Abstract
Objectives
This study sought to elucidate the diagnostic performance and concordance between myocardial flow reserve (MFR) derived from dynamic single-photon emission computed tomography (D-SPECT) and quantitative flow ratio (QFR) in evaluating non-culprit ischemic lesions following ST-elevation myocardial infarction (STEMI). Additionally, the study investigated the integration of MFR with angiographic microvascular resistance (AMR) derived from coronary angiography to determine its utility in screening and stratifying non-culprit ischemic lesions.
Background
The diagnostic performance and agreement between MFR and QFR in evaluating non-culprit ischemic lesions in STEMI patients with multivessel disease are unknown.
Methods
This research encompassed a cohort of 106 STEMI patients with at least 1 intermediate non-culprit ischemic lesion, characterized by 40–80% diameter stenosis. After undergoing percutaneous coronary intervention, patients were evaluated using QFR and, approximately five days later, underwent D-SPECT to assess myocardial blood flow (MBF) and MFR. MFR was evaluated against QFR as a reference for diagnostic performance and agreement, including sensitivity analysis in vessels with normal microvascular function. Furthermore, MFR, combined with AMR, effectively screened and stratified non-culprit ischemic lesions. Non-culprit ischemic lesions were defined by QFR ≤ 0.80 and normal microvascular function by AMR < 255 mmHg*s/m.
Results
Among non-culprit lesions, MFR predicted a QFR ≤ 0.80 with a sensitivity of 85%, specificity of 86%, and accuracy of 86%. The positive predictive value was 56%, and the negative predictive value was 96%. The MFR cut-off was 1.93, with an area under the receiver operating characteristic curve of 0.90 (95% CI: 0.84 to 0.94). MFR showed similar diagnostic performance in patients with normal microcirculation. Moreover, low MFR with normal AMR indicated non-culprit ischemic lesions caused solely by epicardial narrowing, while low MFR with abnormal AMR indicated ischemic lesions complicated by microvascular dysfunction.
Conclusion
MFR derived from D-SPECT exhibits good diagnostic performance and moderate agreement in identifying non-culprit ischemic lesions in patients with STEMI. Combining AMR with MFR effectively screens and stratifies non-culprit ischemic lesions.
Trial registration
ChiCTR.org.cn. ChiCTR2200059934. Registered 13 May 2022.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00259-025-07223-0.
Keywords: Myocardial flow reserve (MFR), Quantitative flow ratio (QFR), ST-Elevation myocardial infarction (STEMI), Non-culprit ischemic lesions, Dynamic single-photon emission computed tomography (D-SPECT)
Introduction
In recent years, positron emission tomography (PET) has been established as the gold standard for evaluating myocardial ischemia in patients with coronary artery disease (CAD), attributed to its superior diagnostic accuracy [1–3]. However, PET imaging technology's high cost, high radiation dose, and low spatial resolution have limited its clinical application and dissemination. In contrast, while traditional SPECT imaging is prevalent in clinical settings, it fails to provide a comprehensive quantitative assessment of myocardial blood flow (MBF) and myocardial flow reserve (MFR). Interestingly, the new generation of dynamic single-photon emission computed tomography (D-SPECT) using cadmium-zinc-telluride (CZT) solid-state detectors not only reduces the required radiation dose but also shows higher sensitivity and resolution in the obese population, enabling complete quantitative evaluations of MFR [4–6]. Additionally, several studies have demonstrated good agreement between D-SPECT and PET in assessing MFR [7–10].
In contemporary clinical practice, guidelines advocate for assessing fractional flow reserve (FFR) using invasive angiography to identify myocardial ischemia and guide revascularization [11–13]. Nonetheless, the invasiveness of the procedure and the dependency on hyperemic agents curtail its application [14]. Interestingly, the introduction of quantitative flow ratio (QFR) technology has effectively surmounted these limitations by eliminating the need for a pressure guidewire and demonstrating high operational feasibility [15]. Prior studies have demonstrated robust agreement and diagnostic accuracy between FFR and QFR, positioning QFR as a critical reference standard for identifying myocardial ischemia [16, 17]. Moreover, coronary microvascular dysfunction (CMD) is a major contributor to non-obstructive myocardial ischemia in patients with ST-elevation myocardial infarction (STEMI). Studies have demonstrated that the discordance between FFR and MFR is largely attributed to the presence of CMD [18, 19]. Consequently, it is particularly important to quantitatively assess CMD when studying ischemic lesions in non-culprit vessels. Currently, the index of microcirculatory resistance (IMR) is one of the most widely used metrics for evaluating CMD. This index provides a precise measure of microvascular resistance by monitoring real-time changes in pressure, blood flow, and temperature at the distal end of the coronary arteries [20, 21]. This index has been extensively validated across a range of clinical conditions, including stable angina, acute myocardial infarction, cardiomyopathies, heart transplantation, and valvular heart disease, underscoring its prognostic and risk stratification capabilities [22–26]. Despite its clinical relevance, the invasive nature, complexity, and high cost of IMR measurements limit its widespread adoption. Notably, the development and validation of the angiography-derived microvascular resistance index (AMR) have addressed these limitations. Based on micro-quantitative flow ratio (μQFR), AMR eliminates the need for pressure wires and adenosine, offering a non-invasive and quantifiable approach to assessing microvascular function. Previous studies have underscored AMR's high reproducibility, rapid execution, and ease of use, positioning it as a viable and effective alternative to IMR in the detection of CMD among patients with coronary artery disease [27–29].
