Explant analysis of AneuRx stent grafts: relationship between structural findings and clinical outcome

AbstractObjectiveWe reviewed the structural findings of explanted AneuRx stent grafts used to treat abdominal aortic aneurysms, and relate the findings to clinical outcome measures.MethodsWe reviewed data for all bifurcated AneuRx stent grafts explanted at surgery or autopsy and returned to the manufacturer from the US clinical trial and worldwide experience of more than 33,000 implants from 1996 to 2003. Devices implanted for more than 1 month with structural analysis are included in this article. Explant results were analyzed in relation to cause of explantation and pre-explant evidence of endoleak, enlargement, or device migration.ResultsOne hundred twenty explanted stent grafts, including 37 from the US clinical trial, were analyzed. Mean implant duration was 22 ± 13 months (range, 1-61 months). Structural abnormalities included stent fatigue fractures, fabric abrasion holes, and suture breaks. The mean number of nitinol stent strut fractures per explanted device was 3 ± 4, which represents less than 0.2% of the total number of stent struts in each device. The mean number of fabric holes per explanted device was 2 ± 3, with a median hole size of 0.5 mm2. Suture breaks were seen in most explanted devices, but composed less than 1.5% of the total number of sutures per device. “For cause” explants (n = 104) had a 10-month longer implant duration (P = .007) compared with “incidental” explants (n = 16). “For cause” explants had more fractures (3 ± 5; P = .005) and fabric holes (2 ± 3; P = .008) per device compared with “incidental” explants, but these differences were not significant (P = .3) when adjusted for duration of device implantation. Among clinical trial explants the number of fabric holes in grafts in patients with endoleak (2 ± 3 per device) was no different from those without endoleak (3 ± 4 per device; P = NS). The number of fatigue fractures or fabric holes was no different in grafts in clinical trial patients with pre-explant aneurysm enlargement compared with those without enlargement. Pre-explant stent-graft migration was associated with a greater number of stent strut fractures (5 ± 7 per device; P = .04) and fabric holes (3 ± 3 per bifurcation; P = .03) compared with explants without migration. Serial imaging studies revealed inadequate proximal, distal, or junctional device fixation as the probable cause of rupture or need for conversion to open surgery in 86% of “for cause” explants. Structural device abnormalities were usually remote from fixation sites, and no causal relationship between device findings and clinical outcome could be established.ConclusionsNitinol stent fatigue fractures, fabric holes, and suture breaks found in explanted AneuRx stent grafts do not appear to be related to clinical outcome measures. Longer term studies are needed to confirm these observations

a CLARIFY trial sub-study

Publisher Copyright: © 2022Background: The difference between expert level (L3) reader and artificial intelligence (AI) performance for quantifying coronary plaque and plaque components is unknown. Objective: This study evaluates the interobserver variability among expert readers for quantifying the volume of coronary plaque and plaque components on coronary computed tomographic angiography (CCTA) using an artificial intelligence enabled quantitative CCTA analysis software as a reference (AI-QCT). Methods: This study uses CCTA imaging obtained from 232 patients enrolled in the CLARIFY (CT EvaLuation by ARtificial Intelligence For Atherosclerosis, Stenosis and Vascular MorphologY) study. Readers quantified overall plaque volume and the % breakdown of noncalcified plaque (NCP) and calcified plaque (CP) on a per vessel basis. Readers categorized high risk plaque (HRP) based on the presence of low-attenuation-noncalcified plaque (LA-NCP) and positive remodeling (PR; ≥1.10). All CCTAs were analyzed by an FDA-cleared software service that performs AI-driven plaque characterization and quantification (AI-QCT) for comparison to L3 readers. Reader generated analyses were compared among readers and to AI-QCT generated analyses. Results: When evaluating plaque volume on a per vessel basis, expert readers achieved moderate to high interobserver consistency with an intra-class correlation coefficient of 0.78 for a single reader score and 0.91 for mean scores. There was a moderate trend between readers 1, 2, and 3 and AI with spearman coefficients of 0.70, 0.68 and 0.74, respectively. There was high discordance between readers and AI plaque component analyses. When quantifying %NCP v. %CP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.23, 0.34 and 0.24, respectively, compared to AI with a spearman coefficient of 0.38, 0.51, and 0.60, respectively. The intra-class correlation coefficient among readers for plaque composition assessment was 0.68. With respect to HRP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.22, 0.26, and 0.17, respectively, and a spearman coefficient of 0.36, 0.35, and 0.44, respectively. Conclusion: Expert readers performed moderately well quantifying total plaque volumes with high consistency. However, there was both significant interobserver variability and high discordance with AI-QCT when quantifying plaque composition.publishersversionpublishe

