Cost-effectiveness of Contemporary Statin Use Guidelines With or Without Coronary Artery Calcium Assessment in African American Individuals.

Importance: Clinical and economic consequences of statin treatment guidelines supplemented by targeted coronary artery calcium (CAC) assessment have not been evaluated in African American individuals, who are at increased risk for atherosclerotic cardiovascular disease and less likely than non-African American individuals to receive statin therapy. Objective: To evaluate the cost-effectiveness of the 2013 American College of Cardiology/American Heart Association (ACC/AHA) guideline without a recommendation for CAC assessment vs the 2018 ACC/AHA guideline recommendation for use of a non-0 CAC score measured on one occasion to target generic-formulation, moderate-intensity statin treatment in African American individuals at risk for atherosclerotic cardiovascular disease. Design, Setting, and Participants: A microsimulation model was designed to estimate life expectancy, quality of life, costs, and health outcomes over a lifetime horizon. African American-specific data from 472 participants in the Jackson Heart Study (JHS) at intermediate risk for atherosclerotic cardiovascular disease and other US population-specific data on individuals from published sources were used. Data analysis was conducted from November 11, 2018, to November 1, 2019. Main Outcomes and Measures: Lifetime costs and quality-adjusted life-years (QALYs), discounted at 3% annually. Results: In a model-based economic evaluation informed in part by follow-up data, the analysis was focused on 472 individuals in the JHS at intermediate risk for atherosclerotic cardiovascular disease; mean (SD) age was 63 (6.7) years. The sample included 243 women (51.5%) and 229 men (48.5%). Of these, 178 of 304 participants (58.6%) who underwent CAC assessment had a non-0 CAC score. In the base-case scenario, implementation of 2013 ACC/AHA guidelines without CAC assessment provided a greater quality-adjusted life expectancy (0.0027 QALY) at a higher cost ($428.97) compared with the 2018 ACC/AHA guideline strategy with CAC assessment, yielding an incremental cost-effectiveness ratio of $158 325/QALY, which is considered to represent low-value care by the ACC/AHA definition. The 2018 ACC/AHA guideline strategy with CAC assessment provided greater quality-adjusted life expectancy at a lower cost compared with the 2013 ACC/AHA guidelines without CAC assessment when there was a strong patient preference to avoid use of daily medication therapy. In probability sensitivity analyses, the 2018 ACC/AHA guideline strategy with CAC assessment was cost-effective compared with the 2013 ACC/AHA guidelines without CAC assessment in 76% of simulations at a willingness-to-pay value of $100 000/QALY when there was a preference to lose 2 weeks of perfect health to avoid 1 decade of daily therapy. Conclusions and Relevance: A CAC assessment-guided strategy for statin therapy appears to be cost-effective compared with initiating statin therapy in all African American individuals at intermediate risk for atherosclerotic cardiovascular disease and may provide greater quality-adjusted life expectancy at a lower cost than a non-CAC assessment-guided strategy when there is a strong patient preference to avoid the need for daily medication. Coronary artery calcium testing may play a role in shared decision-making regarding statin use.

Deep Learning for Automatic Calcium Scoring in CT: Validation Using Multiple Cardiac CT and Chest CT Protocols.

Background Although several deep learning (DL) calcium scoring methods have achieved excellent performance for specific CT protocols, their performance in a range of CT examination types is unknown. Purpose To evaluate the performance of a DL method for automatic calcium scoring across a wide range of CT examination types and to investigate whether the method can adapt to different types of CT examinations when representative images are added to the existing training data set. Materials and Methods The study included 7240 participants who underwent various types of nonenhanced CT examinations that included the heart: coronary artery calcium (CAC) scoring CT, diagnostic CT of the chest, PET attenuation correction CT, radiation therapy treatment planning CT, CAC screening CT, and low-dose CT of the chest. CAC and thoracic aorta calcification (TAC) were quantified using a convolutional neural network trained with (a) 1181 low-dose chest CT examinations (baseline), (b) a small set of examinations of the respective type supplemented to the baseline (data specific), and (c) a combination of examinations of all available types (combined). Supplemental training sets contained 199-568 CT images depending on the calcium burden of each population. The DL algorithm performance was evaluated with intraclass correlation coefficients (ICCs) between DL and manual (Agatston) CAC and (volume) TAC scoring and with linearly weighted κ values for cardiovascular risk categories (Agatston score; cardiovascular disease risk categories: 0, 1-10, 11-100, 101-400, >400). Results At baseline, the DL algorithm yielded ICCs of 0.79-0.97 for CAC and 0.66-0.98 for TAC across the range of different types of CT examinations. ICCs improved to 0.84-0.99 (CAC) and 0.92-0.99 (TAC) for CT protocol-specific training and to 0.85-0.99 (CAC) and 0.96-0.99 (TAC) for combined training. For assignment of cardiovascular disease risk category, the κ value for all test CT scans was 0.90 (95% confidence interval [CI]: 0.89, 0.91) for the baseline training. It increased to 0.92 (95% CI: 0.91, 0.93) for both data-specific and combined training. Conclusion A deep learning calcium scoring algorithm for quantification of coronary and thoracic calcium was robust, despite substantial differences in CT protocol and variations in subject population. Augmenting the algorithm training with CT protocol-specific images further improved algorithm performance. © RSNA, 2020 See also the editorial by Vannier in this issue.

Coronary Calcium Detection using 3D Attention Identical Dual Deep Network Based on Weakly Supervised Learning.

Coronary artery calcium (CAC) is biomarker of advanced subclinical coronary artery disease and predicts myocardial infarction and death prior to age 60 years. The slice-wise manual delineation has been regarded as the gold standard of coronary calcium detection. However, manual efforts are time and resource consuming and even impracticable to be applied on large-scale cohorts. In this paper, we propose the attention identical dual network (AID-Net) to perform CAC detection using scan-rescan longitudinal non-contrast CT scans with weakly supervised attention by only using per scan level labels. To leverage the performance, 3D attention mechanisms were integrated into the AID-Net to provide complementary information for classification tasks. Moreover, the 3D Gradient-weighted Class Activation Mapping (Grad-CAM) was also proposed at the testing stage to interpret the behaviors of the deep neural network. 5075 non-contrast chest CT scans were used as training, validation and testing datasets. Baseline performance was assessed on the same cohort. From the results, the proposed AID-Net achieved the superior performance on classification accuracy (0.9272) and AUC (0.9627).