Concurrent studies indicate good concordance between MFR derived from D-SPECT and FFR in assessing multivessel and stable CAD [8, 10, 30]. However, the agreement between MFR and QFR in identifying non-culprit ischemic lesions in STEMI patients remains unknown. Notably, the failure to appropriately stratify and manage non-culprit lesions in STEMI patients can lead to poor prognosis, especially in those with more advanced disease [31, 32]. Given that D-SPECT offers comprehensive capabilities to concurrently assess left ventricular function, ischemic zones, infarct size (IS), and MFR, this technology provides distinct advantages in managing STEMI patients. In particular, D-SPECT enables a complex quantitative assessment of MFR in non-culprit vessels, providing significant incremental value to patient prognosis. Therefore, we designed a diagnostic test based on comparing MFR derived from D-SPECT with QFR, aiming to evaluate the diagnostic performance and agreement of MFR in identifying non-culprit ischemic lesions in patients with STEMI. Additionally, this study integrates MFR with AMR to further explore the incremental benefits of screening and stratification processes for these ischemic lesions.
Methods
Study population
In this single-center prospective trial conducted at the First Hospital of Lanzhou University between May 2023 and March 2024, STEMI patients who underwent emergency percutaneous coronary intervention (PCI) were investigated for their QFR and D-SPECT-derived MFR. This trial is officially registered with the Chinese Clinical Trial Registry (ChiCTR2200059934). QFR and MFR measurements were meticulously carried out by expert observers, with a second observer overseeing the process to rigorously enforce the principles of blinding, thereby safeguarding the objectivity and accuracy of the diagnostic assessments. The primary inclusion criteria were STEMI patients with multivessel disease, where non-culprit vessel stenosis ranged from 40–80%, and who had successfully undergone coronary angiography (CAG) and PCI. Patient ages ranged from 18–75 years. Exclusion criteria included: 1) patients with a mean heart rate < 55 bpm or systolic blood pressure < 100 mmHg; 2) those requiring hemodialysis due to left main lesions; 3) those requiring hemodialysis due to left main lesions; previous coronary artery bypass grafting (CABG) or PCI, coronary artery calcification or chronic total occlusion, unsuccessful PCI of culprit lesions, or referral for CABG; 4) severe hepatic or renal impairment requiring hemodialysis; 5) chronic pulmonary diseases characterized by persistent wheezing or a significant history of reactive airway disease; 6) second or third-degree atrioventricular block, sick sinus syndrome, or complete bundle branch block without a pacemaker; 7) use of dipyridamole within the last 48 h. The institutional ethics committee approved the study protocol.
QFR and AMR measurement process
This study included patients with STEMI and multivessel disease, where all non-culprit vessels with 40%−80% stenosis were evaluated using QFR. QFR measurements were performed in a blinded fashion by experienced analyst H.W., under the supervision of senior analyst J.L., using the commercialized system (AngioPlus Galley, Shanghai Pulse Medical Technology, Inc., Shanghai, China) [33, 34]. QFR calculations were based on Murray's bifurcation law from a single angiographic view. Specifically, the process involves 3D reconstruction and hemodynamic analysis of two standard angiographic images of non-culprit vessels taken at angles of ≥ 25°. Subsequently, virtual pressure curves generated by computer software were used to compute the QFR of the non-culprit vascular lesions. This study differentiated ischemic and non-ischemic lesions at QFR thresholds of ≤ 0.80 and > 0.8, respectively [35]. Additionally, AMR was quantified through the ratio of distal pressure to hyperemic flow velocity, with a threshold of < 255 mm Hg * s/m defining a normal coronary microcirculatory function [36].
