Dataset Information

Sex differences in machine learning computed tomography-derived fractional flow reserve.

ABSTRACT: Coronary computed tomography angiography (CCTA) derived machine learning fractional flow reserve (ML-FFR_CT) can assess the hemodynamic significance of coronary artery stenoses. We aimed to assess sex differences in the association of ML-FFR_CT and incident cardiovascular outcomes. We studied a retrospective cohort of consecutive patients who underwent clinically indicated CCTA and single photon emission computed tomography (SPECT). Obstructive stenosis was defined as ≥ 70% stenosis severity in non-left main vessels or ≥ 50% in the left main coronary. ML-FFR_CT was computed using a machine learning algorithm with significant stenosis defined as ML-FFR_CT < 0.8. The primary outcome was a composite of death or non-fatal myocardial infarction (D/MI). Our study population consisted of 471 patients with mean (SD) age 65 (13) years, 53% men, and multiple comorbidities (78% hypertension, 66% diabetes, 81% dyslipidemia). Compared to men, women were less likely to have obstructive stenosis by CCTA (9% vs. 18%; p = 0.006), less multivessel CAD (4% vs. 6%; p = 0.25), lower prevalence of ML-FFR_CT < 0.8 (39% vs. 44%; p = 0.23) and higher median (IQR) ML-FFR_CT (0.76 (0.53-0.86) vs. 0.71 (0.47-0.84); p = 0.047). In multivariable adjusted models, there was no significant association between ML-FFR_CT < 0.8 and D/MI [Hazard Ratio 0.82, 95% confidence interval (0.30, 2.20); p = 0.25 for interaction with sex.]. In a high-risk cohort of symptomatic patients who underwent CCTA and SPECT testing, ML-FFR_CT was higher in women than men. There was no significant association between ML-FFR_CT and incident mortality or MI and no evidence that the prognostic value of ML-FFR_CT differs by sex.

SUBMITTER: Al Rifai M

PROVIDER: S-EPMC9381799 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Sex differences in machine learning computed tomography-derived fractional flow reserve.

Al Rifai Mahmoud M Ahmed Ahmed Ibrahim AI Han Yushui Y Saad Jean Michel JM Alnabelsi Talal T Nabi Faisal F Chang Su Min SM Cocker Myra M Schwemmer Chris C Ramirez-Giraldo Juan C JC Zoghbi William A WA Mahmarian John J JJ Al-Mallah Mouaz H MH

Scientific reports 20220816 1

Coronary computed tomography angiography (CCTA) derived machine learning fractional flow reserve (ML-FFR<sub>CT</sub>) can assess the hemodynamic significance of coronary artery stenoses. We aimed to assess sex differences in the association of ML-FFR<sub>CT</sub> and incident cardiovascular outcomes. We studied a retrospective cohort of consecutive patients who underwent clinically indicated CCTA and single photon emission computed tomography (SPECT). Obstructive stenosis was defined as ≥ 70% s ...[more]

PMID: 35974055

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Project description:BackgroundNitroglycerin administration prior to examination improves stenosis assessment of coronary computed tomography (CT) angiography (CCTA). However, whether nitroglycerin influences CT-derived fractional flow reserve (FFR, CT-FFR) evaluation remains unclear. This study aimed to investigate the effect of nitroglycerin on diagnostic performance of CT-FFR.MethodsIn this single-center retrospective study, 107 consecutive patients suspected of coronary artery disease (CAD) with nitroglycerin administration prior to CCTA in 2019 were matched to 107 patients without nitroglycerin in 2016 from Fuwai Hospital. All patients underwent CCTA and invasive FFR in a month. Vessel-based and patient-based accuracy and diagnostic performance of CT-FFR were compared between the two groups, as well as image quality, coronary artery diameter and evaluability. Quantitative variables were compared by Kruskal-Wallis H test. Categorical variables and rates were compared by χ2 test or Fisher exact test.ResultsA total of 214 patients (56.1±8.9 years, 155 male) with 237 target lesion vessels were analyzed, including 120 vessels in nitroglycerin and 117 vessels in non-nitroglycerin group. Per-vessel based accuracy of CT-FFR was higher in nitroglycerin group {80.0% [95% confidence interval (CI): 71.7-86.7%] vs. 68.4% (59.1-76.7%), P=0.041}. On a per-patient basis, nitroglycerin administration improved the accuracy [83.2% (74.7-89.7%) vs. 68.2% (58.5-76.9%), P=0.01], specificity [82.7% (69.7-91.8%) vs. 61.9% (48.8-73.9%), P=0.01], positive predictive value (PPV) [83.6% (73.6-90.4%) vs. 58.6% (50.0-66.9%), P=0.004], and area under the curve (AUC) [0.83 (0.75-0.89) vs. 0.71 (0.61-0.79), P=0.03] of CT-FFR. Vessel diameters (left main arteries: 4.3 vs. 3.8 mm, P<0.001; left anterior descending arteries: 3.1 vs. 2.9 mm, P=0.001; left circumflex arteries: 2.9 vs. 2.7 mm, P=0.01; right coronary arteries: 3.7 vs. 3.4 mm, P=0.001) and number of evaluable coronary arteries (11.0 vs. 8.0, P<0.001) were larger in nitroglycerin group.ConclusionsNitroglycerin administration prior to CCTA has positive effects on diagnostic performance of CT-FFR.

Project description:BackgroundA 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).MethodsThis 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).ResultsOne 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).ConclusionsAn 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.

Project description:Amongst patients with suspected obstructive coronary artery disease (CAD), less than a third of patients have obstructive disease on invasive coronary angiography (ICA) and fewer still have flow-limiting obstructive disease as determined by invasive fractional flow reserve (FFR). FFR is a powerful tool in guiding revascularization of flow-limiting lesions which in turn improves clinical outcome in those with haemodynamically significant obstructive disease. However FFR is infrequently performed due to the cost, time and patient discomfort the procedure entails. Further advances in non-invasive imaging has allowed FFR to be derived non-invasively by applying computational fluid dynamic (CFD) modeling to the coronary computed tomography angiography (CCTA) dataset without the need to induce hyperemia or modify the standard CCTA acquisition protocol. FFR derived from CCTA has been shown to have excellent correlation with invasive FFR and remains diagnostically robust in presence of reduced signal-to-noise ratio (SNR), coronary calcification and motion artifact. More recently, new data have emerged evaluating the clinical impact of fractional flow reserve computed tomography (FFRCT) on the assessment and management of patients with stable chest pain. One such study is the Prospective LongitudinAl trial of FFRCT: Outcome and Resource IMpacts (PLATFORM) study which showed an improved patient selection for ICA using CCTA-FFRCT approach by increasing the likelihood of identifying obstructive CAD at ICA amongst those intended for invasive testing. CCTA-FFRCT may therefore serve as efficacious gatekeeper to ICA that enriches the ICA population. The utility of FFRCT has also helped deepened our understanding of CAD. Through CFD modeling, it is now recognized that there are mechanistic forces of wall shear stress (WSS) and axial plaque force acting on coronary plaques. This has created further interest in exploring the possible interplay between these mechanistic forces on the development of coronary plaque and vulnerability of these plaques to rupture.

Dataset Information

Sex differences in machine learning computed tomography-derived fractional flow reserve.

Publications

Sex differences in machine learning computed tomography-derived fractional flow reserve.

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