Abstract
Background
In patients presenting with acute coronary syndromes (ACS), impaired coronary blood flow (CBF) after percutaneous coronary interventions (PCI) is linked to mortality. We developed a novel angiography‐based approach for blood flow quantification using automatic contrast bolus tracking. Therefore, this study aimed to investigate the clinical impact of angiography‐based blood flow quantification on major adverse cardiovascular events (MACE) after PCI in patients with ACS.
Methods
Prospective, multicenter, nested case–control study of patients presenting ACS. A propensity score was used to match patients with and without MACE at 1 year of follow‐up. MACE was defined as cardiovascular death, myocardial infarction, hospitalization for heart failure, or ischemia‐driven revascularization. CBF was measured automatically from angiograms after PCI.
Results
One hundred sixty‐two patients were included. The mean age was 68.3±13.0 years, 83% were male, and 33% had diabetes. Overall, 66% of patients presented with ST‐segment–elevation myocardial infarction. CBF after PCI was lower after ST‐segment–elevation myocardial infarction compared with other clinical presentations (74.1±47.0 mL/min ST‐segment–elevation myocardial infarction, 89.1±45.8 mL/min, non‐ST‐segment–elevation myocardial infarction, 95.7±48.8 mL/min, unstable angina, P=0.046). Patients with low post‐PCI CBF (<54.3 mL/min) had an increased risk of MACE (hazard ratio, 2.11 [95% CI, 1.35–3.28], P=0.001).
Conclusions
After PCI, automatic quantification of CBF using angiography was associated with MACE in patients with ACS. Risk stratification using post‐PCI CBF‐derived angiography may enable tailored management strategies for individuals with ACS.
Keywords: acute coronary syndrome, angiography, coronary blood flow, coronary flow velocity, heart failure
Subject Categories: Percutaneous Coronary Intervention
Nonstandard Abbreviations and Acronyms
- CBF
coronary blood flow
- MACE
major adverse cardiovascular events
- TIMI
thrombolysis in myocardial infarction
Clinical Perspective.
What Is New?
We developed a novel approach using coronary angiograms for the estimation of absolute coronary blood flow (mL/min) and microvascular resistance.
We found that patients with low CBF and acute coronary syndrome after percutaneous coronary intervention had an increased risk of cardiac events at 1 year compared with those with normal postpercutaneous coronary intervention coronary blood flow.
What Are the Clinical Implications?
This study demonstrates that a novel technology for blood flow quantification based on angiography could significantly contribute to risk stratification in patients undergoing percutaneous coronary intervention for acute coronary syndrome.
Stratifying patients with acute coronary syndromes (ACS) enables personalized care and optimizes resource allocation. Various clinical, procedural, and hemodynamic factors have been identified as predictors of both short‐ and long‐term outcomes following percutaneous coronary intervention (PCI). Notably, incomplete reperfusion, such as slow flow or no reflow, is a strong predictor of poor prognosis in patients with ACS undergoing PCI. 1
The thrombolysis in myocardial infarction (TIMI) frame count after PCI enables blood flow quantification using angiography. This is a simple method that has been shown to correlate with microvascular obstruction and mortality after primary PCI. 2 Nonetheless, due to time constraints and interobserver variability, the TIMI frame count is not widely used in clinical practice. Thus, an automatic TIMI frame count calculation may facilitate the assessment of blood flow in routine clinical practice.
We developed an approach based on conventional angiography that tracks the bolus of contrast and automatically assesses the TIMI frame count. The present study aims to validate this novel method for blood flow quantification based on standard angiograms using invasive flow‐velocity measurements as a reference and to investigate the clinical impact of this novel approach on clinical outcomes after PCI in patients with ACS.
METHODS
The data that support the findings of this study are available from the corresponding author upon reasonable request. The present study consisted of 2 cohorts. The validation cohort (n=27), consisting of patients with stable angina and nonobstructive coronary arteries, validated angiography‐guided blood flow quantification by comparing it with Doppler‐based flow velocity measurements. The clinical cohort (n=162), comprising patients presenting with ACS who underwent PCI, investigated the clinical impact of angiography‐guided blood flow quantification on clinical outcomes.
