Biobanks and Electronic Medical Records: Enabling Cost-Effective Research

Bowton, Erica; Field, Julie R.; Wang, Sunny; Schildcrout, Jonathan S.; Driest, Sara L. Van; Delaney, Jessica; Cowan, James D.; Weeke, Peter; Mosley, Jonathan D.; Wells, Quinn S.; Karnes, Jason H.; Shaffer, Christian M.; Peterson, Josh F.; Denny, Joshua C.; Roden, Dan M.; Pulley, Jill M.

doi:10.1126/scitranslmed.3008604

Cited by 125 publications

(115 citation statements)

References 35 publications

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“…The coding definition excluded two ICD-9-CM groups: 'nephritis, nephrotic syndrome and nephrosis' (580-589) and 'other diseases of urinary system' (590-599). However, a substantial proportion (11.2%) of patients in this study had undergone a kidney transplant [45,56]. Because of the strong correlation between renal osteodystrophy diagnoses and kidney transplant in this dataset (r ϕ = 0.82, p > 10 -20 ), we retested the association between SLC15A2 rs1143672 and renal osteodystrophy after adjustment for kidney transplant status and found that the association was not significant (p = 0.22).…”

Section: Novel Associations Among European-americansmentioning

confidence: 99%

“…The clinical characteristics of the study population are shown in Table 1. A total of 7266 DNA samples were genotyped using the Illumina ® ADME Core Panel, which has been used for previous pharmacogenetic studies in BioVU (Supplementary Table 1) [45]. After quality control (see 'Methods'), the majority of the samples were European-American (n = 6067) and the remainder African-American (n = 762).…”

Section: Study Populationmentioning

confidence: 99%

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Evidence for Extensive Pleiotropy Among Pharmacogenes

et al. 2016

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Section: Novel Associations Among European-americansmentioning

confidence: 99%

Section: Study Populationmentioning

confidence: 99%

Evidence for Extensive Pleiotropy Among Pharmacogenes

et al. 2016

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“…Using automated phenotyping algorithms, investigators have identified cases and controls for diseases of interest to replicate known phenotype-genotype associations and make novel discoveries, [12][13][14][15][16][17] potentially with decreased cost 18 and faster execution than traditional trials.…”

Section: Background and Significancementioning

confidence: 99%

Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals

Teixeira

Wei

Cronin

et al. 2016

Journal of the American Medical Informatics Association

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Objective: Phenotyping algorithms applied to electronic health record (EHR) data enable investigators to identify large cohorts for clinical and genomic research. Algorithm development is often iterative, depends on fallible investigator intuition, and is time-and labor-intensive. We developed and evaluated 4 types of phenotyping algorithms and categories of EHR information to identify hypertensive individuals and controls and provide a portable module for implementation at other sites. Materials and Methods: We reviewed the EHRs of 631 individuals followed at Vanderbilt for hypertension status. We developed features and phenotyping algorithms of increasing complexity. Input categories included International Classification of Diseases, Ninth Revision (ICD9) codes, medications, vital signs, narrative-text search results, and Unified Medical Language System (UMLS) concepts extracted using natural language processing (NLP). We developed a module and tested portability by replicating 10 of the best-performing algorithms at the Marshfield Clinic. Results: Random forests using billing codes, medications, vitals, and concepts had the best performance with a median area under the receiver operator characteristic curve (AUC) of 0.976. Normalized sums of all 4 categories also performed well (0.959 AUC). The best non-NLP algorithm combined normalized ICD9 codes, medications, and blood pressure readings with a median AUC of 0.948. Blood pressure cutoffs or ICD9 code counts alone had AUCs of 0.854 and 0.908, respectively. Marshfield Clinic results were similar. Conclusion: This work shows that billing codes or blood pressure readings alone yield good hypertension classification performance. However, even simple combinations of input categories improve performance. The most complex algorithms classified hypertension with excellent recall and precision.

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“…The combination of a highvolume clinical center and established EMRs allows investigators to identify large cohorts for most clinical phenotypes. Bowton et al 32 recently showed that the median cohort size for 28 validated phenotypes in Vanderbilt's Synthetic Derivative was 1,123 (interquartile range [IQR]: 492-4,158) subjects, compared with 623 (IQR: 273-2,095) subjects in similar cohort studies funded by the National Institutes of Health. Moreover, the median time to construct the EMR-based cohorts was 3 months, compared with 3 years for NIHfunded cohorts.…”

Section: Emr-based Cohorts Versus Traditional Prospective Cohortsmentioning

confidence: 99%

Integration of Complex Data Sources to Provide Biologic Insight into Pulmonary Vascular Disease (2015 Grover Conference Series)

Brittain

Chan

2016

Pulm. circ.

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The application of complex data sources to pulmonary vascular diseases is an emerging and promising area of investigation. The use of -omics platforms, in silico modeling of gene networks, and linkage of large human cohorts with DNA biobanks are beginning to bear biologic insight into pulmonary hypertension. These approaches to high-throughput molecular phenotyping offer the possibility of discovering new therapeutic targets and identifying variability in response to therapy that can be leveraged to improve clinical care. Optimizing the methods for analyzing complex data sources and accruing large, well-phenotyped human cohorts linked to biologic data remain significant challenges. Here, we discuss two specific types of complex data sources-gene regulatory networks and DNA-linked electronic medical record cohorts-that illustrate the promise, challenges, and current limitations of these approaches to understanding and managing pulmonary vascular disease.

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Biobanks and Electronic Medical Records: Enabling Cost-Effective Research

Cited by 125 publications

References 35 publications

Evidence for Extensive Pleiotropy Among Pharmacogenes

Evidence for Extensive Pleiotropy Among Pharmacogenes

Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals

Integration of Complex Data Sources to Provide Biologic Insight into Pulmonary Vascular Disease (2015 Grover Conference Series)

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