Secondary use of clinical data: the Vanderbilt approach.

The last decade has seen an exponential growth in the quantity of clinical data collected nationwide, triggering an increase in opportunities to reuse the data for biomedical research. The Vanderbilt research data warehouse framework consists of identified and de-identified clinical data repositories, fee-for-service custom services, and tools built atop the data layer to assist researchers across the enterprise. Providing resources dedicated to research initiatives benefits not only the research community, but also clinicians, patients and institutional leadership. This work provides a summary of our approach in the secondary use of clinical data for research domain, including a description of key components and a list of lessons learned, designed to assist others assembling similar services and infrastructure.

The use of a DNA biobank linked to electronic medical records to characterize pharmacogenomic predictors of tacrolimus dose requirement in kidney transplant recipients.

OBJECTIVE: Tacrolimus, an immunosuppressive drug widely prescribed in kidney transplantation, requires therapeutic drug monitoring due to its marked interindividual pharmacokinetic variability and narrow therapeutic index. Previous studies have established that CYP3A5 rs776746 is associated with tacrolimus clearance, blood concentration, and dose requirement. The importance of other drug absorption, distribution, metabolism, and elimination (ADME) gene variants has not been well characterized. METHODS: We used novel DNA biobank and electronic medical record resources to identify ADME variants associated with tacrolimus dose requirement. Broad ADME genotyping was performed on 446 kidney transplant recipients, who had been dosed to a steady state with tacrolimus. The cohort was obtained from Vanderbilt's DNA biobank, BioVU, which contains linked deidentified electronic medical record data. Genotyping included Affymetrix drug-metabolizing enzymes and transporters Plus (1936 polymorphisms), custom Sequenom Massarray iPLEX Gold assay (95 polymorphisms), and ancestry-informative markers. The primary outcome was tacrolimus dose requirement defined as blood concentration to dose ratio. RESULTS: In analyses, which adjusted for race and other clinical factors, we replicated the association of tacrolimus blood concentration to dose ratio with CYP3A5 rs776746 (P=7.15×10), and identified associations with nine variants in linkage disequilibrium with rs776746, including eight CYP3A4 variants. No NR1I2 variants were significantly associated. Age, weight, and hemoglobin were also significantly associated with the outcome. In final models, rs776746 explained 39% of variability in dose requirement and 46% was explained by the model containing clinical covariates. CONCLUSION: This study highlights the utility of DNA biobanks and electronic medical records for tacrolimus pharmacogenomic research.

A prognostic model based on readily available clinical data enriched a pre-emptive pharmacogenetic testing program.

OBJECTIVES: We describe the development, implementation, and evaluation of a model to pre-emptively select patients for genotyping based on medication exposure risk. STUDY DESIGN AND SETTING: Using deidentified electronic health records, we derived a prognostic model for the prescription of statins, warfarin, or clopidogrel. The model was implemented into a clinical decision support (CDS) tool to recommend pre-emptive genotyping for patients exceeding a prescription risk threshold. We evaluated the rule on an independent validation cohort and on an implementation cohort, representing the population in which the CDS tool was deployed. RESULTS: The model exhibited moderate discrimination with area under the receiver operator characteristic curves ranging from 0.68 to 0.75 at 1 and 2 years after index dates. Risk estimates tended to underestimate true risk. The cumulative incidences of medication prescriptions at 1 and 2 years were 0.35 and 0.48, respectively, among 1,673 patients flagged by the model. The cumulative incidences in the same number of randomly sampled subjects were 0.12 and 0.19, and in patients over 50 years with the highest body mass indices, they were 0.22 and 0.34. CONCLUSION: We demonstrate that prognostic algorithms can guide pre-emptive pharmacogenetic testing toward those likely to benefit from it.

A genome-wide association study of heparin-induced thrombocytopenia using an electronic medical record.

