed that CFR was independent of sex, which is in itself a major predictor of MACE. Tio et al37 showed that patients with ischemic heart disease (IHD) and low CFR were at high risk of cardiac death. Majmudar et al54 that impaired CFR was associated with MACEs in all patients with cardiomyopathies, irrespective of their ischemic nature. These results might suggest a subgroup of patients in whom CFR could be determined to improve patient management, but additional studies are necessary. A study suggested that CFR could be used as a guide for revascularization deferral in patients with diabetes.56 Combinations of CRF with CAS biomarkers like troponins also showed good value for predicting MACEs,57 and predictive models should be explored in future studies.
Valenta et al33 suggested in 2013 that MBF would be a game-changer in the management of chronic CAD. Guerraty et al51 showed that CFR was more strongly associated with MACEs than the traditional risk factors. It has been suggested that CFR improves the stratification of the risk of MACE in patients with IHD.52 Still, impaired MBF is currently not included in the guidelines from the American College of Cardiology/American Heart Association for patients with stable ischemic heart disease3 even though impaired MBF is not uncommon since there are about 500,000 to 1 million new diagnoses of nonobstructive angina in the United States of America each year,58–60 in whom impaired MBF can be highly suspected.61 Still, conventional CT coronary angiography recommended by the guidelines62,63 fails to detect impaired MBF, which requires additional examinations that might not be readily available everywhere and increase healthcare costs. Still, since the present meta-analysis and the literature strongly suggest the prognostic value of MBF, authoritative organizations should examine the value of MBF. Still, the relationship of MBF with traditional risk factors and their management (eg, diabetes, hypertension, hyperlipidemia, and smoking) need to be studied.28 The ISCHEMIA trial showed that the patients with stable CAD would not benefit from an initial invasive strategy.64 The present meta-analysis was not performed only in patients with stable CAD but in any patients with a heart condition. This meta-analysis might suggest that whether the patients were operated on or not, MBF could be used to follow-up patients with CAD to determine the need for operation or reoperation during follow-up. Future studies should discriminate the value of MBF during follow-up between patients with an initial conservative versus invasive management and among more precise patient populations.
This study has limitations. Despite the best meta-analysis strategy, meta-analyses inherit all the limitations of their included studies, and caution must be considered while extrapolating our results. Not all included studies used a rest and stress protocol.42,45–47 In addition, extracting the MFR was not possible. This meta-analysis only included 6 studies, and the reported data did not allow for a meta-analysis of the cutoff points for prognostic significance. It will have to be determined specifically using studies that report different cutoff points along with sensitivity, specificity, and accuracy. Finally, most of the included studies were single-center observational studies limited by a relatively small sample size.
Quantification of stress MBF using PET/CT and CMR might have incremental predictive value for future MACEs in a population at intermediate to high cardiovascular risk. The results will require validation in large prospective randomized controlled trials.
1. Menees DS, Bates ER. Evaluation of patients with suspected coronary artery disease
. Coron Artery Dis. 2010;21:386–390.
2. Lawton JS. Sex and gender differences in coronary artery disease
. Semin Thorac Cardiovasc Surg. 2011;23:126–130.
3. Fihn SD, Gardin JM, Abrams J, et al.; American College of Cardiology Foundation; American Heart Association Task Force on Practice Guidelines; American College of Physicians; American Association for Thoracic Surgery; Preventive Cardiovascular Nurses Association; Society for Cardiovascular Angiography and Interventions; Society of Thoracic Surgeons. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines, and the American College of Physicians, American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons. J Am Coll Cardiol. 2012;60:e44–e164.
4. Fihn SD, Blankenship JC, Alexander KP, et al. 2014 ACC/AHA/AATS/PCNA/SCAI/STS focused update of the guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines, and the American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons. Circulation. 2014;130:1749–1767.
5. Triant VA, Perez J, Regan S, et al. Cardiovascular risk prediction functions underestimate risk in HIV infection. Circulation. 2018;137:2203–2214.
