Determining Hemodynamically Significant Coronary Artery Disease: Patient-Specific Cutoffs in Quantitative Myocardial Blood Flow Using [15O]H2O PET Imaging.

Currently, cutoffs of quantitative [15O]H2O PET to detect fractional flow reserve (FFR)-defined coronary artery disease (CAD) were derived from a single cohort that included patients without prior CAD. However, prior CAD, sex, and age can influence myocardial blood flow (MBF). Therefore, the present study determined the influence of prior CAD, sex, and age on optimal cutoffs of hyperemic MBF (hMBF) and coronary flow reserve (CFR) and evaluated whether cutoff optimization enhanced diagnostic performance of quantitative [15O]H2O PET against an FFR reference standard. Methods: Patients with chronic coronary symptoms underwent [15O]H2O PET and invasive coronary angiography with FFR. Optimal cutoffs for patients with and without prior CAD and subpopulations based on sex and age were determined. Results: This multicenter study included 560 patients. Optimal cutoffs were similar for patients with (n = 186) and without prior CAD (hMBF, 2.3 vs. 2.3 mL·min-1·g-1; CFR, 2.7 vs. 2.6). Females (n = 190) had higher hMBF cutoffs than males (2.8 vs. 2.3 mL·min-1·g-1), whereas CFRs were comparable (2.6 vs. 2.7). However, female sex-specific hMBF cutoff implementation decreased diagnostic accuracy as compared with the cutoff of 2.3 mL·min-1·g-1 (72% vs. 82%, P < 0.001). Patients aged more than 70 y (n = 79) had lower hMBF (1.7 mL·min-1·g-1) and CFR (2.3) cutoffs than did patients aged 50 y or less, 51-60 y, and 61-70 y (hMBF, 2.3-2.4 mL·min-1·g-1; CFR, 2.7). Age-specific cutoffs in patients aged more than 70 y yielded comparable accuracy to the previously established cutoffs (hMBF, 72% vs. 76%, P = 0.664; CFR, 80% vs. 75%, P = 0.289). Conclusion: Patients with and without prior CAD had similar [15O]H2O PET cutoffs for detecting FFR-defined significant CAD. Stratifying patients according to sex and age led to different optimal cutoffs; however, these values did not translate into an increased overall accuracy as compared with previously established thresholds for MBF.

Development and Validation of a Quantitative Coronary CT Angiography Model for Diagnosis of Vessel-Specific Coronary Ischemia.

BACKGROUND: Noninvasive stress testing is commonly used for detection of coronary ischemia but possesses variable accuracy and may result in excessive health care costs. OBJECTIVES: This study aimed to derive and validate an artificial intelligence-guided quantitative coronary computed tomography angiography (AI-QCT) model for the diagnosis of coronary ischemia that integrates atherosclerosis and vascular morphology measures (AI-QCTISCHEMIA) and to evaluate its prognostic utility for major adverse cardiovascular events (MACE). METHODS: A post hoc analysis of the CREDENCE (Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia) and PACIFIC-1 (Comparison of Coronary Computed Tomography Angiography, Single Photon Emission Computed Tomography [SPECT], Positron Emission Tomography [PET], and Hybrid Imaging for Diagnosis of Ischemic Heart Disease Determined by Fractional Flow Reserve) studies was performed. In both studies, symptomatic patients with suspected stable coronary artery disease had prospectively undergone coronary computed tomography angiography (CTA), myocardial perfusion imaging (MPI), SPECT, or PET, fractional flow reserve by CT (FFRCT), and invasive coronary angiography in conjunction with invasive FFR measurements. The AI-QCTISCHEMIA model was developed in the derivation cohort of the CREDENCE study, and its diagnostic performance for coronary ischemia (FFR ≤0.80) was evaluated in the CREDENCE validation cohort and PACIFIC-1. Its prognostic value was investigated in PACIFIC-1. RESULTS: In CREDENCE validation (n = 305, age 64.4 ± 9.8 years, 210 [69%] male), the diagnostic performance by area under the receiver-operating characteristics curve (AUC) on per-patient level was 0.80 (95% CI: 0.75-0.85) for AI-QCTISCHEMIA, 0.69 (95% CI: 0.63-0.74; P < 0.001) for FFRCT, and 0.65 (95% CI: 0.59-0.71; P < 0.001) for MPI. In PACIFIC-1 (n = 208, age 58.1 ± 8.7 years, 132 [63%] male), the AUCs were 0.85 (95% CI: 0.79-0.91) for AI-QCTISCHEMIA, 0.78 (95% CI: 0.72-0.84; P = 0.037) for FFRCT, 0.89 (95% CI: 0.84-0.93; P = 0.262) for PET, and 0.72 (95% CI: 0.67-0.78; P < 0.001) for SPECT. Adjusted for clinical risk factors and coronary CTA-determined obstructive stenosis, a positive AI-QCTISCHEMIA test was associated with an HR of 7.6 (95% CI: 1.2-47.0; P = 0.030) for MACE. CONCLUSIONS: This newly developed coronary CTA-based ischemia model using coronary atherosclerosis and vascular morphology characteristics accurately diagnoses coronary ischemia by invasive FFR and provides robust prognostic utility for MACE beyond presence of stenosis.

