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.

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.

The effect of coronary calcification on diagnostic performance of machine learning-based CT-FFR: a Chinese multicenter study.

OBJECTIVE: To investigate the effect of coronary calcification morphology and severity on the diagnostic performance of machine learning (ML)-based coronary CT angiography (CCTA)-derived fractional flow reserve (CT-FFR) with FFR as a reference standard. METHODS: A total of 442 patients (61.2 ± 9.1 years, 70% men) with 544 vessels who underwent CCTA, ML-based CT-FFR, and invasive FFR from China multicenter CT-FFR study were enrolled. The effect of calcification arc, calcification remodeling index (CRI), and Agatston score (AS) on the diagnostic performance of CT-FFR was investigated. CT-FFR ≤ 0.80 and lumen reduction ≥ 50% determined by CCTA were identified as vessel-specific ischemia with invasive FFR as a reference standard. RESULTS: Compared with invasive FFR, ML-based CT-FFR yielded an overall sensitivity of 0.84, specificity of 0.94, and accuracy of 0.90 in a total of 344 calcification lesions. There was no statistical difference in diagnostic accuracy, sensitivity, or specificity of CT-FFR across different calcification arc, CRI, or AS levels. CT-FFR exhibited improved discrimination of ischemia compared with CCTA alone in lesions with mild-to-moderate calcification (AUC, 0.89 vs. 0.69, p < 0.001) and lesions with CRI ≥ 1 (AUC, 0.89 vs. 0.71, p < 0.001). The diagnostic accuracy and specificity of CT-FFR were higher than CCTA alone in patients and vessels with mid (100 to 299) or high (≥ 300) AS. CONCLUSION: Coronary calcification morphology and severity did not influence diagnostic performance of CT-FFR in ischemia detection, and CT-FFR showed marked improved discrimination of ischemia compared with CCTA alone in the setting of calcification. KEY POINTS: • CT-FFR provides superior diagnostic performance than CCTA alone regardless of coronary calcification. • No significant differences in the diagnostic performance of CT-FFR were observed in coronary arteries with different coronary calcification arcs and calcified remodeling indexes. • No significant differences in the diagnostic accuracy of CT-FFR were observed in coronary arteries with different coronary calcification score levels.

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.