D-SPECT image acquisition
This study utilized a dedicated D-SPECT cardiac scanner (Spectrum Dynamics, Israel) for dynamic imaging at rest and under stress. A single-day protocol was followed, with patients receiving a fasting intravenous injection of 99mTc-MIBI at a dose of 10 mCi (1 mCi equals 37 MBq). Notably, patients undergoing D-SPECT were instructed to discontinue xanthine-containing drugs, β-blockers, and nitrates 24 h before the exam and to avoid caffeinated beverages such as coffee, cola, and tea. To minimize interference from hepatic and biliary excretion in myocardial perfusion imaging, patients consumed a high-fat meal 30 min before the scan. In this study, D-SPECT parameters were determined in a blinded fashion by a senior analyst (Q.L.) and supervised by another analyst (Z.G.). Resting myocardial perfusion imaging was conducted one hour after the meal, followed by adenosine-stress myocardial perfusion imaging 30 min later. Left ventricular ejection fraction (LVEF), left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), MBF, and MFR were assessed under both resting and stress conditions. Additionally, the QPS/QGS software was used to evaluate the ischemic area and infarct size associated with non-culprit vascular lesions. Specifically, reversible defects, indicated by reduced myocardial tracer uptake under stress with normal uptake at rest, were identified as myocardial ischemia in non-culprit vascular lesions.
Statistical analysis
Continuous variables were described using mean ± standard deviation (x̄ ± s) or median with interquartile ranges (P25, P75). Categorical variables were expressed as frequencies and percentages. The correlation between MFR and QFR in multivessel disease was quantified using Pearson correlation analysis. Agreement between MFR and QFR was evaluated using Bland–Altman plots, which displayed the percentage difference of each measurement pair relative to their average, with the mean percentage difference of all pairs serving as a reference line. The limits of agreement were defined as the mean absolute difference ± 1.96 standard deviations. MFR's diagnostic performance in identifying ischemic lesions in non-culprit vessels (QFR ≤ 0.8) was assessed using sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, and diagnostic accuracy. Furthermore, further Sensitivity analysis was conducted on non-culprit vessels with normal microcirculatory function to evaluate MFR's diagnostic capabilities under these conditions. Optimal cutoff values were determined using the area under the receiver operating characteristic (ROC) curve (AUC) and Youden's index. ROC curve comparisons utilized the DeLong method. A two-tailed P-value of < 0.05 was considered statistically significant. All data analyses were performed using MedCalc version 23.0.1 and SPSS version 27.0 (IBM Inc., Armonk, NY, USA).
Results
Patient characteristics
During the recruitment period at the First Hospital of Lanzhou University, 140 patients were enrolled, 106 of whom successfully underwent D-SPECT and QFR analysis, as shown in Fig. 1. The study cohort's baseline characteristics (Table 1) reveal an average age of 58 ± 9 years, predominantly male (81.1%). In 106 STEMI patients, a total of 145 non-culprit vessels were analyzed. The characteristics of the angiography and D-SPECT for left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA) non-culprit lesions, are listed in Table 2, with counts of 44, 50, and 51, respectively. The cohort included 65 individuals with triple-vessel disease, comprising 61.3% of the total. The mean values for QFR, MFR, and AMR were 0.88 ± 0.11, 2.43 ± 0.70, and 253 ± 73 mmHg*s/m, respectively. LVEF was assessed at 54 ± 14% in resting state and 53 ± 14% under stress, with total ischemic extent relative to left ventricular area averaging 13 (8, 29)%.
Fig. 1.
Flowchart of Study Population. STEMI: ST-segment elevation myocardial infarction, D-SPECT: dynamic single-photon emission computed tomography, QFR: quantitative flow ratio, MFR: myocardial flow reserve, AMR: angiographic microvascular resistance
Table 1.
Baseline Characteristics (N = 106)
| Age, yrs | 58 ± 9 |
| Male | 86 (81.1) |
| Risk factors | |
| Hypertension | 55 (51.9%) |
| Dyslipidemia | 39 (36.8%) |
| Diabetes mellitus | 19 (17.9%) |
| Current smoker | 67 (63.2%) |
| Obesity | 13 (12.3%) |
| Killip class on admission | |
| I | 84 (79.3%) |
| II | 17 (16.0%) |
| III | 3 (2.8%) |
| IV | 2 (1.9%) |
| Medication at discharge | |
| Aspirin | 106 (100%) |
| P2Y12 inhibitor | 106 (100%) |
| Beta-blocker | 99 (93.4%) |
| ACEI, ARB, or ARNI | 90 (84.9%) |
| Statin | 106 (100%) |
Values are mean ± SD or n (%)
ACEI angiotensin-converting enzyme inhibitor, ARB angiotensin II receptor blocker, ARNI angiotensin receptor neprilysin inhibitor
Table 2.