Validation of resting full-cycle ratio and diastolic pressure ratio with [15O]H2O positron emission tomography myocardial perfusion.

Fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) are invasive techniques used to evaluate the hemodynamic significance of coronary artery stenosis. These methods have been validated through perfusion imaging and clinical trials. New invasive pressure ratios that do not require hyperemia have recently emerged, and it is essential to confirm their diagnostic efficacy. The aim of this study was to validate the resting full-cycle ratio (RFR) and the diastolic pressure ratio (dPR), against [15O]H2O positron emission tomography (PET) imaging. A total of 129 symptomatic patients with an intermediate risk of coronary artery disease (CAD) were included. All patients underwent cardiac [15O]H2O PET with quantitative assessment of resting and hyperemic myocardial perfusion. Within a 2 week period, coronary angiography was performed. Intracoronary pressure measurements were obtained in 320 vessels and RFR, dPR, and FFR were computed. PET derived regional hyperemic myocardial blood flow (hMBF) and myocardial perfusion reserve (MPR) served as reference standards. In coronary arteries with stenoses (43%, 136 of 320), the overall diagnostic accuracies of RFR, dPR, and FFR did not differ when PET hyperemic MBF < 2.3 ml min-1 (69.9%, 70.6%, and 77.1%, respectively) and PET MPR < 2.5 (70.6%, 71.3%, and 66.9%, respectively) were considered as the reference for myocardial ischemia. Non-significant differences between the areas under the receiver operating characteristic (ROC) curve were found between the different indices. Furthermore, the integration of FFR with RFR (or dPR) does not enhance the diagnostic information already achieved by FFR in the characterization of ischemia via PET perfusion. In conclusion, the novel non-hyperemic pressure ratios, RFR and dPR, have a diagnostic performance comparable to FFR in assessing regional myocardial ischemia. These findings suggest that RFR and dPR may be considered as an FFR alternative for invasively guiding revascularization treatment in symptomatic patients with CAD.

Hemodynamic Insights into Combined Fractional Flow Reserve and Instantaneous Wave-Free Ratio Assessment Through Quantitative [15O]H2O PET Myocardial Perfusion Imaging.

In patients evaluated for obstructive coronary artery disease (CAD), guidelines recommend using either fractional flow reserve (FFR) or instantaneous wave-free ratio (iFR) to guide coronary revascularization decision-making. The hemodynamic significance of lesions with discordant FFR and iFR measurements is debated. This study compared [15O]H2O PET-derived absolute myocardial perfusion between vessels with concordant and discordant FFR and iFR measurements. Methods: We included 197 patients suspected of obstructive CAD who had undergone [15O]H2O PET perfusion imaging and combined FFR/iFR interrogation in 468 vessels. Resting myocardial blood flow (MBF), hyperemic MBF, and coronary flow reserve (CFR) were compared among 4 groups: FFR low/iFR low (n = 79), FFR high/iFR low (n = 22), FFR low/iFR high (n = 22), and FFR high/iFR high (n = 345). Predefined [15O]H2O PET thresholds for ischemia were 2.3 mL·min-1·g-1 or less for hyperemic MBF and 2.5 or less for CFR. Results: Hyperemic MBF was lower in the concordant low (2.09 ± 0.67 mL·min-1·g-1), FFR high/iFR low (2.41 ± 0.80 mL·min-1·g-1), and FFR low/iFR high (2.40 ± 0.69 mL·min-1·g-1) groups compared with the concordant high group (2.91 ± 0.84 mL·min-1·g-1) (P < 0.001, P = 0.004, and P < 0.001, respectively). A lower CFR was observed in the concordant low (2.37 ± 0.76) and FFR high/iFR low (2.64 ± 0.84) groups compared with the concordant high group (3.35 ± 1.07, P < 0.01 for both). However, for vessels with either low FFR or low iFR, quantitative hyperemic MBF and CFR values exceeded the ischemic threshold in 38% and 49%, respectively. In addition, resting MBF exhibited a negative correlation with iFR (P < 0.001) and was associated with FFR low/iFR high discordance compared with concordant low FFR/low iFR measurements, independent of clinical and angiographic characteristics, as well as hyperemic MBF (odds ratio [OR], 0.41; 95% CI, 0.26-0.65; P < 0.001). Conclusion: We found reduced myocardial perfusion in vessels with concordant low and discordant FFR/iFR measurements. However, FFR/iFR combinations often inaccurately classified vessels as either ischemic or nonischemic when compared with hyperemic MBF and CFR. Furthermore, a lower resting MBF was associated with a higher iFR and the occurrence of FFR low/iFR high discordance. Our study showed that although combined FFR/iFR assessment can be useful to estimate the hemodynamic significance of coronary lesions, these pressure-derived indices provide a limited approximation of [15O]H2O PET-derived quantitative myocardial perfusion as the physiologic standard of CAD severity.

AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD.

BACKGROUND: The recent development of artificial intelligence-guided quantitative coronary computed tomography angiography analysis (AI-QCT) has enabled rapid analysis of atherosclerotic plaque burden and characteristics. OBJECTIVES: This study set out to investigate the 10-year prognostic value of atherosclerotic burden derived from AI-QCT and to compare the spectrum of plaque to manually assessed coronary computed tomography angiography (CCTA), coronary artery calcium scoring (CACS), and clinical risk characteristics. METHODS: This was a long-term follow-up study of 536 patients referred for suspected coronary artery disease. CCTA scans were analyzed with AI-QCT and plaque burden was classified with a plaque staging system (stage 0: 0% percentage atheroma volume [PAV]; stage 1: >0%-5% PAV; stage 2: >5%-15% PAV; stage 3: >15% PAV). The primary major adverse cardiac event (MACE) outcome was a composite of nonfatal myocardial infarction, nonfatal stroke, coronary revascularization, and all-cause mortality. RESULTS: The mean age at baseline was 58.6 years and 297 patients (55%) were male. During a median follow-up of 10.3 years (IQR: 8.6-11.5 years), 114 patients (21%) experienced the primary outcome. Compared to stages 0 and 1, patients with stage 3 PAV and percentage of noncalcified plaque volume of >7.5% had a more than 3-fold (adjusted HR: 3.57; 95% CI 2.12-6.00; P < 0.001) and 4-fold (adjusted HR: 4.37; 95% CI: 2.51-7.62; P < 0.001) increased risk of MACE, respectively. Addition of AI-QCT improved a model with clinical risk factors and CACS at different time points during follow-up (10-year AUC: 0.82 [95% CI: 0.78-0.87] vs 0.73 [95% CI: 0.68-0.79]; P < 0.001; net reclassification improvement: 0.21 [95% CI: 0.09-0.38]). Furthermore, AI-QCT achieved an improved area under the curve compared to Coronary Artery Disease Reporting and Data System 2.0 (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.023) and manual QCT (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.040), although net reclassification improvement was modest (0.09 [95% CI: -0.02 to 0.29] and 0.04 [95% CI: -0.05 to 0.27], respectively). CONCLUSIONS: Through 10-year follow-up, AI-QCT plaque staging showed important prognostic value for MACE and showed additional discriminatory value over clinical risk factors, CACS, and manual guideline-recommended CCTA assessment.

The diagnostic performance of quantitative flow ratio and perfusion imaging in patients with prior coronary artery disease.

AIMS: In chronic coronary syndrome (CCS) patients with documented coronary artery disease (CAD), ischaemia detection by myocardial perfusion imaging (MPI) and an invasive approach are viable diagnostic strategies. We compared the diagnostic performance of quantitative flow ratio (QFR) with single-photon emission computed tomography (SPECT), positron emission tomography (PET), and cardiac magnetic resonance imaging (CMR) in patients with prior CAD [previous percutaneous coronary intervention (PCI) and/or myocardial infarction (MI)]. METHODS AND RESULTS: This PACIFIC-2 sub-study evaluated 189 CCS patients with prior CAD for inclusion. Patients underwent SPECT, PET, and CMR followed by invasive coronary angiography with fractional flow reserve (FFR) measurements of all major coronary arteries (N = 567), except for vessels with a sub-total or chronic total occlusion. Quantitative flow ratio computation was attempted in 488 (86%) vessels with measured FFR available (FFR ≤0.80 defined haemodynamically significant CAD). Quantitative flow ratio analysis was successful in 334 (68%) vessels among 166 patients and demonstrated a higher accuracy (84%) and sensitivity (72%) compared with SPECT (66%, P < 0.001 and 46%, P = 0.001), PET (65%, P < 0.001 and 58%, P = 0.032), and CMR (72%, P < 0.001 and 33%, P < 0.001). The specificity of QFR (87%) was similar to that of CMR (83%, P = 0.123) but higher than that of SPECT (71%, P < 0.001) and PET (67%, P < 0.001). Lastly, QFR exhibited a higher area under the receiver operating characteristic curve (0.89) than SPECT (0.57, P < 0.001), PET (0.66, P < 0.001), and CMR (0.60, P < 0.001). CONCLUSION: QFR correlated better with FFR in patients with prior CAD than MPI, as reflected in the higher diagnostic performance measures for detecting FFR-defined, vessel-specific, significant CAD.