High Dose “HDR-Like” Prostate SBRT: PSA 10-Year Results From a Mature, Multi-Institutional Clinical Trial

Purpose/Objective(s)Although ample intermediate-term prostate stereotactic body radiotherapy (SBRT) outcomes have been reported, 10-year results remain relatively sparse.Materials/MethodsEighteen institutions enrolled 259 low- and intermediate-risk patients. Median follow-up is 5.5 years, with 66 patients followed ≥ 10 years. This SBRT regimen specifically emulated an existing HDR brachytherapy dose schedule and isodose morphology, prescribed to 38 Gy/4 fractions, delivered daily by robotic SBRT, mandating > 150% dose escalation in the peripheral zone. Androgen deprivation therapy was not allowed, and a hydrogel spacer was not available at that time.ResultsMedian pre-SBRT PSA 5.12 ng/mL decreased to 0.1 ng/mL by 3.5 years, with further decrease to a nadir of < 0.1 ng/mL by 7 years, maintained through 10 years. Ten-year freedom from biochemical recurrence measured 100% for low-risk, 84.3% for favorable intermediate risk (FIR), and 68.4% for unfavorable intermediate (UIR) cases. Multivariable analysis revealed that the UIR group bifurcated into two distinct prognostic subgroups. Those so classified by having Gleason score 4 + 3 and/or clinical stage T2 (versus T1b/T1c) had a significantly poorer 10 year freedom from biochemical recurrence rate, 54.8% if either or both factors were present, while UIR patients without these specific factors had a 94.4% 10-year freedom from biochemical recurrence rate. The cumulative incidence of grade 2 GU toxicity modestly increased over time – 16.3% at 5 years increased to 19.2% at 10 years-- while the incidence of grade 3+ GU and GI toxicity remained low and stable to 10 years - 2.6% and 0%, respectively. The grade 2 GI toxicity incidence also remained low and stable to 10 years – 4.1% with no further events after year 5.ConclusionThis HDR-like SBRT regimen prescribing 38 Gy/4 fractions but delivering much higher intraprostatic doses on a daily basis is safe and effective. This treatment achieves a median PSA nadir of <0.1 ng/mL and provides high long-term disease control rates without ADT except for a subgroup of unfavorable intermediate-risk patients

High Dose “HDR-Like” Prostate SBRT: PSA 10-Year Results From a Mature, Multi-Institutional Clinical Trial

Purpose/Objective(s)Although ample intermediate-term prostate stereotactic body radiotherapy (SBRT) outcomes have been reported, 10-year results remain relatively sparse.Materials/MethodsEighteen institutions enrolled 259 low- and intermediate-risk patients. Median follow-up is 5.5 years, with 66 patients followed ≥ 10 years. This SBRT regimen specifically emulated an existing HDR brachytherapy dose schedule and isodose morphology, prescribed to 38 Gy/4 fractions, delivered daily by robotic SBRT, mandating &gt; 150% dose escalation in the peripheral zone. Androgen deprivation therapy was not allowed, and a hydrogel spacer was not available at that time.ResultsMedian pre-SBRT PSA 5.12 ng/mL decreased to 0.1 ng/mL by 3.5 years, with further decrease to a nadir of &lt; 0.1 ng/mL by 7 years, maintained through 10 years. Ten-year freedom from biochemical recurrence measured 100% for low-risk, 84.3% for favorable intermediate risk (FIR), and 68.4% for unfavorable intermediate (UIR) cases. Multivariable analysis revealed that the UIR group bifurcated into two distinct prognostic subgroups. Those so classified by having Gleason score 4 + 3 and/or clinical stage T2 (versus T1b/T1c) had a significantly poorer 10 year freedom from biochemical recurrence rate, 54.8% if either or both factors were present, while UIR patients without these specific factors had a 94.4% 10-year freedom from biochemical recurrence rate. The cumulative incidence of grade 2 GU toxicity modestly increased over time – 16.3% at 5 years increased to 19.2% at 10 years-- while the incidence of grade 3+ GU and GI toxicity remained low and stable to 10 years - 2.6% and 0%, respectively. The grade 2 GI toxicity incidence also remained low and stable to 10 years – 4.1% with no further events after year 5.ConclusionThis HDR-like SBRT regimen prescribing 38 Gy/4 fractions but delivering much higher intraprostatic doses on a daily basis is safe and effective. This treatment achieves a median PSA nadir of &lt;0.1 ng/mL and provides high long-term disease control rates without ADT except for a subgroup of unfavorable intermediate-risk patients

