Aortic valve stenosis-multimodality assessment with PET/CT and PET/MRI

Aortic valve disease is the most common form of heart valve disease in developed countries and a growing healthcare burden with an ageing population. Transthoracic and transoesophageal echocardiography remains central to the diagnosis and surveillance of patients with aortic stenosis, providing gold standard assessments of valve haemodynamics and myocardial performance. However, other multimodality imaging techniques are being explored for the assessment of aortic stenosis, including combined PET/CT and PET/MR. Both approaches provide unique information with respect to disease activity in the valve alongside more conventional anatomic assessments of the valve and myocardium in this condition. This review investigates the emerging use of PET/CT and PET/MR to assess patients with aortic stenosis, examining how the complementary data provided by each modality may be used for research applications and potentially in future clinical practice

Repeatability of quantitative pericoronary adipose tissue attenuation and coronary plaque burden from coronary CT angiography

BACKGROUND: High pericoronary adipose tissue (PCAT) attenuation and non-calcified plaque burden (NCP) measured from coronary CT angiography (CTA) have been implicated in future cardiac events. We aimed to evaluate the interobserver and intraobserver repeatability of PCAT attenuation and NCP burden measurement from CTA, in a sub-study of the prospective SCOT-HEART trial. METHODS: Fifty consecutive CTAs from participants of the CT arm of the prospective SCOT-HEART trial were included. Two experienced observers independently measured PCAT attenuation and plaque characteristics throughout the whole coronary tree from CTA using semi-automatic quantitative software. RESULTS: We analyzed proximal segments in 157 vessels. Intraobserver mean differences in PCAT attenuation and NCP plaque burden were −0.05HU and 0.92% with limits of agreement (LOA) of ±1.54 and ±5.97%. Intraobserver intraclass correlation coefficients (ICC) for PCAT attenuation and NCP burden were excellent (0.999 and 0.978). Interobserver mean differences in PCAT attenuation and NCP plaque burden were 0.13HU [LOA ±1.67HU] and −0.23% (LOA ±9.61%). Interobserver ICC values for PCAT attenuation and NCP burden were excellent (0.998 and 0.944). CONCLUSION: PCAT attenuation and NCP burden on CTA has high intraobserver and interobserver repeatability, suggesting they represent a repeatable and robust method of quantifying cardiovascular risk

Aortic valve imaging using 18F-sodium fluoride: impact of triple motion correction

BACKGROUND: Current (18)F-NaF assessments of aortic valve microcalcification using (18)F-NaF PET/CT are based on evaluations of end-diastolic or cardiac motion-corrected (ECG-MC) images, which are affected by both patient and respiratory motion. We aimed to test the impact of employing a triple motion correction technique (3 × MC), including cardiorespiratory and gross patient motion, on quantitative and qualitative measurements. MATERIALS AND METHODS: Fourteen patients with aortic stenosis underwent two repeat 30-min PET aortic valve scans within (29 ± 24) days. We considered three different image reconstruction protocols; an end-diastolic reconstruction protocol (standard) utilizing 25% of the acquired data, an ECG-gated (four ECG gates) reconstruction (ECG-MC), and a triple motion-corrected (3 × MC) dataset which corrects for both cardiorespiratory and patient motion. All datasets were compared to aortic valve calcification scores (AVCS), using the Agatston method, obtained from CT scans using correlation plots. We report SUV(max) values measured in the aortic valve and maximum target-to-background ratios (TBR(max)) values after correcting for blood pool activity. RESULTS: Compared to standard and ECG-MC reconstructions, increases in both SUV(max) and TBR(max) were observed following 3 × MC (SUV(max): Standard = 2.8 ± 0.7, ECG-MC = 2.6 ± 0.6, and 3 × MC = 3.3 ± 0.9; TBR(max): Standard = 2.7 ± 0.7, ECG-MC = 2.5 ± 0.6, and 3 × MC = 3.3 ± 1.2, all p values ≤ 0.05). 3 × MC had improved correlations (R(2) value) to the AVCS when compared to the standard methods (SUV(max): Standard = 0.10, ECG-MC = 0.10, and 3 × MC = 0.20; TBR(max): Standard = 0.20, ECG-MC = 0.28, and 3 × MC = 0.46). CONCLUSION: 3 × MC improves the correlation between the AVCS and SUV(max) and TBR(max) and should be considered in PET studies of aortic valves using (18)F-NaF. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40658-022-00433-7

