Background: Coronary microvascular disease (CMVD) is associated with adverse cardiovascular outcomes. However, there is no reliable and noninvasive quantitative diagnostic method available for CMVD. The use of a pressure wire to measure the index of microcirculatory resistance (IMR) is possible, but it has inevitable practical restrictions. We hypothesized that computation of the quantitative flow ratio could be used to predict CMVD with symptoms of ischemia and no obstructive coronary artery disease (INOCA). Methods: We retrospectively assessed the diagnostic efficiency of the quantitative flow ratio-derived index of microcirculatory resistance (QMR) in 103 vessels from 66 patients and compared it with invasive IMR using the thermodilution technique. Results: Patients were divided into the CMVD group (41/66, 62.1%) and non-CMVD group (25/66, 37.9%). Pressure wire IMR measurements were made in 103 coronary vessels, including 44 left descending arteries, 18 left circumflex arteries, and 41 right coronary arteries. ROC curve analysis showed a good diagnostic performance of QMR for all arteries (area under the curve = 0.820, 95% confidence interval 0.736–0.904, p < 0.001) in predicting microcirculatory function. The optimal cut-off for QMR to predict microcirculatory function was 266 (sensitivity: 82.9%, specificity: 72.6%, and diagnostic accuracy: 76.7%). Conclusion: QMR is a promising tool for the assessment of coronary microcirculation. The assessment of the IMR without the use of a pressure wire may enable more rapid, convenient, and cost-effective assessment of coronary microvascular function.

Patients who present with symptoms of ischemic heart disease and no obstructive coronary artery disease (INOCA) are increasingly recognized in clinical practice [1‒3]. INOCA is a heterogeneous population with a variety of underlying causes, and approximately 50–65% of patients have coronary microvascular dysfunction (CMD) [4]. Attention needs to be paid to these patients because angina and inflammation of the esophagus are associated with a high risk of cardiovascular events (e.g., acute coronary syndrome, heart failure, and all-cause mortality) compared with a reference population without ischemic heart disease [5, 6].

CMD can be diagnosed, invasively or non-invasively, and accumulating research suggests that CMD predicts a poor outcome in patients with angina and INOCA [2]. At present, function of the coronary microcirculation is primarily detected by cardiac magnetic resonance imaging, myocardial contrast echocardiography, radionuclide myocardial perfusion imaging, and the invasive index of microcirculatory resistance (IMR) measured by the temperature dilution method [6‒8]. Therefore, how to evaluate the coronary microcirculation conveniently and accurately in the cardiovascular field is an important issue.

Impaired microcirculatory conductance can be diagnosed by measuring the coronary flow reserve (CFR) or minimal microcirculatory resistance (inverse of conductance) [2]. The IMR can be measured with a pressure-temperature sensor wire using the thermodilution technique to assess coronary microvascular health in a simple, quantitative, and invasive manner [9, 10]. The IMR is still perceived as a research tool. However, the IMR involves additional procedural time/complexity, has a high procedural cost, and has the potential challenge of pressure wire manipulation for widespread use in clinical practice.

The quantitative flow ratio (QFR) is a possible novel angiography-based index based on computational flow dynamics applied to three-dimensional coronary artery modeling [11]. However, the QFR remains an index for characterization of the coronary epicardial segment and does not provide direct assessment of CMD. This study aimed to determine if the QFR-derived index of microcirculatory resistance (QMR) can be used to predict coronary microvascular disease with symptoms of INOCA. QMR is a new detection method, which not only can be used to diagnose patients with simple coronary microvascular lesions but also can be used in those with coronary microvascular lesions combined with coronary vascular lesions.