Heparin-induced thrombocytopenia (HIT) is an unpredictable, potentially catastrophic adverse effect of heparin treatment resulting from an immune response to platelet factor 4 (PF4)/heparin complexes. No genome-wide evaluations have been performed to identify potential genetic influences on HIT. Here, we performed a genome-wide association study (GWAS) and candidate gene study using HIT cases and controls identified using electronic medical records (EMRs) coupled to a DNA biobank and attempted to replicate GWAS associations in an independent cohort. We subsequently investigated influences of GWAS-associated single nucleotide polymorphisms (SNPs) on PF4/heparin antibodies in non-heparin treated individuals. In a recessive model, we observed significant SNP associations (odds ratio [OR] 18.52; 95% confidence interval [CI] 6.33-54.23; p=3.18×10(-9)) with HIT near the T-Cell Death-Associated Gene 8 (TDAG8). These SNPs are in linkage disequilibrium with a missense TDAG8 SNP. TDAG8 SNPs trended toward an association with HIT in replication analysis (OR 5.71; 0.47-69.22; p=0.17), and the missense SNP was associated with PF4/heparin antibody levels and positive PF4/heparin antibodies in non-heparin treated patients (OR 3.09; 1.14-8.13; p=0.02). In the candidate gene study, SNPs at HLA-DRA were nominally associated with HIT (OR 0.25; 0.15-0.44; p=2.06×10(-6)). Further study of TDAG8 and HLA-DRA SNPs is warranted to assess their influence on the risk of developing HIT.

Facilitating pharmacogenetic studies using electronic health records and natural-language processing: a case study of warfarin.

OBJECTIVE: DNA biobanks linked to comprehensive electronic health records systems are potentially powerful resources for pharmacogenetic studies. This study sought to develop natural-language-processing algorithms to extract drug-dose information from clinical text, and to assess the capabilities of such tools to automate the data-extraction process for pharmacogenetic studies. MATERIALS AND METHODS: A manually validated warfarin pharmacogenetic study identified a cohort of 1125 patients with a stable warfarin dose, in which 776 patients were managed by Coumadin Clinic physicians, and the remaining 349 patients were managed by their providers. The authors developed two algorithms to extract weekly warfarin doses from both data sets: a regular expression-based program for semistructured Coumadin Clinic notes; and an advanced weekly dose calculator based on an existing medication information extraction system (MedEx) for narrative providers' notes. The authors then conducted an association analysis between an automatically extracted stable weekly dose of warfarin and four genetic variants of VKORC1 and CYP2C9 genes. The performance of the weekly dose-extraction program was evaluated by comparing it with a gold standard containing manually curated weekly doses. Precision, recall, F-measure, and overall accuracy were reported. Associations between known variants in VKORC1 and CYP2C9 and warfarin stable weekly dose were performed with linear regression adjusted for age, gender, and body mass index. RESULTS: The authors' evaluation showed that the MedEx-based system could determine patients' warfarin weekly doses with 99.7% recall, 90.8% precision, and 93.8% accuracy. Using the automatically extracted weekly doses of warfarin, the authors successfully replicated the previous known associations between warfarin stable dose and genetic variants in VKORC1 and CYP2C9.

An automated approach to calculating the daily dose of tacrolimus in electronic health records.

Clinical research often requires extracting detailed drug information, such as medication names and dosages, from Electronic Health Records (EHR). Since medication information is often recorded as both structured and unstructured formats in the EHR, extracting all the relevant drug mentions and determining the daily dose of a medication for a selected patient at a given date can be a challenging and time-consuming task. In this paper, we present an automated approach using natural language processing to calculate daily doses of medications mentioned in clinical text, using tacrolimus as a test case. We evaluated this method using data sets from four different types of unstructured clinical data. Our results showed that the system achieved precisions of 0.90-1.00 and recalls of 0.81-1.00.

Cruella: developing a scalable tissue microarray data management system.

CONTEXT: Compared with DNA microarray technology, relatively little information is available concerning the special requirements, design influences, and implementation strategies of data systems for tissue microarray technology. These issues include the requirement to accommodate new and different data elements for each new project as well as the need to interact with pre-existing models for clinical, biological, and specimen-related data. OBJECTIVE: To design and implement a flexible, scalable tissue microarray data storage and management system that could accommodate information regarding different disease types and different clinical investigators, and different clinical investigation questions, all of which could potentially contribute unforeseen data types that require dynamic integration with existing data. DESIGN: The unpredictability of the data elements combined with the novelty of automated analysis algorithms and controlled vocabulary standards in this area require flexible designs and practical decisions. Our design includes a custom Java-based persistence layer to mediate and facilitate interaction with an object-relational database model and a novel database schema. User interaction is provided through a Java Servlet-based Web interface. RESULTS: Cruella has become an indispensable resource and is used by dozens of researchers every day. The system stores millions of experimental values covering more than 300 biological markers and more than 30 disease types. The experimental data are merged with clinical data that has been aggregated from multiple sources and is available to the researchers for management, analysis, and export. CONCLUSION: Cruella addresses many of the special considerations for managing tissue microarray experimental data and the associated clinical information. A metadata-driven approach provides a practical solution to many of the unique issues inherent in tissue microarray research, and allows relatively straightforward interoperability with and accommodation of new data models.