Cost-Utility Analysis of Low-Dose Pioglitazone in a Population With Prediabetes and a History of Stroke or Transient Ischemic Attack.

BACKGROUND: Pioglitazone significantly reduces the risk of stroke in people with diabetes, and in those with prediabetes, it markedly reduces the risk of stroke/myocardial infarction and new-onset diabetes. Low-dose pioglitazone provides most of the clinical benefits of high-dose pioglitazone, with fewer adverse effects. We report an economic evaluation of the cost-effectiveness of low-dose pioglitazone versus placebo from a Canadian public payer perspective in 2023 Canadian dollars. METHODS AND RESULTS: A Markov model was developed at a lifetime horizon with an annual cycle length and 5 health states (event-free, myocardial infarction, stroke, new-onset diabetes, and death). Transition probabilities were extracted from the IRIS (Insulin Resistance Intervention in Stroke) trial. Health state costs and utilities were based on public sources. Annual discount rates of 1.5% were applied in the reference-case analysis. Probabilistic analyses were conducted to deal with parameter uncertainty through 5000 simulations. The costs were estimated as $24 887 (interquartile range [IQR], $14 632-$41507) for low-dose pioglitazone and $57 301 (IQR, $48 730-$67368) for placebo, resulting in a cost saving of -$30 287 (IQR, -$43 374 to -$14 587) in favor of low-dose pioglitazone. Quality-adjusted life years were estimated as 25.99 (IQR, 24.56-26.81) for the low-dose pioglitazone and 19.44 (IQR, 18.68-20.13) for placebo, resulting in a difference of 6.37 (IQR, 5.07-7.36) in favor of low-dose pioglitazone. Consistent findings were observed from scenario analyses and 1-way probability sensitivity analyses. CONCLUSIONS: Holding across a wide range of values in modeling parameters, low-dose pioglitazone is found as the dominant strategy versus a placebo.

Effect of lipid-lowering therapy on carotid plaque burden in older adults.

BACKGROUND: Little is known about the benefits of lipid-lowering medications in those age ≥ 75 years. We assessed the effect of lipid-lowering medications on progression to severe atherosclerosis in patients age > 75. METHODS: Data was retrospectively obtained from the Stroke Prevention & Atherosclerosis Research Centre, Canada. Atherosclerosis burden was measured as carotid total plaque area (TPA), a powerful predictor of cardiovascular risk. Survival time free of severe atherosclerosis (SFSA) was defined as the period when TPA remained <1.19 cm2. Kaplan-Meier, multiple Cox proportional hazard and hierarchical mixed-effect models were used to determine the effects of lipid-lowering medications on progression to severe atherosclerosis. RESULTS: In total 1404 cases (mean age 81 ± 4 years; women 52%) were included. Those taking lipid-lowering medications were more likely to have a history of diabetes and a higher burden of atherosclerosis at baseline. In Kaplan-Meier analysis, the SFSA was significantly longer in those receiving lipid-lowering therapy. In multivariable-adjusted analyses, those not receiving lipid lowering therapy (irrespective of their vascular disease at baseline) were more likely to have TPA > 1.19 cm2 (hazard ratio (HR) = 1.37, 95% confidence interval (CI): 1.09,0.71). Similar findings were observed in mixed effects models when plaque progression was defined as any change >0.05 cm2 per year (odds ratio (OR):2.17, 95% CI:1.38,3.57). CONCLUSION: Lipid-lowering therapy is effective in controlling the burden of atherosclerosis among older adults with and without vascular disease. The measurement of plaque burden can guide selection and follow-up of those who may benefit from treatment.

Carotid Stenosis and Stroke: Historical Perspectives Leading to Current Challenges.

The carotid artery is unique; it is the only vessel to bifurcate into a bulb larger than itself. The history of its anatomic description, understanding of its pathophysiology and evolution of its imaging are relevant to current controversies regarding measurement of stenosis, surgical/endovascular therapies and medical management of carotid stenosis in stroke prevention. Treatment decisions on millions of symptomatic and asymptomatic patients are routinely based on information from clinical trials from over 30 years ago. This article briefly summarizes the highlights of past research in key areas and discuss how they led to current challenges of diagnosis and treatment.

Assessment of atherosclerosis: should coronary calcium score and intima-media thickness be replaced by ultrasound measurement of carotid plaque burden and vessel wall volume?

PURPOSE OF REVIEW: To describe the uses of vessel wall volume (VWV) and measurement of carotid plaque burden, as total plaque area (TPA) and total plaque volume (TPV), and to contrast them with measurement of carotid intima-media thickness (IMT) and coronary calcium (CAC). RECENT FINDINGS: Measurement of carotid plaque burden (CPB) is useful for risk stratification, research into the genetics and biology of atherosclerosis, for measuring effects of new therapies for atherosclerosis, and for treatment of high-risk patients with severe atherosclerosis. It is as predictive of risk as CAC, with important advantages. IMT is only a weak predictor of risk and changes so little over time that it is not useful for assessing effects of therapy. SUMMARY: Measurement of CPB and VWV are far superior to measurement of carotid IMT in many ways, and should replace it. Vessel wall volume can be measured in persons with no plaque as an alternative to IMT. There are important advantages of CPB over coronary calcium; CPB should be more widely used in vascular prevention.

Ultrasound carotid plaque segmentation via image reconstruction-based self-supervised learning with limited training labels.

