Journal of cardiovascular computed tomography最新文献

Background: Conduction abnormalities (CA) after TAVI remain problematic. Membranous septum (MS) depth correlates inversely with new CA though within-patient variability exists.

Objectives: To determine the association of CT-derived MS area with new CA after TAVI.

Methods: MS depth was measured along its width (20 ​% intervals) to calculate MS area in 140 patients without CA. The primary outcome was PPI or new persistent LBBB at discharge.

Results: New CA occurred in 49 (35 ​%) patients of whom 10 (7.1 ​%) required PPI and 39 (27.9 ​%) developed persisting LBBB. MS area was significantly smaller in those with new CA (20.1 [8.6] vs. 41.2 [18.0] mm2; p ​< ​0.01). By multivariable regression, a model including MS area and TAVI contact (MS width∗implant depth): MS area ratio showed better discrimination for new CA compared with a model including MS depth and MS depth - implant depth (AUC 0.89 [95 ​% CI 0.83-0.94] vs. 0.84 [95 ​% CI 0.76-0.90]; p ​= ​0.05, respectively). Optimal cut off point for correct classification of new CA for MS depth was 3.9 ​mm (sensitivity 73 ​%, specificity 76 ​%, PPV 58 ​% and NPV 84 ​%), 28.0 ​mm² for MS area (sensitivity 88 ​%, specificity 78 ​%, PPV 68 ​% and NPV 92 ​%) and 1.88 (sensitivity 63 ​%, specificity 81, PPV 77 ​% and NPV 68 ​%) for TAVI contact: MS area ratio. To minimize new CA, maximal valve implant depth should ≤ (1.88 ∗ MS area)/MS width.

Conclusions: Pre-procedural assessment of the MS area offers additional predictive value for development of new conduction abnormalities after TAVI when compared with MS depth and can guide implant depth.

Despite the promising results, the clinical implications of the CCT-FFR is already debated. This metanalysis aimed to determine the potential benefits of incorporating FFRCT into stable CAD management. After searching for studies comparing outcomes of patients with suspected stable CAD who underwent CCT-FFR as a first strategy versus non-urgent cardiovascular testing after a clinical judgment, we calculated odds ratios (ORs) and 95 ​% confidence intervals (CIs) using a random-effects or fixed-effects meta-analysis model depending on heterogeneity significance. 5 studies (3 RCTs and 2 observational studies) globally encompassing 5282 patients (CCT-FFR ​= ​2604 patients, Control Group ​= ​2678 patients) were included in the quantitative analysis. The rates of ICA overall (OR 1.57, 95%CI 1.36-1.81, p value ​< ​0.001) and those without obstructive CAD (OR 6.63, 95%CI 4.79-9.16, p value ​< ​0.001) were reduced in the CCTAFFR group, as compared to the control group. Moreover, CCT-FFR patients underwent coronary revascularization more frequently than patients in the control arm (OR 0.48,CI 0.38-0.62, p value ​< ​0.001). There was no significance difference between the two strategies in terms of 1 year MACE (OR 1.11,CI 0.86-1.44, p value 0.42), nonfatal MI (OR 0.73, CI 0.41-1.33, p value 0.31), all-cause mortality (OR 1.29,CI 0.47-3.54, p value 0.63) and unplanned revascularization for angina (OR 0.99, 95%CI 0.65-1.49, p value 0.95). In conclusion, in the management of stable CAD, the use of CCT-FFR was associated with lower overall rates of ICA but higher rates of coronary revascularization with comparable 1-year clinical impact.

Background: As a new noninvasive diagnostic technique, computed tomography-derived fraction flow reserve (FFRCT) has been used to identify hemodynamically significant coronary artery stenosis. FFRCT can be calculated using computational fluid dynamics (CFD) or machine learning (ML) approaches. It was hypothesized that ML-based FFRCT (FFRCT_ML) has comparable diagnostic performance with CFD-based FFRCT (FFRCT_CFD). We used invasive FFR as the reference test to evaluate the diagnostic performance of FFRCT_ML vs. FFRCT_CFD.

Methods: We searched PubMed, Cochrane Library, EMBASE, WOS, and Scopus for articles published until March 2024. We analyzed the synthesized sensitivity, specificity, and diagnostic odds ratio (DOR) of FFRCT_ML vs FFRCT_CFD at both the patient and vessel levels. We generated summary receiver operating characteristic curves (SROC) and then calculated the area under the curve (AUC).