6. Yusuf S, Rangarajan S, Teo K, et al.; PURE Investigators. Cardiovascular risk and events in 17 low-, middle-, and high-income countries. N Engl J Med. 2014;371:818–827.
7. WHO CVD Risk Chart Working Group. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Glob Health. 2019;7:e1332–e1345.
8. Khan MA, Hashim MJ, Mustafa H, et al. Global epidemiology of ischemic heart disease: results from the global burden of disease study. Cureus. 2020;12:e9349.
9. Piepoli MF, Hoes AW, Agewall S, et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts): Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur J Prev Cardiol. 2016;23:NP1–NP96.
10. Lacey B, Herrington WG, Preiss D, et al. The role of emerging risk factors in cardiovascular outcomes. Curr Atheroscler Rep. 2017;19:28.
11. Omeh DJ, Shlofmitz E. Angiography. Treasure Island, FL: StatPearls. 2021.
12. Bowman AW, Kantor B, Gerber TC. Coronary computed tomographic angiography: current role in the diagnosis and management of coronary artery disease
. Pol Arch Med Wewn. 2009;119:381–390.
13. Vogel RA, Mancini GB, Bates ER. Cardiac applications of digital subtraction angiography. Int J Card Imaging. 1985;1:233–240.
14. Pelz DM, Fox AJ, Vinuela F. Digital subtraction angiography: current clinical applications. Stroke. 1985;16:528–536.
15. Meaney TF, Weinstein MA, Buonocore E, et al. Digital subtraction angiography of the human cardiovascular system. AJR Am J Roentgenol. 1980;135:1153–1160.
16. Mancini GB, Higgins CB, Norris SL, et al. Cardiac imaging with digital subtraction angiography. Cardiovasc Intervent Radiol. 1983;6:252–262.
17. Greenwood JP, Maredia N, Younger JF, et al. Cardiovascular magnetic resonance and single-photon emission computed tomography for diagnosis of coronary heart disease (CE-MARC): a prospective trial. Lancet. 2012;379:453–460.
18. Laspas F, Pipikos T, Karatzis E, et al. Cardiac magnetic resonance versus single-photon emission computed tomography for detecting coronary artery disease
and myocardial ischemia: comparison with coronary angiography. Diagnostics (Basel). 2020;10:E190.
19. Driessen RS, Raijmakers PG, Stuijfzand WJ, et al. Myocardial perfusion imaging with PET. Int J Cardiovasc Imaging. 2017;33:1021–1031.
20. Ora M, Gambhir S. Myocardial Perfusion imaging: a brief review of nuclear and nonnuclear techniques and comparative evaluation of recent advances. Indian J Nucl Med. 2019;34:263–270.
21. Dewey M, Siebes M, Kachelrieß M, et al.; Quantitative Cardiac Imaging Study Group. Clinical quantitative cardiac imaging for the assessment of myocardial ischaemia. Nat Rev Cardiol. 2020;17:427–450.
22. Finegold JA, Asaria P, Francis DP. Mortality from ischaemic heart disease by country, region, and age: statistics from World Health Organisation and United Nations. Int J Cardiol. 2013;168:934–945.
23. Knuuti J, Wijns W, Saraste A, et al.; ESC Scientific Document Group. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. 2020;41:407–477.
24. Pepine CJ, Anderson RD, Sharaf BL, et al. 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.
25. Ford TJ, Stanley B, Good R, et al. Stratified medical therapy using invasive coronary function testing in angina: the CorMicA trial. J Am Coll Cardiol. 2018;72(23 Pt A):2841–2855.
26. Mehra MR, Crespo-Leiro MG, Dipchand A, et al. International society for heart and lung transplantation working formulation of a standardized nomenclature for cardiac allograft vasculopathy-2010. J Heart Lung Transplant. 2010;29:717–727.
27. Gamba A, Mamprin F, Fiocchi R, et al. The risk of coronary artery disease
after heart transplantation is increased in patients receiving low-dose cyclosporine, regardless of blood cyclosporine levels. Clin Cardiol. 1997;20:767–772.