Impact of cardiac history and myocardial scar on increase of myocardial perfusion after revascularization.

PURPOSE: We sought to assess the impact of coronary revascularization on myocardial perfusion and fractional flow reserve (FFR) in patients without a cardiac history, with prior myocardial infarction (MI) or non-MI percutaneous coronary intervention (PCI). Furthermore, we studied the impact of scar tissue. METHODS: Symptomatic patients underwent [15O]H2O positron emission tomography (PET) and FFR before and after revascularization. Patients with prior CAD, defined as prior MI or PCI, underwent scar quantification by magnetic resonance imaging late gadolinium enhancement. RESULTS: Among 137 patients (87% male, age 62.2 ± 9.5 years) 84 (61%) had a prior MI or PCI. The increase in FFR and hyperemic myocardial blood flow (hMBF) was less in patients with prior MI or non-MI PCI compared to those without a cardiac history (FFR: 0.23 ± 0.14 vs. 0.20 ± 0.12 vs. 0.31 ± 0.18, p = 0.02; hMBF: 0.54 ± 0.75 vs. 0.62 ± 0.97 vs. 0.91 ± 0.96 ml/min/g, p = 0.04). Post-revascularization FFR and hMBF were similar across patients without a cardiac history or with prior MI or non-MI PCI. An increase in FFR was strongly associated to hMBF increase in patients without a cardiac history or with prior MI/non-MI PCI (r = 0.60 and r = 0.60, p < 0.01 for both). Similar results were found for coronary flow reserve. In patients with prior MI scar was negatively correlated to hMBF increase and independently predictive of an attenuated CFR increase. CONCLUSIONS: Post revascularization FFR and perfusion were similar among patients without a cardiac history, with prior MI or non-MI PCI. In patients with prior MI scar burden was associated to an attenuated perfusion increase.

Relation of Gender to Atherosclerotic Plaque Characteristics by Differing Angiographic Stenosis Severity.

It is unknown whether gender influences the atherosclerotic plaque characteristics (APCs) of lesions of varying angiographic stenosis severity. This study evaluated the imaging data of 303 symptomatic patients from the derivation arm of the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial, all of whom underwent coronary computed tomographic angiography and clinically indicated nonemergent invasive coronary angiography upon study enrollment. Index tests were interpreted by 2 blinded core laboratories, one of which performed quantitative coronary computed tomographic angiography using an artificial intelligence application to characterize and quantify APCs, including percent atheroma volume (PAV), low-density noncalcified plaque (LD-NCP), noncalcified plaque (NCP), calcified plaque (CP), lesion length, positive arterial remodeling, and high-risk plaque (a combination of LD-NCP and positive remodeling ≥1.10); the other classified lesions as obstructive (≥50% diameter stenosis) or nonobstructive (<50% diameter stenosis) based on quantitative invasive coronary angiography. The relation between APCs and angiographic stenosis was further examined by gender. The mean age of the study cohort was 64.4 ± 10.2 years (29.0% female). In patients with obstructive disease, men had more LD-NCP PAV (0.5 ± 0.4 vs 0.3 ± 0.8, p = 0.03) and women had more CP PAV (11.7 ± 1.6 vs 8.0 ± 0.8, p = 0.04). Obstructive lesions had more NCP PAV compared with their nonobstructive lesions in both genders, however, obstructive lesions in women also demonstrated greater LD-NCP PAV (0.4 ± 0.5 vs 1.0 ± 1.8, p = 0.03), and CP PAV (17.4 ± 16.5 vs 25.9 ± 18.7, p = 0.03) than nonobstructive lesions. Comparing the composition of obstructive lesions by gender, women had more CP PAV (26.3 ± 3.4 vs 15.8 ± 1.5, p = 0.005) whereas men had more NCP PAV (33.0 ± 1.6 vs 26.7 ± 2.5, p = 0.04). Men had more LD-NCP PAV in nonobstructive lesions compared with women (1.2 ± 0.2 vs 0.6 ± 0.2, p = 0.02). In conclusion, there are gender-specific differences in plaque composition based on stenosis severity.

Relationship between impaired myocardial blood flow by positron emission tomography and low-attenuation plaque burden and pericoronary adipose tissue attenuation from coronary computed tomography: From the prospective PACIFIC trial.