Validation Cohort
Study Population and Design
Patients presenting with stable angina at one center (OLV Clinic Aalst, Belgium) were considered for inclusion. Patients with nonobstructive coronary artery disease based on visual estimation (diameter stenosis <50%) were considered eligible for flow measurements. We included patients who underwent invasive assessment of coronary flow velocity using Doppler. The angiography and physiology data were analyzed by a Core Laboratory (CoreAalst BV, Aalst, Belgium). All patients signed informed consent and the protocol was approved by the local ethics committee. This cohort aimed to validate the angiographic blood velocity quantification using invasive Doppler flow‐velocity measurements as a reference.
Doppler Wire Flow Velocity Assessment
A 6 Fr guiding catheter was advanced into the coronary ostium of the vessel to be studied, and 0.2 mg of nitroglycerin was administered by intracoronary injection. A Doppler flow velocity wire (FloWireUSA) was connected to a Doppler system (FlowMap; Philips/Volcano, San Diego, CA, Philips/Volcano). Coronary flow velocity was measured in the distal segment of the vessel. The phasic and average peak velocity signals of the coronary flow were displayed on the screen and electronically stored on the physiologic tracings recorder (MacLab TM Hemodynamic Recording System; GE Healthcare, Chicago, IL), along with the pressure tracings and the ECG. High‐quality Doppler signals were obtained in all patients after having buckled the distal tip of the wire to stabilize the Doppler crystal and to read the flow in a retrograde mode. Doppler signal tracings with artifacts were excluded from the analysis.
Angiography‐Based Coronary Flow Velocity Quantification
Intracoronary nitroglycerin injection (100–200 μg) was administered before angiography. Coronary angiography data were analyzed offline by trained analysts (K.S. and G.B.) using a novel software (CAAS Workstation 8.5—QCA Contrast Tracking Prototype, Pie Medical Imaging, Maastricht, the Netherlands). The software requires 1 angiographic projection of the target vessel and automatically identifies end‐diastolic frames in the target vessel. After vessel contour detection, coronary flow velocity (mm/s) is fully automatically calculated based on the segment length and transit time. In this first cohort study, the vessel contour was delineated in the angiographic film from the tip position of the guiding catheter to the sensor position of the Doppler wire.
Clinical Cohort
Study Population and Design
Patients from the TACTICS (Tokyo, Kanagawa, Chiba, Shizuoka, and Ibaraki Active Optical Coherence Tomography Applications for ACS) study were included. TACTICS was an investigator‐initiated, prospective, multicenter, observational study conducted at 22 Japanese hospitals between November 2019 and April 2021. 3 Patients with ACS diagnosed within 24 hours of symptom onset who underwent optical coherence tomography‐guided emergency PCI were enrolled. ACS diagnoses included ST‐segment–elevation myocardial infarction (STEMI), non‐STEMI, and unstable angina. The definition of ACS diagnosis 4 , 5 and inclusion/exclusion criteria are shown in Tables S1 and S2. All PCI procedures were performed guided by optical coherence tomography. The protocol was approved by each institution's ethics committee and registered in the University Hospital Medical Information Network Clinical Trials Registry of Japan (UMIN‐CTR, ID 000039050).
The primary end point was the incidence of major adverse cardiac events (MACE) at 12‐month follow‐up. MACE was defined as cardiovascular death, nonfatal MI, heart failure (HF), or ischemia‐driven revascularization. Cardiovascular death was defined as death from cardiovascular causes 6 ; MI according to the Fourth Universal Definition of MI 5 ; and HF as prolonged hospitalization (inpatient) or new hospitalization (post discharge) with new or worsening symptoms with objective signs of HF and treatment initiation or escalation specifically for HF. 7 Ischemia‐driven revascularization was defined as any revascularization with at least 1 of the following: (1) angina symptoms despite optimal medical therapy, (2) new ischemic ECG changes, (3) positive noninvasive test, or (4) positive invasive physiological test. Target lesion revascularization was defined as repeat PCI of the target lesion (the treated segment including the 5‐mm margins proximal and distal to the stent) or bypass surgery of the target vessel. Follow‐up was performed by clinic visits, medical record reviews, and telephone contact. All clinical events were adjudicated by an independent committee, which consisted of clinicians.
