Abstract
Early detection of coronary microvascular dysfunction (CMD) in ST-segment elevation myocardial infarction (STEMI) patients undergoing percutaneous coronary intervention (PCI), is challenging. The index of microcirculatory resistance (caIMR), derived from computational pressure-fluid dynamics (CPFD), allows practical assessment of CMD using only routine coronary angiography. However, the prognostic implications of combining caIMR with microvascular obstruction (MVO) identified through cardiac magnetic resonance (CMR) imaging are unclear. This retrospective study investigates the utility of CPFD-caIMR and CMR-derived MVO in predicting major adverse cardiovascular events (MACEs) in 292 STEMI patients who underwent primary PCI, followed by caIMR and CMR evaluations. Patients were stratified into four groups based on caIMR thresholds (≤ 40 U or > 40 U) and the presence/absence of MVO. The primary endpoint was MACEs, defined as cardiac death, recurrent myocardial infarction, target vessel revascularization, or heart failure readmission. Overall, 101/292 patient exhibited discordant caIMR and MVO results. Specifically, 103 patients had caIMR ≤ 40 U without MVO, while 88 patients showed caIMR > 40 U with MVO. Multivariate analysis identified both caIMR > 40 U and MVO as independent predictors of MACEs, with an HR of 3.572 for each unit increase in CPFD-caIMR > 40 U. Combination of CPFD-caIMR and MVO significantly enhanced predictive accuracy. CPFD-caIMR is a reliable, minimally invasive tool for identifying microvascular dysfunction. Combination with CMR-derived MVO improves risk stratification in STEMI patients following PCI, holding promise for the early identification of high-risk patients, enabling targeted and personalized management.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-025-88942-0.
Keywords: ST-segment elevation myocardial infarction, Primary percutaneous coronary intervention, Computational pressure-fluid dynamics, Microcirculatory resistance, Microcirculatory obstruction, Major adverse cardiovascular events
Subject terms: Interventional cardiology, Risk factors, Myocardial infarction, Outcomes research
Introduction
Current guidelines for the treatment of ST-segment elevation myocardial infarction (STEMI) emphasize the need for rapid restoration of blood flow in the epicardial coronary arteries via primary percutaneous coronary intervention (PPCI), which remains the standard treatment1. Despite achieving epicardial vessel patency through PCI, myocardial perfusion may still be inadequate due to underlying coronary microvascular dysfunction (CMD)2. This dysfunction can result in higher rates of morbidity and mortality3. As such, the early detection of “reperfusion failure” is crucial to identify high-risk patients, allowing improved prognosis through additional therapies4.
Cardiac magnetic resonance (CMR) imaging is the primary diagnostic tool for detecting microcirculatory obstruction (MVO), a critical predictor of adverse clinical outcomes following STEMI5,6. Despite its benefits, the utility of CMR in risk stratification for patients with STEMI is hindered by high costs and safety concerns associated with performing CMR imaging immediately following a STEMI event. Consequently, the number of STEMI patients who can undergo CMR is limited. Furthermore, the time interval between emergency PCI and CMR may restrict the role of the latter in detecting MVO, thereby limiting its use in guiding early adjunctive therapy following vascular reconstruction7. These limitations underscore the need for a more safe and accessible diagnostic approach for risk assessment in these patients when immediate post-PCI CMR imaging is not feasible.
The computed angiography-derived index of microcirculatory resistance (caIMR) is a physiological parameter derived from coronary angiography using computational pressure-flow dynamics, the measurement of which enables the assessment of coronary microvascular resistance, without the need for pressure wires or hyperemic agents. While caIMR eliminates the additional invasive steps associated with traditional IMR, it relies on high-quality angiographic imaging, making it a semi-invasive method. Choi et al.8 previously demonstrated that computational pressure-fluid dynamics (CPFD) applied to the index of microcirculatory resistance derived from CPFD provides high diagnostic accuracy, and is strongly correlated with the invasive IMR obtained from traditional guidewire measurements. Recent studies have further highlighted the prognostic value of the index of microcirculatory resistance derived from CPFD (CPFD-caIMR) in patients with STEMI. Shin et al.9 demonstrated an angio-IMR threshold of > 40 U as an independent predictor of microvascular dysfunction and major adverse cardiac events (MACEs). Their analysis showed robust diagnostic performance, with an area under the ROC curve (AUC) of 0.899 (95% confidence interval [CI]: 0.786–0.949), a sensitivity of 75.0%, a specificity of 84.2%, and an overall accuracy of 80.6%. Similarly, Stouffer et al.10 corroborated the predictive utility of CPFD-IMR > 40 U, identifying a strong association with adverse outcomes, including cardiac death and heart failure (HF)-related rehospitalization. Building on these foundational studies, our analysis further established that the index of microcirculatory resistance derived from CPFD (CPFD-caIMR) > 40 U is significantly associated with an elevated risk of cardiac death or HF-related rehospitalization. Our previous studies have further shown the prognostic relevance of CPFD-caIMR > 40 U, highlighting its potential as a valuable tool for risk stratification and clinical decision-making in patients with STEMI11.
Based on the existing research, we hypothesized that combining CPFD-caIMR and MVO would offer superior prognostic prediction for patients with STEMI following PPCI compared with each method alone. Therefore, in the present study, we evaluated the combined value of CPFD-caIMR and MVO in predicting MACEs in patients with STEMI who underwent PPCI.
Methods
Study design
This retrospective cohort study enrolled 425 patients with STEMI who underwent CMR imaging at the Affiliated Hospital of Xuzhou Medical University in China between January 2020 and January 2021. After screening, 292 of these patients were included in the final analysis. The research was approved by the Medical Ethics Committee of the Affiliated Hospital of Xuzhou Medical University (XYFY2024-KL042-01), and was conducted in accordance with the principles outlined in the Declaration of Helsinki. Due to the retrospective study design, the ethics committee waived the requirement for written informed consent.