BACKGROUND: Positron emission tomography (PET) is the clinical gold standard for quantifying myocardial blood flow (MBF). Pericoronary adipose tissue (PCAT) attenuation may detect vascular inflammation indirectly. We examined the relationship between MBF by PET and plaque burden and PCAT on coronary CT angiography (CCTA). METHODS: This post hoc analysis of the PACIFIC trial included 208 patients with suspected coronary artery disease (CAD) who underwent [15O]H2O PET and CCTA. Low-attenuation plaque (LAP, < 30HU), non-calcified plaque (NCP), and PCAT attenuation were measured by CCTA. RESULTS: In 582 vessels, 211 (36.3%) had impaired per-vessel hyperemic MBF (≤ 2.30 mL/min/g). In multivariable analysis, LAP burden was independently and consistently associated with impaired hyperemic MBF (P = 0.016); over NCP burden (P = 0.997). Addition of LAP burden improved predictive performance for impaired hyperemic MBF from a model with CAD severity and calcified plaque burden (P < 0.001). There was no correlation between PCAT attenuation and hyperemic MBF (r = - 0.11), and PCAT attenuation was not associated with impaired hyperemic MBF in univariable or multivariable analysis of all vessels (P > 0.1). CONCLUSION: In patients with stable CAD, LAP burden was independently associated with impaired hyperemic MBF and a stronger predictor of impaired hyperemic MBF than NCP burden. There was no association between PCAT attenuation and hyperemic MBF.

Sex differences in computed tomography angiography-derived coronary plaque burden in relation to invasive fractional flow reserve.

BACKGROUND: Distinct sex-related differences exist in coronary artery plaque burden and distribution. We aimed to explore sex differences in quantitative plaque burden by coronary CT angiography (CCTA) in relation to ischemia by invasive fractional flow reserve (FFR). METHODS: This post-hoc analysis of the PACIFIC trial included 581 vessels in 203 patients (mean age 58.1 â€‹± â€‹8.7 years, 63.5% male) who underwent CCTA and per-vessel invasive FFR. Quantitative assessment of total, calcified, non-calcified, and low-density non-calcified plaque burden were performed using semiautomated software. Significant ischemia was defined as invasive FFR ≤0.8. RESULTS: The per-vessel frequency of ischemia was higher in men than women (33.5% vs. 7.5%, p â€‹< â€‹0.001). Women had a smaller burden of all plaque subtypes (all p â€‹< â€‹0.01). There was no sex difference on total, calcified, or non-calcified plaque burdens in vessels with ischemia; only low-density non-calcified plaque burden was significantly lower in women (beta: -0.183, p â€‹= â€‹0.035). The burdens of all plaque subtypes were independently associated with ischemia in both men and women (For total plaque burden (5% increase): Men, OR: 1.15, 95%CI: 1.06-1.24, p â€‹= â€‹0.001; Women, OR: 1.96, 95%CI: 1.11-3.46, p â€‹= â€‹0.02). No significant interaction existed between sex and total plaque burden for predicting ischemia (interaction p â€‹= â€‹0.108). The addition of quantitative plaque burdens to stenosis severity and adverse plaque characteristics improved the discrimination of ischemia in both men and women. CONCLUSIONS: In symptomatic patients with suspected CAD, women have a lower CCTA-derived burden of all plaque subtypes compared to men. Quantitative plaque burden provides independent and incremental predictive value for ischemia, irrespective of sex.

Diabetes, Atherosclerosis, and Stenosis by AI.

OBJECTIVE: This study evaluates the relationship between atherosclerotic plaque characteristics (APCs) and angiographic stenosis severity in patients with and without diabetes. Whether APCs differ based on lesion severity and diabetes status is unknown. RESEARCH DESIGN AND METHODS: We retrospectively evaluated 303 subjects from the Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia (CREDENCE) trial referred for invasive coronary angiography with coronary computed tomographic angiography (CCTA) and classified lesions as obstructive (≥50% stenosed) or nonobstructive using blinded core laboratory analysis of quantitative coronary angiography. CCTA quantified APCs, including plaque volume (PV), calcified plaque (CP), noncalcified plaque (NCP), low-density NCP (LD-NCP), lesion length, positive remodeling (PR), high-risk plaque (HRP), and percentage of atheroma volume (PAV; PV normalized for vessel volume). The relationship between APCs, stenosis severity, and diabetes status was assessed. RESULTS: Among the 303 patients, 95 (31.4%) had diabetes. There were 117 lesions in the cohort with diabetes, 58.1% of which were obstructive. Patients with diabetes had greater plaque burden (P = 0.004). Patients with diabetes and nonobstructive disease had greater PV (P = 0.02), PAV (P = 0.02), NCP (P = 0.03), PAV NCP (P = 0.02), diseased vessels (P = 0.03), and maximum stenosis (P = 0.02) than patients without diabetes with nonobstructive disease. APCs were similar between patients with diabetes with nonobstructive disease and patients without diabetes with obstructive disease. Diabetes status did not affect HRP or PR. Patients with diabetes had similar APCs in obstructive and nonobstructive lesions. CONCLUSIONS: Patients with diabetes and nonobstructive stenosis had an association to similar APCs as patients without diabetes who had obstructive stenosis. Among patients with nonobstructive disease, patients with diabetes had more total PV and NCP.