In the present study, we performed a nested case study of patients presenting with and without MACE from the TACTICS registry. This analysis aimed to investigate the clinical impact of automatic angiography‐based coronary blood flow (CBF) on clinical outcomes after PCI in patients with ACS.
Angiography‐Based Coronary Blood Flow Quantification
We assessed angiography‐based CBF using the final angiography during PCI in patients with ACS. TIMI flow grade and myocardial blush grade were also evaluated using the final angiogram, 8 and angiograms with post‐PCI TIMI < II or no reflow were excluded. We defined the distal position of each coronary artery as the midsegment (segment 8) in left anterior descending artery, end of segment 13 in left circumflex artery, and end of segment 3 in right coronary artery. Angiography‐based CBF (mL/min) was calculated by dividing the vessel volume by blood velocity normalized by the length as provided by the software. Vessel volume was extracted from 3‐dimensional quantitative coronary angiography (QCA, CAAS Workstation 8.5, Pie Medical Imaging, Maastricht, the Netherlands). The calculation flow of angio‐derived CBF is shown in Figure S1.
Statistical Analysis
Continuous variables are expressed as mean±SD and median (interquartile range) for normally and nonnormally distributed data, respectively. Continuous variables were compared using the ANOVA, student's t‐test (or Mann–Whitney tests, Kruskal–Wallis test as appropriate), and categorical variables were compared using the chi‐square (or Fisher's exact test as appropriate). The Pearson correlation coefficient was used to assess the relationship between continuous variables.
In the study of the clinical cohort, propensity score‐matching was performed 1 to 1 to minimize differences between groups. Specifically, nearest‐neighbor matching by propensity score was performed to match all cases without replacement. The factors used for evaluating propensity scores were determined a priori, based on variables known to be associated with cardiovascular events. These factors included sex, age, comorbidities such as hypertension, dyslipidemia, and diabetes, left ventricular ejection fraction, baseline creatinine levels, presentation of ACS, and the culprit vessel in ACS. Univariate Cox proportional hazards regression analyses were used to assess the association with cardiac events. Kaplan–Meier curves were used to compare survival free from cardiac events between patients with low and normal post‐PCI CBF. Low post‐PCI CBF was defined using the lowest tertile of the distribution of angiography‐based CBF. Time‐to‐event data are presented as Kaplan–Meier estimates and were compared using the log‐rank test. Multiple comparisons were adjusted by the Bonferroni method analyses were performed A P value of 0.05 or less was considered to indicate statistical significance. All statistical using R statistical software version 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria).
RESULTS
Validation Cohort
Thirty‐five patients with coronary flow velocity measurement with Doppler were screened. After evaluating the tracings' quality and the angiograms' suitability for flow‐velocity assessment, 27 patients with Doppler and angiography‐derived flow velocity were included. The clinical and angiographic characteristics are shown in Table S3. The correlation in coronary flow velocity between Doppler and angiography was good (r=0.71 [95% CI, 0.45–0.86], P<0.001, Figure S2). The interobserver reproducibility of angiography‐derived flow velocity was excellent (r=0.97 [95% CI, 0.94–0.99], P<0.001, Figure S3).
Clinical Cohort
Seven hundred and two patients with ACS treated with PCI were eligible. After propensity score matching, 162 patients (81 pairs) were analyzed. There were no differences between groups after propensity matching (Figure S4). The patient's clinical characteristics before and after matching are shown in Tables S4 and S5. The study flow chart is shown in Figure S5.
