A cost-effectiveness analysis of over-the-counter statins.

OBJECTIVES: To estimate the costs and benefits of over-the-counter (OTC) statins under the 2013 American College of Cardiology/American Heart Association guidelines. STUDY DESIGN: A 10-year cost-effectiveness model using a health system perspective was developed to analyze the impact of making an OTC statin drug available. METHODS: We calibrated the model by using nationally representative survey data on statin use and cardiovascular risk, data from clinical studies on the safety and efficacy of statins, and data from a study on consumer decisions to use an OTC statin. RESULTS: We estimated that OTC statins would result in 252,359 fewer major coronary events, 41,133 fewer strokes, and 135,299 fewer coronary revascularization procedures over 10 years, as well as reduce coronary heart disease- and stroke-related deaths by 68,534 over the same time frame. These averted events would save more than $10.8 billion in healthcare costs while the costs of drug therapy would increase by $28.3 billion. Increased statin utilization is estimated to cause 3864 more cases of rhabdomyolysis-a very rare but severe side effect of statins. The estimated incremental cost-effectiveness ratio (ICER) of OTC statins was $5667 per quality-adjusted life-year, and the 95% CI of the ICER was $1384 to $12,701. CONCLUSIONS: With proper labeling and consumer education, it is highly likely that OTC statins would be cost-effective, as they significantly improve population health without large increases in healthcare costs.

Automated classification of atherosclerotic plaque from magnetic resonance images using predictive models.

The information contained within multicontrast magnetic resonance images (MRI) promises to improve tissue classification accuracy, once appropriately analyzed. Predictive models capture relationships empirically, from known outcomes thereby combining pattern classification with experience. In this study, we examine the applicability of predictive modeling for atherosclerotic plaque component classification of multicontrast ex vivo MR images using stained, histopathological sections as ground truth. Ten multicontrast images from seven human coronary artery specimens were obtained on a 9.4 T imaging system using multicontrast-weighted fast spin-echo (T1-, proton density-, and T2-weighted) imaging with 39-mum isotropic voxel size. Following initial data transformations, predictive modeling focused on automating the identification of specimen's plaque, lipid, and media. The outputs of these three models were used to calculate statistics such as total plaque burden and the ratio of hard plaque (fibrous tissue) to lipid. Both logistic regression and an artificial neural network model (Relevant Input Processor Network-RIPNet) were used for predictive modeling. When compared against segmentation resulting from cluster analysis, the RIPNet models performed between 25 and 30% better in absolute terms. This translates to a 50% higher true positive rate over given levels of false positives. This work indicates that it is feasible to build an automated system of plaque detection using MRI and data mining.