Results: This meta-analysis included 23 studies reporting FFRCT_CFD diagnostic performance and 18 studies reporting FFRCT_ML diagnostic performance. In the FFRCT_CFD group, 2501 patients and 3764 vessels or lesions were analyzed. In the FFRCT_ML group, 1323 patients and 4194 vessels or lesions were analyzed. Our results showed that at the per-patient level, FFRCT_CFD and FFRCT_ML had comparable pooled specificity (Z ​= ​-0.59, P ​= ​0.55) and AUC (P ​= ​0.5). At the per-vessel level, FFRCTCFD and FFRCTML also showed comparable specificity (Z ​= ​0.94, P ​= ​0.34), DOR (Z ​= ​0.7, P ​= ​0.48), and AUC (P ​= ​0.74). However, the sensitivity of FFRCT_ML was significantly lower compared to FFRCT_CFD at both patient (Z ​= ​-3.85, P ​= ​0.0001) and vessel (Z ​= ​-2.05, P ​= ​0.04) levels.

Conclusion: The FFRCT_ML technique was comparable to standard CFD approaches in terms of AUC and specificity. However, it did not achieve the same level of sensitivity as FFRCT_CFD.

Background: Aortic calcification, often incidentally detected during coronary artery calcium (CAC) scans, is underutilized in cardiovascular risk assessments due to manual quantification challenges. This study evaluates a deep learning model for automating aortic calcification detection and quantification in coronary CT angiography (CTA) images. We validate against manual assessments and compare the association of manual and automated assessments with incident major adverse cardiovascular events (MACE).

Methods: A deep learning algorithm was applied to CAC scans from 670 participants in the CORE320 and CORE64 studies. Aortic calcification in the aortic root, ascending, and descending aorta was quantified manually and automatically. Concordance correlation coefficients (CCC) assessed agreement, and Cox regression and ROC analyses evaluated association with incident MACE.

Results: Automated scoring demonstrated high concordance with manual methods (CCC: 0.926-0.992), supporting its reliability in assessing aortic calcifications. ROC analysis revealed that the automated method was as effective as the manual technique in predicting MACE (p ​> ​0.05).

Conclusion: Automated aortic calcification scoring is a reliable alternative to manual methods, offering consistency and efficiency in the analysis of incidental findings on CAC scans.

Background: The association between high-risk plaque (HRP) on coronary computed tomography angiography (CTA) and the level of perivascular inflammation has not been fully investigated.

Methods: Patients who underwent both CTA and optical coherence tomography (OCT) were included. The level of perivascular inflammation was assessed by pericoronary adipose tissue (PCAT) attenuation at two levels: lesion-specific and the proximal segment of the culprit vessel. HRP features included positive remodeling (PR), low-attenuation plaque (LAP), napkin-ring sign (NRS), and spotty calcification (SC).

Results: OCT features of plaque vulnerability were evaluated in culprit vessels. A total of 1360 lesions (413 culprit lesions and 947 non-culprit lesions) from 413 patients were evaluated. Lesion-specific PCAT attenuation was higher in lesions with any HRP feature except SC (present vs. absent: PR -71.3 ​± ​10.1 vs. -74.1 ​± ​11.7, P ​< ​0.001; LAP -71.7 ​± ​9.9 vs. -73.0 ​± ​11.4, P ​= ​0.025; NRS -70.3 ​± ​9.6 vs. -72.9 ​± ​11.1, P ​= ​0.001; and SC -71.9 ​± ​9.9 vs. -73.0 ​± ​11.5, P ​= ​0.082). After adjusting for confounders, only PR was associated with higher lesion-specific PCAT attenuation. The number of lesions with PR significantly correlated with higher levels of perivascular inflammation measured by culprit vessel PCAT attenuation. The number of lesions with PR was associated with higher lipid index and macrophage grade at culprit vessels.

Conclusions: Among 4 HRP features, only PR was significantly associated with higher lesion-specific PCAT attenuation. The number of plaques with PR correlated with the level of perivascular inflammation and vulnerability.

Trial registration: clinicaltrials.gov Identifier: NCT04523194.

In an era of rapidly expanding use of transcatheter aortic valve replacement (TAVR), cardiovascular computed tomography (CCT) has become an essential component in the evaluation process for the growing number of patients. Because of the nature of the guideline-recommended protocol -involving several different CCT acquisitions-it represents a unique dataset for comprehensive phenotyping of the patient with significant aortic stenosis. A substantial body of data has established CCT as a central tool in pre-procedural implantation planning. However, emerging evidence suggests a potential new role for CCT in phenotyping patient risk beyond the index procedure. This new role could represent a unique opportunity in patient selection, medication optimization and follow up post TAVR aiming to improve long-term prognosis. This review highlights emerging data on CCT imaging features for risk stratification in patients during long-term follow-up after TAVR. We summarize the existing literature on this topic and explore whether comprehensive CCT-derived information could be integrated into clinical practice, potentially enhancing TAVR patient selection and post-procedural care.