The distributions of angiography‐based coronary flow velocity and CBF are shown in Figure 1. The median of angiography‐based CBF is 68.6 (49.9–102.7) mL/min, and the lowest tertile is 54.3 mL/min. Angiography‐based CBF after PCI was lower in patients with MACE during 1‐year follow‐up than those without MACE (58.5 [38.6–102.7] mL/min versus 76.1 [56.1–104.9] mL/min, P=0.023, Table S6). As for clinical presentation, angiography‐based CBF after PCI was lower after STEMI, followed by non‐STEMI and unstable angina (Figure 2). Angiography‐based CBF was significantly lower in the left anterior descending artery compared with non‐left anterior descending artery vessels (Figure S6) and did not differ between different ACS causes (Figure S7).
Figure 1. Distribution of angiography‐based flow velocity and coronary blood flow.
Left, the distribution of angiography‐based flow velocity. Right, the distribution of CBF. Patients with MACE are shown in orange, and controls are shown in gray. CBF indicates coronary blood flow; and MACE, major adverse cardiovascular events.
Figure 2. Comparison of CBF between clinical presentations of ACS.
The plot shows the comparison of angiography‐based CBF between the clinical presentation of ACS (STEMI in red, NSTEMI in yellow, and UAP in green). ACS indicates acute coronary syndrome; CBF, coronary blood flow; NSTEMI, non‐ST‐segment–elevation myocardial infarction; STEMI, ST‐segment–elevation myocardial infarction; and UAP, unstable angina.
The patient's clinical characteristics stratified by angiography‐based CBF are shown in Table 1. In the present study cohort, the median age was 68.0 (59.0–79.0) years, 83% were male, and 33% had diabetes. STEMI accounted for two thirds of cases. The value of left ventricular ejection fraction was lower in patients with low angiography‐based CBF than those with normal angiography‐based CBF (48.5 [39.0–57.8]% versus 57.0 [46.8–62.0]%, P=0.005). Patients with low angiography‐based CBF had a significantly higher value of peak creatine kinase than those with normal angiography‐based CBF (976.0 [329.2–3119.0] IU/L versus 379.5 [146.2–1593.8] IU/L, P=0.007). Patients with low post‐PCI CBF had lower TIMI flow grades at baseline (P=0.006) and lower myocardial blush grade (P=0.006). Furthermore, post‐PCI CBF was significantly associated with myocardial blush grade (Figure S8).
Table 1.
Clinical Characteristics
| Variables | All | Normal post‐PCI CBF | Low post‐PCI CBF* | P value |
|---|---|---|---|---|
| Number of patients, n | 162 | 108 | 54 | |
| Age, y (median [IQR]) | 68.0 (59.0–79.0) | 68.5 (57.8–78.0) | 68.0 (60.3–80.0) | 0.266 |
| Male, n (%) | 135 (83.3) | 91 (84.3) | 44 (81.5) | 0.823 |
| Body mass index, (median [IQR]) | 23.2 (21.3–26.1) | 23.7 (21.4–26.9) | 22.9 (21.1–25.5) | 0.164 |
| Hypertension, n (%) | 102 (63.0) | 69 (63.9) | 33 (61.1) | 0.863 |
| Dyslipidemia, n (%) | 76 (46.9) | 52 (48.1) | 24 (44.4) | 0.781 |