Selection of Patients

We retrospectively analyzed patients with CCS class I-III of angina pectoris [2] who underwent invasive coronary angiography at the Department of Cardiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, between January 2019 and May 2021. The inclusion criteria were patients who had risk factors for CMD (e.g., diabetes, metabolic syndrome, myocardial hypertrophy, and female sex) and were indicated for coronary angiography. They were aged between 30 and 80 years. The exclusion criteria were as follows: a history of coronary artery bypass graft surgery; prior percutaneous coronary intervention; suspicion of or recent acute coronary syndrome; contraindication to adenosine; complicated complex congenital heart disease; severe arrhythmia; prior artificial pacemaker or internal defibrillator leads implanted; an implanted artificial heart valve; impaired chronic renal function (serum creatinine concentrations >1.5 times the upper limit of normal); allergic to iodinated contrast; state of pregnancy; body mass index >35 kg/m2; requirement for emergency procedures; severe distortion in the blood vessel; unstable hemodynamics, such as abrupt chest pain cardiogenic shock, unstable blood pressure (systolic blood pressure <90 mm Hg), severe congestive heart failure, and pulmonary edema; evidence of life-threatening diseases (life expectancy <2 months); prior Tako Tsubo syndrome; and patients who were judged as being inappropriate for participation by clinicians.

Study Procedure

Of the 166 patients remaining after applying the inclusion and exclusion criteria, 109 underwent invasive coronary angiography and IMR assessment. Nineteen patients were excluded, among whom 11 did not have the IMR data and 8 withdrew informed consent. Of these 109 patients, 90 patients were assessed for eligibility for QMR analysis (Fig. 1). Of these 90 patients, 24 were excluded, among whom 12 had insufficient image quality, 6 had arrhythmia, and 6 were excluded for other reasons (e.g., chronic total occlusions and missing calibration). Finally, 66 patients were included in the study with 103 diseased vessels available for analysis. A flowchart depicting the details of selecting the patients is shown in Figure 1.

Fig. 1.

Study flowchart. IMR, index of microcirculatory resistance; QMR, quantitative flow ratio-derived index of microcirculatory resistance.

Fig. 1.

Study flowchart. IMR, index of microcirculatory resistance; QMR, quantitative flow ratio-derived index of microcirculatory resistance.

Close modal

Data Collection

The patients’ baseline demographics and procedural characteristics were prospectively collected and recorded in a dedicated electronic case report form. Angiographic images were transferred to the core laboratory (Pulse Medical Imaging Technology, Shanghai, China), where the characteristics and severity of coronary atherosclerotic lesions were quantified by two independent, blinded researchers. Informed consent to participate in this study was obtained from the patients or their relatives. The protocol was approved by the Ethics Committee of Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, for medical research investigating human subjects.

IMR Measurement

ICA was performed in accordance with the American College of Cardiology/American Heart Association guidelines for coronary angiography and intervention [12]. An intracoronary pressure and temperature sensor-tipped guidewire (St. Jude Medical, USA) was used to measure distal coronary pressure and the index of coronary flow derived from the coronary thermodilution method according to standard operating procedures [9]. After intracoronary injection of 200 μg isosorbide dinitrate, the conduction time (Tmn) was measured when the cardiac muscle reached the maximum hyperemic state at a dose of 160 µg/kg/min of adenosine 5′-triphosphate [13]. The temperature curve was sensed by a temperature sensor induced at room temperature with 0.9% sodium chloride solution (3 mL). The mean conduction time was calculated after three consecutive operations. Mean aortic pressure (Pa), mean distal pressure (Pd), fractional flow reserve (FFR), and CFR values were obtained at baseline and at hyperemia. If the FFR was >0.80 or coronary artery stenosis was mild to moderate, the IMR was calculated using the following simplified formula: IMR = Pd × Tmn [14]. However, when severe coronary artery stenosis or the FFR was ≤0.80, IMR = Pa × Tmn ([Pd−Pw]/[Pa−Pw]) [15]. Pw represents the wedge pressure of the coronary artery (i.e., mean pressure at the distal end of the lesion after complete coronary stenosis or balloon incarceration). Representative images of match are shown in Figure 2.