AI Evaluation of Stenosis on Coronary CTA, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve: A CREDENCE Trial Substudy.

BACKGROUND: Clinical reads of coronary computed tomography angiography (CTA), especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. Artificial intelligence (AI)-based solutions applied to coronary CTA may overcome these limitations. OBJECTIVES: This study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography (AI-QCT) angiography analyses to core lab-interpreted coronary CTA, core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR). METHODS: Coronary CTA, FFR, and QCA data from 303 stable patients (64 ± 10 years of age, 71% male) from the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia) trial were retrospectively analyzed using an Food and Drug Administration-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. RESULTS: Disease prevalence was high, with 32.0%, 35.0%, 21.0%, and 13.0% demonstrating ≥50% stenosis in 0, 1, 2, and 3 coronary vessel territories, respectively. Average AI-QCT analysis time was 10.3 ± 2.7 minutes. AI-QCT evaluation demonstrated per-patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 94%, 68%, 81%, 90%, and 84%, respectively, for ≥50% stenosis, and of 94%, 82%, 69%, 97%, and 86%, respectively, for detection of ≥70% stenosis. There was high correlation between stenosis detected on AI-QCT evaluation vs QCA on a per-vessel and per-patient basis (intraclass correlation coefficient = 0.73 and 0.73, respectively; P < 0.001 for both). False positive AI-QCT findings were noted in in 62 of 848 (7.3%) vessels (stenosis of ≥70% by AI-QCT and QCA of <70%); however, 41 (66.1%) of these had an FFR of <0.8. CONCLUSIONS: A novel AI-based evaluation of coronary CTA enables rapid and accurate identification and exclusion of high-grade stenosis and with close agreement to blinded, core lab-interpreted quantitative coronary angiography. (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia [CREDENCE]; NCT02173275).

Machine Learning From Quantitative Coronary Computed Tomography Angiography Predicts Fractional Flow Reserve-Defined Ischemia and Impaired Myocardial Blood Flow.

BACKGROUND: A pathophysiological interplay exists between plaque morphology and coronary physiology. Machine learning (ML) is increasingly being applied to coronary computed tomography angiography (CCTA) for cardiovascular risk stratification. We sought to assess the performance of a ML score integrating CCTA-based quantitative plaque features for predicting vessel-specific ischemia by invasive fractional flow reserve (FFR) and impaired myocardial blood flow (MBF) by positron emission tomography (PET). METHODS: This post-hoc analysis of the PACIFIC trial (Prospective Comparison of Cardiac Positron Emission Tomography/Computed Tomography [CT]' Single Photon Emission Computed Tomography/CT Perfusion Imaging and CT Coronary Angiography with Invasive Coronary Angiography) included 208 patients with suspected coronary artery disease who prospectively underwent CCTA' [15O]H2O PET, and invasive FFR. Plaque quantification from CCTA was performed using semiautomated software. An ML algorithm trained on the prospective NXT trial (484 vessels) was used to develop a ML score for the prediction of ischemia (FFR≤0.80), which was then evaluated in 581 vessels from the PACIFIC trial. Thereafter, the ML score was applied for predicting impaired hyperemic MBF (≤2.30 mL/min per g) from corresponding PET scans. The performance of the ML score was compared with CCTA reads and noninvasive FFR derived from CCTA (FFRCT). RESULTS: One hundred thirty-nine (23.9%) vessels had FFR-defined ischemia, and 195 (33.6%) vessels had impaired hyperemic MBF. For the prediction of FFR-defined ischemia, the ML score yielded an area under the receiver-operating characteristic curve of 0.92, which was significantly higher than that of visual stenosis grade (0.84; P<0.001) and comparable with that of FFRCT (0.93; P=0.34). Quantitative percent diameter stenosis and low-density noncalcified plaque volume had the greatest ML feature importance for predicting FFR-defined ischemia. When applied for impaired MBF prediction, the ML score exhibited an area under the receiver-operating characteristic curve of 0.80; significantly higher than visual stenosis grade (area under the receiver-operating characteristic curve 0.74; P=0.02) and comparable with FFRCT (area under the receiver-operating characteristic curve 0.77; P=0.16). CONCLUSIONS: An externally validated ML score integrating CCTA-based quantitative plaque features accurately predicts FFR-defined ischemia and impaired MBF by PET, performing superiorly to standard CCTA stenosis evaluation and comparably to FFRCT.

Functional stress imaging to predict abnormal coronary fractional flow reserve: the PACIFIC 2 study.

