FFR CT and Static Computed Tomography Myocardial Perfusion Imaging for Therapeutic Decision-making and Prognosis in Patients With Coronary Artery Disease.

PURPOSE: The purpose of this study was to investigate the effect of integrated evaluation of resting static computed tomography perfusion (CTP) and coronary computed tomography angiography (CCTA)-derived fractional flow reserve (FFR CT ) on therapeutic decision-making and predicting major adverse cardiovascular events (MACEs) in patients with suspected coronary artery disease. MATERIALS AND METHODS: In this post hoc analysis of a prospective trial of CCTA in patients assigned to either CCTA or CCTA plus FFR CT arms, 500 patients in the CCTA plus FFR CT arm were analyzed. Both resting static CTP and FFR CT were evaluated by using the conventional CCTA. Perfusion defects in the myocardial segments with ≥50% degree of stenosis in the supplying vessels were defined as resting static CTP positive, and any vessel with an FFR CT value of ≤0.80 was considered positive. Patients were divided into 3 groups: (1) negative CTP-FFR CT match group (resting static CTP-negative and FFR CT -negative group); (2) mismatch CTP-FFR CT group (resting static CTP-positive and FFR CT -negative or resting static CTP-negative and FFR CT -positive group); and (3) positive CTP-FFR CT match group (resting static CTP-positive and FFR CT -positive group). We compared the revascularization-to-invasive coronary angiography ratio and the MACE rate among 3 subgroups at 1- and 3-year follow-ups. The adjusted Cox hazard proportional model was used to assess the prognostic value of FFR CT and resting static CTP to determine patients at risk of MACE. RESULTS: Patients in the positive CTP-FFR CT match group were more likely to undergo revascularization at the time of invasive coronary angiography compared with those in the mismatch CTP-FFR CT group (81.4% vs 57.7%, P =0.033) and the negative CTP-FFR CT match group (81.4% vs 33.3%, P= 0.001). At 1- and 3-year follow-ups, patients in the positive CTP-FFR CT match group were more likely to have MACE than those in the mismatch CTP-FFR CT group (10.5% vs 4.2%, P= 0.046; 35.6% vs 9.4%, P <0.001) and the negative CTP-FFR CT match group (10.5% vs 0.9%, P <0.001; 35.6% vs 5.4%, P <0.001). A positive CTP-FFR CT match was strongly related to MACE at 1-year (hazard ratio=8.06, P= 0.003) and 3-year (hazard ratio=6.23, P <0.001) follow-ups. CONCLUSION: In patients with suspected coronary artery disease, the combination of FFR CT with resting static CTP could guide therapeutic decisions and have a better prognosis with fewer MACE in a real-world scenario.

Role of Quantitative Plaque Analysis and Fractional Flow Reserve Derived From Coronary Computed Tomography Angiography to Assess Plaque Progression.

PURPOSE: To explore the role of quantitative plaque analysis and fractional flow reserve (CT-FFR) derived from coronary computed angiography (CCTA) in evaluating plaque progression (PP). METHODS: A total of 248 consecutive patients who underwent serial CCTA examinations were enrolled. All patients' images were analyzed quantitatively by plaque analysis software. The quantitative analysis indexes included diameter stenosis (%DS), plaque length, plaque volume (PV), calcified PV, noncalcified PV, minimum lumen area (MLA), and remodeling index (RI). PP is defined as PAV (percentage atheroma volume) change rate >1%. CT-FFR analysis was performed using the cFFR software. RESULTS: A total of 76 patients (30.6%) and 172 patients (69.4%) were included in the PP group and non-PP group, respectively. Compared with the non-PP group, the PP group showed greater %DS, smaller MLA, larger PV and non-calcified PV, larger RI, and lower CT-FFR on baseline CCTA (all P <0.05). Logistic regression analysis showed that RI≥1.10 (odds ratio [OR]: 2.709, 95% CI: 1.447-5.072), and CT-FFR≤0.85 (OR: 5.079, 95% CI: 2.626-9.283) were independent predictors of PP. The model based on %DS, quantitative plaque features, and CT-FFR (area under the receiver-operating characteristics curve [AUC]=0.80, P <0.001) was significantly better than that based rarely on %DS (AUC=0.61, P =0.007) and that based on %DS and quantitative plaque characteristics (AUC=0.72, P <0.001). CONCLUSIONS: Quantitative plaque analysis and CT-FFR are helpful to identify PP. RI and CT-FFR are important predictors of PP. Compared with the prediction model only depending on %DS, plaque quantitative markers and CT-FFR can further improve the predictive performance of PP.

