Circulation: Cardiovascular Imaging最新文献

Background: A recent simulation study proposed that stenosis measurements on coronary computed tomography (CT) angiography are influenced by the improved spatial resolution of photon-counting detector (PCD)-CT. The aim of the current study was to evaluate the impact of ultrahigh-spatial-resolution (UHR) on coronary stenosis measurements and Coronary Artery Disease Reporting and Data System (CAD-RADS) reclassification rates in patients undergoing coronary CT angiography on both PCD-CT and energy-integrating detector (EID)-CT and to compare measurements against quantitative coronary angiography.

Methods: Patients with coronary calcification on EID-CT (collimation, 192×0.6 mm) were prospectively enrolled for a research coronary CT angiography with UHR PCD-CT (collimation, 120×0.2 mm) within 30 days (between April 1, 2023 and January 31, 2024). PCD-CT was acquired with the same or lower CT dose index and equivalent contrast media volume as EID-CT. Percentage diameter stenosis (PDS) for calcified, partially calcified, and noncalcified lesions were compared between scanners. Patient-level reclassification rates for CAD-RADS were evaluated. The accuracy of PDS measurements was validated against quantitative coronary angiography in patients who underwent invasive coronary angiography.

Results: In total, PDS of 278 plaques were quantified in 49 patients (calcified, 202; partially calcified, 51; noncalcified, 25). PCD-CT-based PDS values were lower than EID-CT measurements for calcified (45.1±20.7 versus 54.6±19.2%; P<0.001) and partially calcified plaques (44.3±19.6 versus 54.9±20.0%; P<0.001), without significant differences for noncalcified lesions (39.1±15.2 versus 39.0±16.0%; P=0.98). The reduction in stenosis degrees led to a 49.0% (24/49) reclassification rate to a lower CAD-RADS with PCD-CT. In a subset of 12 patients with 56 lesions, UHR-based PDS values showed higher agreement with quantitative coronary angiography (mean difference, 7.3%; limits of agreement, -10.7%/25.2%) than EID-CT measurements (mean difference, 17.4%; limits of agreement, -6.9%/41.7%).

Conclusions: Compared with conventional EID-CT, UHR PCD-CT results in lower PDS values and more accurate stenosis measurements in coronary plaques with calcified components and leads to a substantial Coronary Artery Disease Reporting and Data System reclassification rate in 49.0% of patients.

Background: In patients with coronary artery disease, coronary plaques with high-risk features and low-attenuation plaque burden are independent measures associated with major adverse cardiovascular events (MACEs). Patients with chronic kidney failure may have different coronary artery disease characteristics. The aim was to assess the association of coronary plaque characteristics and coronary artery disease extent with MACE and all-cause mortality in patients with chronic kidney failure.

Methods: Potential kidney transplant candidates who underwent coronary computed tomography angiography as part of the cardiac screening program before kidney transplantation were included. We evaluated high-risk plaques and diameter stenosis semiqualitatively and quantified coronary artery calcium score and plaque burden (percentage atheroma volume).

Results: In 484 patients with chronic kidney failure and few or no symptoms of coronary artery disease (mean age, 53±12 years; 62% men; 32% on dialysis), 56 (12%) patients suffered MACE and 69 (14%) patients died during a median follow-up of 4.9 years. High-risk plaques were present in 39 (70%) patients with MACE. Median calcified plaque burden was 3.7% in patients with MACE versus 0.2% in patients without MACE. The median low-attenuation plaque burden was 0.3% versus 0.03%, respectively. In semiqualitative analyses, the presence of high-risk plaque and a higher coronary artery calcium score were associated with increased risk of MACE (hazard ratio (HR), 2.0 [95% CI, 1.0-3.7]; P=0.040; HR, 1.2 [95% CI, 1.0-1.3]; P=0.014), respectively. Neither were associated with all-cause mortality. In quantified analysis, increasing levels of both calcified and low-attenuation plaque burdens were associated with risk of MACE (HR, 2.6 [95% CI, 1.8-3.7]; P<0.001; HR, 2.6 [95% CI, 1.5-4.5]; P=0.001 [per variable doubling, respectively]) and all-cause mortality (HR, 1.6 [95% CI, 1.2-2.1]; P=0.002; HR, 1.8 [95% CI, 1.1-3.0]; P=0.028, respectively).

Conclusions: In patients with chronic kidney failure, calcified and low-attenuation plaque burdens were independently associated with MACE and all-cause mortality, while high-risk plaques and coronary artery calcium score were only associated with MACE.

Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01344434.

Cardiovascular imaging has rapidly advanced over the past decades. Traditional imaging techniques such as echocardiography, computed tomography, and cardiovascular magnetic resonance are essential for assessing the structural and functional aspects of the cardiovascular system but often fall short in providing direct insights into disease activity. This gap is increasingly being bridged by molecular nuclear imaging techniques, including positron emission tomography and single-photon emission computed tomography, which enable the visualization of disease processes at the molecular and cellular levels. This review highlights the role of cardiovascular molecular imaging, emphasizing its current and potential applications in diagnosing and managing cardiovascular disease. With advancements in positron emission tomography scanners, novel radiotracers, and sophisticated imaging software, molecular imaging is set to play an essential role in precision medicine by enhancing our understanding of disease mechanisms, accelerating the development of targeted therapies, and facilitating personalized patient care.

Background: Plaque quantification from coronary computed tomography angiography has emerged as a valuable predictor of cardiovascular risk. Deep learning can provide automated quantification of coronary plaque from computed tomography angiography. We determined per-patient age- and sex-specific distributions of deep learning-based plaque measurements and further evaluated their risk prediction for myocardial infarction in external samples.

Methods: In this international, multicenter study of 2803 patients, a previously validated deep learning system was used to quantify coronary plaque from computed tomography angiography. Age- and sex-specific distributions of coronary plaque volume were determined from 956 patients undergoing computed tomography angiography for stable coronary artery disease from 5 cohorts. Multicenter external samples were used to evaluate associations between coronary plaque percentiles and myocardial infarction.

Results: Quantitative deep learning plaque volumes increased with age and were higher in male patients. In the combined external sample (n=1847), patients in the ≥75th percentile of total plaque volume (unadjusted hazard ratio, 2.65 [95% CI, 1.47-4.78]; P=0.001) were at increased risk of myocardial infarction compared with patients below the 50th percentile. Similar relationships were seen for most plaque volumes and persisted in multivariable analyses adjusting for clinical characteristics, coronary artery calcium, stenosis, and plaque volume, with adjusted hazard ratios ranging from 2.38 to 2.50 for patients in the ≥75th percentile of total plaque volume.

Conclusions: Per-patient age- and sex-specific distributions for deep learning-based coronary plaque volumes are strongly predictive of myocardial infarction, with the highest risk seen in patients with coronary plaque volumes in the ≥75th percentile.

Background: Current guidelines recommend intervention for asymptomatic rheumatic mitral stenosis (MS) with mitral valve area ≤1.5 cm² based on indicators including pulmonary arterial systolic pressure (PASP) >50 mm Hg and new-onset atrial fibrillation; however, evidence supporting this is lacking.

Methods: This single-center retrospective study included patients with rheumatic MS between 2006 and 2022. Pulmonary hypertension was evaluated by using echocardiography to estimate PASP. Primary outcomes were major adverse cardiovascular events (MACE), including all-cause mortality, hospitalization for heart failure, and arterial thromboembolic events for up to 5 years.

Results: Overall, 287 patients with severe rheumatic MS were enrolled (mean age, 62.5±11.3 years; 74.6% women). During a median follow-up of 2.52 years, MACE occurred in 99 patients. There were no differences in echocardiographic parameters, such as the mean mitral valve pressure gradient, mitral valve area, and proportion of mitral valve area <1.0 cm², between patients who developed primary outcomes and those who did not. Survival analysis showed a worse prognosis in patients with estimated PASP (ePASP) >50 mm Hg than in those with ePASP ≤50 mm Hg (log-rank P<0.001); however, atrial fibrillation was not a significant prognostic indicator. As a continuous variable, ePASP (mm Hg) was a significant predictor of MACE (adjusted hazard ratio, 1.027 [95% CI, 1.011-1.042]; P<0.001). Receiver operating characteristic analysis revealed an optimal ePASP threshold of >45 mm Hg, which was an independent predictor of MACE in patients with severe rheumatic MS (adjusted hazard ratio, 2.127 [95% CI, 1.424-3.177]; P<0.001). Competing risk analysis considering mitral valve intervention as a competing risk showed similar results.

Conclusions: Our study demonstrated the prognostic significance of ePASP, rather than atrial fibrillation, in relation to MACE among patients with severe rheumatic MS. Additionally, we proposed a lower ePASP threshold (>45 mm Hg) as a predictor of an unfavorable prognosis.