Study population and eligibility criteria
Patients were eligible for inclusion if they met the following criteria: (1) diagnosis of STEMI according to current guidelines1, (2) successful completion of PPCI, (3) follow-up data available for more than 1 year, (4) available angiography images of sufficient quality for reconstruction, and (5) high-quality CMR images obtained 2–7 days post-PCI.
Patients were excluded if they had: (1) a lack of necessary clinical information, (2) missing or poor-quality digital subtraction angiography images, (3) incomplete or poor-quality CMR sequences, or (4) angiographic features precluding adequate contour detection by the FlashAngio IMR software (e.g., poor vessel opacification, severe branch overlap, or distortion).
CPFD-caIMR measurement procedure
Figure 1 presents a diagram illustrating the typical performance of caIMR. To ensure accurate reconstruction, the procedure required at least two high-quality angiographic projections, obtained at an angle > 30° apart and free of vascular overlap. Angiographic projections and pressures were transmitted to the FlashAngio console by aligning the aortic pressures from surgical records closest to the time of angiography. Thereafter, a computer-generated three-dimensional mesh reconstruction of the coronary arteries was created using two-dimensional image processing. The coronary angiography-derived fractional flow reserve (caFFR) was calculated by combining pressure with resting flow velocity, obtained using the thrombolysis in myocardial infarction frame count method, and the optimized CPFD algorithm. The image frame rate was 15 fps.
Fig. 1.
Schematic showing the performance of caIMR. CPFD-caIMR, computational pressure fluid dynamics applied to the index of microcirculatory resistance.
The FlashAngio IMR system, comprising the FlashAngio IMR console, specialized software, and a FlashPressure IMR transducer (RainMed Ltd., Suzhou, China), was used to calculate a novel physiological parameter, caIMR (mmHg·s/mm), defined as:
caIMR = (Pa)hyp∙ caFFR ∙L /K∙ Vdiastole.
where: caFFR = (Pd)hyp/(Pa)hyp, and caFFR represents the fractional flow reserve derived from coronary angiography.
Unlike traditional fractional flow reserve, caFFR is computed non-invasively using computational fluid dynamics algorithms and high-quality coronary angiographic images. This approach eliminates the need for pressure wires or adenosine-induced hyperemia, significantly reducing procedural risks and time. The constant L approximates the distance from the inlet to the distal point, which is indicated by two pressure sensors on a wire (mm). The mean pressures (mmHg) at the aorta and distal position during maximal hyperemia are represented by (Pa)hyp and(Pd)hyp, respectively, with “hyp” denoting the maximal hyperemia state. Vdiastole refers to the mean flow velocity (mm/s) at the distal position during diastole, and K is a constant simulating flow velocity during maximal hyperemia.
All CPFD-caIMR values were analyzed offline using the FlashAngio system. This approach eliminates the need for invasive pressure wires, thereby avoiding the associated risks and limitations, including additional procedural time, potential vessel trauma, and patient discomfort. For patients with STEMI, the normal reference range was defined as CPFD-caIMR ≤ 40 U, based on prior studies8–11, with further validation in this study cohort.
CMR imaging protocol
CMR scans were performed 2–7 days post-procedure using a 3.0-T scanner (Ingenia 3.0 T, Philips; Netherlands). A modified standard imaging protocol was followed, with short-axis cine images covering the left ventricle (10–12 slices) taken following the administration of a gadolinium-based contrast agent (0.1 mmol/kg). Delayed enhancement images (late gadolinium enhancement) were captured 10–15 min later. The cvi42 software (Circle Cardiovascular Imaging Inc.; Calgary, Alberta, CA) was utilized to analyze the short-axis cine images by two independent operators, blinded to the clinical, procedural, and coronary physiology information. Any discrepancies were resolved by consensus. Cine images were applied to assess functional parameters including left ventricular ejection fraction [LVEF], end-diastolic volume, end-systolic volume, and ejection fraction, incorporating the papillary muscles in the left ventricular volume calculations. Late gadolinium enhancement was assessed by placing a reference region of interest in the remote myocardium and setting the signal intensity threshold at 2 and 5 standard deviations above the mean of the reference region of interest12. MVOs appeared as hypointense regions within the hyper-enhanced area in the late gadolinium enhancement images13 (Fig. 2).
Fig. 2.
Example plots of microcirculatory obstruction (MVO) after myocardial infarction identified by Cvi42: the orange-yellow area (within the purple line) represents the infarcted size, and the orange-yellow area (within the blue-green line) represents the MVO at the infarction center.
Study outcomes
The median follow-up period for the enrolled patients was 17 months (Q1–Q3: 12–24). Throughout this period, we tracked the occurrence of MACEs, including (1) cardiac death, excluding non-cardiac causes such as cancer; (2) Readmission for HF (e.g., new HF symptoms and/or the prescription of diuretics, reduced LVEF < 50% on echocardiography, or elevated NT-proBNP levels), requiring intensified diuretic therapy and leading to a diagnosis of congestive HF; (3) recurrent myocardial infarction, identified by CK-MB or troponin levels exceeding normal limits, alongside ischemic symptoms or electrocardiographic signs of ischemia; and (4) unplanned revascularization, characterized by stenosis ≥ 50% within 5 mm of the original lesion, with recurrence of angina pectoris, and confirmed by positive noninvasive or invasive physiological tests.
Group definitions
Patients were divided into the following four groups, based on the final CPFD-caIMR value and the presence of MVO: (1) CPFD-caIMR ≤ 40 U without MVO, (2) CPFD-caIMR > 40 U without MVO, (3) CPFD-caIMR ≤ 40 U with MVO, and (4) CPFD-caIMR > 40 U with MVO.
Statistical analysis
All statistical analyses were conducted using BM SPSS Statistics for Windows, version 26 (IBM Corp.; Armonk, NY, USA). Continuous and categorical variables are reported as the mean (standard deviation), median (interquartile range), or proportions (%), as appropriate. One-way analysis of variance, chi-squared tests, or Kruskal–Wallis rank tests were employed as needed. Univariate and multivariable binary Cox regression analyses were applied to estimate hazard ratios (HRs) for MACEs. All probability values were two-sided in the binary logistic and Cox regression models, with p-values < 0.05 considered statistically significant.