Functional CAD-RADS using FFRCT on therapeutic management and prognosis in patients with coronary artery disease.

OBJECTIVES: To propose a novel functional Coronary Artery Disease-Reporting and Data System (CAD-RADS) category system integrated with coronary CT angiography (CCTA)-derived fractional flow reserve (FFRCT) and to validate its effect on therapeutic decision and prognosis in patients with coronary artery disease (CAD). METHODS: Firstly, we proposed a novel functional CAD-RADS and evaluated the performance of functional CAD-RADS for guiding treatment strategies with actual clinical treatment as a reference standard in a retrospective multicenter cohort with CCTA and invasive FFR performed in all patients (n = 466). Net reclassification improvement (NRI) of functional CAD-RADS over anatomical CAD-RADS was calculated. Secondly, the prognostic value of functional CAD-RADS in a prospective two-arm cohort (566 [FFRCT arm] vs. 567 [CCTA arm]) was calculated, after a 1-year follow-up, functional CAD-RADS in FFRCT arm (n = 513) and anatomical CAD-RADS in CCTA arm (n = 511) to determine patients at risk of adverse outcomes were compared with a Cox hazard proportional model. RESULTS: Functional CAD-RADS demonstrated superior value over anatomical CAD-RADS (AUC: 0.828 vs. 0.681, p < 0.001) and comparable performance to FFR (AUC: 0.828 vs. 0.848, p = 0.253) in guiding therapeutic decisions. Functional CAD-RADS resulted in the revision of management plan as determined by anatomical CAD-RADS in 30.0% of patients (n = 140) (NRI = 0.369, p < 0.001). Functional CAD-RADS was an independent predictor for 1-year outcomes with indexes of concordance of 0.795 and the corresponding value was 0.751 in anatomical CAD-RADS. CONCLUSION: The novel functional CAD-RADS gained incremental value in guiding therapeutic decision-making compared with anatomical CAD-RADS and comparable power in 1-year prognosis with anatomical CAD-RADS in a real-world scenario. KEY POINTS: • The novel functional CAD-RADS category system with FFRCT integrated into the anatomical CAD-RADS categories was originally proposed. • The novel functional CAD-RADS category system was validated superior value over anatomical CAD-RADS (AUC: 0.828 vs. 0.681, p < 0.001) in guiding therapeutic decisions and revised management plan in 30.0% of patients as determined by anatomical CAD-RADS (net reclassification improvement index = 0.369, p < 0.001). • Functional CAD-RADS was an independent predictor with an index of concordance of 0.795 and 0.751 in anatomical CAD-RADS for 1-year prognosis of adverse outcomes.

One-year outcomes of CCTA alone versus machine learning-based FFRCT for coronary artery disease: a single-center, prospective study.

OBJECTIVES: To explore downstream management and outcomes of machine learning (ML)-based CT derived fractional flow reserve (FFRCT) strategy compared with an anatomical coronary computed tomography angiography (CCTA) alone assessment in participants with intermediate coronary artery stenosis. METHODS: In this prospective study conducted from April 2018 to March 2019, participants were assigned to either the CCTA or FFRCT group. The primary endpoint was the rate of invasive coronary angiography (ICA) that demonstrated non-obstructive disease at 90 days. Secondary endpoints included coronary revascularization and major adverse cardiovascular events (MACE) at 1-year follow-up. RESULTS: In total, 567 participants were allocated to the CCTA group and 566 to the FFRCT group. At 90 days, the rate of ICA without obstructive disease was higher in the CCTA group (33.3%, 39/117) than that (19.8%, 19/96) in the FFRCT group (risk difference [RD] = 13.5%, 95% confidence interval [CI]: 8.4%, 18.6%; p = 0.03). The ICA referral rate was higher in the CCTA group (27.5%, 156/567) than in the FFRCT group (20.3%, 115/566) (RD = 7.2%, 95% CI: 2.3%, 12.1%; p = 0.003). The revascularization-to-ICA ratio was lower in the CCTA group than that in the FFRCT group (RD = 19.8%, 95% CI: 14.1%, 25.5%, p = 0.002). MACE was more common in the CCTA group than that in the FFRCT group at 1 year (HR: 1.73; 95% CI: 1.01, 2.95; p = 0.04). CONCLUSION: In patients with intermediate stenosis, the FFRCT strategy appears to be associated with a lower rate of referral for ICA, ICA without obstructive disease, and 1-year MACE when compared to the anatomical CCTA alone strategy. KEY POINTS: • In stable patients with intermediate stenosis, ML-based FFRCT strategy was associated with a lower referral ICA rate, a lower normalcy rate of ICA, and higher revascularization-to-ICA ratio than the CCTA strategy. • Compared with the CCTA strategy, ML-based FFRCTshows superior outcome prediction value which appears to be associated with a lower rate of 1-year MACE. • ML-based FFRCT strategy as a non-invasive "one-stop-shop" modality may be the potential to change diagnostic workflows in patients with suspected coronary artery disease.

