Identifying populations of heart failure (HF) patients is paramount to research efforts aimed at developing strategies to effectively reduce the burden of this disease. The use of electronic medical record (EMR) data for this purpose is challenging given the syndromic nature of HF and the need to distinguish HF with preserved or reduced ejection fraction. Using a gold standard cohort of manually abstracted cases, an EMR-driven phenotype algorithm based on structured and unstructured data was developed to identify all the cases. The resulting algorithm was executed in two cohorts from the Electronic Medical Records and Genomics (eMERGE) Network with a positive predictive value of > 95%. The algorithm was expanded to include three hierarchical definitions of HF (i.e., Definite, Probable, Possible) based on the degree of confidence of the classification to capture HF cases in a whole population whereby increasing the algorithm utility for use in e-Epidemiologic research.
Background and objective: We designed an algorithm to identify abdominal aortic aneurysm cases and controls from electronic health records to be shared and executed within the "electronic Medical Records and Genomics" (eMERGE) Network.
Background/Aims: Triple therapy [adding protease inhibitors to standard of care (SOC)] dramatically increases treatment response in selected patients with hepatitis C virus (HCV). Interleukin 28B (IL28Β) genotyping helps predict responsiveness in these patients; however, the economic implications of IL28Β genotyping in HCV genotype 2 or 3 infected patients are unknown. Short- and long-term costs and outcomes of SOC therapy were calculated and used to determine the cost-effectiveness thresholds for using triple therapy in HCV genotype 2 or 3 infected patients. Methods: Costs and outcomes were calculated by conducting cohort simulations on decision trees modeling SOC and triple therapy. Quality-adjusted life expectancies and long-term costs were predicted through Markov modeling. Results: For triple therapy to be cost-effective, sustained virologic response (SVR) rates must improve (depending on age) by 7.91-11.11 and 9.06-12.8% for HCV genotype 2 and 3 cohorts, respectively. When triple therapy is guided by 2 IL28Β variants, a 2.63-3.72% improvement in SVR is needed for cost-effectiveness, and when guided by only one variant, a 1.4-8.91% improvement is needed. Conclusions: Markov modeling revealed that modest increases in SVR rates from IL28Β-guided triple therapy can lead to both lower costs and better health outcomes than SOC therapy in the long run.
Abstracts biobank total twelve million dollars, representing a several-fold return on investment. Conclusions: The seamless integration of the automated MyCode process with existing clinical infrastructure maximizes the efficiency of research sample collection and minimizes costs. The process allows resources to be focused on interaction with the patients during consent. The opt-in consent allows samples to be linked to EMR data.
Conclusions:We designed an ePhenotyping algorithm to identify AAA cases and controls from the EMR with high PPV and sensitivity necessary for research purposes. The VDW provides an excellent opportunity to broaden the study population characteristics and replicate the findings.
Background/Aims: Rural health describes a set of health issues, health care challenges and research priorities driven by a single geo-demographic factor: low population density. Rural areas compared to urbanized areas have fewer providers per capita, longer distance to care, lower socioeconomic status, higher rates of untreated illness, greater exposure to agricultural chemicals, and higher rates of alcohol use, fatal motor vehicle crashes, and suicide. Accessing clinical data for large numbers of rural residents can be challenging. To meet this challenge, seven sites formed the HMORN Rural Health Scientific Interest Group (SIG). Methods: VDW data from seven HMORN sites were analyzed. Rural-Urban Commuting Area (RUCA) codes describe commuting flow but include data on urban, town, and rural tracts; RUCA codes were used to categorize areas as urban, large rural town, small rural town, or isolated rural area. We determined prevalence of chronic conditions by rural status and age group (child, adult, seniors). Results: Common diseases were hypertension, obesity, dyslipidemia, diabetes, alcohol/drug use, depression, and cancers. Most sites saw stable rates of rural vs. urban patients over the years. Rates of pediatric obesity increased at all sites. Adult obesity increased markedly among seniors while dyslipidemia and diabetes increased in all age groups. Cancer among adults also trended upward over time and exceeded national averages. Hypertension among adults appeared lower than US national average in 2010 (32%). Conclusions: Economic challenges and other factors may further accentuate existing health and health care disparities experienced by many Americans living in rural areas. The Rural Health SIG of the HMORN is poised to conduct meaningful, multi-site research addressing health care issues, health care delivery and care follow-up for this special patient population. Future analyses will explore variation in chronic disease by rural status and the influence of economic factors within geographies.
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