Results
Clinical characteristics and procedural parameters
In this study, we retrospectively analyzed 292 patients with STEMI with available data for CPFD coronary physiology and CMR assessments, followed for more than 12 months (Fig. 3). Tables 1 and 2 present the clinical and angiographic characteristics of the study cohort. Patients with and without MACEs showed significant disparities in sex (31 women [12.3%] in the former group vs. 10 [25.6%], p = 0.025), diuretic use (83 [32.8%] vs. 25 [64.1%], p = 0.001), and right coronary artery (RCA) (92 [36.4%] vs. 7 [17.9%], p = 0.024). Patients with MACEs showed no significant differences in time from pain to balloon (5.0 [3.0, 8.5] vs. 5.5 [3.5, 9.5] h, p = 0.523) and door to balloon (68 [55, 93.8] vs. 68 [56, 131] min, p = 0.586) compared to those without MACEs after achieving improvements in chest pain center constructions.
Fig. 3.
Flow chart of the study. Abbreviations: STEMI, ST-segment elevation myocardial infarction; PPCI, primary percutaneous coronary intervention; DE-CMR, delayed enhancement cardiac magnetic resonance imaging; CPFD-caIMR, computational pressure-fluid dynamics applied to the index of microcirculatory resistance, derived from coronary angiography; MVO, microcirculatory obstruction.
Table 1.
Baseline characteristics of the study population grouped by MACEs.
| Overall (n = 292) |
Non-MACEs (n = 253) |
MACEs (n = 39) |
P-value | |
|---|---|---|---|---|
| Patient characteristics | ||||
| Age, years | 58.7 ± 37.8 | 58.6 ± 40.2 | 59.2 ± 13.9 | 0.924 |
| Female, n (%) | 41(14.0) | 31(12.3) | 10(25.6) | 0.025 |
| BMI, kg/m2 | 25.9 ± 3.5 | 26.0 ± 3.5 | 24.9 ± 3.6 | 0.076 |
| Cadiovascular risk factors | ||||
| Hypertension, n (%) | 139(47.6) | 122(48.2) | 17(43.6) | 0.590 |
| Diabetes melbitisus, n (%) | 60(20.5) | 53(20.9) | 7(17.9) | 0.666 |
| Cronic kidney disease, n (%) | 3(1.0) | 2(0.8) | 1(2.6) | 0.351 |
| Current smoker, n (%) | 135(46.2) | 115(45.5) | 20(51.3) | 0.497 |
| Family history, n (%) | 7(2.4) | 6(2.4) | 1(2.6) | 1.000 |
| Hemodynatic parameters | ||||
| SBP, mmHg | 127.8 ± 20.8 | 127.5 ± 21.0 | 129.4 ± 19.5 | 0.597 |
| DBP, mmHg | 81.2 ± 13.6 | 81.1 ± 13.9 | 81.7 ± 11.8 | 0.776 |
| Laboratory profiles | ||||
| Peak hsTnT, ng/mL | 3569.1(2988.0, 3039.5) | 2739.0(869.8, 5557.0) | 3973.5(1432.5, 5623.25) | 0.299 |
| LDL-C, mmol/L | 2.7(2.0, 3.2) | 2.6(2.0, 3.3) | 2.9(2.2, 3.2) | 0.482 |
| Scr, mg/dL | 71.3 ± 14.5 | 71.7 ± 14.5 | 68.3 ± 14.7 | 0.168 |
| CRP, mg/L | 12.2(4.9, 32.6) | 12.0(5.0, 32.5) | 18.1(4.3, 46.9) | 0.512 |
| Medication | ||||
| Aspirin, n (%) | 292(100) | 253(100) | 39(100) | 1.000 |
| P2Y12 Inhibitor, n (%) | 292(100) | 253(100) | 39(100) | 1.000 |
| Beta blocker, n (%) | 265(90.8) | 228(90.1) | 37(94.9) | 0.551 |
| RAASI, n (%) | 225(77.1) | 193(76.3) | 32(82.1) | 0.425 |
| Statin, n (%) | 292(100) | 253(100) | 39(100) | 1.000 |
| Diuretic, n (%) | 108(38.0) | 83(32.8) | 25(64.1) | 0.001 |
Abbreviations: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; hsTnT, high sensitivity troponin T; CK-MB, MB isoenzyme of creatine kinase; CRP, C-reactive protein; LDL-C, low-density lipoprotein cholesterol; Scr, serum creatinine; RAASI, renin-angiotensin-aldosterone system inhibitor.
Table 2.
Characteristics of the PPCI process of the study population grouped by MACEs.
| Overall (n = 292) |
Non-MACEs (n = 253) |
MACEs (n = 39) |
P-value | |
|---|---|---|---|---|
| Infarct-related artery | ||||
| LAD, n (%) | 154(52.7) | 131(51.8) | 23(59.0) | 0.402 |
| LCX, n (%) | 39(13.4) | 30(11.9) | 9(23.1) | 0.055 |
| RCA, n (%) | 99(33.9) | 92(36.4) | 7(17.9) | 0.024 |
| PTOB, h | 5.0(3.0, 8.5) | 5.0(3.0, 8.5) | 5.5(3.5, 9.5) | 0.523 |
| DTOB, min | 68(56, 97) | 68(55, 93.8) | 68(56, 131) | 0.586 |
| TIMI flow grade at baseline | ||||
| 0 | 189(65.6) | 167(66.5) | 22(59.5) | 0.608 |
| 1 | 12(4.2) | 10(4.0) | 2(5.4) | |
| 2 | 23(8.0) | 21(8.4) | 2(5.4) | |
| 3 | 64(22.2) | 23(21.1) | 11(29.7) | |
| TIMI folw grade post-PCI | ||||
| 3 | 292(100) | 253(100) | 39(100) | 1.000 |
| Single stent, n (%) | 230(82.1) | 201(82.4) | 29(80.6) | 0.790 |
| Mean stent diameters, mm | 3.1 ± 1.4 | 3.1 ± 1.5 | 2.9 ± 0.4 | 0.418 |
| Total length of stents, mm | 31.6 ± 13.9 | 31.4 ± 13.8 | 33.1 ± 14.7 | 0.487 |
| IABP, n (%) | 3(1.0) | 2(0.8) | 1(2.6) | 0.574 |
| Thrombus aspiration, n (%) | 34(11.6) | 29(11.5) | 5(12.8) | 0.966 |
| Killip grade, n (%) | ||||
| I-II | 283(96.9) | 245(96.8) | 38(97.4) | 1.000 |
| III-IV | 9(3.1) | 8(3.2) | 1(2.6) | |
| Multivessel disease, n (%) | 161(55.1) | 137(54.2) | 24(61.5) | 0.545 |
Abbreviations: LAD, left anterior descending; LCX, left circumflex; RCA, right coronary artery; PTOB, time from pain to balloon; DTOB, time from door to balloon; IABP, intra-aortic balloon pump.
