Web of Science: 23 cites, Scopus: 27 cites, Google Scholar: cites,
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
Yu, Dahai (Keele University)
Jordan, Kelvin P.. (Keele University)
Snell, Kym I E. (Keele University)
Riley, Richard D. (Keele University)
Bedson, John (Keele University)
Edwards, John James (Keele University)
Mallen, Christian D. (Keele University)
Tan, Valerie (Keele University)
Ukachukwu, Vincent (Keele University)
Prieto-Alhambra, Daniel (Institut Universitari d'Investigació en Atenció Primària Jordi Gol)
Walker, Christine (Keele University)
Peat, George (Keele University)
Universitat Autònoma de Barcelona

Data: 2018
Resum: The 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. We 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. 45 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). Two 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.
Nota: Altres ajuts: This study was funded by NIHR School for Primary Care Research Funding Round 9 (Project No: 258) and by Public Health England. CDM is funded by the NIHR Collaborations for Leadership in Applied Health Research and Care West Midlands, the NIHR School for Primary Care Research and a NIHR Research Professorship in General Practice (NIHR-RP-2014-04-026). JE is a NIHR Academic Clinical Lecturer. The views expressed in this paper are those of the author(s) and not necessarily those of the NHS, the NIHR, Public Health England, or the Department of Health. This research is funded by the National Institute for Health Research School for Primary Care Research (NIHR SPCR).
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Matèria: Osteoarthritis ; Total hip replacement ; Total knee replacement ; Primary care ; Rlectronic health record ; Record-wide association study ; Risk prediction model
Publicat a: Annals of the rheumatic diseases, Vol. 78 (october 2018) , p. 91-99, ISSN 1468-2060

DOI: 10.1136/annrheumdis-2018-213894
PMID: 30337425


9 p, 1.2 MB

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