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                Data Mining

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          Data Mining at the Center for Veterinary MedicineData Mining Data Mining at the Center for Veterinary Medicine
  
  	
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    PrintThe goal of a veterinary pharmacovigilance program is to ensure the continued safety and effectiveness of animal drugs once they are being used in a wide and diverse population of animals. Current Activities The Adverse Drug Event (ADE) Review team is primarily responsible for ADE surveillance related to pharmaceutical products approved for use in dogs and cats (including antiparasitics, endectocides, hormones, oncology products).  In addition, we are responsible for international standardization and harmonization activities related to ADE reporting   Current data mining activities include: Incorporation of data mining into routine periodic surveillance of newly marketed products, as well as products that have been on the market for longer periods of time.     Investigation into performance of data mining algorithms within CVM database for cats and dogs.     Investigation into the effects of limiting denominator products by pharmaceutical classification.     Investigation into the effects of limiting denominator signs by excluding reports indicating only lack of efficacy.     Ongoing communications with other regulatory authorities about their experiences with utilizing disproportionality analysis as a surveillance tool for adverse events.     Maintaining and enhancing clinical sign and product problem code lists (including hierarchal structure) for use in data mining efforts.The Adverse Event Review Team is responsible for post-market ADE surveillance and evaluation for approved products used in large and food animal species, which include cattle, pigs, horses, poultry and fish.  Additionally, this team monitors and evaluates ADEs for unapproved products (including devices) used in animals, including compounded drugs. The team also performs safety surveillance and evaluation of adverse events submitted to FDA for pet foods. **Herd considerations** – a unique aspect of Veterinary ADE reporting for food animal species (typically cattle, pigs, and poultry but can even include fish) is that individual ADE reports will often involve many affected animals on a farm or from a herd. Animal numbers per report can vary widely for food animal species; single reports may contain only one reacting animal, or could involve hundreds or many thousands of affected animals, and there is often a wide range of between reports for the same product. Data mining runs can be set to calculate results based on number of cases (reports) or number of animals affected; however, this issue presents an ongoing challenge to development of the best approaches for these large animal products and to the subsequent interpretation of results.     **EBGM challenges** – Inconsistencies in data mining results have occasionally been obtained with the use of the EBGM in the *PVAnalyser* software program used at CVM, especially when used for herd animal data, somewhat limiting its usefulness. At this time, the PRR has been used most consistently and is performing well.     **Current activities and future plans** – Data mining runs for safety signal detection are performed within animal species, and typically by pharmaceutical class to help reduce run times, which can be quite long. Runs are being developed to support ongoing safety signal detection and evaluation for all HFV-242 products on a regular basis. The wide variety of approved products for large animal species presents unique challenges to the development of successful large animal signal detection strategies.The Data Management and Analysis team is responsible for ADE surveillance related to pharmaceutical products approved for use in dogs and cats (including antibiotics, gastrointestinal, renal and urinary tract agents, non-steroidal anti-inflammatory agents and analgesics). Also, monitor 3 Day FAR reports, 1932a and voluntary reports to CVM from other Centers as well as monitors and evaluates medication errors and product defects. Using data mining in drug product reviews primarily of newer products     Evaluating and comparing use of various denominator groups in data mining runs. We generally break down at least by species but are looking at pharmaceutical class, and considering ingredient when multiple products, including generics, are involved.     Evaluating different alert level options; PRR, PRR and chi square, etc     Investigating possible uses for examining medication errors/product defect issuesReferences related to Veterinary Pharmacovigilance European Medicine Agency (EMA) Guidelines related to signal detection and signal management     US FDA CVM guidance documents describing the Data Elements and Code Lists for Submission of Veterinary Adverse Event Reports     GFI # 188 Data Elements for Submission of Veterinary Adverse Event Reports to the Center for Veterinary Medicine         GFI #143 Pharmacovigilance of Veterinary Medicinal Products: Controlled List of Terms     VeDDRA Terms: The VeDDRA Vocabulary is the VICH-harmonized list for submission of adverse event terms. VeDDRA medical terminology describes adverse clinical manifestations. The most current list, as well as explanation and Guidance Notes on the Use of VeDDRA Terminology for Reporting Adverse Events in Animal, can be downloaded from the European Medicines Agency Website     CVM has a list of additional terms used internally to capture terms describing product defects, medication errors and adverse clinical manifestations for which no term exists in VeDDRA. Examples include laboratory and pathology terms, as well as more specific types of reported lack of expected efficacy (LOEE).  The current list is available under the supporting documents section of CVM’sVeterinary Adverse Event Reporting for Manufacturers webpage     Book Chapter:     Bright-Ponte, Susan J. and Lee Anne Myers Palmer. "Reporting Adverse Events to the Food and Drug Administration - Center for Veterinary Medicine." Bonagura, John D. and David C. Twedt. Kirk's Current Veterinary Therapy XV. St. Louis: Elsevier, 2014. e35 - e42. Print.

                                                                                  Content current as of:
      03/27/2018Data Mining
 
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