Coronary microvascular indices and CMR findings
Table 3 presents the CMR and coronary angiography data obtained in this study. Among 292 patients, 27.1% (n = 78) had an index of microcirculatory resistance derived from CPFD-caIMR > 40 U, while 51.4% (n = 150) had MVO. The analysis revealed significant discrepancies in LVEF% (46.3 ± 10.6 vs. 37.7 ± 12.7, p = 0.001), MVO% (0.09 [0, 0.46] vs. 0.33 [0, 1.19], p = 0.004), MVO (122 [48.2] vs. 28 [71.8], p = 0.006), infarct size (IS) % (25.0 ± 13.4 vs. 30.0 ± 16.5, p = 0.009), CPFD-caIMR (24.5 [17.6, 37.9] vs. 40.1 [19.8, 60.9], p = 0.030), and CPFD-caIMR > 40 U (58 [22.9] vs. 21 [53.8], p < 0.001) between patients with and without MACE.
Table 3.
CMR and coronary angiography data of the study population grouped by MACEs. Abbreviations: CMR, cardiac magnetic resonance; LVEF, left ventricular ejection fraction; MVO, microcirculation obstruction; IS, infarct size; CPFD-caFFR, coronary angiography-derived fractional flow reserve; CPFD-caIMR, coronary angiography-derived index of microcirculatory resistance.
| Overall (n = 292) |
Non-MACEs (n = 253) |
MACEs (n = 39) |
P-value | |
|---|---|---|---|---|
| LVEF,% | 45.2 ± 11.2 | 46.3 ± 10.6 | 37.7 ± 12.7 | 0.001 |
| MVO,% | 0.11(0, 0.58) | 0.09(0, 0.46) | 0.33(0, 1.19) | 0.004 |
| MVO, n (%) | 150(51.4) | 122(48.2) | 28(71.8) | 0.006 |
| IS,% | 24.4 ± 14.0 | 25.0 ± 13.4 | 30.0 ± 16.5 | 0.009 |
| CPFD-caFFR | 0.92 ± 0.07 | 0.92 ± 0.06 | 0.93 ± 0.07 | 0.513 |
| CPFD-caIMR | 25.0(17.7, 41.6) | 24.5(17.6,37.9) | 40.1(19.8,60.9) | 0.030 |
| CPFD-caIMR>40U, n (%) | 78(27.1) | 58(22.9) | 21(53.8) | <0.001 |
Predictors of MACE
Single-factor logistic regression analysis revealed that MACE was significantly associated with female sex (HR: 2.469 [95%CI: 1.097–5.557; p = 0.029]), RCA (HR: 0.383, 95%CI: 0.162–0.902; p = 0.028), LVEF% (HR: 0.936, 95%CI: 0.907–0.966; p < 0.001), MVO (HR: 2.733, 95%CI: 1.304–5.727; p = 0.008), IS% (HR: 1.032; 95%CI: 1.007–1.057; p = 0.010), and CPFD-caIMR > 40 U (HR: 3.922, 95%CI: 1.959–7.855; p < 0.001). MVO (HR: 2.750, 95%CI: 1.085–6.973; p = 0.033) and CPFD-caIMR > 40 U (HR: 3.572, 95%CI: 1.639–7.786; p = 0.001) were predictors of MACEs over 12 months (Table 4). For every 1-unit increase above the threshold of CPFD-caIMR > 40 U, the risk of MACE increased approximately 3.6-fold (HR: 3.572; 95%CI: 2.14–5.92; p < 0.001). Receiver operating characteristic (ROC) curve analysis showed that MACEs were effectively predicted by CPFD-caIMR > 40 U (AUC: 0.724, 95%CI: 0.577–0.757; p < 0.001) or MVO (AUC: 0.667, 95%CI: 0.577–0.757; p = 0.001). The integration of CPFD-caIMR with MVO resulted in a further increase in the AUC to 0.820 (95%CI: 0.747–0.892; p < 0.001; Fig. 4).
Table 4.
Logistic regression analysis of MACEs. Abbreviations: BMI, body mass index; hsTnT, high sensitivity troponin T; LAD, left anterior descending; LCX, left circumflex; RCA, right coronary artery; LVEF, left ventricular ejection fraction; MVO, microcirculation obstruction; IS, infarct size; CPFD-caIMR, coronary angiography-derived index of microcirculatory resistance.
