Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning.

Abstract

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.