Coronary artery assessment on pre transcatheter aortic valve implantation computed tomography may avoid the need for additional coronary angiography

Two birds with one stone: pre-TAVI coronary CT angiography combined with FFR helps screen for coronary stenosis

OBJECTIVES

Since coronary artery disease (CAD) is a common comorbidity in patients with aortic valve stenosis, invasive coronary angiography (ICA) can be avoided if significant CAD can be screened with the non-invasive coronary CT angiography (cCTA). This study aims to evaluate the ability of machine learning-based CT coronary fractional flow reserve (CT-FFR) derived from cCTA to aid in the diagnosis of comorbid CAD in patients undergoing transcatheter aortic valve implantation (TAVI).

METHODS

A total of 100 patients who underwent both cCTA and ICA assessments prior to TAVI procedure between January 2021 and July 2023 were included. Coronary stenosis was assessed using both cCTA data and machine learning-generated CT-FFR image information for patients/major coronary vessels. Coronary lesions with CT-FFR ≤ 0.80 were defined as hemodynamically significant, with ICA serving as the diagnostic gold standard.

RESULTS

A total of 400 major coronary vessels were identified in 100 eligible patients who underwent TAVI. CT-FFR was 86.4% sensitive and 66.1% specific to diagnose CAD, with a positive predictive value (PPV) of 66.7% and a negative predictive value (NPV) of 86.0%. The diagnostic accuracy (Acc) was 75.0%, with a false positive rate (FPR) of 33.9%. At the vessel level, CT-FFR showed a sensitivity of 77.6% and a specificity of 76.9%. The PPV was 44.0% and the NPV was 93.6%. The Acc was 77.0% and the FPR was 23.1%. For all patient/vessel units, CT-FFR outperformed cCTA.

CONCLUSION

Machine learning-based CT-FFR can effectively detect coronary hemodynamic abnormalities. Combined with preoperative cCTA in TAVI patients, it is an effective tool to rule out significant CAD, reducing unnecessary coronary angiography in this high-risk population.

CLINICAL TRIAL NUMBER

Not applicable.

Imaging in France: 2024 Update

Radiology in France has made major advances in recent years through innovations in research and clinical practice. French institutions have developed innovative imaging techniques and artificial intelligence applications in the field of diagnostic imaging and interventional radiology. These include, but are not limited to, a more precise diagnosis of cancer and other diseases, research in dual-energy and photon-counting computed tomography, new applications of artificial intelligence, and advanced treatments in the field of interventional radiology. This article aims to explore the major research initiatives and technological advances that are shaping the landscape of radiology in France. By highlighting key contributions in diagnostic imaging, artificial intelligence, and interventional radiology, we provide a comprehensive overview of how these innovations are improving patient outcomes, enhancing diagnostic accuracy, and expanding the possibilities for minimally invasive therapies. As the field continues to evolve, France's position at the forefront of radiological research ensures that these innovations will play a central role in addressing current healthcare challenges and improving patient care on a global scale.

Coronary CTA and CT-FFR in trans-catheter aortic valve implantation candidates: a systematic review and meta-analysis

OBJECTIVES

Screening for obstructive coronary artery disease (CAD) with coronary computed tomography angiography (CCTA) could prevent unnecessary invasive coronary angiography (ICA) procedures during work-up for trans-catheter aortic valve implantation (TAVI). CT-derived fractional flow reserve (CT-FFR) improves CCTA accuracy in chest pain patients. However, its reliability in the TAVI population is unknown. This systematic review and meta-analysis assesses CCTA and CT-FFR in TAVI candidates.

METHODS

PubMed, Embase and Web of Science were searched for studies regarding CCTA and/or CT-FFR in TAVI candidates. Primary endpoint was correct identification and rule-out of obstructive CAD. Results were pooled in a meta-analysis.

RESULTS

Thirty-four articles were part of the meta-analysis, reporting results for CCTA and CT-FFR in 7235 and 1269 patients, respectively. Reference standard was mostly anatomical severity of CAD. At patient level, pooled CCTA sensitivity was 94.0% and specificity 72.4%. CT-FFR sensitivity was 93.2% and specificity 70.3% with substantial variation between studies. However, in studies that compared both, CT-FFR performed better than CCTA. Sensitivity of CCTA versus CT-FFR was 74.9% versus 83.9%, and specificity was 65.5% versus 89.8%.

