Prognostic Value of Computed Tomography-Derived Fractional Flow Reserve Comparison With Myocardial Perfusion Imaging |
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| Institution: | 1. Houston Methodist Debakey Heart and Vascular Center, Houston, Texas, USA;2. Baylor College of Medicine, Houston, Texas, USA;3. Computed Tomography-Research Collaborations, Siemens Healthineers, Malvern, Pennsylvania, USA;4. Computed Tomography-Research and Development, Siemens Healthcare GmbH, Forchheim, Germany |
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| Abstract: | ObjectivesThe aim of this study was to compare the incremental prognostic value of coronary computed tomography (CT) angiography (CCTA)-derived machine learning fractional flow reserve CT (ML-FFRct) versus that of ischemia detected on single-photon emission-computed tomography (SPECT) myocardial perfusion imaging (MPI) on incident cardiovascular outcomes.BackgroundSPECT MPI and ML-FFRct are noninvasive tools that can assess the hemodynamic significance of coronary atherosclerotic disease.MethodsWe studied a retrospective cohort of consecutive patients who underwent clinically indicated CCTA and SPECT MPI. ML-FFRct was computed using a ML prototype. The primary outcome was all-cause mortality and nonfatal myocardial infarction (D/MI), and the secondary outcome was D/MI and unplanned revascularization, percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) occurring more than 90 days postimaging. Multiple nested multivariate cox regression was used to model a scenario wherein an initial anatomical assessment was followed by a functional assessment.ResultsA total of 471 patients (mean age: 64 ± 13 year; 53% males) were included. Comorbidities were prevalent (78% hypertension, 66% diabetes, 81% dyslipidemia). ML-FFRct was <0.8 in at least 1 proximal/midsegment was present in 41.6% of patients, and ischemia on MPI was present in 13.8%. After a median follow-up of 18 months, 7% of patients (n = 33) experienced D/MI. On multivariate Cox proportional analysis, the presence of ischemia on MPI but not ML-FFRct significantly predicted D/MI (HR: 2.3; 95% CI: 1.0-5.0; P = 0.047; or HR: 0.7; 95% CI: 0.3-1.4; P = 0.306 respectively) when added to CCTA obstructive stenosis. Furthermore, the model with SPECT ischemia had higher global chi-square result and significantly improved reclassification. Results were similar using the secondary outcome and on several sensitivity analyses.ConclusionsIn a high-risk patient cohort, SPECT MPI but not ML-FFRct adds independent and incremental prognostic information to CCTA-based anatomical assessment and clinical risk factors in predicting incident outcomes. |
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| Keywords: | CCTA ML-FFRct SPECT AIC"} {"#name":"keyword" "$":{"id":"kwrd0030"} "$$":[{"#name":"text" "_":"Akaike information criterion CABG"} {"#name":"keyword" "$":{"id":"kwrd0040"} "$$":[{"#name":"text" "_":"coronary artery bypass graft CAD"} {"#name":"keyword" "$":{"id":"kwrd0050"} "$$":[{"#name":"text" "_":"coronary artery disease CCTA"} {"#name":"keyword" "$":{"id":"kwrd0060"} "$$":[{"#name":"text" "_":"coronary computed tomography angiography D/MI"} {"#name":"keyword" "$":{"id":"kwrd0070"} "$$":[{"#name":"text" "_":"death or myocardial infarction computed tomography-derived fractional flow reserve MACE"} {"#name":"keyword" "$":{"id":"kwrd0090"} "$$":[{"#name":"text" "_":"major adverse cardiac event MPI"} {"#name":"keyword" "$":{"id":"kwrd0100"} "$$":[{"#name":"text" "_":"myocardial perfusion imaging ML"} {"#name":"keyword" "$":{"id":"kwrd0110"} "$$":[{"#name":"text" "_":"machine learning PCI"} {"#name":"keyword" "$":{"id":"kwrd0120"} "$$":[{"#name":"text" "_":"percutaneous coronary intervention SPECT"} {"#name":"keyword" "$":{"id":"kwrd0130"} "$$":[{"#name":"text" "_":"single-photon emission-computed tomography |
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