Open AccessPredicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning
Author(s) -
Bradley Richard,
Tagkopoulos Ilias,
Kim Minseung,
Kokkinos Yiannis,
Panagiotakos Theodoros,
Kennedy James,
De Meyer Geert,
Watson Phillip,
Elliott Jonathan
Publication year - 2019
Publication title -
journal of veterinary internal medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.356
H-Index - 103
eISSN - 1939-1676
pISSN - 0891-6640
DOI - 10.1111/jvim.15623
Subject(s) - medicine , kidney disease , machine learning , cats , artificial intelligence , artificial neural network , disease , creatinine , clinical practice , intensive care medicine , physical therapy , computer science
Background Advanced machine learning methods combined with large sets of health screening data provide opportunities for diagnostic value in human and veterinary medicine. Hypothesis/Objectives To derive a model to predict the risk of cats developing chronic kidney disease (CKD) using data from electronic health records (EHRs) collected during routine veterinary practice. Animals A total of 106 251 cats that attended Banfield Pet Hospitals between January 1, 1995, and December 31, 2017. Methods Longitudinal EHRs from Banfield Pet Hospitals were extracted and randomly split into 2 parts. The first 67% of the data were used to build a prediction model, which included feature selection and identification of the optimal neural network type and architecture. The remaining unseen EHRs were used to evaluate the model performance. Results The final model was a recurrent neural network (RNN) with 4 features (creatinine, blood urea nitrogen, urine specific gravity, and age). When predicting CKD near the point of diagnosis, the model displayed a sensitivity of 90.7% and a specificity of 98.9%. Model sensitivity decreased when predicting the risk of CKD with a longer horizon, having 63.0% sensitivity 1 year before diagnosis and 44.2% 2 years before diagnosis, but with specificity remaining around 99%. Conclusions and clinical importance The use of models based on machine learning can support veterinary decision making by improving early identification of CKD.