Deep learning powered coronary CT angiography for detecting obstructive coronary artery disease: The effect of reader experience, calcification and image quality.

OBJECTIVES: To investigate the effect of reader experience, calcification and image quality on the performance of deep learning (DL) powered coronary CT angiography (CCTA) in automatically detecting obstructive coronary artery disease (CAD) with invasive coronary angiography (ICA) as reference standard. METHODS: A total of 165 patients (680 vessels and 1505 segments) were included in this study. Three sessions were performed in order: (1) The artificial intelligence (AI) software automatically processed CCTA images, stenosis degree and processing time were recorded for each case; (2) Six cardiovascular radiologists with different experiences (low/ intermediate/ high experience) independently performed image post-processing and interpretation of CCTA, (3) AI + human reading was performed. Luminal stenosis ≥50% was defined as obstructive CAD in ICA and CCTA. Diagnostic performances of AI, human reading and AI + human reading were evaluated and compared on a per-patient, per-vessel and per-segment basis with ICA as reference standard. The effects of calcification and image quality on the diagnostic performance were also studied. RESULTS: The average post-processing and interpretation times of AI was 2.3 ± 0.6 min per case, reduced by 76%, 72%, 69% compared with low/ intermediate/ high experience readers (all P < 0.001), respectively. On a per-patient, per-vessel and per-segment basis, with ICA as reference method, the AI overall diagnostic sensitivity for detecting obstructive CAD were 90.5%, 81.4%, 72.9%, the specificity was 82.3%, 93.9%, 95.0%, with the corresponding areas under the curve (AUCs) of 0.90, 0.90, 0.87, respectively. Compared to human readers, the diagnostic performance of AI was higher than that of low experience readers (all P < 0.001). The diagnostic performance of AI + human reading was higher than human reading alone, and AI + human readers' ability to correctly reclassify obstructive CAD was also improved, especially for low experience readers (Per-patient, the net reclassification improvement (NRI) = 0.085; per-vessel, NRI = 0.070; and per-segment, NRI = 0.068, all P < 0.001). The diagnostic performance of AI was not significantly affected by calcification and image quality (all P > 0.05). CONCLUSIONS: AI can substantially shorten the post-processing time, while AI + human reading model can significantly improve the diagnostic performance compared with human readers, especially for inexperienced readers, regardless of calcification severity and image quality.

Feasibility and prognostic role of machine learning-based FFRCT in patients with stent implantation.

OBJECTIVES: To investigate the feasibility and prognostic implications of coronary CT angiography (CCTA) derived fractional flow reserve (FFRCT) in patients who have undergone stents implantation. METHODS: Firstly, the feasibility of FFRCT in stented vessels was validated. The diagnostic performance of FFRCT in identifying hemodynamically in-stent restenosis (ISR) in 33 patients with invasive FFR ≤ 0.88 as reference standard, intra-group correlation coefficient (ICC) between FFRCT and FFR was calculated. Secondly, prognostic value was assessed with 115 patients with serial CCTA scans after PCI. Stent characteristics (location, diameter, length, etc.), CCTA measurements (minimum lumen diameter [MLD], minimum lumen area [MLA], ISR), and FFRCT measurements (FFRCT, ΔFFRCT, ΔFFRCT/stent length) both at baseline and follow-up were recorded. Longitudinal analysis included changes of MLD, MLA, ISR, and FFRCT. The primary endpoint was major adverse cardiovascular events (MACE). RESULTS: Per-patient accuracy of FFRCT was 0.85 in identifying hemodynamically ISR. FFRCT had a good correlation with FFR (ICC = 0.84). 15.7% (18/115) developed MACE during 25 months since follow-up CCTA. Lasso regression identified age and follow-up ΔFFRCT/length as candidate variables. In the Cox proportional hazards model, age (hazard ratio [HR], 1.102 [95% CI, 1.032-1.177]; p = 0.004) and follow-up ΔFFRCT/length (HR, 1.014 [95% CI, 1.006-1.023]; p = 0.001) were independently associated with MACE (c-index = 0.856). Time-dependent ROC analysis showed AUC was 0.787 (95% CI, 0.594-0.980) at 25 months to predict adverse outcome. After bootstrap validation with 1000 resamplings, the bias-corrected c-index was 0.846. CONCLUSIONS: Noninvasive ML-based FFRCT is feasible in patients following stents implantation and shows prognostic value in predicting adverse events after stents implantation in low-moderate risk patients. KEY POINTS: • Machine-learning-based FFRCT is feasible to evaluate the functional significance of in-stent restenosis in patients with stent implantation. • Follow-up △FFRCT along with the stent length might have prognostic implication in patients with stent implantation and low-to-moderate risk after 2 years follow-up. The prognostic role of FFRCT in patients with moderate-to-high or high risk needs to be further studied. • FFRCT might refine the clinical pathway of patients with stent implantation to invasive catheterization.

Prognostic implication of CT-FFR based functional SYNTAX score in patients with de novo three-vessel disease.

AIMS: This study was aimed at investigating whether a machine learning (ML)-based coronary computed tomographic angiography (CCTA) derived fractional flow reserve (CT-FFR) SYNTAX score (SS), 'Functional SYNTAX score' (FSSCTA), would predict clinical outcome in patients with three-vessel coronary artery disease (CAD). METHODS AND RESULTS: The SS based on CCTA (SSCTA) and ICA (SSICA) were retrospectively collected in 227 consecutive patients with three-vessel CAD. FSSCTA was calculated by combining the anatomical data with functional data derived from a ML-based CT-FFR assessment. The ability of each score system to predict major adverse cardiac events (MACE) was compared. The difference between revascularization strategies directed by the anatomical SS and FSSCTA was also assessed. Two hundred and twenty-seven patients were divided into two groups according to the SSCTA cut-off value of 22. After determining FSSCTA for each patient, 22.9% of patients (52/227) were reclassified to a low-risk group (FSSCTA ≤ 22). In the low- vs. intermediate-to-high (>22) FSSCTA group, MACE occurred in 3.2% (4/125) vs. 34.3% (35/102), respectively (P < 0.001). The independent predictors of MACE were FSSCTA (OR = 1.21, P = 0.001) and diabetes (OR = 2.35, P = 0.048). FSSCTA demonstrated a better predictive accuracy for MACE compared with SSCTA (AUC: 0.81 vs. 0.75, P = 0.01) and SSICA (0.81 vs. 0.75, P < 0.001). After FSSCTA was revealed, 52 patients initially referred for CABG based on SSCTA would have been changed to PCI. CONCLUSION: Recalculating SS by incorporating lesion-specific ischaemia as determined by ML-based CT-FFR is a better predictor of MACE in patients with three-vessel CAD. Additionally, the use of FSSCTA may alter selected revascularization strategies in these patients.

Impact of machine learning-based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease.

OBJECTIVES: This study investigated the impact of machine learning (ML)-based fractional flow reserve derived from computed tomography (FFRCT) compared to invasive coronary angiography (ICA) for therapeutic decision-making and patient outcome in patients with suspected coronary artery disease (CAD). METHODS: One thousand one hundred twenty-one consecutive patients with stable chest pain who underwent coronary computed tomography angiography (CCTA) followed ICA within 90 days between January 2007 and December 2016 were included in this retrospective study. Medical records were reviewed for the endpoint of major adverse cardiac events (MACEs). FFRCT values were calculated using an artificial intelligence (AI) ML platform. Disagreements between hemodynamic significant stenosis via FFRCT and severe stenosis on qualitative CCTA and ICA were also evaluated. RESULTS: After FFRCT results were revealed, a change in the proposed treatment regimen chosen based on ICA results was seen in 167 patients (14.9%). Over a median follow-up time of 26 months (4-48 months), FFRCT ≤ 0.80 was associated with MACE (HR, 6.84 (95% CI, 3.57 to 13.11); p < 0.001), with superior prognostic value compared to severe stenosis on ICA (HR, 1.84 (95% CI, 1.24 to 2.73), p = 0.002) and CCTA (HR, 1.47 (95% CI, 1.01 to 2.14, p = 0.045). Reserving ICA and revascularization for vessels with positive FFRCT could have reduced the rate of ICA by 54.5% and lead to 4.4% fewer percutaneous interventions. CONCLUSIONS: This study indicated ML-based FFRCT had superior prognostic value when compared to severe anatomic stenosis on CCTA and adding FFRCT may direct therapeutic decision-making with the potential to improve efficiency of ICA. KEY POINTS: • ML-based FFRCT shows superior outcome prediction value when compared to severe anatomic stenosis on CCTA. • FFRCT noninvasively informs therapeutic decision-making with potential to change diagnostic workflows and enhance efficiencies in patients with suspected CAD. • Reserving ICA and revascularization for vessels with positive FFRCT may reduce the normalcy rate of ICA and improve its efficiency.