Fig. 2.

a Three-dimensional reconstruction of the human coronary artery, hemodynamic simulation, and calculation of coronary microcirculation resistance. b Human coronary angiography images and the coronary microcirculation resistance index calculated by a pressure guidewire and the temperature dilution method. QMR, quantitative flow ratio-derived index of microcirculatory resistance; Pa, proximal aortic pressure; QFR, flow quantitative flow ratio; IMR, index of microcirculatory resistance; Pd, intracoronary distal pressure; Tmn, mean transit time.

Fig. 2.

a Three-dimensional reconstruction of the human coronary artery, hemodynamic simulation, and calculation of coronary microcirculation resistance. b Human coronary angiography images and the coronary microcirculation resistance index calculated by a pressure guidewire and the temperature dilution method. QMR, quantitative flow ratio-derived index of microcirculatory resistance; Pa, proximal aortic pressure; QFR, flow quantitative flow ratio; IMR, index of microcirculatory resistance; Pd, intracoronary distal pressure; Tmn, mean transit time.

Close modal

Computation of the QFR

At the same time points when the IMR was measured, and only when measurement of the IMR was completed, angiographic images were acquired at 15 frames/second with manual injection of a contrast dye during maximal hyperemia. The QFR was computed using the AngioPlus system (Pulse Medical Imaging Technology) by two independent operators who were blinded to the data.

The QMR was calculated as follows: (coronary Pd−coronary vein pressure [Pv])/coronary blood flow. As a result of the fixed unit cross-sectional area of coronary epicardial vascular, coronary blood flow flowing through the unit cross-sectional area was expressed as the coronary blood flow rate. The coronary blood rate equals the length of the target coronary vessel (L)/time (t), which was calculated by the number of frames. Because Pv was close to zero in a state of hyperemia, the QMR formula was simply expressed as follows: QMR = Pd/Q = Pa × Pd/(Pa × Q) = Pa × QFR × 15 L/N, where t = N/15, N is the number of frames, and Q represents coronary blood flow. The rate of radiography acquisition was 15 × N every second. QMR was obtained by measuring Pa and QFR and measuring the length from the head of the contrast tube to the reference point of the count of the number of frames. Additionally, the number of frames where the contrast agent moved to the reference point of the count of the number of frames was recorded.

Statistical Analysis

All analyses were conducted using SPSS, version 20.0. All measurement data were tested for normality and homogeneity of variance. Numerical results are presented as the mean ± standard deviation or the median with interquartile range. Categorical covariates were compared using the χ2 test or Fisher’s exact test. With regard to continuous variables, median values were compared with the Mann-Whitney U test, while mean values were compared with the t test or one-way analysis of variance. We performed receiver-operator characteristic (ROC) curve analysis for QMR to determine its sensitivity and specificity for diagnosing coronary microvascular disease. A p value of <0.05 was considered to indicate statistical significance. The p values were two-tailed.

Baseline Characteristics

Overall, 66 (103 vessels) patients were included in the current analysis. The mean age was 67.74 ± 9.38 years (56.06% men). Clinical and procedural characteristics of the patients are shown in Table 1. The characteristics of no obstructive coronary artery disease (n = 103) are shown in Table 2.

Table 1.

Clinical characteristics of the patients

n = 66
Age, years 67.74±9.38 
Male, n (%) 37 (56.06) 
BMI 24.49±3.28 
Hypertension, n (%) 37 (56.06) 
Hypercholesterolemia, n (%) 10 (15.15) 
Diabetes, n (%) 9 (13.64) 
Current smoker, n (%) 16 (24.24) 
Prior PCI history, n (%) 0 (0) 
Prior CABG history, n (%) 0 (0) 
LVEF, % 65.52±7.21 
Laboratory 
 SCr, μmol/L 86.62±29.86 
 FBG, mmol/L 5.19±1.13 
 TG, mmol/L 1.66±1.08 
 TC, mmol/L 4.20±1.00 
 LDL-C, mmol/L 2.25±0.76 
 HLD-C, mmol/L 1.15±0.32 
 apo-A1, g/L 1.34±0.21 
 CRP, mg/L 3.77±4.08 
 HbA1c, % 5.79±0.84 
n = 66
Age, years 67.74±9.38 
Male, n (%) 37 (56.06) 
BMI 24.49±3.28 
Hypertension, n (%) 37 (56.06) 
Hypercholesterolemia, n (%) 10 (15.15) 
Diabetes, n (%) 9 (13.64) 
Current smoker, n (%) 16 (24.24) 
Prior PCI history, n (%) 0 (0) 
Prior CABG history, n (%) 0 (0) 
LVEF, % 65.52±7.21 
Laboratory 
 SCr, μmol/L 86.62±29.86 
 FBG, mmol/L 5.19±1.13 
 TG, mmol/L 1.66±1.08 
 TC, mmol/L 4.20±1.00 
 LDL-C, mmol/L 2.25±0.76 
 HLD-C, mmol/L 1.15±0.32 
 apo-A1, g/L 1.34±0.21 
 CRP, mg/L 3.77±4.08 
 HbA1c, % 5.79±0.84 

BMI, body mass index; PCI, percutaneous coronary intervention; CABG, coronary artery bypass graft; LVEF, left ventricular ejection fraction; SCr, serum creatinine; FBG, fasting blood glucose; TG, triglyceride; TC, total cholesterol; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; CRP, C-reactive protein.

Table 2.

Characteristics of no obstructive coronary artery disease

n = 103
No obstructive coronary artery localization 
 LAD, n (%) 44 (42.72) 
 LCX, n (%) 18 (17.47) 
 RCA, n (%) 41 (39.81) 
IMR parameters 
 Pa 79.59±14.68 
 Pd 72.59±15.70 
 FFR 0.93±0.06 
 Tnm 0.34±0.18 
 CFR 3.94±1.86 
 IMR 24.26±11.10 
Physiological indices 
 fQFR 0.93±0.10 
 cQFR 0.94±0.05 
 Flow velocity 15.15±4.68 
 QMR 265.64±50.94 
n = 103
No obstructive coronary artery localization 
 LAD, n (%) 44 (42.72) 
 LCX, n (%) 18 (17.47) 
 RCA, n (%) 41 (39.81) 
IMR parameters 
 Pa 79.59±14.68 
 Pd 72.59±15.70 
 FFR 0.93±0.06 
 Tnm 0.34±0.18 
 CFR 3.94±1.86 
 IMR 24.26±11.10 
Physiological indices 
 fQFR 0.93±0.10 
 cQFR 0.94±0.05 
 Flow velocity 15.15±4.68 
 QMR 265.64±50.94 

LAD, left descending artery; LCX, left circumflex artery; RCA, right coronary artery; Pa, proximal aortic pressure; Pd, intracoronary distal pressure; FFR, fractional flow reserve; Tmn, mean transit time; CFR, coronary flow reserve; IMR, index of microcirculatory resistance; fQFR, fixed-flow quantitative flow ratio; cQFR, contrast-flow quantitative flow ratio; QMR, quantitative flow ratio-derived index of microcirculatory resistance.

Diagnostic Efficiency of QMR in Predicting Microvascular Dysfunction

The IMR and QMR were significantly correlated in the overall sample of 103 arteries, including 44 left descending arteries, 18 left circumflex arteries, and 41 right coronary arteries. A graphical representation of this comparison is shown in Figure 3.

Fig. 3.

QMR effectively predicts coronary microvascular dysfunction in INOCA. In a, ROC curve shows excellent diagnostic efficiency of QMR and the optimal QMR cut-off value for assessing microvascular dysfunction in patients with INOCA for all arteries. b LCX artery. c LAD artery. d RCA artery. QMR, quantitative flow ratio-derived index of microcirculatory resistance; INOCA, ischemia and no obstructive coronary artery disease; AUC, areas under the receiver-operator characteristics curve; LCX, left circumflex artery; LAD, left descending artery; RCA, right coronary artery.

Fig. 3.