| Diabetes, n (%) | 54 (33.3) | 36 (33.3) | 18 (33.3) | 1.000 |
| Current smoking, n (%) | 45 (27.8) | 33 (30.6) | 12 (22.2) | 0.221 |
| Prior MI, n (%) | 8 (4.9) | 6 (5.6) | 2 (3.7) | 0.898 |
| Prior PCI, n (%) | 12 (7.4) | 10 (9.3) | 2 (3.7) | 0.340 |
| Prior coronary artery bypass grafting, n (%) | 1 (0.6) | 1 (0.9) | 0 (0.0) | 1.000 |
| Left ventricular ejection fraction, % (median [IQR]) | 55.0 (45.0–60.8) | 57.0 (46.8–62.0) | 48.5 (39.0–57.8) | 0.005 |
| Creatinine clearance, mL/min (median [IQR]) | 71.0 (44.0–89.3) | 72.7 (45.0–92.7) | 67.2 (41.3–86.0) | 0.500 |
| Hemodialysis user, n (%) | 7 (4.3) | 5 (4.6) | 2 (3.7) | 1.000 |
| Killip's classification, n (%) | 0.058 | |||
| Class I, n (%) | 122 (78.2) | 87 (84.5) | 35 (66.0) | |
| Class II, n (%) | 13 (8.3) | 7 (6.8) | 6 (11.3) | |
| Class III, n (%) | 11 (7.1) | 5 (4.9) | 6 (11.3) | |
| Class IV, n (%) | 10 (6.4) | 4 (3.9) | 6 (11.3) | |
| Culprit vessels | 0.001 | |||
| Left main trunk, n (%) | 0 (0.0) | 0 (0.0) | 0 (0.0) | |
| Left anterior descending artery, n (%) | 88 (54.3) | 48 (44.4) | 40 (74.1) | |
| Left circumflex artery, n (%) | 7 (4.3) | 5 (4.6) | 2 (3.7) | |
| Right coronary artery, n (%) | 67 (41.4) | 55 (50.9) | 12 (22.2) | |
| Clinical diagnosis | 0.160 | |||
| STEMI, n (%) | 107 (66.0) | 66 (61.1) | 41 (75.9) | |
| Non‐STEMI, n (%) | 49 (30.2) | 37 (34.3) | 12 (22.2) | |
| Unstable angina, n (%) | 6 (3.7) | 5 (4.6) | 1 (1.9) | |
| Cause of acute coronary syndrome | 0.734 | |||
| Plaque rupture, n (%) | 104 (64.2) | 66 (61.1) | 38 (70.4) | |
| Plaque erosion, n (%) | 32 (19.8) | 24 (22.2) | 8 (14.8) | |
| Eruptive calcified nodules, n (%) | 9 (5.6) | 6 (5.6) | 3 (5.6) | |
| Initial TIMI flow grade | 0.006 | |||
| TIMI 0, n (%) | 81 (50.0) | 47 (43.5) | 34 (63.0) | |
| TIMI I, n (%) | 18 (11.1) | 13 (12.0) | 5 (9.3) | |
| TIMI II, n (%) | 34 (21.0) | 21 (19.4) | 13 (24.1) | |
| TIMI III, n (%) | 29 (17.9) | 27 (25.0) | 2 (3.7) | |
| Procedure outcomes | ||||
| Peak creatine kinase, IU/L (median [IQR]) | 530.0 (164.0–2072.0) | 379.5 (146.3–1593.8) | 976.0 (329.3–3119.0) | 0.007 |
| Minimum stent expansion rate, % (median [IQR]) | 74.8 (64.3–87.0) | 74.3 (65.4–87.4) | 79.9 (63.1–86.9) | 0.817 |
| Residual Synergy Between PCI with Taxus and Cardiac Surgery score, (median [IQR]) | 3.0 (0.0–8.8) | 4.0 (0.0–9.0) | 0.50 (0.0–6.0) | 0.171 |
| Myocardial blush grade | 0.006 | |||
| Grade 0, n (%) | 15 (9.3) | 6 (5.6) | 9 (16.7) | |
| Grade 1, n (%) | 33 (20.4) | 17 (15.7) | 16 (29.6) | |
| Grade 2, n (%) | 60 (37.0) | 42 (38.9) | 18 (33.3) | |
| Grade 3, n (%) | 54 (33.3) | 43 (39.8) | 11 (20.4) | |
CBF indicates coronary blood flow; IQR, interquartile range; MI, myocardial infarction; PCI, percutaneous coronary intervention; STEMI, ST‐segment–elevation myocardial infarction; and TIMI, thrombolysis in myocardial infarction.
Low post‐PCI CBF was defined using the lowest tertile of the distribution of angiography‐based CBF (<54.3 mL/min).