| Univariate | Mulivariate | |||
|---|---|---|---|---|
| HR (95% CI) | p value | HR (95% CI) | p value | |
| Age | 1.000[0.992–1.009] | 0.923 | ||
| Female | 2.469[1.097–5.557] | 0.029 | 2.503[0.905–6.919] | 0.077 |
| BMI | 0.908[0.819–1.006] | 0.065 | ||
| Hypertension | 0.830[0.421–1.637] | 0.590 | ||
| Diabetes melbitisus | 0.825[0.345–1.974] | 0.666 | ||
| Current smoker | 1.263[0.643–2.481] | 0.497 | ||
| Peak hsTnT | 1.000[1.000–1.000] | 0.371 | ||
| LAD | 1.339[0.675–2.653] | 0.403 | ||
| LCX | 2.230[0.966–5.149] | 0.060 | ||
| RCA | 0.383[0.162–0.902] | 0.028 | 0.672[0.250–1.803] | 0.430 |
| LVEF | 0.936[0.907–0.966] | <0.001 | 0.939[0.957–1.019] | 0.426 |
| MVO | 2.733[1.304–5.727] | 0.008 | 2.750[1.085–6.973] | 0.033 |
| IS | 1.032[1.007–1.057] | 0.010 | 0.987[0.957–1.019] | 0.426 |
| CPFD-caIMR>40U | 3.922[1.959–7.855] | <0.001 | 3.572[1.639–7.786] | 0.001 |
Fig. 4.
ROC curve showing the MACE predictive abilities of MVO, CPFD-caIMR, IS, and CPFD-caIMR combined with MVO. ROC CPFD-caIMR vs. ROC CPFD-caIMR + MVO = 0.724 vs. 0.820, p < 0.001; ROC MVO vs. ROC CPFD-caIMR + MVO = 0.667 vs. 0.820, p = 0.004; ROC IS vs. ROC CPFD-caIMR + MVO = 0.650 vs. 0.820, p < 0.001.
CPFD-caIMR and MVO discordance
Of the 292 included patients, revealed discrepancies between the index of microcirculatory resistance derived from CPFD-caIMR and MVO were found in 101 (34.6%). Further, 103 (35.3%) exhibited CPFD-caIMR ≤ 40 U without MVO, and 88 (30.1%) displayed CPFD-caIMR values > 40 U with MVO on CMR imaging. In the context of inconsistencies between CPFD-caIMR and MVO, 62 of 101 cases presented with MVO despite having CPFD-caIMR values ≤ 40 U, and 39 of 101 cases showed no MVO despite having CPFD-caIMR values > 40 U (Fig. 5).
Fig. 5.
Distribution of caIMR and MVO in the population.
Relationships between groups and prognosis
The four groups showed no significant different in baseline characteristics, except for the infarct-related arteries (left anterior descending, p = 0.042; RCA, p = 0.002; Tables 5 and 6). However, analyzing each event separately revealed significant differences in events such as cardiac death (p = 0.027) and readmission for HF (p = 0.007; Table 7). Univariate analysis further showed that MACEs were associated with Group 2 (HR: 4.416, 95%CI: 1.290–15.122; p = 0.018), Group 3 (HR: 3.510, 95%CI: 1.155–10.665; p = 0.027), and Group 4 (HR: 9.658, 95%CI: 3.175–29.382; p < 0.001; Table 8). During the 12-month follow-up period, Group 4 showed worse clinical outcomes (HR: 8.765, 95%CI: 2.854–26.912; p < 0.001) than Group 1 (Fig. 6).
Table 5.
Baseline characteristics of the study population grouped by CPFD-caIMR and MVO.
| Group 1 (No MVO and CPFD-caIMR ≤ 40)(n = 103) |
Group 2 (No MVO和CPFD-caIMR>40)(n = 39) |
Group 3 (MVO and CPFD-caIMR ≤ 40)(n = 62) |
Group 4 (MVO和CPFD-caIMR>40)(n = 88) |
P-value | |
|---|---|---|---|---|---|
| Patient characteristics | |||||
| Age, years | 55.6 ± 12.9 | 55.3 ± 13.2 | 62.9 ± 59.3 | 58.2 ± 10.8 | 0.252 |
| Male, n (%) | 15(14.6) | 9(23.1) | 5(8.1) | 12(13.6) | 0.212 |
| BMI, kg/m2 | 25.6 ± 3.4 | 25.2 ± 3.3 | 26.0 ± 4.3 | 26.3 ± 3.6 | 0.264 |
| Cadiovascular risk factors | |||||
| Hypertension, n (%) | 50(48.5) | 19(48.7) | 27(43.5) | 43(48.9) | 0.919 |
| Diabetes melbitisus, n (%) | 21(20.4) | 4(10.3) | 11(17.7) | 24(27.3) | 0.153 |
| Cronic kidney disease, n (%) | 1(1.0) | 0(0) | 1(1.6) | 1(1.1) | 0.890 |
| Current smoker, n (%) | 50(48.5) | 16(41.0) | 28(45.2) | 41(46.6) | 0.878 |
| Family history, n (%) | 1(1.0) | 0(0) | 2(3.2) | 4(4.5) | 0.287 |
| Hemodynatic parameters | |||||
| SBP, mmHg | 128.1 ± 22.7 | 131.2 ± 20.7 | 125.2 ± 19.2 | 130.7 ± 20.1 | 0.526 |
| DBP, mmHg | 80.7 ± 14.7 | 81.5 ± 13.6 | 80.3 ± 12.7 | 84.6 ± 13.2 | 0.143 |
| Laboratory profiles | |||||
| Peak hsTnT, ng/mL | 1327.5(408.4, 3462.5) | 3306.0(1090.0, 5262.0) | 4105.0(1901.0, 6904.3) | 4769.5(2048.8, 6604.5) | <0.001 |
| LDL-C, mmol/L | 2.5(2.0, 3.2) | 3.0(2.4, 3.2) | 2.9(2.2, 3.3) | 2.5(1.9, 3.3) | 0.398 |
| Scr, mg/dL | 72.7 ± 14.8 | 66.9 ± 12.1 | 72.4 ± 14.5 | 68.4 ± 15.2 | 0.127 |
| CRP, mg/L | 9.5(3.4, 23.6) | 5.3(1.9, 15.4) | 17.7(8.3, 40.1) | 13.2(7.4, 40.3) | <0.001 |
| NT-proBNP, pg/mL | 1213.2 ± 2035.4 | 1912.3 ± 3140.2 | 2045.6 ± 2669.3 | 2965.2 ± 5058.6 | 0.034 |
| Medication | |||||
| Aspirin, n (%) | 103(100) | 39(100) | 62(100) | 88(100) | 1.000 |
| P2Y12 Inhibitor, n (%) | 103(100) | 39(100) | 62(100) | 88(100) | 1.000 |
| Beta blocker, n (%) | 91(88.3) | 35(89.7) | 56(90.3) | 83(94.3) | 0.551 |
| RAASI, n (%) | 73(70.9) | 29(74.4) | 52(83.9) | 71(80.7) | 0.198 |
| Statin, n (%) | 103(100) | 39(100) | 62(100) | 88(100) | 1.000 |
| Diuretic, n (%) | 21(20.4) | 15(38.5) | 28(45.2) | 44(50.0) | 0.001 |
Abbreviations: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; hsTnT, high sensitivity troponin T; CK-MB, MB isoenzyme of creatine kinase; CRP, C-reactive protein; LDL-C, low-density lipoprotein cholesterol; Scr, serum creatinine; RAASI, renin-angiotensin-aldosterone system inhibitor.