Carotid total plaque area (TPA) is an important contributing measurement to the evaluation of stroke risk. Deep learning provides an efficient method for ultrasound carotid plaque segmentation and TPA quantification. However, high performance of deep learning requires datasets with many labeled images for training, which is very labor-intensive. Thus, we propose an image reconstruction-based self-supervised learning algorithm (IR-SSL) for carotid plaque segmentation when few labeled images are available. IR-SSL consists of pre-trained and downstream segmentation tasks. The pre-trained task learns region-wise representations with local consistency by reconstructing plaque images from randomly partitioned and disordered images. The pre-trained model is then transferred to the segmentation network as the initial parameters in the downstream task. IR-SSL was implemented with two networks, UNet++ and U-Net, and evaluated on two independent datasets of 510 carotid ultrasound images from 144 subjects at SPARC (London, Canada) and 638 images from 479 subjects at Zhongnan hospital (Wuhan, China). Compared to the baseline networks, IR-SSL improved the segmentation performance when trained on few labeled images (n = 10, 30, 50 and 100 subjects). For 44 SPARC subjects, IR-SSL yielded Dice-similarity-coefficients (DSC) of 80.14-88.84%, and algorithm TPAs were strongly correlated (r=0.962-0.993, p < 0.001) with manual results. The models trained on the SPARC images but applied to the Zhongnan dataset without retraining achieved DSCs of 80.61-88.18% and strong correlation with manual segmentation (r=0.852-0.978, p < 0.001). These results suggest that IR-SSL could improve deep learning when trained on small labeled datasets, making it useful for monitoring carotid plaque progression/regression in clinical use and trials.

Carotid Vessel-Wall-Volume Ultrasound Measurement via a UNet++ Ensemble Algorithm Trained on Small Data Sets.

Vessel wall volume (VWV) is a 3-D ultrasound measurement for the assessment of therapy in patients with carotid atherosclerosis. Deep learning can be used to segment the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) and to quantify VWV automatically; however, it typically requires large training data sets with expert manual segmentation, which are difficult to obtain. In this study, a UNet++ ensemble approach was developed for automated VWV measurement, trained on five small data sets (n = 30 participants) and tested on 100 participants with clinically diagnosed coronary artery disease enrolled in a multicenter CAIN trial. The Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), Pearson correlation coefficient (r), Bland-Altman plots and coefficient of variation (CoV) were used to evaluate algorithm segmentation accuracy, agreement and reproducibility. The UNet++ ensemble yielded DSCs of 91.07%-91.56% and 87.53%-89.44% and ASSDs of 0.10-0.11 mm and 0.33-0.39 mm for the MAB and LIB, respectively; the algorithm VWV measurements were correlated (r = 0.763-0.795, p < 0.001) with manual segmentations, and the CoV for VWV was 8.89%. In addition, the UNet++ ensemble trained on 30 participants achieved a performance similar to that of U-Net and Voxel-FCN trained on 150 participants. These results suggest that our approach could provide accurate and reproducible carotid VWV measurements using relatively small training data sets, supporting deep learning applications for monitoring atherosclerosis progression in research and clinical trials.

Quantification of Local Vessel Wall and Plaque Volume Change for Assessment of Effects of Therapies on Carotid Atherosclerosis Based on 3-D Ultrasound Imaging.

We developed a new method to measure the voxel-based vessel-wall-plus-plaque volume (VWV). In addition to quantifying local thickness change as in the previously introduced vessel-wall-plus-plaque thickness (VWT) metric, voxel-based VWV further considers the circumferential change associated with vascular remodeling. Three-dimensional ultrasound images were acquired at baseline and 1 y afterward. The vessel wall region was divided into small voxels with the voxel-based VWV change (ΔVVol%) computed by taking the percentage volume difference between corresponding voxels in the baseline and follow-up images. A 3-D carotid atlas was developed to allow visualization of the local thickness and circumferential change patterns in the pomegranate versus the placebo groups. A new patient-based biomarker was obtained by computing the mean ΔVVol% over the entire 3-D map for each patient (ΔVVol%¯). ΔVVol%¯ detected a significant difference between patients randomized to pomegranate juice/extract and placebo groups (p = 0.0002). The number of patients required by ΔVVol%¯ to establish statistical significance was approximately a third of that required by the local VWT biomarker. The increased sensitivity afforded by the proposed biomarker improves the cost-effectiveness of clinical studies evaluating new anti-atherosclerotic treatments.

Why do guidelines recommend screening for abdominal aortic aneurysms, but not for asymptomatic carotid stenosis? A plea for a randomized controlled trial.

BACKGROUND: Current guidelines do not recommend screening for asymptomatic carotid artery stenosis (AsxCS). The rationale behind this recommendation is that detection of AsxCS may lead to an unnecessary carotid intervention. In contrast, screening for abdominal aortic aneurysms is strongly recommended. METHODS: A critical analysis of the literature was performed to evaluate the implications of detecting AsxCS. RESULTS: Patients with AsxCS are at high risk for future stroke, myocardial infarction and vascular death. Population-wide screening for AsxCS should not be recommended. Additionally, screening of high-risk individuals for AsxCS with the purpose of identifying candidates for a carotid intervention is inappropriate. Instead, selective screening for AsxCS should be considered and should be viewed as an opportunity to identify individuals at high risk for atherosclerotic cardiovascular disease and future cardiovascular events for the timely initiation of intensive medical therapy and risk factor modification. CONCLUSIONS: Although mass screening should not be recommended, there are several arguments suggesting that selective screening for AsxCS should be considered. The rationale supporting such selective screening is to optimize risk factor control and to initiate intensive medical therapy for prevention of future cardiovascular events, rather than to identify candidates for an intervention.