Post‐PCI angiography‐derived functional assessment is shown in Table 2. Post‐PCI vessel fractional flow reserve was lower in patients with low angiography‐based CBF than in those with normal angiography‐based CBF (0.88 [0.82–0.94] versus 0.93 [0.89–0.97]%, P<0.001). Angiography‐based coronary flow velocity was lower in patients with low angiography‐based CBF than in those with normal angiography‐based CBF (8.7 [6.6–11.9] cm/s versus 18.7 [13.6–24.1] cm/s, P<0.001). Patients with low angiography‐based CBF had a significantly smaller vessel volume than those with normal angiography‐based CBF (520.4 [409.8–731.6] mm3 versus 707.9 [498.8–974.0] mm3, P=0.002).
Table 2.
Post‐PCI Angiography‐Derived Flow Assessment
| All | Normal post‐PCI CBF | Low post‐PCI CBF* | P value | |
|---|---|---|---|---|
| Number of patients, n | 162 | 108 | 54 | |
| Post PCI vessel fractional flow reserve, (median [IQR]) | 0.92 (0.86–0.96) | 0.93 (0.89–0.97) | 0.88 (0.82–0.94) | <0.001 |
| Coronary flow velocity, cm/s (median [IQR]) | 14.9 (9.9–21.6) | 18.7 (13.6–24.1) | 8.7 (6.6–11.9) | <0.001 |
| Vessel volume, mm3 (median [IQR]) | 623.9 (480.5–933.3) | 707.9 (498.8–974.0) | 520.4 (409.8–731.6) | 0.002 |
| CBF, mL/min (median [IQR]) | 68.6 (49.9–102.7) | 86.8 (68.6–119.2) | 37.2 (29.3–49.6) | <0.001 |
CBF indicates coronary blood flow; IQR, interquartile range; PCI, percutaneous coronary intervention.
Low post‐PCI CBF was defined using the lowest tertile of the distribution of angiography‐based CBF (<54.3 mL/min).
The median follow‐up duration was 371 days (interquartile range, 362– 403). Of 81 MACE, 8 were cardiovascular deaths, 10 were nonfatal MI, 42 were ischemia‐driven revascularization, and 33 were hospitalizations for HF (Table 3). Patients with low angiography‐based CBF exhibited a higher incidence of MACE compared with those with normal angiography‐based CBF (66.7% versus 41.7%, P=0.005), with HF hospitalizations being the predominant contributor (35.2% versus 13.0%, P=0.002). Patients with low post‐PCI CBF had an increased risk of MACE (hazard ratio [HR], 2.11 [95% CI, 1.35–3.28], P=0.001) compared with those with normal post‐PCI CBF. Moreover, patients with low CBF experience a higher incidence of HF hospitalizations (HR, 3.04 [95% CI, 1.52–6.06], P=0.002; Figure 3). The associations of angiography‐based coronary flow velocity with clinical outcomes are shown in the Table S7 and Figure S9.
Table 3.
Clinical End Points at 12‐Month Follow‐Up
| Variable* | All | Normal post‐PCI CBF | Low post‐PCI CBF† | P value |
|---|---|---|---|---|
| Number of patients, n | 162 | 108 | 54 | |
| Major adverse cardiovascular events, n (%) | 81 (50.0) | 45 (41.7) | 36 (66.7) | 0.005 |
| Cardiovascular death, n (%) | 8 (4.9) | 4 (3.7) | 4 (7.4) | 0.522 |
| Nonfatal myocardial infarction, n (%) | 10 (6.2) | 6 (5.6) | 4 (7.4) | 0.908 |
| Ischemia‐driven revascularization, n (%) | 42 (25.9) | 29 (26.9) | 13 (24.1) | 0.849 |
| Hospitalizations for heart failure, n (%) | 33 (20.4) | 14 (13.0) | 19 (35.2) | 0.002 |
CBF indicates coronary blood flow; and PCI, percutaneous coronary intervention.
These clinical events are considered in a nonhierarchical approach.
