Dataset Information

Unravelling the health status of brachycephalic dogs in the UK using multivariable analysis.

ABSTRACT: Brachycephalic dog breeds are regularly asserted as being less healthy than non-brachycephalic breeds. Using primary-care veterinary clinical data, this study aimed to identify predispositions and protections in brachycephalic dogs and explore differing inferences between univariable and multivariable results. All disorders during 2016 were extracted from a random sample of 22,333 dogs within the VetCompass Programme from a sampling frame of 955,554 dogs under UK veterinary care in 2016. Univariable and multivariable binary logistic regression modelling explored brachycephaly as a risk factor for each of a series of common disorders. Brachycephalic dogs were younger, lighter and less likely to be neutered than mesocephalic, dolichocephalic and crossbred dogs. Brachycephalic differed to non-brachycephalic types in their odds for 10/30 (33.33%) common disorders. Of these, brachycephalic types were predisposed for eight disorders and were protected for two disorders. Univariable and multivariable analyses generated differing inference for 11/30 (30.67%) disorders. This study provides strong evidence that brachycephalic breeds are generally less healthy than their non-brachycephalic counterparts. Results from studies that report only univariable methods should be treated with extreme caution due to potential confounding effects that have not been accounted for during univariable study design or analysis.

SUBMITTER: O'Neill DG

PROVIDER: S-EPMC7560694 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Unravelling the health status of brachycephalic dogs in the UK using multivariable analysis.

O'Neill D G DG Pegram C C Crocker P P Brodbelt D C DC Church D B DB Packer R M A RMA

Scientific reports 20201014 1

Brachycephalic dog breeds are regularly asserted as being less healthy than non-brachycephalic breeds. Using primary-care veterinary clinical data, this study aimed to identify predispositions and protections in brachycephalic dogs and explore differing inferences between univariable and multivariable results. All disorders during 2016 were extracted from a random sample of 22,333 dogs within the VetCompass Programme from a sampling frame of 955,554 dogs under UK veterinary care in 2016. Univari ...[more]

PMID: 33057051

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Project description:A key goal of disease surveillance is to identify outbreaks of known or novel diseases in a timely manner. Such an outbreak occurred in the UK associated with acute vomiting in dogs between December 2019 and March 2020. We tracked this outbreak using the clinical free text component of anonymised electronic health records (EHRs) collected from a sentinel network of participating veterinary practices. We sourced the free text (narrative) component of each EHR supplemented with one of 10 practitioner-derived main presenting complaints (MPCs), with the 'gastroenteric' MPC identifying cases involved in the disease outbreak. Such clinician-derived annotation systems can suffer from poor compliance requiring retrospective, often manual, coding, thereby limiting real-time usability, especially where an outbreak of a novel disease might not present clinically as a currently recognised syndrome or MPC. Here, we investigate the use of an unsupervised method of EHR annotation using latent Dirichlet allocation topic-modelling to identify topics inherent within the clinical narrative component of EHRs. The model comprised 30 topics which were used to annotate EHRs spanning the natural disease outbreak and investigate whether any given topic might mirror the outbreak time-course. Narratives were annotated using the Gensim Library LdaModel module for the topic best representing the text within them. Counts for narratives labelled with one of the topics significantly matched the disease outbreak based on the practitioner-derived 'gastroenteric' MPC (Spearman correlation 0.978); no other topics showed a similar time course. Using artificially injected outbreaks, it was possible to see other topics that would match other MPCs including respiratory disease. The underlying topics were readily evaluated using simple word-cloud representations and using a freely available package (LDAVis) providing rapid insight into the clinical basis of each topic. This work clearly shows that unsupervised record annotation using topic modelling linked to simple text visualisations can provide an easily interrogable method to identify and characterise outbreaks and other anomalies of known and previously un-characterised diseases based on changes in clinical narratives.

Dataset Information

Unravelling the health status of brachycephalic dogs in the UK using multivariable analysis.

Publications

Unravelling the health status of brachycephalic dogs in the UK using multivariable analysis.

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