CONCLUSIONS

Negative CCTA accurately rules out CAD in the TAVI population. CCTA could lead to significant reduction in pre-TAVI ICA, but false positives remain high. Diagnostic accuracy of CT-FFR was comparable to that of CCTA in our meta-analyses, but in studies performing a direct comparison, CT-FFR performed better than CCTA. However, as most studies were small and used CT-FFR software exclusively available for research, a large study on CT-FFR in TAVI work-up using commercially available CT-FFR software would be appropriate before considering routine implementation.

KEY POINTS

Question Coronary artery disease (CAD) screening with invasive coronary angiography before trans-catheter aortic valve implantation (TAVI) is often retrospectively unnecessary, revealing no obstructive CAD. Findings Coronary CTA ruled out CAD in approximately half of TAVI candidates. CT-derived fractional flow reserve (CT-FFR) performed similarly overall but better than coronary CTA in direct comparison. Clinical relevance Addition of coronary CTA to TAVI planning-CT to screen for obstructive CAD could reduce negative invasive coronary angiographies in TAVI work-up. CT-FFR could reduce false-positive coronary CTA results, improving its gatekeeper function in this population, but more data is necessary.

Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care

Artificial intelligence (AI) serves as a powerful tool that can revolutionize how personalized, patient-focused care is provided within interventional cardiology. Specifically, AI can augment clinical care across the spectrum for acute coronary syndrome, coronary artery disease, and valvular heart disease, with applications in coronary and structural heart interventions. This has been enabled by the potential of AI to harness various types of health data. We review how AI-driven technologies can advance diagnosis, preprocedural planning, intraprocedural guidance, and prognostication in interventional cardiology. AI automates clinical tasks, increases efficiency, improves reliability and accuracy, and individualizes clinical care, establishing its potential to transform care. Furthermore, AI-enabled, community-based screening programs are yet to be implemented to leverage the full potential of AI to improve patient outcomes. However, to transform clinical practice, AI tools require robust and transparent development processes, consistent performance across various settings and populations, positive impact on clinical and care quality outcomes, and seamless integration into clinical workflows. Once these are established, AI can reshape interventional cardiology, improving precision, efficiency, and patient outcomes.

Coronary artery disease evaluation during transcatheter aortic valve replacement work-up using photon-counting CT and artificial intelligence

PURPOSE

The purpose of this study was to evaluate the capabilities of photon-counting (PC) CT combined with artificial intelligence-derived coronary computed tomography angiography (PC-CCTA) stenosis quantification and fractional flow reserve prediction (FFRai) for the assessment of coronary artery disease (CAD) in transcatheter aortic valve replacement (TAVR) work-up.

MATERIALS AND METHODS

Consecutive patients with severe symptomatic aortic valve stenosis referred for pre-TAVR work-up between October 2021 and June 2023 were included in this retrospective tertiary single-center study. All patients underwent both PC-CCTA and ICA within three months for reference standard diagnosis. PC-CCTA stenosis quantification (at 50% level) and FFRai (at 0.8 level) were predicted using two deep learning models (CorEx, Spimed-AI). Diagnostic performance for global CAD evaluation (at least one significant stenosis ≥ 50% or FFRai ≤ 0.8) was assessed.

RESULTS

A total of 260 patients (138 men, 122 women) with a mean age of 78.7 ± 8.1 (standard deviation) years (age range: 51-93 years) were evaluated. Significant CAD on ICA was present in 126/260 patients (48.5%). Per-patient sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 96.0% (95% confidence interval [CI]: 91.0-98.7), 68.7% (95% CI: 60.1-76.4), 74.3 % (95% CI: 69.1-78.8), 94.8% (95% CI: 88.5-97.8), and 81.9% (95% CI: 76.7-86.4) for PC-CCTA, and 96.8% (95% CI: 92.1-99.1), 87.3% (95% CI: 80.5-92.4), 87.8% (95% CI: 82.2-91.8), 96.7% (95% CI: 91.7-98.7), and 91.9% (95% CI: 87.9-94.9) for FFRai. Area under the curve of FFRai was 0.92 (95% CI: 0.88-0.95) compared to 0.82 for PC-CCTA (95% CI: 0.77-0.87) (P < 0.001). FFRai-guidance could have prevented the need for ICA in 121 out of 260 patients (46.5%) vs. 97 out of 260 (37.3%) using PC-CCTA alone (P < 0.001).

