Pathobiology and Population Sciences, The Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield, AL9 7TA, Herts, UK.
Clinical Science and Services, The Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield, AL9 7TA, Herts, UK.
Sci Rep. 2021 Apr 27;11(1):9035. doi: 10.1038/s41598-021-88440-z.
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.
库欣综合征是一种影响患病动物生活质量的犬类内分泌疾病。库欣综合征的确诊具有一定挑战性，因此需要新的诊断方法。本研究使用英国 VetCompass 计划中的结构化临床数据，应用 4 种机器学习算法来预测库欣综合征的未来诊断。分析纳入了疑似患有库欣综合征的犬，并根据其临床记录中的最终报告诊断进行分类。模型中纳入了就诊兽医首次怀疑时的人口统计学和临床特征。这些机器学习方法能够对记录的库欣综合征诊断进行分类，具有良好的预测性能。当应用于测试集时，LASSO 惩罚回归模型的 AUROC 为 0.85（95%CI 0.80-0.89），灵敏度为 0.71，特异性为 0.82，PPV 为 0.75，NPV 为 0.78，表现最佳。本研究结果表明，机器学习方法可以预测执业兽医的未来诊断。使用这些方法的新方法可以支持临床决策，并有助于提高犬库欣综合征的诊断水平。
