Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice

Schofield, Imogen; Brodbelt, David C.; Kennedy, Noel; Niessen, Stijn J. M.; Church, David B.; Geddes, Rebecca F.; O’Neill, Dan G.

Abstract

Cushing’s syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing’s syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-learning algorithms were applied to predict a future diagnosis of Cushing's syndrome, using structured clinical data from the VetCompass programme in the UK. Dogs suspected of having Cushing's syndrome were included in the analysis and classified based on their final reported diagnosis within their clinical records. Demographic and clinical features available at the point of first suspicion by the attending veterinarian were included within the models. The machine-learning methods were able to classify the recorded Cushing’s syndrome diagnoses, with good predictive performance. The LASSO penalised regression model indicated the best overall performance when applied to the test set with an AUROC = 0.85 (95% CI 0.80–0.89), sensitivity = 0.71, specificity = 0.82, PPV = 0.75 and NPV = 0.78. The findings of our study indicate that machine-learning methods could predict the future diagnosis of a practicing veterinarian. New approaches using these methods could support clinical decision-making and contribute to improved diagnosis of Cushing’s syndrome in dogs.

Citation

Schofield, I., Brodbelt, D. C., Kennedy, N., Niessen, S. J. M., Church, D. B., Geddes, R. F., & O’Neill, D. G. (2021). Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice. Scientific Reports, 11(1), https://doi.org/10.1038/s41598-021-88440-z

• Journal Article Type: Article
• Acceptance Date: Apr 8, 2021
• Online Publication Date: Apr 27, 2021
• Publication Date: 2021-12
• Deposit Date: May 24, 2021
• Publicly Available Date: May 24, 2021
• Journal: Scientific Reports
• Publisher: Nature Research
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 1
• DOI: https://doi.org/10.1038/s41598-021-88440-z
• Keywords: Multidisciplinary
• Public URL: https://rvc-repository.worktribe.com/output/1549046
• Additional Information: Received: 21 October 2020; Accepted: 8 April 2021; First Online: 27 April 2021; : I.S is supported at the RVC by an award from Dechra Veterinary Products Ltd. S.J.M.N has undertaken consultancy work for Dechra Veterinary Products Ltd. The remaining authors have no conflicts of interest to declare.