ArticleAnnals of the rheumatic diseases2019
Development and validation of prediction models to estimate risk of primary total hip and knee replacements using data from the UK: two prospective open cohorts using the UK Clinical Practice Research Datalink.
Article in Annals of the rheumatic diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Accuracy of machine learning in identifying candidates for total knee arthroplasty (TKA) surgery: a systematic review and meta-analysis.European journal of medical research · 2025Pooled it
- Forecasting knee arthroplasty surgery with deep learning : an integrated approach using routine clinical data and radiographs.Bone & joint open · 2026Article
- Clinical care patterns in the 10-years prior to primary total hip replacement: a matched population-based case-control analysis in England.BMC medicine · 2026Article
- Supervised machine learning model to predict total knee replacement in a large osteoartrhitis real-world evidence dataset using retrospectively 20-year insurance data from Israel.BMC medical informatics and decision making · 2026Article
- Predicting adverse outcomes in dilated cardiomyopathy using 3D echocardiography: penalised Cox regression versus machine learning.BMC cardiovascular disorders · 2026Article
- Primary care prediction of hip and knee replacement 1-5 years in advance using Temporal Graph-based Convolutional Neural Networks (TG-CNNs).Rheumatology (Oxford, England) · 2025Article
- Metabolic profiling of smoking, associations with type 2 diabetes and interaction with genetic susceptibility.European journal of epidemiology · 2024Article
- Real-world evidence that among atrial fibrillation patients warfarin is associated with reduced nonelective admissions compared with those on DOACs.Clinical cardiology · 2023Article
- Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration.BMJ (Clinical research ed.) · 2023Article
- Predicting total knee replacement at 2 and 5 years in osteoarthritis patients using machine learning.BMJ surgery, interventions, & health technologies · 2023Article
- Developing and internally validating a prediction model for total knee replacement surgery in patients with osteoarthritis.Osteoarthritis and cartilage open · 2022Article
- Proton pump inhibitor therapy and risk of knee replacement surgery: a general population-based cohort study.Osteoarthritis and cartilage · 2022Article
- Digoxin targets low density lipoprotein receptor-related protein 4 and protects against osteoarthritis.Annals of the rheumatic diseases · 2022Article
- Prediction models for the risk of total knee replacement: development and validation using data from multicentre cohort studies.The Lancet. Rheumatology · 2022Article
- Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis.Systematic reviews · 2021Article
- A Decline in Walking Speed Is Associated With Incident Knee Replacement in Adults With and at Risk for Knee Osteoarthritis.The Journal of rheumatology · 2021Article
- Effect of femoral posterior condyle offset on knee joint function after total knee replacement: a network meta-analysis and a sequential retrospective cohort study.Journal of orthopaedic surgery and research · 2021Article
- Cardiovascular risks and bleeding with non-vitamin K antagonist oral anticoagulant versus warfarin in patients with type 2 diabetes: a tapered matching cohort study.Cardiovascular diabetology · 2020Article
- Predicting Total Knee Replacement from Symptomology and Radiographic Structural Change Using Artificial Neural Networks-Data from the Osteoarthritis Initiative (OAI).Journal of clinical medicine · 2020Article
- Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 2 countries.
Funding
Abstract
objectivesThe ability to efficiently and accurately predict future risk of primary total hip and knee replacement (THR/TKR) in earlier stages of osteoarthritis (OA) has potentially important applications. We aimed to develop and validate two models to estimate an individual's risk of primary THR and TKR in patients newly presenting to primary care.
methodsWe identified two cohorts of patients aged ≥40 years newly consulting hip pain/OA and knee pain/OA in the Clinical Practice Research Datalink. Candidate predictors were identified by systematic review, novel hypothesis-free 'Record-Wide Association Study' with replication, and panel consensus. Cox proportional hazards models accounting for competing risk of death were applied to derive risk algorithms for THR and TKR. Internal-external cross-validation (IECV) was then applied over geographical regions to validate two models.
results45 predictors for THR and 53 for TKR were identified, reviewed and selected by the panel. 301 052 and 416 030 patients newly consulting between 1992 and 2015 were identified in the hip and knee cohorts, respectively (median follow-up 6 years). The resultant model C-statistics is 0.73 (0.72, 0.73) and 0.79 (0.78, 0.79) for THR (with 20 predictors) and TKR model (with 24 predictors), respectively. The IECV C-statistics ranged between 0.70-0.74 (THR model) and 0.76-0.82 (TKR model); the IECV calibration slope ranged between 0.93-1.07 (THR model) and 0.92-1.12 (TKR model).
conclusionsTwo prediction models with good discrimination and calibration that estimate individuals' risk of THR and TKR have been developed and validated in large-scale, nationally representative data, and are readily automated in electronic patient records.
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