CONCLUSION

Deep learning-based photon-counting FFRai evaluation improves the accuracy of PC-CCTA ≥ 50% stenosis detection, reduces the need for ICA, and may be incorporated into the clinical TAVR work-up for the assessment of CAD.

A simple coronary CT angiography-based jeopardy score for the identification of extensive coronary artery disease: Validation against invasive coronary angiography

PURPOSE

The invasive British Cardiovascular Intervention Society Jeopardy Score (iBCIS-JS) is a simple angiographic scoring system, enabling quantification of the extent of jeopardized myocardium related to clinically significant coronary artery disease (CAD). The purpose of this study was to develop and validate the coronary CT angiography-based BCIS-JS (CT-BCIS-JS) against the iBCIS-JS in patients with suspected or stable CAD.

MATERIALS AND METHODS

Patients who underwent coronary CT angiography followed by invasive coronary angiography, within 90 days were retrospectively included. CT-BCIS-JS and iBCIS-JS were calculated, with a score ≥ 6 indicating extensive CAD. Correlation between the CT-BCIS-JS and iBCIS-JS was searched for using Spearman's coefficient, and agreement with weighted Kappa (κ) analyses.

RESULTS

A total of 122 patients were included. There were 102 men and 20 women with a median age of 62 years (Q1, Q3: 54, 68; age range: 19-83 years). No differences in median CT-BCIS-JS (4; Q1, Q3: 0, 8) and median iBCIS-JS (4; Q1, Q3: 0, 8) were found (P = 0.18). Extensive CAD was identified in 53 (43.4%) and 52 (42.6%) patients using CT-BCIS-JS and iBCIS-JS, respectively (P = 0.88). CT-based and iBCIS-JS showed excellent correlation (r = 0.98; P < 0.001) and almost perfect agreement (κ = 0.93; 95% confidence interval: 0.90-0.97). Agreement for identification of an iBCIS-JS ≥ 6 was almost perfect (κ = 0.94; 95 % confidence interval: 0.87-0.99).

CONCLUSION

The CT-BCIS-JS represents a feasible, and accurate method for quantification of CAD, with capabilities not different from those of iBCIS-JS. It enables simple, non-invasive identification of patients with anatomically extensive CAD.

Utility of Coronary Computed Tomography Angiography in Patients Undergoing Transcatheter Aortic Valve Implantation: A Meta-Analysis and Meta-Regression Based on Published Data from 7458 Patients

BACKGROUND

Coronary CT angiography (CCTA) may detect coronary artery disease (CAD) in transcatheter aortic valve implantation (TAVI) patients and may obviate invasive coronary angiography (ICA) in selected patients. We assessed the diagnostic accuracy of CCTA for detecting CAD in TAVI patients based on published data.

METHODS

Meta-analysis and meta-regression were performed based on a comprehensive electronic search, including relevant studies assessing the diagnostic accuracy of CCTA in the setting of TAVI patients compared to ICA. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated on a patient and per segment level.

RESULTS

Overall, 27 studies (total of 7458 patients) were included. On the patient level, the CCTA's pooled sensitivity and NPV were 95% (95% CI: 93-97%) and 97% (95% CI: 95-98%), respectively, while the specificity and PPV were at 73% (95% CI: 62-82%) and 64% (95% CI: 57-71%), respectively. On the segmental coronary vessel level, the sensitivity and NPV were 90% (95% CI: 79-96%) and 98% (95% CI: 97-99%).

CONCLUSIONS

This meta-analysis highlights CCTA's potential as a first-line diagnostic tool although its limited PPV and specificity may pose challenges when interpreting heavily calcified arteries. This study underscores the need for further research and protocol standardization in this area.