QMR effectively predicts coronary microvascular dysfunction in INOCA. In a, ROC curve shows excellent diagnostic efficiency of QMR and the optimal QMR cut-off value for assessing microvascular dysfunction in patients with INOCA for all arteries. b LCX artery. c LAD artery. d RCA artery. QMR, quantitative flow ratio-derived index of microcirculatory resistance; INOCA, ischemia and no obstructive coronary artery disease; AUC, areas under the receiver-operator characteristics curve; LCX, left circumflex artery; LAD, left descending artery; RCA, right coronary artery.

Close modal

ROC curve analysis showed an excellent diagnostic performance of QMR in predicting CMD for all arteries (area under the curve = 0.820, 95% confidence interval (CI): 0.736–0.904, p < 0.001; Fig. 3a). The optimal cut-off of QMR for predicting CMD was 266 (sensitivity: 82.9%, specificity: 72.6%, negative predictive value: 86.5%, positive predictive value: 66.7%, and diagnostic accuracy: 76.7%). With regard to the left descending artery, the area under the curve was 0.865 (95% CI: 0.723–1.000, p < 0.001; Fig. 3c). The optimal cut-off of QMR for predicting CMD was 270 (sensitivity: 81.8%, specificity: 84.8%, negative predictive value: 93.3%, positive predictive value: 64.3%, and diagnostic accuracy: 84.1%). With regard to the right coronary artery, the area under the curve was 0.801 (95% CI: 0.656–0.946, p < 0.001; Fig. 3d). The optimal cut-off of QMR for predicting CMD was 270 (sensitivity: 80.0%, specificity: 75.0%, negative predictive value: 70.6%, positive predictive value: 83.3%, and diagnostic accuracy: 78.0%). However, there was no statistical difference in left circumflex artery (95% CI: 0.494–0.968, p = 0.139; Fig. 3c).

Head-To-Head Comparison of QMR and the Invasive IMR

On the basis of the good diagnostic efficiency of QMR analysis in predicting microcirculatory function in patients with INOCA, we performed a head-to-head comparison of QMR and the invasive IMR. This comparison was performed to further validate this method in coronary microvessels, in which hemodynamic relevance was assessed with a staged IMR (n = 103). QMR and the invasive IMR presented a strong positive linear correlation (95% CI: 1.815–3.317, p < 0.001; Fig. 4a; 95% CI: 1.365–1.586, p < 0.001; Fig. 4b). A graphical representation of this comparison is shown in Figure 4.

Fig. 4.

Head-to-head comparison of QMR and invasive IMR. In a, a strong linear correlation between QMR and IMR of microvascular dysfunction is shown. In b, Bland-Altman plot shows a good agreement between IMR and QMR. QMR, quantitative flow ratio-derived index of microcirculatory resistance; IMR, index of microcirculatory resistance.

Fig. 4.

Head-to-head comparison of QMR and invasive IMR. In a, a strong linear correlation between QMR and IMR of microvascular dysfunction is shown. In b, Bland-Altman plot shows a good agreement between IMR and QMR. QMR, quantitative flow ratio-derived index of microcirculatory resistance; IMR, index of microcirculatory resistance.

Close modal

In the current study, we found that QMR, as established by the QFR, was effective in assessing microcirculation disorders in patients with ischemia and no obstructive coronary artery disease. We found the following: (1) QMR showed good agreement with pressure wire-determined standard IMR measurements; (2) a value of 266 U appeared to be the best threshold of QMR to predict an abnormal IMR in INOCA; and (3) the QMR value could be a potential index to predict CMD.

CMD could interfere in the calculation of FFR and might lead to an overestimation of FFR [16]. However, the effect of QMR on the QFR remains unclear. CMD decreases the diagnostic performance of the QFR [16, 17]. Despite CMD, the QFR as measured by FFR remains superior to angiography in determining the severity of functional stenosis. The presence of high IMR values and high acute coronary syndrome is an independent predictor of discordance between the QFR and FFR [16]. Although there is no universally accepted CFR cut-off to diagnose CMD and various studies have used CFD values ranging from 1.5 to 2.6, a myocardial perfusion reserve >2.4 is generally considered normal [18]. Schumann et al. [19] studied 66 patients with INOCA who underwent stress cardiac magnetic resonance with calculation of myocardial perfusion reserve and found that 59% (myocardial perfusion reserve ≤2.4) had definite or borderline CMD. The clinical risk is high, regardless of the presence of CMD, in patients with INOCA, according to this previous finding. The patients with INOCA in our study had a high risk factor burden (high rates of hypertension, hyperlipidemia, and smoking).