Angiographic and D-SPECT Characteristics (N = 106)
| The time between pPCI and D-SPECT (days) | 6 ± 2 |
| Infarct-related artery | |
| LAD/LCX/RCA | 56/20/35 |
| Multivessel disease | |
| Two vessel | 41 (38.7%) |
| Triple vessel | 65 (61.3%) |
| Non-culprit lesion location (number of vessels) | |
| LAD/LCX/RCA | 44/50/51 |
| Non-culprit vessels | |
| QFR | 0.88 ± 0.11 |
| With QFR ≤ 0.80 | 0.71 ± 0.13 |
| With QFR > 0.80 | 0.91 ± 0.05 |
| MFR | 2.43 ± 0.70 |
| AMR (mmHg*s/m) | 253 ± 73 |
| With AMR < 255 | 206 ± 37 |
| With AMR ≥ 255 | 326 ± 51 |
| LV function and volumes at rest | |
| LVEF (%) | 54 ± 14 |
| LV EDV (ml/m2) | 100 ± 40 |
| LV ESV (ml/m2) | 50 ± 35 |
| LV function and volumes at stress | |
| LVEF (%) | 53 ± 14 |
| LV EDV (ml/m2) | 105 ± 41 |
| LV ESV (ml/m2) | 54 ± 37 |
| Total ischemic extent (% of LV) | 13 (8, 29) |
Values are mean ± SD, n (%), or median (interquartile range)
D-SPECT dynamic single-photon emission computed tomography, LAD left anterior descending artery, LCX left circumflex artery, RCA right coronary artery, LV left ventricle, pPCI primary percutaneous coronary intervention, EF ejection fraction, EDV end-diastolic volume, ESV end-systolic volume, QFR quantitative flow ratio, MFR myocardial flow reserve, AMR angiographic microvascular resistance
Analysis of correlation and agreement between MFR and QFR
The correlation analysis between QFR and MFR is presented in Fig. 4. A representative case of QFR and MFR comparisons is shown in Fig. 2, while their agreement is detailed in Fig. 3. Across the overall non-culprit vessels, the analysis revealed a moderate correlation between QFR and MFR (r = 0.43; p < 0.001), with a notably stronger correlation in LAD non-culprit vessels (r = 0.57; p < 0.001). In contrast, LCX and RCA non-culprit vessels showed moderate correlations (LCX: r = 0.40; p < 0.05; RCA: r = 0.33; p < 0.05). According to the Bland–Altman analysis in Fig. 3, the LAD non-culprit vessels demonstrated moderate agreement with an average difference of 91.2% and agreement limits spanning from 59.4% to 123.0%. The LCX vessels showed a similar agreement pattern with an average difference of 86.0% and limits ranging from 46.9% to 125.1%. In contrast, RCA vessels exhibited a more favorable consistency, with an average difference of 94.0% and a range of 53.6% to 134.4% (Fig. 3).
Fig. 4.
The Correlation Between MFR and QFR. A Correlation of MFR with QFR in all noncriminal vascular lesions. B Correlation of MFR with QFR in LAD. C Correlation of MFR with QFR in LCX. D Correlation of MFR with QFR in RCA. D-SPECT: dynamic single photon emission computed tomography, LAD: left anterior descending artery, LCX: left circumflex artery, RCA: right coronary artery, QFR: quantitative flow ratio, MFR: myocardial flow reserve
Fig. 2.
Case Example. A D-SPECT evaluates MBF in the LAD, LCX, and RCA at rest and under stress. The MFR is the ratio of MBF under stress to MBF at rest. The MFR of LCX is 2.8. B The QFR value for the LCX is determined to be 0.91 through QCA; The AMR, derived from coronary angiographic evaluation, is calculated to be 230 mm Hg * s / m. C Luminal diameter and QFR pullback in the LCX non- culprit lesions. D-SPECT: dynamic single-photon emission computed tomography, LAD: left anterior descending artery, LCX: left circum- flex artery, RCA: right coronary artery, QCA: Three-dimensional quantitative coronary angiography, QFR: quantitative flow ratio, MFR: myocardial flow reserve, AMR: angiographic microvascular resistance
Fig. 3.
Diagnostic Performance and Agreement of MFR and QFR Across Different Coronary Vessels. Top: MFR for the LAD, middle: MFR for the LCX, bottom: MFR for the RCA. Results of ROC curves and Bland–Altman analysis for detecting non-culprit vessels with QFR ≤ 0.80. MFR: myocardial flow reserve; QFR: quantitative flow ratio; LAD: left anterior descending artery; LCX: left circumflex artery; RCA: right coronary artery; AUC: area under the curve
Diagnostic performance of MFR in predicting non-culprit ischemia lesions
The diagnostic performance of MFR in predicting ischemic lesions in non-culprit vessels is presented in Figs. 3 and 5 and Table 3. Across all non-culprit vessel lesions, MFR predicted a QFR ≤ 0.80 with a sensitivity of 85%, a specificity of 86%, an accuracy of 86%, a positive predictive value of 56%, and a negative predictive value of 96%. The corresponding ROC curve had an AUC of 0.90 (95% CI: 0.84 to 0.94). Moreover, the rates of false positives and false negatives for predicting QFR ≤ 0.80 were 14.3% and 15.4%, respectively. Results in Fig. 5 suggest the optimal MFR cutoff value for predicting QFR ≤ 0.80 is 1.93, with a Youden index of 0.70. In vessel-specific analyses, RCA showed the best diagnostic performance with a sensitivity of 83%, specificity of 89%, and AUC of 0.93. In contrast, LAD and LCX showed slightly lower diagnostic performances. LAD had a sensitivity of 80%, a specificity of 94%, and an AUC of 0.90, while LCX had the highest specificity at 95%, a sensitivity of 70%, and AUC of 0.88. Overall, MFR derived from D-SPECT shows good diagnostic performance in identifying non-culprit ischemic lesions.
