People: Camilla Pegram, Dan O'Neill, David Brodbelt, Ruby Chang, Noel Kennedy

Vet Compass Project Type: Dog

Project name
VetCompass eClinical Trials (VETs) – Generating Interventional Evidence from Observational Data

Project intro
The study aims to develop innovative statistical approaches to veterinary electronic patient records to evaluate the effectiveness of clinical interventions in dogs.

Camilla Pegram, Dan O’Neill, Karla Diaz-Ordaz, David Brodbelt, Ruby Chang, Noel Kennedy, David Church

Veterinarians are encouraged to apply ‘evidence based principles’ but the paucity of relevant published evidence on veterinary interventions has limited the current clinical welfare gains for dogs (1). Evidence based medicine that reports the clinical effects of therapy relies heavily on randomised control trials that are invariably complex, slow, expensive and frequently ethically challenged.
The RVC has invested substantial resources over the past decade into companion animal health surveillance via the VetCompass Programme (2). Routinely collected data are collected daily from over 1800 UK practices across the UK. VetCompass disorder-specific studies have reported on disorder occurrence, clinical management and outcomes on disorders including heat stroke (3), demodicosis (4), lipoma (5), Cushing’s syndrome (6, 7), dystocia (8, 9) and corneal ulceration (10). VetCompass research is ongoing to apply machine learning techniques to automate data extraction and develop prediction tools for VetCompass data (11).
However, to date, inference from veterinary electronic health records initiatives has been limited to exploring associations rather than causality between interventions and outcomes. To circumvent similar constraints in human medicine, the US FDA has promoted the use of electronic records data in clinical investigations to support ‘real world evidence’ (U.S. Department of Health and Human Services et al., 2018).

This project aims to develop and apply novel causal inference methods that evaluate real world interventions via routinely collected veterinary EPRs. These methods will be applied to VetCompass data to provide real world inference for some key interventions.
1. Extend and adapt novel causal inference and statistical methods for existing clinical data for Virtual eClinical Trials (VETs) to generate evidence comparable to RCT studies.
2. Estimate and compare the clinical effectiveness of real-world interventions in 4 conditions with welfare importance in dogs in the UK: osteoarthritis, otitis externa, chronic diarrhoea and cruciate ligament rupture.
3. Train and develop a companion animal epidemiologist in EPR analysis and to use these novel causal inferential methods.
This project will harness the VetCompass Big Data resource of de-identified electronic patient record (EPR) data from ~ 7.5 million UK dogs. VetCompass clinical data include demographics such as breed, sex, neuter, date of birth and bodyweight. Over 200 million free-text clinical notes hold detailed information on disorder diagnoses, clinical reasoning, diagnostic testing, co-therapy and clinical outcomes. Additionally, VetCompass holds detailed treatments information on over half a billion treatments including dosages and dates on administered or sold for every episode of veterinary care including on-site consultations and surgical procedures as well as telephone and other remote interactions, house visits and over-the-counter sales.

The novel results from these studies offer immediate welfare impacts by improving the clinical decisions made by veterinary professionals for common conditions of dogs.
Benefits from the proposed project to canine welfare
1. This work supports the goals of replacement, refinement and reduction of the use of dogs in research (12).
2. The evidential outputs from this study will have immediate application in veterinary medicine to improve the treatment of common canine conditions and the welfare of affected dogs.
3. This study will develop and apply novel scientific methods that will lead the world in this area of research and provide a strong foundation for future eClinical trials.
4. This project offers a unique opportunity for welfare and technological gains from existing observational veterinary clinical data.

We thank Dogs Trust for funding this project

1. Dean RS. Veterinary clinical trials are on trial. Veterinary Record. 2017;181(8):193.
2. VetCompass. VetCompass™ Programme London: RVC Electronic Media Unit; 2020 [Available from:
3. Hall EJ, Carter AJ, O'Neill DG. Incidence and risk factors for heat-related illness (heatstroke) in UK dogs under primary veterinary care in 2016. Scientific Reports. 2020;10(1):9128.
4. O'Neill DG, Turgoose E, Church DB, Brodbelt DC, Hendricks A. Juvenile-onset and adult-onset demodicosis in dogs in the UK: prevalence and breed associations. Journal of Small Animal Practice. 2020;61(May 2020):325.
5. O'Neill DG, Corah CH, Church DB, Brodbelt DC, Rutherford L. Lipoma in dogs under primary veterinary care in the UK: prevalence and breed associations. Canine Genetics and Epidemiology. 2018;5(1):9.
6. O'Neill DG, Scudder C, Faire JM, Church DB, McGreevy PD, Thomson PC, et al. Epidemiology of hyperadrenocorticism among 210,824 dogs attending primary-care veterinary practices in the UK from 2009 to 2014. Journal of Small Animal Practice. 2016;57(7):365-73.
7. Schofield I, Brodbelt DC, Wilson ARL, Niessen S, Church D, O'Neill D. Survival analysis of 219 dogs with hyperadrenocorticism attending primary care practice in England. Veterinary Record. 2019:vetrec-2018-105159.
8. O'Neill DG, O’Sullivan AM, Manson EA, Church DB, McGreevy PD, Boag AK, et al. Canine dystocia in 50 UK first-opinion emergency care veterinary practices: clinical management and outcomes. Veterinary Record. 2019;184:409.
9. O'Neill DG, O'Sullivan AM, Manson EA, Church DB, Boag AK, McGreevy PD, et al. Canine dystocia in 50 UK first-opinion emergency-care veterinary practices: prevalence and risk factors. Veterinary Record. 2017;181(4).
10. O'Neill DG, Lee MM, Brodbelt DC, Church DB, Sanchez RF. Corneal ulcerative disease in dogs under primary veterinary care in England: epidemiology and clinical management. Canine Genetics and Epidemiology. 2017;4(1):5.
11. Kennedy N, Brodbelt DC, Church DB, O'Neill DG. Detecting false-positive disease references in veterinary clinical notes without manual annotations. npj Digital Medicine. 2019;2(1):33.
12. NC3Rs. National Centre for the Replacement, Refinement and Reduction of Animals in Research: NC3Rs.; 2020 [Available from:

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