6
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Association between oral anticoagulants and osteoporosis: Real-world data mining using a multi-methodological approach

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Introduction: Warfarin and direct oral anticoagulants (DOACs) have been widely used in antithrombotic therapy. Although warfarin use has been suspected to be associated with osteoporosis risk, several studies have shown otherwise. Conversely, a few reports have found an association between DOACs and osteoporosis. This study therefore clarifies the association between oral anticoagulants and osteoporosis by analyzing real-world data using different methodologies, algorithms, and databases.

          Methods: Real-world data from the US Food and Drug Administration Adverse Event Reporting System (FAERS; 2004-2016) and Japanese administrative claims database (2005-2017; JMDC Inc., Tokyo) were used. Reporting odds ratio (ROR) and information component (IC) were calculated through disproportionality analysis (DPA) using reports recorded in the FAERS. Sequence symmetry analysis (SSA) was employed to calculate the adjusted sequence ratio (SR) using the JMDC Claims Database. For the adjusted SR and ROR, a significant signal was detected when the lower limit of the two-sided 95% confidence interval (CI) was more than 1. For the IC, a significant signal was detected when the lower limit of the 95% CI was more than 0.

          Results: DPA for warfarin found significant signals for osteoporosis in ROR (1.43, 95% CI: 1.32-1.54) and IC (0.50, 95% CI: 0.39-0.61). SSA showed a significant association between warfarin use and osteoporosis or bisphosphonate use. Moreover, a significant association was observed in males and females, albeit only for warfarin.

          Conclusion: Multi-methodological data mining revealed that warfarin use, not DOACs, is significantly associated with osteoporosis regardless of sex difference.

          Related collections

          Most cited references34

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Data Mining of the Public Version of the FDA Adverse Event Reporting System

          The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS, formerly AERS) is a database that contains information on adverse event and medication error reports submitted to the FDA. Besides those from manufacturers, reports can be submitted from health care professionals and the public. The original system was started in 1969, but since the last major revision in 1997, reporting has markedly increased. Data mining algorithms have been developed for the quantitative detection of signals from such a large database, where a signal means a statistical association between a drug and an adverse event or a drug-associated adverse event, including the proportional reporting ratio (PRR), the reporting odds ratio (ROR), the information component (IC), and the empirical Bayes geometric mean (EBGM). A survey of our previous reports suggested that the ROR provided the highest number of signals, and the EBGM the lowest. Additionally, an analysis of warfarin-, aspirin- and clopidogrel-associated adverse events suggested that all EBGM-based signals were included in the PRR-based signals, and also in the IC- or ROR-based ones, and that the PRR- and IC-based signals were in the ROR-based ones. In this article, the latest information on this area is summarized for future pharmacoepidemiological studies and/or pharmacovigilance analyses.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Japanese 2011 guidelines for prevention and treatment of osteoporosis—executive summary

            Introduction In 1998, the first Japanese practice guidelines on osteoporosis was published. It has been updated several times, with the most recent being the full-scale 2011 edition and its abridged edition. The present guidelines provide information for the managements of primary osteoporosis in postmenopausal women and men over 50 years old, a summary of the evidence for the treatment of secondary osteoporosis, and a summary of the evidence for the prevention of osteoporosis in younger people. Method The present Executive Summary is primarily based on the content of the 2011 Japanese abridged edition. One of the key changes is revision of the criteria for initiation of pharmacological treatment, along with an introduction of the fracture risk factors used in FRAX®. Key figures and tables were selected from the Japanese abridged edition and a reference list was added. Result and conclusions The essential points of the Japanese practice guidelines on osteoporosis were translated into English for the first time. It is hoped that the content of the guidelines becomes known throughout the world.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A Bayesian neural network method for adverse drug reaction signal generation.

              The database of adverse drug reactions (ADRs) held by the Uppsala Monitoring Centre on behalf of the 47 countries of the World Health Organization (WHO) Collaborating Programme for International Drug Monitoring contains nearly two million reports. It is the largest database of this sort in the world, and about 35,000 new reports are added quarterly. The task of trying to find new drug-ADR signals has been carried out by an expert panel, but with such a large volume of material the task is daunting. We have developed a flexible, automated procedure to find new signals with known probability difference from the background data. Data mining, using various computational approaches, has been applied in a variety of disciplines. A Bayesian confidence propagation neural network (BCPNN) has been developed which can manage large data sets, is robust in handling incomplete data, and may be used with complex variables. Using information theory, such a tool is ideal for finding drug-ADR combinations with other variables, which are highly associated compared to the generality of the stored data, or a section of the stored data. The method is transparent for easy checking and flexible for different kinds of search. Using the BCPNN, some time scan examples are given which show the power of the technique to find signals early (captopril-coughing) and to avoid false positives where a common drug and ADRs occur in the database (digoxin-acne; digoxin-rash). A routine application of the BCPNN to a quarterly update is also tested, showing that 1004 suspected drug-ADR combinations reached the 97.5% confidence level of difference from the generality. Of these, 307 were potentially serious ADRs, and of these 53 related to new drugs. Twelve of the latter were not recorded in the CD editions of The physician's Desk Reference or Martindale's Extra Pharmacopoea and did not appear in Reactions Weekly online. The results indicate that the BCPNN can be used in the detection of significant signals from the data set of the WHO Programme on International Drug Monitoring. The BCPNN will be an extremely useful adjunct to the expert assessment of very large numbers of spontaneously reported ADRs.
                Bookmark

                Author and article information

                Journal
                Int J Med Sci
                Int J Med Sci
                ijms
                International Journal of Medical Sciences
                Ivyspring International Publisher (Sydney )
                1449-1907
                2020
                4 February 2020
                : 17
                : 4
                : 471-479
                Affiliations
                [1 ]Division of Clinical Drug Informatics, School of Pharmacy, Kindai University, 3-4-1 Kowakae, Higashi-osaka, Osaka 577-8502, Japan
                [2 ]Department of Pharmacy, Kindai University Hospital, 377-2 Ohno-higashi, Osaka-Sayama, Osaka 589-8511, Japan
                Author notes
                ✉ Corresponding author: Satoshi Yokoyama, Ph. D., Division of Clinical Drug Informatics, School of Pharmacy, Kindai University, 3-4-1 Kowakae, Higashi-osaka, Osaka, 577-8502, Japan. Telephone number: +81-6-6721-2332; Fax number: +81-6-6730-1394; E-mail address: yokoyama@ 123456phar.kindai.ac.jp

                Competing Interests: The authors have declared that no competing interest exists.

                Article
                ijmsv17p0471
                10.7150/ijms.39523
                7053309
                32174777
                b665931d-6949-417a-aece-ac6fc8b3c357
                © The author(s)

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.

                History
                : 21 August 2019
                : 8 January 2020
                Categories
                Research Paper

                Medicine
                warfarin,direct oral anticoagulant,osteoporosis,disproportionality analysis,sequence symmetry analysis,data mining

                Comments

                Comment on this article