28. Marinescu MA, Löffler AI, Ouellette M, et al. Coronary microvascular dysfunction, microvascular angina, and treatment strategies. JACC Cardiovasc Imaging. 2015;8:210–220.
29. Jespersen L, Hvelplund A, Abildstrøm SZ, et al. 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.
30. Brainin P, Frestad D, Prescott E. The prognostic value of coronary endothelial and microvascular dysfunction in subjects with normal or non-obstructive coronary artery disease
: A systematic review and meta-analysis. Int J Cardiol. 2018;254:1–9.
31. Knott KD, Camaioni C, Ramasamy A, et al. Quantitative myocardial perfusion in coronary artery disease
: A perfusion mapping study. J Magn Reson Imaging. 2019;50:756–762.
32. Brown LAE, Onciul SC, Broadbent DA, et al. Fully automated, inline quantification of myocardial blood flow
with cardiovascular magnetic resonance: repeatability of measurements in healthy subjects. J Cardiovasc Magn Reson. 2018;20:48.
33. Valenta I, Dilsizian V, Quercioli A, et al. Quantitative PET/CT measures of myocardial flow reserve and atherosclerosis for cardiac risk assessment and predicting adverse patient outcomes. Curr Cardiol Rep. 2013;15:344.
34. Herzog BA, Husmann L, Valenta I, et al. Long-term prognostic value of 13N-ammonia myocardial perfusion positron emission tomography
added value of coronary flow reserve. J Am Coll Cardiol. 2009;54:150–156.
35. Schindler TH, Cardenas J, Prior JO, et al. Relationship between increasing body weight, insulin resistance, inflammation, adipocytokine leptin, and coronary circulatory function. J Am Coll Cardiol. 2006;47:1188–1195.
36. Murthy VL, Naya M, Taqueti VR, et al. Effects of sex on coronary microvascular dysfunction and cardiac outcomes. Circulation. 2014;129:2518–2527.
37. Tio RA, Dabeshlim A, Siebelink HM, et al. Comparison between the prognostic value of left ventricular function and myocardial perfusion reserve in patients with ischemic heart disease. J Nucl Med. 2009;50:214–219.
38. Selçuk AA. A guide for systematic reviews: PRISMA. Turk Arch Otorhinolaryngol. 2019;57:57–58.
39. Aslam S, Emmanuel P. Formulating a researchable question: a critical step for facilitating good clinical research. Indian J Sex Transm Dis AIDS. 2010;31:47–50.
40. Lo CK, Mertz D, Loeb M. Newcastle-Ottawa scale: comparing reviewers’ to authors’ assessments. BMC Med Res Methodol. 2014;14:45.
41. Higgins JPT, Thomas J, Chandler J, et al. Cochrane Handbook for Systematic Reviews of Interventions version 6.1. London: Cochrane Collaboration; 2020.
42. Farhad H, Dunet V, Bachelard K, et al. Added prognostic value of myocardial blood flow
quantitation in rubidium-82 positron emission tomography
imaging. Eur Heart J Cardiovasc Imaging. 2013;14:1203–1210.
43. Knott KD, Seraphim A, Augusto JB, et al. The prognostic significance of quantitative myocardial perfusion: an artificial intelligence-based approach using perfusion mapping. Circulation. 2020;141:1282–1291.
44. Meinel FG, Wichmann JL, Schoepf UJ, et al. Global quantification of left ventricular myocardial perfusion at dynamic CT imaging: prognostic value. J Cardiovasc Comput Tomogr. 2017;11:16–24.
45. Hamaya R, Kanaji Y, Hada M, et al. Prognostic implication of global myocardial blood flow
in patients with ST-segment elevation myocardial infarction. Heart Vessels. 2020;35:936–945.
46. Harms HJ, Bravo PE, Bajaj NS, et al. Cardiopulmonary transit time: A novel PET imaging biomarker of in vivo physiology for risk stratification of heart transplant recipients. J Nucl Cardiol. 2021. doi: 10.1007/s12350-020-02465-x
47. Monroy-Gonzalez AG, Tio RA, de Groot JC, et al. Long-term prognostic value of quantitative myocardial perfusion in patients with chest pain and normal coronary arteries. J Nucl Cardiol. 2019;26:1844–1852.