Fig. 5.
Receiver-Operating Characteristic Curves of Myocardial Flow Reserve, Sensitivity, Specificity, and Youden Index for the Cutoff of Myocardial Flow Reserve to Predict Quantitative Flow Ratio ≤ 0.80. All non-culprit vessels (A and B); non-culprit vessels with normal microvascular function (C and D). A The area under the curve (AUC) of Myocardial Flow Reserve (MFR) was 0.90 (95% CI: 0.84 to 0.94). B The optimal MFR cutoff value for predicting Quantitative Flow Ratio (QFR) ≤ 0.80 was 1.93 (sensitivity 85%, specificity 86%, Youden index 0.70). C The area under the curve (AUC) of Myocardial Flow Reserve (MFR) was 0.90 (95% CI: 0.82 to 0.95). D The optimal MFR cutoff value for predicting Quantitative Flow Ratio (QFR) ≤ 0.80 was 1.93 (sensitivity 83%, specificity 88%, Youden index 0.71)
Table 3.
Diagnostic Performance of D-SPECT for Diagnosing Non-Culprit Lesions With QFR ≤ 0.80
| All vessels | LAD | LCX | RCA | |
|---|---|---|---|---|
| Sensitivity (%) | 85 (65–96) | 80 (44–98) | 70 (35–93) | 83 (36–100) |
| Specificity (%) | 86 (78–92) | 94 (80–99) | 95 (83–99) | 89 (76–96) |
| Accuracy (%) | 86 (79–91) | 91 (78–98) | 90 (78–97) | 88 (76–96) |
| False positive (%) | 17 (14.3) | 2 (5.9) | 2 (5.0) | 5 (11.1) |
| False negative (%) | 4 (15.4) | 2 (20.0) | 3 (30) | 1 (16.7) |
| Positive predictive value (%) | 56 (45–67) | 80 (50–94) | 78 (46–94) | 50 (29–71) |
| Negative predictive value (%) | 96 (91–98) | 94 (82–98) | 93 (83–97) | 98 (87–100) |
| Positive likelihood ratio | 5.9 (3.7–9.5) | 13.6 (3.4–54.1) | 14.0 (3.4–57.4) | 7.5 (3.1–18.5) |
| Negative likelihood ratio | 0.2 (0.1–0.4) | 0.2 (0.1–0.7) | 0.3 (0.1–0.8) | 0.2 (0.1–1.1) |
| AUC | 0.90 (0.84–0.94) | 0.90 (0.77–0.97) | 0.88 (0.75–0.95) | 0.93 (0.82–0.98) |
| cutoff | 1.93 | 1.91 | 1.7 | 1.93 |
Values are n (%) or n (95% confidence interval)
Abbreviations as in Table 2
Diagnostic performance of MFR in identifying non-culprit ischemia lesions: an analysis from another perspective
Table S1 presents a detailed comparison of MFR and QFR in diagnosing ischemic lesions in non-culprit vessels. In D-SPECT perfusion imaging, reversible defects, characterized by decreased myocardial tracer uptake under stress or normal uptake at rest, are recognized as ischemic lesions in non-culprit vessels. The optimal cutoffs for QFR and MFR are 0.8 and 1.9, respectively. Analytically, QFR offers 78% sensitivity and 86% both specificity and accuracy, with an AUC of 0.82, while MFR provides a perfect sensitivity of 100%, with 81% specificity and 82% accuracy, supported by an AUC of 0.92.
Sensitivity analysis for detecting ischemic lesions in non-culprit vessels with normal microvascular function
This study's sensitivity analysis evaluates ischemic lesions in non-culprit vessels through MFR and QFR metrics, presented in Figs. 5 and 6 and Table 4. Of all non-culprit vessels, 88 (61%) exhibited AMR values below 255 mm Hg * s/m. Among these, 24 vessels (27%) demonstrated a QFR ≤ 0.8. Figure 5 illustrates the diagnostic performance of MFR in identifying ischemic lesions in non-culprit vessels with AMR < 255 mm Hg * s/m. Figure 6 further shows the variation in MFR values across different groups. In this sensitivity analysis, a QFR ≤ 0.8 was used as the reference standard for identifying ischemic lesions in non-culprit vessels, with MFR predicting a QFR ≤ 0.80 with a sensitivity of 83%, specificity of 88%, accuracy of 86%, positive predictive value of 71%, and negative predictive value of 93% (Table 4). The AUC for the ROC curve was 0.90 (95% CI: 0.82 to 0.95) (Table 4). Results from Fig. 5 suggest the optimal MFR cutoff value for predicting QFR ≤ 0.80 is 1.93, with a corresponding Youden index of 0.71. Moreover, regardless of whether in all non-culprit vessels or those with normal microvascular function, the ischemic lesion group had significantly lower MFR than the non-ischemic group, as shown in Fig. 6. Therefore, in non-culprit vessels with normal AMR values, the diagnostic performance of MFR derived from D-SPECT remains stable without significant changes. Notably, 28 vessels (19%) with normal AMR and MFR ≤ 1.93 and 11 vessels (8%) with abnormal AMR and MFR ≤ 1.93 indicate that combining MFR with AMR effectively stratifies ischemic lesions in non-culprit vessels.
