The BioImage Study: novel approaches to risk assessment in the primary prevention of atherosclerotic cardiovascular disease--study design and objectives.

The identification of asymptomatic individuals at risk for near-term atherothrombotic events to ensure optimal preventive treatment remains a challenging goal. In the BioImage Study, novel approaches are tested in a typical health-plan population. Based on certain demographic and risk characteristics on file with Humana Inc, a total of 7,687 men 55 to 80 years of age and women 60 to 80 years of age without evidence of atherothrombotic disease but presumed to be at risk for near-term atherothrombotic events were enrolled between January 2008 and June 2009. Those who met the prespecified eligibility criteria were randomized to a telephonic health survey only (survey only: n = 865), standard risk assessment (Framingham only: n = 718), or comprehensive risk assessment in a dedicated mobile facility equipped with advanced imaging tools (n = 6,104). Baseline examination included assessment of cardiovascular risk factors and screening for subclinical (asymptomatic) atherosclerosis with quantification of coronary artery calcification by computed tomography (CT), measurement of intima-media thickness, presence of carotid atherosclerotic plaques and abdominal aortic aneurysm by ultrasound, and ankle brachial index. Participants with one or more abnormal screening test results underwent advanced imaging with contrast-enhanced magnetic resonance imaging for carotid and aortic plaques, contrast-enhanced coronary CT angiography for luminal stenosis and noncalcified plaques, and 18F-fluorodeoxyglucose-positron emission tomography/CT for carotid and aortic plaque inflammation. Plasma, PAXgene RNA, and DNA samples were obtained, frozen, and stored for future biomarker discovery studies. All individuals will be followed until 600 major atherothrombotic events have occurred in those undergoing imaging. The BioImage Study will help identify those patients with subclinical atherosclerosis who are at risk for near-term atherothrombotic events and enable a more personalized management of care.

Transformative learning during a nursing externship program: the reflections of senior nursing students.

Many nursing students participate in summer extern programs to augment their clinical experience in school and ease the gap between education and the real world of nursing practice. The purpose of this descriptive, qualitative research study is to gain an understanding of the transformative learning that takes place during a summer externship program. Examining outcomes of externship programs will help nurse educators, practitioners, and administrators understand the full dimension of these programs. Seventy-eight nurse externs, from baccalaureate, associate, and diploma nursing programs, participated in an eight-week extern program at a large, inner-city trauma hospital in the northeastern United States. Data collection took place throughout the program. Individual discussions, midpoint focus group discussions, and reflective surveys administered immediately following the externship experience were used as data sources. Findings indicated that some of the externs' perceptions, values, and beliefs were transformed as a result of the program. Three themes emerged from the analysis of the findings: affirming assumptions, validating values, and banishing some core beliefs. The results suggest implications for nursing education, practice, and research.

Novel predictive models for metabolic syndrome risk: a "big data" analytic approach.

OBJECTIVES: We applied a proprietary "big data" analytic platform--Reverse Engineering and Forward Simulation (REFS)--to dimensions of metabolic syndrome extracted from a large data set compiled from Aetna's databases for 1 large national customer. Our goals were to accurately predict subsequent risk of metabolic syndrome and its various factors on both a population and individual level. STUDY DESIGN: The study data set included demographic, medical claim, pharmacy claim, laboratory test, and biometric screening results for 36,944 individuals. The platform reverse-engineered functional models of systems from diverse and large data sources and provided a simulation framework for insight generation. METHODS: The platform interrogated data sets from the results of 2 Comprehensive Metabolic Syndrome Screenings (CMSSs) as well as complete coverage records; complete data from medical claims, pharmacy claims, and lab results for 2010 and 2011; and responses to health risk assessment questions. RESULTS: The platform predicted subsequent risk of metabolic syndrome, both overall and by risk factor, on population and individual levels, with ROC/AUC varying from 0.80 to 0.88. We demonstrated that improving waist circumference and blood glucose yielded the largest benefits on subsequent risk and medical costs. We also showed that adherence to prescribed medications and, particularly, adherence to routine scheduled outpatient doctor visits, reduced subsequent risk. CONCLUSIONS: The platform generated individualized insights using available heterogeneous data within 3 months. The accuracy and short speed to insight with this type of analytic platform allowed Aetna to develop targeted cost-effective care management programs for individuals with or at risk for metabolic syndrome.