Patients with ST elevation myocardial infarction show an excellent correlation between angiography-derived microcirculatory resistance and wire-based myocardial resistance [20]. Sheng et al. [21] also reported that QFR computation can be used to predict microvascular function in patients with ST elevation myocardial infarction. Our study showed that the IMR derived from QFR, represented as QMR, was able to be measured in patients with INOCA. We found a significant correlation between the IMR and QMR, as confirmed by the ROC curve analysis. For decision-making purposes, values of an IMR ≥25 U are indicative of abnormal microcirculatory function [22]. Notably, in our study, QMR showed a similar upper cut-off of 266 U to predict an abnormal IMR.

The flow velocity in coronary arteries reflects not only that in epicardial vessels but also microvascular function. Coronary flow velocity reserve can estimate coronary flow at rest and in hyperemia and has been applied as a technique for diagnosing MVD in patients without obstructive epicardial coronary disease [23, 24]. As a result of the high variability and low reproducibility of coronary flow velocity reserve during hyperemia, it has not been widely applied in clinical practice. A previous study showed that hyperemic flow velocity was increased and angiography-derived microcirculatory resistance was decreased in intermediate lesions with a QFR ≤0.8. However, in the current study, QMR showed good agreement with the pressure wire-determined standard IMR measurements in patients with INOCA and a QFR >0.8. Because of the adenosine-free nature of QFR, previous studies and our study used non-hyperemic indices to estimate microvascular resistance [21, 25, 26]. Furthermore, a QFR pilot study showed a good correlation between contrast velocity and adenosine-induced hyperemic velocity [11]. Nevertheless, the cut-offs of angiography-based microcirculatory indices for predicting microvascular dysfunction may need to be evaluated in future studies in hyperemic and non-hyperemic conditions.

The present study has some limitations. First, this was a single-center study with a relatively limited sample size and associated limitations in the number of predictors that could be included in the multivariable regression analysis. Comparing characteristics of study cohorts is limited by a small sample size. Second, angiographic images were collected retrospectively, which would have affected the feasibility and reliability of the QFR analysis. Third, QMR was calculated using validated equations under the non-hyperemic state. Therefore, future studies are warranted to confirm our findings in hyperemic conditions. Although the results of this study partly showed a relationship between coronary microcirculation resistance-microvascular disease and QFR computation, the true translation of our findings in clinical practice should be verified in other patients with potential MVD.

There is substantial anatomical-functional discordance between combined invasive IMR parameters and the QFR. QMR is an easy, quick, and cost-effective test for the routine assessment of microvascular function in the catheter lab. QMR facilitates prognostic stratification and early triage of therapies for patients with coronary disease.

We thank Liwen Bianji (Edanz) (http://www.liwenbianji.cn/) for editing the English text of a draft of this manuscript.

This study was approved by the Ethics Committee of Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine (2019-033-01), and informed consent will be obtained for all enrolled patients, and written informed consent was obtained for participation in this study.

The authors declare that there is no conflict of interest.

This research was supported by the Key R&D Project through the Science and Technology Department of Zhejiang Province (Grant No. 2020C03018).

Jinyu Huang and Guoxin Tong conceived and designed the study. Beibei Gao wrote the draft of the paper. Guomin Wu, Jianchang Xie, Peng Xu, Yufeng Qian, Junjie Gu, and Xiangbo Jin recruited patients and performed QMR analysis and IMR analysis. Wei Li collected data. Jie Ruan performed the statistical analysis. All authors contributed to revision and approved the final version of the manuscript.

All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.

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