Low post‐PCI CBF was defined using the lowest tertile of the distribution of angiography‐based CBF (<54.3 mL/min).
Figure 3. Kaplan–Meier survival curve from primary revascularization for cardiovascular events in patients with acute coronary syndromes.
This figure illustrates the 1‐year survival rates over time for patients with low angiography‐based CBF (blue line) and normal CBF (red line). Low CBF was defined as less than the lowest tertile in this cohort (<54.3 mL/min). The left panel shows Kaplan–Meier survival curve for MACE, composed of cardiovascular death, nonfatal myocardial infarction, hospitalization of HF, or ischemia‐driven revascularization. The right panel shows Kaplan–Meier survival curve for hospitalization of HF. CBF indicates coronary blood flow; HF, heart failure; HR, hazard ratio; and MACE, major adverse cardiovascular events.
DISCUSSION
The present study validated the novel approach for blood flow quantification using automatic contrast bolus tracking. The main findings can be summarized as follows: (1) angiography‐derived coronary flow velocity using automatic bolus tracking strongly correlated with average peak velocity derived from Doppler; (2) after PCI, patients presenting with STEMI had lower angiography‐based CBF than those with non‐STEMI and unstable angina; and (3) low post‐PCI CBF was associated with the occurrence of adverse cardiovascular events, especially hospitalizations due to HF.
Reperfusion of the infarct‐related coronary artery with PCI is the main goal for ACS management. In patients with STEMI, PCI reduces infarct size, minimizes myocardial damage, preserves left ventricular function, and decreases mortality. 9 Myocardial injury occurs due to ischemia and reperfusion injury. Impaired reperfusion angiography manifested as slow flow or no reflow has been associated with microvascular obstruction, which is an independent predictor of mortality and HF after primary PCI. 10 , 11 Blood flow can also be assessed angiographically by TIMI flow grade, corrected TIMI frame count, and myocardial blush grade. 8 , 12 Blood flow can also be assessed invasively with thermodilution or Doppler methods. Studies have shown that reduced epicardial blood flow either assessed by TIMI frame count or the index of microvascular resistance is linked to a worse prognosis. Nevertheless, these techniques are not widely used in clinical practice due to time constraints, logistical aspects, and interobserver variability. 13 In the present study, we tested a novel software to track the contrast bolus and calculate flow velocity. This novel approach resembles the TIMI frame counts but leverages a novel algorithm providing blood flow quantification automatically.
The present study showed that this novel approach identified patients at higher risk for MACE after PCI. The increased risk for events was mainly driven by a higher rate of hospitalization for HF at 1 year. This finding aligns with previous studies, which showed that post‐PCI blood flow was independently associated with 1‐year mortality or hospitalization rates for HF. 14 , 15 The potential mechanism explaining these findings is the strong association between epicardial blood flow restoration, microvascular obstruction, infarct size, and myocardial necrosis. 16 We also found that patients with STEMI had lower angiography‐based CBF compared with those with non‐STEMI and unstable angina. Patients presenting with STEMI had a higher rate of thrombotic occlusions, potentially leading to more distal embolization impairing post‐PCI CBF. It is important to highlight that we excluded patients presenting with slow flow and no reflow after PCI; therefore, the current approach refined the quantification of CBF in patients considered to have adequate reperfusion on visual angiographic assessment after PCI.
CBF can be calculated invasively using Doppler or thermodilution‐based techniques. In the present study, we used Doppler as a reference to validate the velocity calculated from the automatic contrast bolus tracking in patients without coronary artery disease. The velocity derived from the angiographic methods showed a high level of agreement compared with Doppler‐derived velocity with excellent reproducibility. In patients with stable coronary artery disease, the assessment of flow and pressure allows for calculating microvascular resistance. An increased microvascular resistance is the hallmark of coronary microvascular dysfunction. Although we focused on assessing clinical outcomes after PCI in ACS, this novel software may also be applied to patients suspected of having coronary microvascular dysfunction. In this study, we showed a high level of agreement between invasive and angiography‐derived blood flow velocity in stable patients, a larger validation study in this subgroup of patients is required.