Fig. 6.
MFR Corresponding to Different Regions in Non-culprit Vascular Territories. Variations in MFR values among all non-culprit vessels and in vessels with normal microvascular function. Ischemic diseases in narrowed vessels are further stratified according to QFR. Abbreviations are the same as in Fig. 5
Table 4.
Results of Sensitivity Analysis in Non-Culprit Vessel Lesions with Normal Microvascular Function
| Sensitivity (%) | 83 (63–95) |
|---|---|
| Specificity (%) | 88 (77–95) |
| Accuracy (%) | 86 (77–93) |
| False positive (%) | 8 (12.5) |
| False negative (%) | 4 (16.7) |
| Positive predictive value (%) | 71 (56–83) |
| Negative predictive value (%) | 93 (85–97) |
| Positive likelihood ratio | 6.7 (3.4–13.1) |
| Negative likelihood ratio | 0.2 (0.1–0.5) |
| AUC | 0.90 (0.82–0.95) |
| cutoff | 1.93 |
Values are n (%) or n (95% confidence interval)
Abbreviations as in Table 2
Discussion
To our knowledge, this is the first study to investigate the diagnostic performance and agreement of MFR derived from D-SPECT and QFR in assessing non-culprit ischemic lesions among STEMI patients undergoing PCI with multivessel disease. The critical findings of our study are summarized as follows: 1. MFR derived from D-SPECT demonstrates good diagnostic performance and moderate-to-good agreement in identifying non-culprit ischemic lesions of STEMI patients (QFR ≤ 0.8); 2. It retains similar diagnostic performance in patients with normal microvascular function; 3. Combining angiographically derived AMR with MFR effectively facilitates the stratification and screening of non-culprit ischemic lesions.
Evaluating diagnostic agreement of MFR and QFR
Multiple studies have explored the diagnostic value of MFR derived from D-SPECT in identifying ischemic lesions or significant stenosis in CAD. In particular, in patients with multivessel disease, an MFR below 2 has demonstrated a sensitivity of 80% and a specificity of 85% for detecting obstructive coronary artery lesions [30]. Furthermore, comparative studies between D-SPECT and PET have indicated that an MFR cutoff of 2.1 in D-SPECT identified myocardial ischemia in stable CAD with a sensitivity of 81.1% and a specificity of 93% [8]. Recent comparative studies between D-SPECT and PET further confirmed that when the MFR cutoff in D-SPECT was set at 2.1, it achieved diagnostic performance similar to PET in detecting obstructive CAD [10]. It is noteworthy that MFR derived from CMR has also shown good consistency in identifying non-culprit ischemic lesions in STEMI patients [37]. Similar to previous findings, our study demonstrated that MFR derived from D-SPECT also showed good diagnostic performance and moderate consistency in assessing non-culprit ischemic lesions of STEMI patients, with an optimal cutoff value of 1.93 and sensitivity, specificity, and accuracy rates of 85%, 86%, and 86%, respectively. Intriguingly, regardless of whether in overall non-culprit vessels or at the level of other branch vessels, the specificity of MFR in identifying non-culprit ischemic lesions was consistently higher than its sensitivity, and the negative predictive value also exceeded the positive predictive value. These findings suggest that MFR has a significant advantage in ruling out non-culprit ischemic lesions, potentially reducing the need for unnecessary invasive procedures in patients. Nevertheless, it is important to note that the predictive power of MFR for QFR diminishes once the MFR value exceeds the cutoff value. Additionally, the correlation and consistency between MFR and QFR are moderate. Despite MFR’s strong diagnostic performance in identifying ischemic lesions in non-culprit vessels, the correlation and consistency with QFR remain less than ideal. This is not surprising given the differing measurement principles between MFR and QFR. MFR is calculated as the ratio of myocardial blood flow during maximal hyperemia to resting blood flow, offering a comprehensive reflection of the functional reserve of the coronary vasculature, including both the large vessels and microcirculation. Conversely, QFR primarily reflects the hemodynamic status of the main coronary vessel and serves as a physiological parameter for describing ischemic function. Interestingly, our correlation analysis revealed that as MFR approaches 0, QFR tends to stabilize at 0.5 (Fig. 4). This observation is likely a result of the differing underlying principles of MFR and QFR measurement. Specifically, patients with microvascular dysfunction often present with low MFR yet normal QFR [18, 19]. However, to date, no studies have directly compared MFR derived from D-SPECT with a QFR ≤ 0.8 as a reference standard for non-culprit ischemic lesions. This is the novelty of our research, which extends and complements the data on MFR derived from D-SPECT in predicting non-culprit ischemic lesions of STEMI patients. Moreover, unlike previous studies that included non-culprit vessel lesions, our study included multivessel