Table 6.
Characteristics of the PPCI process of the study population grouped by CPFD-caIMR and MVO. Abbreviations: PPCI, primary percutaneous coronary intervention; LAD, left anterior descending; LCX, left circumflex; RCA, right coronary artery; PTOB, time from pain to balloon; DTOB, time from door to balloon; IABP, intra-aortic balloon pump.
| Group 1 (No MVO and CPFD-caIMR ≤ 40)(n = 103) |
Group 2 (No MVO和CPFD-caIMR>40)(n = 39) |
Group 3 (MVO and CPFD-caIMR ≤ 40)(n = 62) |
Group 4 (MVO和CPFD-caIMR>40)(n = 88) |
P-value | |
|---|---|---|---|---|---|
| Infarct-related artery | |||||
| LAD, n (%) | 44(42.7) | 25(64.1) | 32(51.6) | 53(60.2) | 0.042 |
| LCX, n (%) | 12(11.7) | 5(12.8) | 6(9.7) | 16(18.2) | 0.429 |
| RCA, n (%) | 47(45.6) | 9(23.1) | 24(38.7) | 19(21.6) | 0.002 |
| PTOB, h | 5.5(3.5, 9.0) | 4.5(3.5, 7.0) | 5.0(3.0, 9.5) | 5.0(3.3, 6.5) | 0.623 |
| DTOB, min | 73.0(54.0, 98.5) | 73.5(60.0, 116.5) | 65.0(55.0, 89.0) | 61.5(51.0, 105.0) | 0.594 |
| TIMI flow grade at baseline | |||||
| 0 | 59(57.8) | 21(55.3) | 745(72.6) | 64(74.4) | 0.219 |
| 1 | 4(3.9) | 2(5.3) | 2(3.2) | 4(4.7) | |
| 2 | 11(10.8) | 2(5.3) | 4(6.5) | 6(7.0) | |
| 3 | 28(27.5) | 13(34.2) | 11(17.7) | 12(14.0) | |
| TIMI flow grade post-PCI | |||||
| 3 | 103(100) | 39(100) | 62(100) | 88(100) | 1.000 |
| Single stent, n (%) | 85(82.5) | 31(79.5) | 50(80.6) | 64(72.7) | 0.775 |
| Mean stent diameters, mm | 3.2 ± 2.2 | 3.1 ± 0.5 | 3.0 ± 0.5 | 3.1 ± 0.5 | 0.414 |
| Total length of stents, mm | 30.5 ± 12.8 | 31.9 ± 12.9 | 36.6 ± 17.9 | 31.6 ± 13.9 | 0.058 |
| IABP, n (%) | 1(1.0) | 0(0) | 0(0) | 2(2.3) | 0.495 |
| Thrombus aspiration, n (%) | 11(10.7) | 3(7.7) | 7(11.3) | 13(14.8) | 0.675 |
| Killip grade | |||||
| I-II | 100(97.1) | 38(97.4) | 59(95.2) | 86(97.7) | 0.831 |
| III-IV | 3(2.9) | 1(2.6) | 3(4.8) | 2(2.3) | |
| Multivessel disease, n (%) | 57(55.3) | 20(51.3) | 37(59.7) | 47(53.4) | 0.835 |
Table 7.
Adverse cardiovascular events at follow-up.
| Group 1 (No MVO and CPFD-caIMR ≤ 40)(n = 103) |
Group 2 (No MVO和CPFD-caIMR>40)(n = 39) |
Group 3 (MVO and CPFD-caIMR ≤ 40)(n = 62) |
Group 4 (MVO和CPFD-caIMR>40)(n = 88) |
P-value | |
|---|---|---|---|---|---|
| Primary endpoint | 4(3.9) | 7(17.9) | 10(16.1) | 18(20.5) | 0.005 |
| Cardiac death | 0(0) | 1(2.6) | 2(3.2) | 7(8.0) | 0.027 |
| Readmission due to Heart Failure | 2(1.9) | 4(10.3) | 5(8.1) | 14(15.9) | 0.007 |
| Recurrent myocardial infarction | 2(1.9) | 1(2.6) | 2(3.2) | 3(3.4) | 0.929 |
| Unplanned revascularization | 1(1) | 4(10.3) | 4(6.5) | 8(9.1) | 0.056 |
Table 8.