In the past decade, we have witnessed the evolution of different systems that extract physiologic information from angiograms. Several angiography‐derived fractional flow reserve solutions have been validated and are now undergoing clinical testing in randomized clinical trials. In addition, angiograms can also be processed to extract wall‐shear stress with clinical implications. The present work extends the utility of angiography for calculating blood flow, demonstrating its accuracy and association with clinical outcomes. In the near future, these solutions will likely be available after a standard angiogram, providing a quantification of the blood flow with clinical implications and may result in an enhanced stratification of the patients after PCI.
Limitations
The present study has several limitations. First, although we use propensity score matching to minimize selection bias, the present findings are susceptible to unmeasured confounding factors, and potential biases in propensity‐matched control subjects. Second, patients with post‐PCI TIMI < II were excluded. This group of patients with no‐reflow phenomenon or slow flow may be at a high risk of HF due to incomplete or suboptimal myocardial reperfusion. The main reason for this exclusion is that the software necessitates detection of the contrast bolus and, in severe cases (no reflow), does not provide a flow estimation. Thus, these findings apply to patients with an adequate TIMI flow, where this angiography‐based flow quantification further stratified patient's risk following PCI. Third, both the validation and clinical cohorts have relatively small sample sizes, necessitating further studies in larger cohorts with different ethnicities. Fourth, as this study is an exploratory subanalysis of the TACTICS registry, a priori sample size calculations were not performed. Therefore, these findings should be interpreted as hypothesis generating. Fifth, the validation cohort consisted of patients with angina and nonobstructive coronary artery disease, this may have affected the results due to potential disease in the coronary microcirculation. Sixth, this software is not commercially available yet. Seventh, all CBF measurements were obtained from the final angiograms following nitrate administration, as generally recommended; the impact of vasodilators on our findings remains to be determined. Eighth, we did not assess the impact of thrombectomy in the present study.
CONCLUSIONS
CBF can be estimated from standard angiograms and demonstrated to be accurate when compared with invasive Doppler measurements. Angiography‐based CBF was associated with the occurrence of adverse events after PCI in patients presenting with an ACS. Low angiography‐based CBF after PCI was associated with an increased risk of cardiovascular events, particularly HF hospitalizations at 1‐year follow‐up.
Sources of Funding
This study was sponsored by Abbott Medical Japan LLC (Minato‐ku, Tokyo, Japan). Other than financial sponsorship, the company had no role in study protocol development or implementation, management, data collection, or analysis. The authors and colleagues were solely responsible for the design and execution of this study.
Disclosures
Takuya Mizukami received consultancy fees from Zeon Medical Inc., research grants from Boston Scientific, and speaker fees from Abbott Vascular, Cathworks, and Boston Scientific. Junichi Yamaguchi was endowed by Abbott Medical Japan LLC, Boston Scientific, Medtronic, and Terumo. Toshiro Shinke received personal fees and research grants from Abbott Medical Japan LLC. Adriaan Wilgenhof has been supported by a research grant provided by the DigiCardiopaTh PhD program. Chris Bouwman and Jean‐Paul Aben are employees of Pie Medical Imaging. Carlos Collet reports receiving research grants from Biosensor, Coroventis Research, Medis Medical Imaging, Pie Medical Imaging, CathWorks, Boston Scientific, Siemens, HeartFlow Inc, Abbott Vascular, and consultancy fees from HeartFlow Inc, OpSens, Abbott Vascular, and Philips Volcano. The remaining authors have no disclosures to report.
Supporting information
Tables S1–S7
Figures S1–S9
Acknowledgments
We gratefully acknowledge the work of coinvestigators in TACTICS registry and past and present members of our Core Laboratory.
This article was sent to Rushi V. Parikh, MD, Associate Editor, for review by expert referees, editorial decision, and final disposition.
Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.124.038770
For Sources of Funding and Disclosures, see page 9.
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Associated Data
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Supplementary Materials
Tables S1–S7
Figures S1–S9