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Lookup NU author(s): Dr Claire WelshORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2016 Elsevier BV. Electronic medical records from first opinion equine veterinary practice may represent a unique resource for epidemiologic research. The appropriateness of this resource for risk factor analyses was explored as part of an investigation into clinical and pharmacologic risk factors for laminitis. Amalgamated medical records from seven UK practices were subjected to text mining to identify laminitis episodes, systemic or intra-synovial corticosteroid prescription, diseases known to affect laminitis risk and clinical signs or syndromes likely to lead to corticosteroid use. Cox proportional hazard models and Prentice, Williams, Peterson models for repeated events were used to estimate associations with time to first, or subsequent laminitis episodes, respectively. Over seventy percent of horses that were diagnosed with laminitis suffered at least one recurrence. Risk factors for first and subsequent laminitis episodes were found to vary. Corticosteroid use (prednisolone only) was only significantly associated with subsequent, and not initial laminitis episodes. Electronic medical record use for such analyses is plausible and offers important advantages over more traditional data sources. It does, however, pose challenges and limitations that must be taken into account, and requires a conceptual change to disease diagnosis which should be considered carefully.
Author(s): Welsh CE, Duz M, Parkin TDH, Marshall JF
Publication type: Article
Publication status: Published
Journal: Preventive Veterinary Medicine
Year: 2017
Volume: 136
Pages: 11-18
Print publication date: 01/01/2017
Online publication date: 22/11/2016
Acceptance date: 21/11/2016
Date deposited: 17/10/2019
ISSN (print): 0167-5877
ISSN (electronic): 1873-1716
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.prevetmed.2016.11.012
DOI: 10.1016/j.prevetmed.2016.11.012
PubMed id: 28010903
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