48. Green R, Cantoni V, Acampa W, et al. Prognostic value of coronary flow reserve in patients with suspected or known coronary artery disease
referred to PET myocardial perfusion imaging: a meta-analysis. J Nucl Cardiol. 2021;28:904–918.
49. Taqueti VR, Di Carli MF. Coronary microvascular disease pathogenic mechanisms and therapeutic options: JACC State-of-the-Art review. J Am Coll Cardiol. 2018;72:2625–2641.
50. Chilian WM. Coronary microcirculation in health and disease. Summary of an NHLBI workshop. Circulation. 1997;95:522–528.
51. Guerraty MA, Rao HS, Anjan VY, et al. The role of resting myocardial blood flow
and myocardial blood flow
reserve as a predictor of major adverse cardiovascular outcomes. PLoS One. 2020;15:e0228931.
52. van de Hoef TP, Echavarria-Pinto M, van Lavieren MA, et al. Diagnostic and prognostic implications of coronary flow capacity: a comprehensive cross-modality physiological concept in ischemic heart disease. JACC Cardiovasc Interv. 2015;8:1670–1680.
53. Fernandes J, Ferreira MJ, Leite L. Update on myocardial blood flow
quantification by positron emission tomography
. Rev Port Cardiol (Engl Ed). 2020;39:37–46.
54. Majmudar MD, Murthy VL, Shah RV, et al. Quantification of coronary flow reserve in patients with ischaemic and non-ischaemic cardiomyopathy and its association with clinical outcomes. Eur Heart J Cardiovasc Imaging. 2015;16:900–909.
55. Gdowski MA, Murthy VL, Doering M, et al. 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.
56. Van Belle E, Cosenza A, Baptista SB, et al.; PRIME-FFR Study Group. Usefulness of routine fractional flow reserve for clinical management of coronary artery disease
in patients with diabetes. JAMA Cardiol. 2020;5:272–281.
57. Hamaya R, Yonetsu T, Kanaji Y, et al. Interrelationship in the prognostic efficacy of regional coronary flow reserve, fractional flow reserve, high-sensitivity cardiac troponin-I and NT-proBNP in patients with stable coronary artery disease
. Heart Vessels. 2019;34:410–418.
58. Go AS, Mozaffarian D, Roger VL, et al.; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics–2013 update: a report from the American Heart Association. Circulation. 2013;127:e6–e245.
59. Selker HP, Zalenski RJ, Antman EM, et al. An evaluation of technologies for identifying acute cardiac ischemia in the emergency department: a report from a National Heart Attack Alert Program Working Group. Ann Emerg Med. 1997;29:13–87.
60. Patel MR, Peterson ED, Dai D, et al. Low diagnostic yield of elective coronary angiography. N Engl J Med. 2010;362:886–895.
61. Corcoran D, Young R, Adlam D, et al. Coronary microvascular dysfunction in patients with stable coronary artery disease
: the CE-MARC 2 coronary physiology sub-study. Int J Cardiol. 2018;266:7–14.
62. Montalescot G, Sechtem U, Achenbach S, et al.; Task Force Members; ESC Committee for Practice Guidelines; Document Reviewers. 2013 ESC guidelines on the management of stable coronary artery disease
: the Task Force on the management of stable coronary artery disease
of the European Society of Cardiology. Eur Heart J. 2013;34:2949–3003.
63. Skinner JS, Smeeth L, Kendall JM, et al.; Chest Pain Guideline Development Group. NICE guidance. Chest pain of recent onset: assessment and diagnosis of recent onset chest pain or discomfort of suspected cardiac origin. Heart. 2010;96:974–978.
64. Maron DJ, Hochman JS, Reynolds HR, et al.; ISCHEMIA Research Group. Initial invasive or conservative strategy for stable coronary disease. N Engl J Med. 2020;382:1395–1407.