Cox regression analysis of MACEs.Abbreviations: BMI, body mass index; LAD, left anterior descending; LCX, left circumflex; RCA, right coronary artery; MVO, microcirculation obstruction; CPFD-caIMR, coronary angiography-derived index of microcirculatory resistance.
| Univariate | Mulivariate | |||
|---|---|---|---|---|
| HR(95% CI) | p value | HR(95% CI) | p value | |
| Age | 1.001[0.993–1.008] | 0.884 | ||
| Female | 2.304[1.122–4.729] | 0.023 | 3.237[1.513–6.926] | 0.002 |
| BMI | 0.918[0.835–1.009] | 0.077 | ||
| Diabetes melbitisus | 0.871[0.384–1.975] | 0.741 | ||
| Current smoker | 1.235[0.659–2.313] | 0.511 | ||
| LAD | 1.300[0.687–2.461] | 0.420 | ||
| LCX | 2.221[1.053–4.685] | 0.036 | 1.970[0.904–4.290] | 0.088 |
| RCA | 0.396[0.175–0.898] | 0.026 | 0.497[0.206–1.199] | 0.120 |
| Group 2(no MVO and CPFD-caIMR>40) | 4.416[1.290-15.122] | 0.018 | 3.554[1.028–12.289] | 0.045 |
| Group 3( MVO and CPFD-caIMR ≤ 40) | 3.510[1.155–10.665] | 0.027 | 3.474[1.138–10.607] | 0.029 |
| Group 4(MVO and CPFD-caIMR>40) | 9.658[3.175–29.382] | <0.001 | 8.765[2.854–26.912] | <0.001 |
Fig. 6.
Kaplan–Meier curve stratified by CPFD-caIMR and MVO showing the time of MACEs in each of the four groups. Thee adjusted risk ratio of other groups was obtained by comparison with group 1 (CPFD-caIMR ≤ 40 and no MVO); group 4 (CPFD-caIMR > 40 and MVO) had the highest risk.
Discussion
In the present study, we assessed the added value of CPFD-caIMR and MVO in predicting MACE in patients who underwent PPCI for STEMI. The three main findings of the current work are as follows: (1) a CPFD-caIMR of > 40 U is an effective predictor of prognosis in patients with STEMI; (2) when combined with CMR imaging, CPFD-caIMR > 40 U provides additional prognostic value for PPCI-treated STEMI patients; and (3) patients in Group 4, who had severe CMD, faced the highest risk of adverse outcomes in the 12 months post-intervention, compared with patients with no CMD (Group 1) or elevated IMR/MVO (Groups 2 and 3).
PPCI is widely recognized as the most effective method for achieving epicardial reperfusion and reducing the risk of CMVO. Understanding the mechanisms underlying CMVO and utilizing advanced technologies to identify high-risk patients swiftly are essential for advancing research in this area14,15. The primary goal in the management of acute myocardial infarction, particularly STEMI, remains the rapid restoration of coronary blood flow. Although significant progress has been made in reperfusion therapies, patients who survive AMI often face risks of both short- and long-term complications, which are strongly linked to the extent of myocardial damage incurred during the acute phase3.
Research findings have suggested that HF develops in approximately 13% of patients within the first month of myocardial infarction, with this percentage increasing to 20–30% in subsequent years. These observations highlight the critical need to refine early intervention strategies and ensure comprehensive long-term care to minimize complications and enhance outcomes for this vulnerable patient population16. The patients in this study successfully underwent PPCI and achieved thrombolysis in myocardial infarction grade 3 blood flow. The coronary circulation is both a culprit and victim in AMI. Ischemia and reperfusion injury affect not only cardiomyocytes, but also the coronary microcirculation, leading to endothelial dysfunction, increased permeability, and MVO. These processes involve microembolization, release of soluble factors, platelet and leukocyte activation, capillary structural damage, and the no-reflow phenomenon17,18.
Efforts in the field of cardioprotection have predominantly focused on minimizing infarct size and enhancing outcomes following myocardial infarction; however, translating preclinical successes into consistent clinical improvements has proven challenging. Emerging evidence has indicated that targeting the coronary microcirculation may offer an overlooked opportunity for advancing cardioprotective strategies19. Nevertheless, when CMD was evaluated using CPFD-caIMR and CMR technology, MVO was observed in 51.4% of patients, indicating a high prevalence of CMD in patients with STEMI. Among patients without myocardial infarction, a CPFD-caIMR value of 25 U could be expected, whereas a value exceeding 40 U indicates severe CMD in patients with STEMI. In the present study, we identified a cutoff value of 39.7 for CPFD-caIMR to predict MACEs, which aligns with the results of prior studies11. Overall, our findings suggest that CPFD-caIMR measurements should be performed immediately following PPCI to detect clinically significant CMD. However, immediate CMR imaging post-PPCI is often not feasible. The MVO area remains stable for 2–9 days post-reperfusion, making this period optimal for screening20; in agreement with this, median time for CMR examination in the present study was 3.76 days.
While both MVO and CPFD-caIMR are useful for assessing microcirculatory disorders, 34.6% of patients showed inconsistencies in these two indicators. These observed discrepancies could be interpreted in the context of several key factors. Firstly, both caIMR and MVO are dynamic parameters whose abnormalities tend to diminish after the acute phase of STEMI. The timing of these assessments is thus crucial for revealing their distinctions. caIMR can be assessed immediately following the procedure, whereas CMR to evaluate MVO is usually performed 2–7 days post-operatively. Furthermore, CMR-MVO serves as an anatomical marker, outlining the structural characteristics of microvascular obstruction. Conversely, caIMR offers a functional evaluation that mirrors the physiological condition and reactivity of the coronary microcirculation. Despite these methodological variances, there is a notable correlation between caIMR and MVO, highlighting their complementary roles in assessing microvascular integrity21. Overall, it is crucial to recognize that no individual index can completely forecast patient prognosis. Therefore, combining both indices is necessary to ensure a thorough evaluation.
CPFD-caIMR is gaining attention due its many advantages. Primarily, it only requires coronary angiography images, with no pressure–temperature guidewire, adenosine, or the use of other coronary dilating drugs. While congestion is necessary for pressure wire-guided fractional flow reserve, it is not required for caFFR and caIMR. An algorithm based on resting pressure simulates congestive pressure, which has been validated in previous studies. Simultaneously, microcirculatory dysfunction can be diagnosed during angiography. Choi et al.8 previously reported that patients with caIMR > 40 U had a significantly greater risk of cardiogenic death and HF readmission than those with caIMR ≤ 40 U (46.7% vs. 16.6%, adjusted HR: 2.909). Our data further showed that CPFD-caIMR > 40 U and MVO were independent predictors of composite endpoints, even after adjusting for female sex, RCA disease, LVEF, MVO, and IS. Furthermore, based on survival analysis, patients with high CPFD-caIMR and MVO exhibited a higher risk of developing MACE.