non-culprit lesions with stenoses of 40–80%, in line with the latest ACC/AHA/SCAI guidelines, which recommend that for non-culprit vessels with less than 90% stenosis, functional assessment should be combined to evaluate myocardial ischemia before deciding on revascularization treatment [12, 37]. This definition of lesion severity helps to identify patients at higher risk of ischemia earlier, which is crucial for subsequent clinical management as timely interventions may significantly improve outcomes in STEMI patients. Notably, our study also used reversible perfusion defects identified by D-SPECT as a reference standard for ischemia, validating the accuracy of QFR ≤ 0.8 as a reference standard for ischemia and the accuracy of the MFR cutoff [8, 10, 30, 37]. Interestingly, MFR identified non-culprit ischemic lesions with a sensitivity of 100%, specificity of 81%, and accuracy of 82% (Table S1). The high diagnostic performance of MFR in identifying non-culprit ischemic lesions is not surprising, as both MFR and the identification of reversible perfusion defects are obtained through D-SPECT. The final results confirm that using a QFR ≤ 0.8 as a reference standard for non-culprit ischemic lesions is accurate and feasible. Additionally, we have validated that MFR ≤ 1.9 identifies non-culprit ischemic lesions from another perspective, indicating a stable diagnostic capability. In summary, MFR derived from D-SPECT accurately identifies non-culprit ischemic lesions in STEMI patients.
The impact of microvascular function on the diagnostic performance of non-culprit ischemic lesions and its clinical implications
Myocardial ischemia in non-culprit vessels may result from epicardial stenosis, microvascular dysfunction, or a combination of both. The FAME trial indicated that in moderately stenosed coronary lesions, only about one-third caused myocardial ischemia [38]. This finding suggests that relying solely on the degree of coronary stenosis to predict non-culprit ischemic lesions is insufficient and must be combined with functional assessment of the coronary arteries. In recent years, AMR, derived from coronary angiography as a non-invasive assessment metric, has been proven to evaluate microvascular function accurately [29]. Sensitivity analysis from this study reveals that even in non-culprit vessels with normal microvascular function, MFR still demonstrates good diagnostic performance, further confirming the robustness of MFR in diagnosing non-culprit ischemic lesions. Furthermore, our study delved into the reasons for inconsistencies between MFR and QFR. Previous studies have noted that while concordance between MFR and FFR is common, inconsistencies between MFR and FFR occur in about 20–40% of coronary lesions [18, 19, 37]. These inconsistencies may arise from coronary microcirculatory dysfunction, segmental coronary narrowing, or diffuse non-obstructive coronary disease [18, 37, 39]. Nevertheless, our trial data indicated only a 14% inconsistency between MFR and QFR in non-culprit vessels. This lower inconsistency rate may relate to different vascular functional parameters used in our study, as we compared the consistency of QFR with MFR, not FFR with MFR. Intriguingly, non-culprit vessels with microvascular dysfunction identified through AMR represented 39% of such lesions, significantly exceeding the inconsistency rates between MFR and QFR. This result is consistent with the findings of Everaars et al., who also noted a higher prevalence of microvascular dysfunction assessed by the index of microcirculatory resistance compared to inconsistencies between MFR and FFR [37]. Further analysis showed that microvascular dysfunction in non-culprit vessels appeared not only in groups with discrepancies between QFR and MFR but also in the consistent groups. Indeed, in our cohort, inconsistencies caused by microvascular dysfunction were only observed in non-culprit vessels with low MFR and normal QFR, aligning with previous findings [18, 19]. Thus, these data suggest that inconsistencies between MFR and QFR may be attributable to microvascular dysfunction. Notably, the assessment method combining MFR with AMR not only effectively screens for non-culprit ischemic lesions caused by epicardial stenosis but also identifies those with concomitant microvascular dysfunction. Specifically, low MFR with normal AMR represents non-culprit ischemic lesions solely due to epicardial stenosis, while low MFR with abnormal AMR indicates those combined with microvascular dysfunction. Given that D-SPECT technology can identify non-culprit ischemic lesions from multiple angles, we recommend integrating MFR with AMR for stratified assessment of non-culprit ischemic lesions in clinical practice rather than combining QFR with AMR. Consequently, integrating MFR with AMR can stratify the risk of non-culprit ischemic lesions, potentially offering a therapeutic target for preventative revascularization in STEMI patients' non-culprit vessels.