The accuracy of coronary CT angiography in patients with coronary calcium score above 1000 Agatston Units:Comparison with quantitative coronary angiography: Coronary CT Angiography in High Coronary Calcium

BACKGROUND: High amounts of coronary artery calcium (CAC) pose challenges in interpretation of coronary CT angiography (CCTA). The accuracy of stenosis assessment by CCTA in patients with very extensive CAC is uncertain. METHODS: Retrospective study was performed including patients who underwent clinically directed CCTA with CAC score >1000 and invasive coronary angiography within 90 days. Segmental stenosis on CCTA was graded by visual inspection with two-observer consensus using categories of 0%, 1–24%, 25–49%, 50–69%, 70–99%, 100% stenosis, or uninterpretable. Blinded quantitative coronary angiography (QCA) was performed on all segments with stenosis ≥25% by CCTA. The primary outcome was vessel-based agreement between CCTA and QCA, using significant stenosis defined by diameter stenosis ≥ 70%. Secondary analyses on a per-patient basis and inclusive of uninterpretable segments were performed. RESULTS: 726 segments with stenosis ≥25% in 346 vessels within 119 patients were analyzed. Median coronary calcium score was 1616 (1221–2118). CCTA identification of QCA-based stenosis resulted in a per-vessel sensitivity of 79%, specificity of 75%, positive predictive value (PPV) of 45%, negative predictive value (NPV) of 93%, and accuracy 76% (68 false positive and 15 false negative). Per-patient analysis had sensitivity 94%, specificity 55%, PPV 63%, NPV 92%, and accuracy 72% (30 false-positive and 3 false-negative). Inclusion of uninterpretable segments had variable effect on sensitivity and specificity, depending on whether they are considered as significant or non-significant stenosis. CONCLUSIONS: In patients with very extensive CAC (>1000 Agatston units), CCTA retained a negative predictive value > 90% to identify lack of significant stenosis on a per-vessel and per-patient level, but frequently overestimated stenosis

Machine-learning with 18F-sodium fluoride PET and quantitative plaque analysis on CT angiography for the future risk of myocardial infarction

Coronary (18)F-sodium fluoride ((18)F-NaF) PET and CT angiography–based quantitative plaque analysis have shown promise in refining risk stratification in patients with coronary artery disease. We combined both of these novel imaging approaches to develop an optimal machine-learning model for the future risk of myocardial infarction in patients with stable coronary disease. Methods: Patients with known coronary artery disease underwent coronary (18)F-NaF PET and CT angiography on a hybrid PET/CT scanner. Machine-learning by extreme gradient boosting was trained using clinical data, CT quantitative plaque analysis, measures and (18)F-NaF PET, and it was tested using repeated 10-fold hold-out testing. Results: Among 293 study participants (65 ± 9 y; 84% male), 22 subjects experienced a myocardial infarction over the 53 (40–59) months of follow-up. On univariable receiver-operator-curve analysis, only (18)F-NaF coronary uptake emerged as a predictor of myocardial infarction (c-statistic 0.76, 95% CI 0.68–0.83). When incorporated into machine-learning models, clinical characteristics showed limited predictive performance (c-statistic 0.64, 95% CI 0.53–0.76) and were outperformed by a quantitative plaque analysis-based machine-learning model (c-statistic 0.72, 95% CI 0.60–0.84). After inclusion of all available data (clinical, quantitative plaque and (18)F-NaF PET), we achieved a substantial improvement (P = 0.008 versus (18)F-NaF PET alone) in the model performance (c-statistic 0.85, 95% CI 0.79–0.91). Conclusion: Both (18)F-NaF uptake and quantitative plaque analysis measures are additive and strong predictors of outcome in patients with established coronary artery disease. Optimal risk stratification can be achieved by combining clinical data with these approaches in a machine-learning model