In recent years, caIMR has emerged as a cost-effective and clinically valuable alternative for assessing CMD. Unlike traditional invasive techniques, this method eliminates the need for expensive pressure-temperature guidewires or pharmacological agents, while still delivering accurate and reliable diagnostic results. This innovative approach is particularly well-suited for healthcare systems with constrained resources, offering the dual benefits of reduced procedural costs and broader accessibility, without compromising diagnostic precision.
This study provides the first evidence that combining MVO and CPFD-caIMR offers significant incremental value for the early risk stratification of CMD in STEMI patients, outperforming the predictive accuracy of either parameter alone (combined AUC: 0.820 vs. MVO: 0.667 and CPFD-caIMR: 0.724). Although this improvement may appear modest, it nevertheless highlights the potential of this combined approach as an important step toward personalized and precise risk assessment in clinical practice. In our study, Group 4 demonstrated significantly higher rates of MACEs (20.5%), cardiac death (8.0%), and HF readmission (15.9%) than other groups, whereas Groups 2 and 3 had risks similar to those in Group 1. To further clarify the differences between Groups 2 and 3, we compared HF occurrence and NT-proBNP levels during hospitalization, observing significant disparities in both (p < 0.01). Nonetheless, no significant disparities were identified in IS, LVEF, or NT-proBNP levels. Moreover, there were no significant differences in HF occurrence during hospitalization (p = 0.85), death during follow-up (p = 0.98), or HF (p = 0.61) (Supplemental Figs. 1,2). The differences in the primary endpoint across the groups was generally attributed to the development of HF during follow-up, indicating the need for a longer follow-up period to validate these findings. After accounting for clinical risk factors, including female sex and sized of the left circumflex and right coronary arteries, Group 4 had an 8.8-fold higher risk of MACEs than Group 1. Scarsini et al.22 reported similarly unfavorable outcomes in IMR and MVO comparisons after a median follow-up of 40.1 months. In our study, patients in the MVO and CPFD-caIMR > 40 U groups had a 3-fold higher risk than those without MVO and with CPFD-caIMR < 40 U. However, survival analysis did not show any significant discrepancies. Although the designs of these two studies were somewhat similar, our study employed more advanced indicators and algorithms. Further demonstrating the value of caIMR could lead to a safer, faster, and more cost-effective approach for patients.
Overall, both IMR and MVO show good prospects for use in prognosis stratification; however, as prognostic indicators, it may be difficult to prioritize their evaluation when treating patients because of limited medical resources and insurance policies. The high cost associated with pressure-temperature guidewires and CMR imaging makes it difficult to popularize these approaches in clinical practice. Considering the results of both the present and prior studies, we recommend CPFD-caIMR as an alternative to IMR. When combined with CMR, this indicator can help to stratify patients with STEMI by prognosis early, thereby facilitating the provision of additional treatment for patients with underlying CMD. CPFD-caIMR can also distinguish patients with severe CMD very early as compared with CMR, particularly among patients who are severely ill and cannot undergo CMR examination.
Despite its small sample size and short follow-up period, this single-center, retrospective observational study employed a novel multimodal approach to evaluate post-ischemic CMD in individual patients using non-invasive caIMR and CMR measurements to assess myocardial MVO. When calculating caIMR, we aligned the aortic pressure with the angiographic timing closest to the surgical records. While this approach may have introduced minor errors in calculations, this is acceptable for retrospective studies. We acknowledge the limitations of this study inherent to its retrospective and non-randomized design, including the potential for residual confounding factors despite multivariate adjustments. To address this, we plan to conduct prospective, multi-center studies to further validate the robustness and generalizability of these findings.
In conclusion, this study demonstrated that MVO can differentiate between the two primary types of CMD: functional and anatomic. Nevertheless, the occurrence of CMD following acute myocardial infarction, characterized by the coexistence of CPFD-caIMR > 40 U and CMD-MVO, was linked to a markedly elevated short-term hazard and an over 8-fold rise in long-term unfavorable events. These findings have the potential to enhance risk stratification and aid in selecting patients for regional myocardial therapy.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
The authors would like to thank the makers of the offline software provided for the analysis and instructions from the scientists. This research received funding from the Key Research and Development Plan (Social Development) of the Science and Technology Project of Xuzhou City, Jiangsu Province (Grant No. KC23274), and the Affiliated Hospital of Xuzhou Medical University (Grant No. 2022ZL.01). All authors are fully responsible for the reliability, impartiality, and accuracy of the data and itsinterpretation.
Author contributions
Conceptualization: Yang Duan, Qianran Yin, Yuan Lu, Yafeng Zhou; Methodology: Yang Duan, Qianran Yin, Yuan Lu; Data Curation: Yinshuang Yang, Hao Miao, Shuguang Han; Formal Analysis: Shuguang Han, Qiuming Chi, Haomin Lv; Writing—Original Draft Preparation: Yang Duan, Qianran Yin, Yafeng Zhou; Writing—Review and Editing: Yang Duan, Qianran Yin, Yuan Lu, Yafeng Zhou. Both Yang Duan and Qianran Yin made equal contributions to this work. Every author has read and approved the final version of the manuscript for publication.
Data availability
The datasets generated during and/or analyzed during the current study are available by request from the corresponding author (Xyfyduanyang0125@163.com).
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yang Duan and Qianran Yin contributed equally to this work.
Contributor Information
Yuan Lu, Email: luyuan329@163.com.
Yafeng Zhou, Email: zhouyafeng73@126.com.
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Associated Data
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Supplementary Materials
Data Availability Statement
The datasets generated during and/or analyzed during the current study are available by request from the corresponding author (Xyfyduanyang0125@163.com).