Exploration of treatment strategies for non-culprit vessel lesions in STEMI patients
In STEMI patients with multivessel disease, consensus on the optimal treatment strategy for non-culprit vessel lesions remains elusive. Current strategies include lifestyle modifications, pharmacological therapy, and revascularization. Previous studies consistently indicated that preventive revascularization for non-culprit vessel lesions in STEMI patients significantly reduced the incidence of major adverse cardiovascular events compared to pharmacological therapy alone [32, 40, 41]. Yet, pharmacological treatment alone is insufficient for improving outcomes in certain patients. Moreover, the ability to accurately identify asymptomatic ischemia in coronary arteries post-myocardial infarction is pivotal for formulating optimal treatment strategies and improving long-term patient outcomes. The Swiss II randomized controlled trial provided evidence that identifying and treating asymptomatic ischemia with preventive revascularization after myocardial infarction can significantly reduce the risk of long-term cardiovascular complications [42]. Despite this, the ideal timing and conditions for revascularization of non-culprit vessels in STEMI patients remain unclear. Interestingly, D-SPECT can assess both infarct size and prognostic value, as well as identify non-culprit ischemic lesions from multiple perspectives. Our study demonstrated that QFR and D-SPECT-derived MFR exhibited good diagnostic performance and moderate-to-good consistency in identifying ischemic lesions in non-culprit vessels post-STEMI. Additionally, combining MFR and AMR effectively identifies and stratifies non-culprit ischemic lesions, providing potential targets for preventive revascularization. In fact, it remains unclear whether the optimal treatment strategy for non-culprit vessel lesions based on D-SPECT guidance can be translated into effective revascularization strategies. Therefore, future randomized controlled trials are warranted to explore the best treatment strategies for non-culprit vessel lesions guided by D-SPECT.
Study limitations
Firstly, this study is a single-center investigation. Given the limited number of STEMI patients, the analysis was restricted to individual vessel levels. Furthermore, as the study population primarily consisted of STEMI patients, our results cannot be generalized to all types of myocardial infarction patients. Secondly, measurements of MFR and QFR were carried out by two experienced specialists but without repeated measurements and interobserver consistency testing, possibly affecting result reliability. Finally, the reference standard for ischemic lesions in non-culprit vessels of STEMI patients was QFR ≤ 0.8. Although we validated the accuracy of QFR ≤ 0.8 as a reference standard for non-culprit ischemic lesions by identifying reversible perfusion defects via D-SPECT, this standard is not considered the gold standard for non-culprit vessel ischemic lesions. Nevertheless, studies on the diagnostic performance and consistency between MFR derived from D-SPECT and QFR in patients with STEMI multivessel disease have not been reported, which makes the data from this study unique. Therefore, the findings of this study should be interpreted with caution in clinical practice.
Conclusion
MFR derived from D-SPECT shows good diagnostic performance and moderate consistency in identifying non-culprit ischemic lesions. Remarkably, MFR maintains similar diagnostic accuracy even in non-culprit vessels with normal microvascular function. Furthermore, combining MFR with AMR effectively identifies and stratifies ischemic lesions, offering potential therapeutic targets for preventive revascularization in STEMI patients.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We thank the clinical and research personnel at the Department of Nuclear Medicine, First Affiliated Hospital of Lanzhou University, for their technical support and insightful discussions.
Author contributions
Study conceptualization: M.Z., X.N., and M.B. Data acquisition and analyses: J.Z., F. C., and M. C. Writing-original draft preparation: M.Z., and E. X. Writing-review and editing: M. Z., E. X., J. Z., F. C., M. C., A. L., C. P., L. B., J. Z., Y. W., T. W., Y. Z., X. N., and M. B. All authors read and approved the final manuscript.
Funding
This work was supported by grants from the Gansu Provincial Talent Program (NO.2025QNTD27), Special Fund for Civil-Military Integration Development of Gansu Province (NO.2060303), the Science and Technology Program of Gansu Province (NO. 24JRRA293), the Scientific Research Project of Health Industry of Gansu Province (NO.GSWSKY2020-64), the Science and Technology Planning Project of Lanzhou City (NO.2020-ZD-72), the Youth Doctoral Fund Project of the Department of Education in Gansu Province (2022QB-011) and the Foundation for Scientific Research of the First Hospital of Lanzhou University (NO.ldyyyn2021-115). No other potential conflict of interest relevant to this article was reported.
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval
This study is conducted following the Declaration of Helsinki and approved by the Ethics Committee of The First Hospital of Lanzhou University, China (study number LDYYLL2022-297).
Consent to participate
Written informed consent is obtained from all individual participants included in the study.
Consent to publish
The authors affirm that human research participants provide informed consent for the publication of the images.
Conflicts of interest
The authors declare no conflicts of interest.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Maomao Zhao and Erkun Xing contributed equally to this work.
Xiaowei Niu and Ming Bai contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.