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Headline
Tools were developed, using large data sets, to predict the outcome of hip and knee arthroplasty, but it would not currently be cost-effective to implement them in the NHS.
Abstract
Background:
Although hip and knee arthroplasties are considered to be common elective cost-effective operations, up to one-quarter of patients are not satisfied with the operation. A number of risk factors for implant failure are known, but little is known about the predictors of patient-reported outcomes.
Objectives:
(1) Describe current and future needs for lower limb arthroplasties in the UK; (2) describe important risk factors for poor surgery outcomes and combine them to produce predictive tools (for hip and knee separately) for poor outcomes; (3) produce a Markov model to enable a detailed health economic analysis of hip/knee arthroplasty, and for implementing the predictive tool; and (4) test the practicality of the prediction tools in a pragmatic prospective cohort of lower limb arthroplasty.
Design:
The programme was arranged into four work packages. The first three work packages used the data from large existing data sets such as Clinical Practice Research Datalink, Hospital Episode Statistics and the National Joint Registry. Work package 4 established a pragmatic cohort of lower limb arthroplasty to test the practicality of the predictive tools developed within the programme.
Results:
The estimated number of total knee replacements (TKRs) and total hip replacements (THRs) performed in the UK in 2015 was 85,019 and 72,418, respectively. Between 1991 and 2006, the estimated age-standardised rates (per 100,000 person-years) for a THR increased from 60.3 to 144.6 for women and from 35.8 to 88.6 for men. The rates for TKR increased from 42.5 to 138.7 for women and from 28.7 to 99.4 for men. The strongest predictors for poor outcomes were preoperative pain/function scores, deprivation, age, mental health score and radiographic variable pattern of joint space narrowing. We found a weak association between body mass index (BMI) and outcomes; however, increased BMI did increase the risk of revision surgery (a 5-kg/m2 rise in BMI increased THR revision risk by 10.4% and TKR revision risk by 7.7%). We also confirmed that osteoarthritis (OA) severity and migration pattern of the hip predicted patient-reported outcome measures. The hip predictive tool that we developed performed well, with a corrected R2 of 23.1% and had good calibration, with only slight overestimation of Oxford Hip Score in the lowest decile of outcome. The knee tool developed performed less well, with a corrected R2 of 20.2%; however, it had good calibration. The analysis was restricted by the relatively limited number of variables available in the extant data sets, something that could be addressed in future studies. We found that the use of bisphosphonates reduced the risk of revision knee and hip surgery by 46%. Hormone replacement therapy reduced the risk by 38%, if used for at least 6 months postoperatively. We found that an increased risk of postoperative fracture was prevented by bisphosphonate use. This result, being observational in nature, will require confirmation in a randomised controlled trial. The Markov model distinguished between outcome categories following primary and revision procedures. The resulting outcome prediction tool for THR and TKR reduced the number and proportion of unsatisfactory outcomes after the operation, saving NHS resources in the process. The highest savings per quality-adjusted life-year (QALY) forgone were reported from the oldest patient subgroups (men and women aged ≥ 80 years), with a reported incremental cost-effectiveness ratio of around £1200 saved per QALY forgone for THRs. In the prospective cohort of arthroplasty, the performance of the knee model was modest (R2 = 0.14) and that of the hip model poor (R2 = 0.04). However, the addition of the radiographic OA variable improved the performance of the hip model (R2 = 0.125 vs. 0.110) and high-sensitivity C-reactive protein improved the performance of the knee model (R2 = 0.230 vs. 0.216). These data will ideally need replication in an external cohort of a similar design. The data are not necessarily applicable to other health systems or countries.
Conclusion:
The number of total hip and knee replacements will increase in the next decade. High BMI, although clinically insignificant, is associated with an increased risk of revision surgery and postoperative complications. Preoperative pain/function, the pattern of joint space narrowing, deprivation index and level of education were found to be the strongest predictors for THR. Bisphosphonates and hormone therapy proved to be beneficial for patients undergoing lower limb replacement. The addition of new predictors collected from the prospective cohort of arthroplasty slightly improved the performance of the predictive tools, suggesting that the potential improvements in both tools can be achieved using the plethora of extra variables from the validation cohort. Although currently it would not be cost-effective to implement the predictive tools in a health-care setting, we feel that the addition of extensive risk factors will improve the performances of the predictive tools as well as the Markov model, and will prove to be beneficial in terms of cost-effectiveness. Future analyses are under way and awaiting more promising provisional results.
Future work:
Further research should focus on defining and predicting the most important outcome to the patient.
Funding:
The National Institute for Health Research Programme Grants for Applied Research programme.
Contents
- Plain English summary
- Scientific summary
- Chapter 1. Introduction
- Chapter 2. Work package 1: current and future rates of lower limb arthroplasties
- Design and setting
- Main exposure
- