The A B C and Ps of Biomedical/Translational Research

1. The A B C and Ps of Biomedical/Translational Research and the Case for an Integrated Data Repository* (IDR) 1Ketty Mobed, PhD MSPH, 2Mark Weiner, MD, 1Rob Wynden, BSCS 1University of California, San Francisco, CA, 2University of Pennsylvania, Philadelphia, PA • Introduction • Time is of the essence when conducting biomedical / translational research. • Biomedical / translational research is comprised of different research arenas, but often with similar research methodologies and study flows and procedures. • A biomedical / translational investigator can greatly benefit from having access to a comprehensive centralized data and information mart to significantly shorten the start-to-finish project period. • A biomedical / translational integrated data repository (IDR) with both the ability to safeguard protected health information, as well as to easily construct a study cohort or extract detailed aggregate health information will enhance research productivity. Workflows • Use Case Examples • Animal Research: Using a Mouse Model for the Evaluation of Pathogenesis and Immunity to Specific Influenza Virus Strains Isolated from Humans • For this time-sensitive study, in order to infect mice with viable influenza virus strains, the investigator gets notified by IDR of the date and the clinic lab location where newly positive influenza isolates are stored and available. • Bench Research: Comparison of Genetic Ancestry and Genetic Markers of Small Cell and Non-Small Cell Lung Cancer Patients • Assuming that data of a retrospective lung cancer study, including lab location and storage information on collected blood or sputum is available in the IDR, study subjects’ study ID numbers and histology codes can be extracted from the study data and linked to the lab database to identify lab numbers linked to the stored blood or saliva samples. Based on the lab id number, the correct frozen blood specimen can be pulled and genetically analyzed and linked back again to subjects’ individual data for further analyses. • Clinical Research: A Randomized Clinical Trial to Compare the Therapeutic Effects of Combination Drugs and Singly Dispensed Drugs in Post-Coronary Angioplasty Patients • Projected as a prospective clinical trial over a 12-month period, patients who are either scheduled or just have undergone coronary angioplasty will be identified through IDR and patient information such as date and time of surgery, patient identifiers such as DOB, contact information, surgeon name and other specified demographic data will be extracted and compiled from clinic files housed in IDR and forwarded to the investigator on a periodic basis. • Population Research: Prenatal Care, Delivery Procedure, Length of Post-Partum Stay, Health Insurance, and Demographic Characteristics at Time of Delivery in Three In-system Hospitals in California • Retrospective and de-identified, but individual information on prenatal care frequency, delivery method, duration of post-partum hospital stay, health insurance, and demographic characteristics are extracted and compiled from the 3 in-system California-based hospitals’ patient databases and compared to the publically available California OSHPD data. Workflow 1. Biomedical/translational research can be classified into four major research arenas: Animal Research (AR), Bench Research (BR), Clinical Research (CR), and Population Research (PR). All exhibit similar paths of research actions and processes. • Background • Electronic data collection forms and access to biomedical data are increasingly a requirement for the successful conduct of biomedical / translational research [1,2]. • Biomedical / translational research is increasingly multi-disciplinary and collaborative to find more efficient solutions to impact the continuum of health care from the public health or community level to the clinic level to prevent diseases and improve health outcomes [3,4,5]. • The architecture of a practical and versatile biomedical / translational data warehouse, such as an IDR depends on accurate and detailed knowledge of the required flows and processes in biomedical research and the researcher, which have been reviewed in other articles [6,7,8]. Workflow 2. The continuum of processes from pre-research to post-research is illustrated here and shows how a researcher would make use of an available IDR. • Conclusion • Biomedical/translational research is a continuum and its researchers have distinct, yet similar requirements when implementing a study. • A well defined and inclusive IDR can be of great benefit to biomedical/translational researchers, substantially decreasing the start-to-finish project time as well as streamlining needed resources to conduct health research. • Goals • To describe in a clear yet comprehensive way, using a workflow, the distinct research arenas, i.e. Animal, Bench, Clinical and Population (ABC and Ps), and research requirements which make up the continuum of biomedical/translational research. (Workflow 1.) • To present common investigator related study activities and their potential interactions with an available IDR. (Workflow 2.) • To illustrate the components and research processes of an optimal IDR, as is currently being implemented at UCSF. (Workflow 3.) • To illustrate by means of use case examples how a biomedical/translational researcher can productively use an IDR. References 1. Shortliffe EH, Sondik EJ. The public health informatics infrastructure: anticipating its role in cancer. Cancer Causes Control. 2006 Sep;17(7):861-9. 2. Shortliffe EH, Perreault LE (eds). Medical Informatics: Computer Applications in Health Care and Biomedicine. Springer Pubs. 2001. 3. Khoury MJ, Gwinn M, Yoon PW, Dowling N, Moore CA, Bradley L. The continuum of translation research in genomic medicine: how can we accelerate the appropriate integration of human genome discoveries into health care and disease prevention? Genet Med. 2007 Oct;9(10):665-74. Review. 4. Kuhn KA, Knoll A, Mewes HW, Schwaiger M, Bode A, Broy M, et al. Informatics and medicine--from molecules to populations. Methods Inf Med. 2008;47(4):283-95. 5. Kulikowski CA. Introducing Kuhn et al.'s paper "Informatics and medicine: from molecules to populations" and invited papers on this special topic. Methods Inf Med. 2008;47(4):279-82. 6. Goble C, Stevens R, Hull D, Wolstencroft K, Lopez R. Data curation + process curation=data integration + science. Brief Bioinform. 2008 Nov;9(6):506-17. Epub 2008 Dec 6. 7. Stevens R, Goble C, Baker P, Brass A. A classification of tasks in bioinformatics. Bioinformatics. 2001 Feb;17(2):180-8. 8. Hull D, Wolstencroft K, Stevens R, Goble C, Pocock MR, Li P, Oinn T. Taverna: a tool for building and running workflows of services. Nucleic Acids Res. 2006 Jul 1;34 (Web Server issue):W729-32. Workflow 3. How the IDR is populated and how a researcher can access and obtain the data is shown in this workflow. *Supported and funded by UCSF and CTSA Grant #1U54RR023566-01