Dataset Information

Computed Tomography Angiography for the Diagnosis of Coronary Artery Disease Among Patients Undergoing Transcatheter Aortic Valve Implantation.

ABSTRACT:

Background

Computed tomography angiography (CTA) is used to plan TAVI procedures. We investigated the performance of pre-TAVI CTA for excluding coronary artery disease (CAD).

Methods

In total 127 patients were included. CTA images were analyzed for the presence of ≥ 50% (significant CAD) and ≥ 70% (severe CAD) diameter stenoses in proximal coronary arteries. Results were compared with invasive coronary angiography (ICA) at vessel and patient levels. Primary endpoint was the negative predictive value (NPV) of CTA for the presence of CAD.

Results

A total of 342 vessels were analyzable. NPV of CTA was 97.5% for significant CAD and 96.3% for severe CAD. Positive predictive value and accuracy were 44.8% and 87.1% for significant CAD and 56.3% and 94.4% for severe CAD. At patient level, NPV for significant CAD was 88.6%.

Conclusion

Pre-TAVI CTA shows good performance for ruling out CAD and could be used as a gatekeeper for ICA in selected patients.

SUBMITTER: Meier D

PROVIDER: S-EPMC8575747 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo evaluate the diagnostic accuracy of coronary computed tomography angiography (CCTA) for the evaluation of obstructive coronary artery disease (CAD) in patients referred for transcatheter aortic valve implantation (TAVI).MethodsEMBASE, PubMed/MEDLINE, and CENTRAL were searched for studies reporting accuracy of CCTA for the evaluation of obstructive CAD compared with invasive coronary angiography (ICA) as the reference standard. QUADAS-2 tool was used to assess the risk of bias. A bivariate random effects model was used to analyze, pool, and plot the diagnostic performance measurements across studies. Pooled sensitivity, specificity, positive ( + LR) and negative (-LR) likelihood ratio, diagnostic odds ratio (DOR), and hierarchical summary ROC curve (HSROC) were evaluated. Prospero registration number: CRD42021252527.ResultsFourteen studies (2533 patients) were included. In the intention-to-diagnose patient-level analysis, sensitivity and specificity for CCTA were 97% (95% CI: 94-98%) and 68% (95% CI: 56-68%), respectively, and + LR and -LR were 3.0 (95% CI: 2.1-4.3) and 0.05 (95% CI: 0.03 - 0.09), with DOR equal to 60 (95% CI: 30-121). The area under the HSROC curve was 0.96 (95% CI: 0.94-0.98). No significant difference in sensitivity was found between single-heartbeat and other CT scanners (96% (95% CI: 90 - 99%) vs. 97% (95% CI: 94-98%) respectively; p = 0.37), whereas the specificity of single-heartbeat scanners was higher (82% (95% CI: 66-92%) vs. 60% (95% CI: 46 - 72%) respectively; p < 0.0001). Routine CCTA in the pre-TAVI workup could save 41% (95% CI: 34 - 47%) of ICAs if a disease prevalence of 40% is assumed.ConclusionsCCTA proved an excellent diagnostic accuracy for assessing obstructive CAD in patients referred for TAVI; the use of single-heartbeat CT scanners can further improve these findings.Key points• CCTA proved to have an excellent diagnostic accuracy for assessing obstructive CAD in patients referred for TAVI. • Routine CCTA in the pre-TAVI workup could save more than 40% of ICAs. • Single-heartbeat CT scanners had higher specificity than others in the assessment of obstructive CAD in patients referred for TAVI.

Project description:AimsWe aimed to investigate the role of aortic valve tissue composition from quantitative cardiac computed tomography angiography (CTA) in patients with severe aortic stenosis (AS) for the differentiation of disease subtypes and prognostication after transcatheter aortic valve implantation (TAVI).Methods and resultsOur study included 447 consecutive AS patients from six high-volume centres reporting to a prospective nationwide registry of TAVI procedures (POL-TAVI), who underwent cardiac CTA before TAVI, and 224 matched controls with normal aortic valves. Components of aortic valve tissue were identified using semi-automated software as calcific and non-calcific. Volumes of each tissue component and composition [(tissue component volume/total tissue volume) × 100%] were quantified. Relationship of aortic valve composition with clinical outcomes post-TAVI was evaluated using Valve Academic Research Consortium (VARC)-2 definitions.High-gradient (HG) AS patients had significantly higher aortic tissue volume compared to low-flow low-gradient (LFLG)-AS (1672.7 vs. 1395.3 mm3, P < 0.001) as well as controls (509.9 mm3, P < 0.001), but increased non-calcific tissue was observed in LFLG compared to HG patients (1063.6 vs. 860.2 mm3, P < 0.001). Predictive value of aortic valve calcium score [area under the curve (AUC) 0.989, 95% confidence interval (CI): 0.981-0.996] for severe AS was improved after addition of non-calcific tissue volume (AUC 0.995, 95% CI: 0.991-0.999, P = 0.011). In the multivariable analysis of clinical and quantitative computed tomography parameters of aortic valve tissue, non-calcific tissue volume [odds ratio (OR) 5.2, 95% CI 1.8-15.4, P = 0.003] and history of stroke (OR 2.6, 95% CI 1.1-6.5, P = 0.037) were independent predictors of 30-day major adverse cardiovascular event (MACE).ConclusionQuantitative CTA assessment of aortic valve tissue volume and composition can improve detection of severe AS, differentiation between HG and LFLG-AS in patients referred for TAVI as well as prediction of 30-day MACEs post-TAVI, over the current clinical standard.