AIMS: The diagnostic performance of non-invasive imaging in patients with prior coronary artery disease (CAD) has not been tested in prospective head-to-head comparative studies. The aim of this study was to compare the diagnostic performance of qualitative single-photon emission computed tomography (SPECT), quantitative positron emission tomography (PET), and qualitative magnetic resonance imaging (MRI) in patients with a prior myocardial infarction (MI) or percutaneous coronary intervention (PCI). METHODS AND RESULTS: In this prospective clinical study, all patients with prior MI and/or PCI and new symptoms of ischaemic CAD underwent 99mTc-tetrofosmin SPECT, [15O]H2O PET, and MRI, followed by invasive coronary angiography with fractional flow reserve (FFR) in all coronary arteries. All modalities were interpreted by core laboratories. Haemodynamically significant CAD was defined by at least one coronary artery with an FFR ≤0.80. Among the 189 enrolled patients, 63% had significant CAD. Sensitivity was 67% (95% confidence interval 58-76%) for SPECT, 81% (72-87%) for PET, and 66% (56-75%) for MRI. Specificity was 61% (48-72%) for SPECT, 65% (53-76%) for PET, and 62% (49-74%) for MRI. Sensitivity of PET was higher than SPECT (P = 0.016) and MRI (P = 0.014), whereas specificity did not differ among the modalities. Diagnostic accuracy for PET (75%, 68-81%) did not statistically differ from SPECT (65%, 58-72%, P = 0.03) and MRI (64%, 57-72%, P = 0.052). Using FFR < 0.75 as a reference, accuracies increased to 69% (SPECT), 79% (PET), and 71% (MRI). CONCLUSION: In this prospective head-to-head comparative study, SPECT, PET, and MRI did not show a significantly different accuracy for diagnosing FFR defined significant CAD in patients with prior PCI and/or MI. Overall diagnostic performances, however, were discouraging and the additive value of non-invasive imaging in this high-risk population is questionable.

Coronary CTA With AI-QCT Interpretation: Comparison With Myocardial Perfusion Imaging for Detection of Obstructive Stenosis Using Invasive Angiography as Reference Standard.

BACKGROUND. Deep learning frameworks have been applied to interpretation of coronary CTA performed for coronary artery disease (CAD) evaluation. OBJECTIVE. The purpose of our study was to compare the diagnostic performance of myocardial perfusion imaging (MPI) and coronary CTA with artificial intelligence quantitative CT (AI-QCT) interpretation for detection of obstructive CAD on invasive angiography and to assess the downstream impact of including coronary CTA with AI-QCT in diagnostic algorithms. METHODS. This study entailed a retrospective post hoc analysis of the derivation cohort of the prospective 23-center Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia (CREDENCE) trial. The study included 301 patients (88 women and 213 men; mean age, 64.4 ± 10.2 [SD] years) recruited from May 2014 to May 2017 with stable symptoms of myocardial ischemia referred for nonemergent invasive angiography. Patients underwent coronary CTA and MPI before angiography with quantitative coronary angiography (QCA) measurements and fractional flow reserve (FFR). CTA examinations were analyzed using an FDA-cleared cloud-based software platform that performs AI-QCT for stenosis determination. Diagnostic performance was evaluated. Diagnostic algorithms were compared. RESULTS. Among 102 patients with no ischemia on MPI, AI-QCT identified obstructive (≥ 50%) stenosis in 54% of patients, including severe (≥ 70%) stenosis in 20%. Among 199 patients with ischemia on MPI, AI-QCT identified nonobstructive (1-49%) stenosis in 23%. AI-QCT had significantly higher AUC (all p < .001) than MPI for predicting ≥ 50% stenosis by QCA (0.88 vs 0.66), ≥ 70% stenosis by QCA (0.92 vs 0.81), and FFR < 0.80 (0.90 vs 0.71). An AI-QCT result of ≥ 50% stenosis and ischemia on stress MPI had sensitivity of 95% versus 74% and specificity of 63% versus 43% for detecting ≥ 50% stenosis by QCA measurement. Compared with performing MPI in all patients and those showing ischemia undergoing invasive angiography, a scenario of performing coronary CTA with AIQCT in all patients and those showing ≥ 70% stenosis undergoing invasive angiography would reduce invasive angiography utilization by 39%; a scenario of performing MPI in all patients and those showing ischemia undergoing coronary CTA with AI-QCT and those with ≥ 70% stenosis on AI-QCT undergoing invasive angiography would reduce invasive angiography utilization by 49%. CONCLUSION. Coronary CTA with AI-QCT had higher diagnostic performance than MPI for detecting obstructive CAD. CLINICAL IMPACT. A diagnostic algorithm incorporating AI-QCT could substantially reduce unnecessary downstream invasive testing and costs. TRIAL REGISTRATION. Clinicaltrials.gov NCT02173275.

