ePhenotyping for Abdominal Aortic Aneurysm in the Electronic Medical Records and Genomics (eMERGE) Network: Algorithm Development and Konstanz Information Miner Workflow.

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. MATERIALS AND METHODS: Structured Query Language, was used to script the algorithm utilizing "Current Procedural Terminology" and "International Classification of Diseases" codes, with demographic and encounter data to classify individuals as case, control, or excluded. The algorithm was validated using blinded manual chart review at three eMERGE Network sites and one non-eMERGE Network site. Validation comprised evaluation of an equal number of predicted cases and controls selected at random from the algorithm predictions. After validation at the three eMERGE Network sites, the remaining eMERGE Network sites performed verification only. Finally, the algorithm was implemented as a workflow in the Konstanz Information Miner, which represented the logic graphically while retaining intermediate data for inspection at each node. The algorithm was configured to be independent of specific access to data and was exportable (without data) to other sites. RESULTS: The algorithm demonstrated positive predictive values (PPV) of 92.8% (CI: 86.8-96.7) and 100% (CI: 97.0-100) for cases and controls, respectively. It performed well also outside the eMERGE Network. Implementation of the transportable executable algorithm as a Konstanz Information Miner workflow required much less effort than implementation from pseudo code, and ensured that the logic was as intended. DISCUSSION AND CONCLUSION: This ePhenotyping algorithm identifies abdominal aortic aneurysm cases and controls from the electronic health record with high case and control PPV necessary for research purposes, can be disseminated easily, and applied to high-throughput genetic and other studies.

A Robust e-Epidemiology Tool in Phenotyping Heart Failure with Differentiation for Preserved and Reduced Ejection Fraction: the Electronic Medical Records and Genomics (eMERGE) Network.

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.

Cost-effectiveness of IL28Β genotype-guided protease inhibitor triple therapy versus standard of care treatment in patients with hepatitis C genotypes 2 or 3 infection.

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.