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

Autor: Schofield I; Pathobiology and Population Sciences, The Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield, AL9 7TA, Herts, UK. ischofield6@rvc.ac.uk., Brodbelt DC; Pathobiology and Population Sciences, The Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield, AL9 7TA, Herts, UK., Kennedy N; Pathobiology and Population Sciences, The Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield, AL9 7TA, Herts, UK., Niessen SJM; Clinical Science and Services, The Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield, AL9 7TA, Herts, UK.; Veterinary Specialist Consultations, Loosdrechtseweg 56, 1215JX, Hilversum, The Netherlands., Church DB; Clinical Science and Services, The Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield, AL9 7TA, Herts, UK., Geddes RF; Clinical Science and Services, The Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield, AL9 7TA, Herts, UK., O'Neill DG; Pathobiology and Population Sciences, The Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield, AL9 7TA, Herts, UK.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2021 Apr 27; Vol. 11 (1), pp. 9035. Date of Electronic Publication: 2021 Apr 27.
DOI: 10.1038/s41598-021-88440-z
Abstrakt: 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.
Databáze: MEDLINE
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