An artificial neural network‐based model to predict chronic kidney disease in aged cats

• Main Authors: Vincent Biourge, Sebastien Delmotte, Alexandre Feugier, Richard Bradley, Molly McAllister, Jonathan Elliott
• Format: Article
• Language: English
• Published: Wiley 2020-09-01
• Series: Journal of Veterinary Internal Medicine
• Subjects: artificial intelligence; CKD modeling; prediction tool; prevention; senior health check
• Online Access: https://doi.org/10.1111/jvim.15892

Abstract Background Chronic kidney disease (CKD) frequently causes death in older cats; its early detection is challenging. Objectives To build a sensitive and specific model for early prediction of CKD in cats using artificial neural network (ANN) techniques applied to routine health screening data. Animals Data from 218 healthy cats ≥7 years of age screened at the Royal Veterinary College (RVC) were used for model building. Performance was tested using data from 3546 cats in the Banfield Pet Hospital records and an additional 60 RCV cats—all initially without a CKD diagnosis. Methods Artificial neural network (ANN) modeling used a multilayer feed‐forward neural network incorporating a back‐propagation algorithm. Clinical variables from single cat visits were selected using factorial discriminant analysis. Independent submodels were built for different prediction time frames. Two decision threshold strategies were investigated. Results Input variables retained were plasma creatinine and blood urea concentrations, and urine specific gravity. For prediction of CKD within 12 months, the model had accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 88%, 87%, 70%, 53%, and 92%, respectively. An alternative decision threshold increased specificity and PPV to 98% and 87%, but decreased sensitivity and NPV to 42% and 79%, respectively. Conclusions and Clinical Importance A model was generated that identified cats in the general population ≥7 years of age that are at risk of developing CKD within 12 months. These individuals can be recommended for further investigation and monitoring more frequently than annually. Predictions were based on single visits using common clinical variables.