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      Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality

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          Abstract

          Objectives Drug repurposing, which finds new indications for existing drugs, has received great attention recently. The goal of our work is to assess the feasibility of using electronic health records (EHRs) and automated informatics methods to efficiently validate a recent drug repurposing association of metformin with reduced cancer mortality.

          Methods By linking two large EHRs from Vanderbilt University Medical Center and Mayo Clinic to their tumor registries, we constructed a cohort including 32 415 adults with a cancer diagnosis at Vanderbilt and 79 258 cancer patients at Mayo from 1995 to 2010. Using automated informatics methods, we further identified type 2 diabetes patients within the cancer cohort and determined their drug exposure information, as well as other covariates such as smoking status. We then estimated HRs for all-cause mortality and their associated 95% CIs using stratified Cox proportional hazard models. HRs were estimated according to metformin exposure, adjusted for age at diagnosis, sex, race, body mass index, tobacco use, insulin use, cancer type, and non-cancer Charlson comorbidity index.

          Results Among all Vanderbilt cancer patients, metformin was associated with a 22% decrease in overall mortality compared to other oral hypoglycemic medications (HR 0.78; 95% CI 0.69 to 0.88) and with a 39% decrease compared to type 2 diabetes patients on insulin only (HR 0.61; 95% CI 0.50 to 0.73). Diabetic patients on metformin also had a 23% improved survival compared with non-diabetic patients (HR 0.77; 95% CI 0.71 to 0.85). These associations were replicated using the Mayo Clinic EHR data. Many site-specific cancers including breast, colorectal, lung, and prostate demonstrated reduced mortality with metformin use in at least one EHR.

          Conclusions EHR data suggested that the use of metformin was associated with decreased mortality after a cancer diagnosis compared with diabetic and non-diabetic cancer patients not on metformin, indicating its potential as a chemotherapeutic regimen. This study serves as a model for robust and inexpensive validation studies for drug repurposing signals using EHR data.

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          Most cited references43

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          Large Scale Prediction and Testing of Drug Activity on Side-Effect Targets

          Summary Discovering the unintended “off-targets” that predict adverse drug reactions (ADRs) is daunting by empirical methods alone. Drugs can act on multiple protein targets, some of which can be unrelated by traditional molecular metrics, and hundreds of proteins have been implicated in side effects. We therefore explored a computational strategy to predict the activity of 656 marketed drugs on 73 unintended “side effect” targets. Approximately half of the predictions were confirmed, either from proprietary databases unknown to the method or by new experimental assays. Affinities for these new off-targets ranged from 1 nM to 30 μM. To explore relevance, we developed an association metric to prioritize those new off-targets that explained side effects better than any known target of a given drug, creating a Drug-Target-ADR network. Among these new associations was the prediction that the abdominal pain side effect of the synthetic estrogen chlorotrianisene was mediated through its newly discovered inhibition of the enzyme COX-1. The clinical relevance of this inhibition was borne-out in whole human blood platelet aggregation assays. This approach may have wide application to de-risking toxicological liabilities in drug discovery.
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            Use of electronic health records in U.S. hospitals.

            Despite a consensus that the use of health information technology should lead to more efficient, safer, and higher-quality care, there are no reliable estimates of the prevalence of adoption of electronic health records in U.S. hospitals. We surveyed all acute care hospitals that are members of the American Hospital Association for the presence of specific electronic-record functionalities. Using a definition of electronic health records based on expert consensus, we determined the proportion of hospitals that had such systems in their clinical areas. We also examined the relationship of adoption of electronic health records to specific hospital characteristics and factors that were reported to be barriers to or facilitators of adoption. On the basis of responses from 63.1% of hospitals surveyed, only 1.5% of U.S. hospitals have a comprehensive electronic-records system (i.e., present in all clinical units), and an additional 7.6% have a basic system (i.e., present in at least one clinical unit). Computerized provider-order entry for medications has been implemented in only 17% of hospitals. Larger hospitals, those located in urban areas, and teaching hospitals were more likely to have electronic-records systems. Respondents cited capital requirements and high maintenance costs as the primary barriers to implementation, although hospitals with electronic-records systems were less likely to cite these barriers than hospitals without such systems. The very low levels of adoption of electronic health records in U.S. hospitals suggest that policymakers face substantial obstacles to the achievement of health care performance goals that depend on health information technology. A policy strategy focused on financial support, interoperability, and training of technical support staff may be necessary to spur adoption of electronic-records systems in U.S. hospitals. 2009 Massachusetts Medical Society
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              Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetes.

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                Author and article information

                Journal
                J Am Med Inform Assoc
                J Am Med Inform Assoc
                jamia
                jaminfo
                Journal of the American Medical Informatics Association : JAMIA
                Oxford University Press
                1067-5027
                1527-974X
                January 2015
                22 July 2014
                : 22
                : 1
                : 179-191
                Affiliations
                1The University of Texas School of Biomedical Informatics at Houston, Houston, Texas, USA
                2Department of Thoracic Surgery, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
                3Division of Epidemiology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
                4Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
                5Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
                6Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
                7Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
                8Department of Biomedical Informatics, Columbia University, New York, New York, USA
                9Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
                Author notes
                Correspondence to Dr Hua Xu, The University of Texas School of Biomedical Informatics at Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, USA; hua.xu@ 123456uth.tmc.edu

                MCA and HX contributed equally to the work.

                Article
                amiajnl-2014-002649
                10.1136/amiajnl-2014-002649
                4433365
                25053577
                dd4acdb8-b938-42b5-b706-789bb8db1f20
                © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.comFor numbered affiliations see end of article.

                History
                : 15 January 2014
                : 10 June 2014
                : 3 July 2014
                Page count
                Pages: 13
                Categories
                Research and Applications

                Bioinformatics & Computational biology
                drug repurposing,electronic health records,natural language processing,metformin

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