Interobserver Variability Among Expert Readers Quantifying Plaque Volume and Plaque Characteristics on Coronary CT Angiography: A CLARIFY Trial Sub-Study

Background: The difference between expert level (L3) reader and artificial intelligence (AI) performance for quantifying coronary plaque and plaque components is unknown. Objective: This study evaluates the interobserver variability among expert readers for quantifying the volume of coronary plaque and plaque components on coronary computed tomographic angiography (CCTA) using an artificial intelligence enabled quantitative CCTA analysis software as a reference (AI-QCT). Methods: This study uses CCTA imaging obtained from 232 patients enrolled in the CLARIFY (CT EvaLuation by ARtificial Intelligence For Atherosclerosis, Stenosis and Vascular MorphologY) study. Readers quantified overall plaque volume and the % breakdown of noncalcified plaque (NCP) and calcified plaque (CP) on a per vessel basis. Readers categorized high risk plaque (HRP) based on the presence of low-attenuation-noncalcified plaque (LA-NCP) and positive remodeling (PR; ≥1.10). All CCTAs were analyzed by an FDA-cleared software service that performs AI-driven plaque characterization and quantification (AI-QCT) for comparison to L3 readers. Reader generated analyses were compared among readers and to AI-QCT generated analyses. Results: When evaluating plaque volume on a per vessel basis, expert readers achieved moderate to high interobserver consistency with an intra-class correlation coefficient of 0.78 for a single reader score and 0.91 for mean scores. There was a moderate trend between readers 1, 2, and 3 and AI with spearman coefficients of 0.70, 0.68 and 0.74, respectively. There was high discordance between readers and AI plaque component analyses. When quantifying %NCP v. %CP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.23, 0.34 and 0.24, respectively, compared to AI with a spearman coefficient of 0.38, 0.51, and 0.60, respectively. The intra-class correlation coefficient among readers for plaque composition assessment was 0.68. With respect to HRP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.22, 0.26, and 0.17, respectively, and a spearman coefficient of 0.36, 0.35, and 0.44, respectively. Conclusion: Expert readers performed moderately well quantifying total plaque volumes with high consistency. However, there was both significant interobserver variability and high discordance with AI-QCT when quantifying plaque composition

Rationale and design of the CONFIRM2 (Quantitative COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) study.

BACKGROUND In the last 15 years, large registries and several randomized clinical trials have demonstrated the diagnostic and prognostic value of coronary computed tomography angiography (CCTA). Advances in CT scanner technology and developments of analytic tools now enable accurate quantification of coronary artery disease (CAD), including total coronary plaque volume (TPV) and low attenuation plaque volume (LAP). The primary aim of CONFIRM2, (Quantitative COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) is to perform comprehensive quantification of CCTA findings, including coronary, non-coronary cardiac, non-cardiac vascular, non-cardiac findings, and relate them to clinical variables and cardiovascular clinical outcomes. DESIGN CONFIRM2 is a multicenter, international observational cohort study designed to evaluate multidimensional associations between quantitative phenotype of cardiovascular disease and future adverse clinical outcomes in subjects undergoing clinically indicated CCTA. The targeted population is heterogenous and includes patients undergoing CCTA for atherosclerotic evaluation, valvular heart disease, congenital heart disease or pre-procedural evaluation. Automated software will be utilized for quantification of coronary plaque, stenosis, vascular morphology and cardiac structures for rapid and reproducible tissue characterization. Up to 30,000 patients will be included from up to 50 international multi-continental clinical CCTA sites and followed for 3-4 years. SUMMARY CONFIRM2 is one of the largest CCTA studies to establish the clinical value of a multiparametric approach to quantify the phenotype of cardiovascular disease by CCTA using automated imaging solutions

The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography

Publisher Copyright: © 2022 The AuthorsObjectives: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. Background: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. Methods: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). Results: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had 400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. Conclusion: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. Condensed abstract: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient's BMI or heart rate at time of scan affect the software's diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters.publishersversionpublishe

Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence

Objective: The study evaluates the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT). Methods: This is a post-hoc analysis of data from 303 subjects enrolled in the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial who were referred for invasive coronary angiography and subsequently underwent coronary computed tomographic angiography (CCTA). In this study, a blinded core laboratory analysing quantitative coronary angiography images classified lesions as obstructive (≥50%) or non-obstructive (\u3c50%) while AI software quantified APCs including plaque volume (PV), low-density non-calcified plaque (LD-NCP), non-calcified plaque (NCP), calcified plaque (CP), lesion length on a per-patient and per-lesion basis based on CCTA imaging. Plaque measurements were normalised for vessel volume and reported as % percent atheroma volume (%PAV) for all relevant plaque components. Data were subsequently stratified by age \u3c65 and ≥65 years. Results: The cohort was 64.4±10.2 years and 29% women. Overall, patients \u3e65 had more PV and CP than patients \u3c65. On a lesion level, patients \u3e65 had more CP than younger patients in both obstructive (29.2 mm3 vs 48.2 mm3; p\u3c0.04) and non-obstructive lesions (22.1 mm3 vs 49.4 mm3; p\u3c0.004) while younger patients had more %PAV (LD-NCP) (1.5% vs 0.7%; p\u3c0.038). Younger patients had more PV, LD-NCP, NCP and lesion lengths in obstructive compared with non-obstructive lesions. There were no differences observed between lesion types in older patients. Conclusion: AI-QCT identifies a unique APC signature that differs by age and degree of stenosis and provides a foundation for AI-guided age-based approaches to atherosclerosis identification, prevention and treatment

The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography

Objectives: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. Background: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. Methods: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm\u27s diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). Results: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had \u3c50% stenosis in all vessel territories evaluated by QCA. Average AI analysis time was 10.3 ± 2.7 min. On a per vessel basis, there were significant differences only in sensitivity for ≥50% stenosis based on contrast type (iso-osmolar 70.0% vs non isoosmolar 92.1% p = 0.0345) and iodine concentration (\u3c350 mg/ml 70.0%, 350-369 mg/ml 90.0%, 370-400 mg/ml 90.0%, \u3e400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. Conclusion: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. Condensed abstract: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient\u27s BMI or heart rate at time of scan affect the software\u27s diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters

Quantitative plaque analysis with A.I.-augmented CCTA in end-stage renal disease and complex CAD

BACKGROUND: Adverse cardiovascular events are a significant cause of mortality in end-stage renal disease (ESRD) patients. High-risk plaque anatomy may be a significant contributor. However, their atherosclerotic phenotypes have not been described. We sought to define atherosclerotic plaque characteristics (APC) in dialysis patients using artificial-intelligence augmented CCTA. METHODS: We retrospectively analyzed ESRD patients referred for CCTA using an FDA approved artificial-intelligence augmented-CCTA program (Cleerly). Coronary lesions were evaluated for APCs by CCTA. APCs included percent atheroma volume(PAV), low-density non-calcified-plaque (LD-NCP), non-calcified-plaque (NCP), calcified-plaque (CP), length, and high-risk-plaque (HRP), defined by LD-NCP and positive arterial remodeling \u3e1.10 (PR). RESULTS: 79 ESRD patients were enrolled, mean age 65.3 years, 32.9% female. Disease distribution was non-obstructive (65.8%), 1-vessel disease (21.5%), 2-vessel disease (7.6%), and 3-vessel disease (5.1%). Mean total plaque volume (TPV) was 810.0 mm, LD-NCP 16.8 mm, NCP 403.1 mm, and CP 390.1 mm. HRP was present in 81.0% patients. Patients with at least one \u3e50% stenosis, or obstructive lesions, had significantly higher TPV, LD-NCP, NCP, and CP. Patients \u3e65 years had more CP and higher PAV. CONCLUSION: Our study provides novel insight into ESRD plaque phenotypes and demonstrates that artificial-intelligence augmented CCTA analysis is feasible for CAD characterization despite severe calcification. We demonstrate elevated plaque burden and stenosis caused by predominantly non-calcified-plaque. Furthermore, the quantity of calcified-plaques increased with age, with men exhibiting increased number of 2-feature plaques and higher plaque volumes. Artificial-intelligence augmented CCTA analysis of APCs may be a promising metric for cardiac risk stratification and warrants further prospective investigation