ArticleRheumatology (Oxford, England)2025
Primary care prediction of hip and knee replacement 1-5 years in advance using Temporal Graph-based Convolutional Neural Networks (TG-CNNs).
Article in Rheumatology (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Explainable temporal graph-based CNNs for predicting hip replacement risk using EHR data.BMJ digital health & AI · 2026Article
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Authors and funding
5 authors.
Funding
Abstract
objectiveThis study aimed to predict the risk of requiring a primary hip or knee replacement 1 and 5 years in advance, using clinical codes.
methodsPrimary care clinical codes, sourced from ResearchOne Electronic Health Records between 1999 and 2014, were used to represent patient pathways prior to hip or knee replacement. Patient records were used to train and test models for hip or knee replacement, 1 and 5 years in advance. Temporal graphs were constructed from clinical codes to predict hip and knee replacement risk, where nodes are clinical codes, and edges are the time between primary care visits. Hip and knee replacement cases were matched to controls by age, sex and Index of Multiple Deprivation. The model was validated on unseen data, with performance measured using area under the receiver operator curve (AUROC), calibration and area under the precision recall curve (AUPRC), recalibrating for class imbalance.
resultsFor knee replacement prediction, AUROC was 0.915 (95% CI 0.914, 0.916) (1 year) and 0.955 (95% CI 0.954, 0.956) (5 years) with AUPRCs of 0.353 (95% CI 0.302, 0.403) and 0.442 (95% CI 0.382, 0.503), respectively. For hip replacement prediction, AUROC was 0.919 (95% CI 0.918, 0.920) (1 year) and 0.967 (95% CI 0.966, 0.968) (5 years), with AUPRCs of 0.409 (95% CI 0.366, 0.451) and 0.879 (95% CI 0.833, 0.924), respectively.
conclusionHip and knee replacement risk can be predicted up to 5 years in advance, with a temporal-graph based artificial intelligence model achieving the best performance. This may be used for planning preventative treatment or triaging patients.
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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.