The relation of RAAS activity and endothelin-1 levels to coronary atherosclerotic burden and microvascular dysfunction in chest pain patients.

BACKGROUND AND AIMS: In this study, we investigated whether increased renin angiotensin aldosterone system (RAAS) activation and endothelin-1 levels are related to coronary artery calcium (CAC) score, total plaque volume (TPV), high risk plaque, hyperemic myocardial blood flow (MBF) and coronary microvascular dysfunction (CMD). METHODS: In a prospective, observational, cross-sectional cohort, renin as a marker for RAAS activation and endothelin-1 were measured in peripheral venous blood of 205 patients (64% men; age 58 ± 8.7 years) with suspected coronary artery disease (CAD) who underwent coronary computed tomography angiography (CCTA), [15O]H2O positron emission tomography (PET) perfusion imaging and invasive fractional flow reserve (FFR) measurements. Patients were categorized into three groups based on FFR (≤0.80) and hyperemic MBF <2.3 ml/min/g: [1] obstructive CAD (n = 92), [2] CMD (n = 26) or [3] no or non-obstructive CAD (n = 85). RESULTS: After correction for baseline characteristics, including RAAS inhibiting therapy, renin associated positively with CAC score and TPV, but not with hyperemic MBF (p < 0.01; p = 0.02 and p = 0.23). Patients with high risk plaque displayed higher levels of renin (mean logarithmic renin 1.25 ± 0.43 vs. 1.12 ± 0.35 pg/ml; p = 0.04), but not endothelin-1. Compared to no or non-obstructive CAD patients, renin was significantly elevated in obstructive CAD patients but not in CMD patients (mean logarithmic renin 1.06 ± 0.34 vs. 1.23 ± 0.36; p < 0.01 and 1.06 ± 0.34 vs. 1.16 ± 0.41 pg/ml; p = 0.65). Endothelin-1 did not differ between the three patient groups. CONCLUSIONS: Our report provides evidence that RAAS activity measured by renin concentration is elevated in patients with coronary atherosclerosis and high risk plaque but not in patients with CMD, whereas endothelin-1 is not related to either.

The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography.

OBJECTIVES: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. BACKGROUND: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. METHODS: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). RESULTS: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had <50% stenosis in all vessel territories evaluated by QCA. Average AI analysis time was 10.3 ± 2.7 min. On a per vessel basis, there were significant differences only in sensitivity for ≥50% stenosis based on contrast type (iso-osmolar 70.0% vs non isoosmolar 92.1% p = 0.0345) and iodine concentration (<350 mg/ml 70.0%, 350-369 mg/ml 90.0%, 370-400 mg/ml 90.0%, >400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. CONCLUSION: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. CONDENSED ABSTRACT: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient's BMI or heart rate at time of scan affect the software's diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters.

Comparison between cardiac magnetic resonance stress T1 mapping and [15O]H2O positron emission tomography in patients with suspected obstructive coronary artery disease.

AIMS: To compare cardiac magnetic resonance (CMR) measurement of T1 reactivity (ΔT1) with [15O]H2O positron emission tomography (PET) measurements of quantitative myocardial perfusion. METHODS AND RESULTS: Forty-three patients with suspected obstructed coronary artery disease underwent [15O]H2O PET and CMR at 1.5-T, including rest and adenosine stress T1 mapping (ShMOLLI) and late gadolinium enhancement to rule out presence of scar tissue. ΔT1 was determined for the three main vascular territories and compared with [15O]H2O PET-derived regional stress myocardial blood flow (MBF) and myocardial flow reserve (MFR). ΔT1 showed a significant but poor correlation with stress MBF (R2 = 0.04, P = 0.03) and MFR (R2 = 0.07, P = 0.004). Vascular territories with impaired stress MBF (i.e. ≤2.30 mL/min/g) demonstrated attenuated ΔT1 compared with vascular territories with preserved stress MBF (2.9 ± 2.2% vs. 4.1 ± 2.2%, P = 0.008). In contrast, ΔT1 did not differ between vascular territories with impaired (i.e. <2.50) and preserved MFR (3.2 ± 2.6% vs. 4.0 ± 2.1%, P = 0.25). Receiver operating curve analysis of ΔT1 resulted in an area under the curve of 0.66 [95% confidence interval (CI): 0.57-0.75, P = 0.009] for diagnosing impaired stress MBF and 0.62 (95% CI: 0.53-0.71, P = 0.07) for diagnosing impaired MFR. CONCLUSIONS: CMR stress T1 mapping has poor agreement with [15O]H2O PET measurements of absolute myocardial perfusion. Stress T1 and ΔT1 are lower in vascular territories with reduced stress MBF but have poor accuracy for detecting impaired myocardial perfusion.