Association of Lipoprotein(a) With Atherosclerotic Plaque Progression

BACKGROUND: Lipoprotein(a) [Lp(a)] is associated with increased risk of myocardial infarction, although the mechanism for this observation remains uncertain. OBJECTIVES: This study aims to investigate whether Lp(a) is associated with adverse plaque progression. METHODS: Lp(a) was measured in patients with advanced stable coronary artery disease undergoing coronary computed tomography angiography at baseline and 12 months to assess progression of total, calcific, noncalcific, and low-attenuation plaque (necrotic core) in particular. High Lp(a) was defined as Lp(a) ≥ 70 mg/dL. The relationship of Lp(a) with plaque progression was assessed using linear regression analysis, adjusting for body mass index, segment involvement score, and ASSIGN score (a Scottish cardiovascular risk score comprised of age, sex, smoking, blood pressure, total and high-density lipoprotein [HDL]–cholesterol, diabetes, rheumatoid arthritis, and deprivation index). RESULTS: A total of 191 patients (65.9 ± 8.3 years of age; 152 [80%] male) were included in the analysis, with median Lp(a) values of 100 (range: 82 to 115) mg/dL and 10 (range: 5 to 24) mg/dL in the high and low Lp(a) groups, respectively. At baseline, there was no difference in coronary artery disease severity or plaque burden. Patients with high Lp(a) showed accelerated progression of low-attenuation plaque compared with low Lp(a) patients (26.2 ± 88.4 mm(3) vs −0.7 ± 50.1 mm(3); P = 0.020). Multivariable linear regression analysis confirmed the relation between Lp(a) and low-attenuation plaque volume progression (β = 10.5% increase for each 50 mg/dL Lp(a), 95% CI: 0.7%-20.3%). There was no difference in total, calcific, and noncalcific plaque volume progression. CONCLUSIONS: Among patients with advanced stable coronary artery disease, Lp(a) is associated with accelerated progression of coronary low-attenuation plaque (necrotic core). This may explain the association between Lp(a) and the high residual risk of myocardial infarction, providing support for Lp(a) as a treatment target in atherosclerosis

Serum Lipoprotein(a) and Bioprosthetic Aortic Valve Degeneration

AIMS: Bioprosthetic aortic valve degeneration demonstrates pathological similarities to aortic stenosis. Lipoprotein(a) [Lp(a)] is a well-recognized risk factor for incident aortic stenosis and disease progression. The aim of this study is to investigate whether serum Lp(a) concentrations are associated with bioprosthetic aortic valve degeneration. METHODS AND RESULTS: In a post hoc analysis of a prospective multimodality imaging study (NCT02304276), serum Lp(a) concentrations, echocardiography, contrast-enhanced computed tomography (CT) angiography, and 18F-sodium fluoride (18F-NaF) positron emission tomography (PET) were assessed in patients with bioprosthetic aortic valves. Patients were also followed up for 2 years with serial echocardiography. Serum Lp(a) concentrations [median 19.9 (8.4-76.4) mg/dL] were available in 97 participants (mean age 75 ± 7 years, 54% men). There were no baseline differences across the tertiles of serum Lp(a) concentrations for disease severity assessed by echocardiography [median peak aortic valve velocity: highest tertile 2.5 (2.3-2.9) m/s vs. lower tertiles 2.7 (2.4-3.0) m/s, P = 0.204], or valve degeneration on CT angiography (highest tertile n = 8 vs. lower tertiles n = 12, P = 0.552) and 18F-NaF PET (median tissue-to-background ratio: highest tertile 1.13 (1.05-1.41) vs. lower tertiles 1.17 (1.06-1.53), P = 0.889]. After 2 years of follow-up, there were no differences in annualized change in bioprosthetic hemodynamic progression [change in peak aortic valve velocity: highest tertile [0.0 (-0.1-0.2) m/s/year vs. lower tertiles 0.1 (0.0-0.2) m/s/year, P = 0.528] or the development of structural valve degeneration. CONCLUSION: Serum lipoprotein(a) concentrations do not appear to be a major determinant or mediator of bioprosthetic aortic valve degeneration