ArticleScientific reports2022
An early prediction model for canine chronic kidney disease based on routine clinical laboratory tests.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 27 citations in OpenAlex.
- Transforming growth factor-β in kidney disease in dogs and cats: a review of the evidence of its role in renal fibrosis.Journal of veterinary internal medicine · 2026Review
- Article
- Artificial Intelligence in Veterinary Clinical Pathology-An Introduction and Review.Veterinary clinical pathology · 2025Review
- Risk Factors Associated with Azotemia in Dogs Presented to the Chiang Mai University Veterinary Teaching Hospital, Thailand: A Retrospective Study (2017-2021).Animals : an open access journal from MDPI · 2025Article
- Real-world data on canine chronic kidney disease in Greece: clinical and quality of life insights.Frontiers in veterinary science · 2025Article
- Gaining insights into pet owner understanding/lived experience of canine chronic kidney disease using survey and social media data.Frontiers in veterinary science · 2025Article
- Renal Disease in Cats and Dogs-Lessons Learned from Text-Mined Trends in Humans.Animals : an open access journal from MDPI · 2024Review
- A comprehensive analysis of albuminuria in canine chronic kidney disease.Veterinary medicine and science · 2024Article
- Construction of diagnostic prediction model for canine nasal diseases using less invasive examinations without anesthesia.The Journal of veterinary medical science · 2023Article
- The MARS PETCARE BIOBANK protocol: establishing a longitudinal study of health and disease in dogs and cats.BMC veterinary research · 2023Article
- Predicting health outcomes in dogs using insurance claims data.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to derive a model to predict the risk of dogs developing chronic kidney disease (CKD) using data from electronic health records (EHR) collected during routine veterinary practice. Data from 57,402 dogs were included in the study. Two thirds of the EHRs were used to build the model, which included feature selection and identification of the optimal neural network type and architecture. The remaining unseen EHRs were used to evaluate model performance. The final model was a recurrent neural network with 6 features (creatinine, blood urea nitrogen, urine specific gravity, urine protein, weight, age). Identifying CKD at the time of diagnosis, the model displayed a sensitivity of 91.4% and a specificity of 97.2%. When predicting future risk of CKD, model sensitivity was 68.8% at 1 year, and 44.8% 2 years before diagnosis. Positive predictive value (PPV) varied between 15 and 23% and was influenced by the age of the patient, while the negative predictive value remained above 99% under all tested conditions. While the modest PPV limits its use as a stand-alone diagnostic screening tool, high specificity and NPV make the model particularly effective at identifying patients that will not go on to develop CKD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.