ArticleBMJ open2024
Risk equations for prosthetic joint infections (PJIs) in UK: a retrospective study using the Clinical Practice Research Datalink (CPRD) AURUM and GOLD databases.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Prediction Models for Periprosthetic Joint Infection: A Systematic Review of Traditional and Machine Learning Approaches.Mayo Clinic proceedings. Digital health · 2026Review
- Bromelain and N-acetylcysteine (BromAc) as novel adjunctive therapy for prosthetic joint infection: a narrative review.Journal of bone and joint infection · 2026Review
- Antimicrobial resistance in orthopedics: microbial insights, clinical impact, and the necessity of a multidisciplinary approach-a review.Acta orthopaedica · 2025Review
- Early Cost-Effectiveness Analysis of Intra-articular Delivery of a PBAE-DEX Conjugate for Osteoarthritis in a UK Population.Clinical drug investigation · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
backgroundProsthetic joint infections (PJIs) are a serious negative outcome of arthroplasty with incidence of about 1%. Risk of PJI could depend on local treatment policies and guidelines; no UK-specific risk scoring is currently available.
objectiveTo determine a risk quantification model for the development of PJI using electronic health records.
designRecords in Clinical Practice Research Datalink (CPRD) GOLD and AURUM of patients undergoing hip or knee arthroplasty between January 2007 and December 2014, with linkage to Hospital Episode Statistics and Office of National Statistics, were obtained. Cohorts' characteristics and risk equations through parametric models were developed and compared between the two databases. Pooled cohort risk equations were determined for the UK population and simplified through stepwise selection.
resultsAfter applying the inclusion/exclusion criteria, 174 905 joints (1021 developed PJI) were identified in CPRD AURUM and 48 419 joints (228 developed PJI) in CPRD GOLD. Patients undergoing hip or knee arthroplasty in both databases exhibited different sociodemographic characteristics and medical/drug history. However, the quantification of the impact of such covariates (coefficients of parametric models fitted to the survival curves) on the risk of PJI between the two cohorts was not statistically significant. The log-normal model fitted to the pooled cohorts after stepwise selection had a C-statistic >0.7.
conclusionsThe risk prediction tool developed here could help prevent PJI through identifying modifiable risk factors pre-surgery and identifying the patients most likely to benefit from close monitoring/preventive actions. As derived from the UK population, such tool will help the National Health Service reduce the impact of PJI on its resources and patient lives.
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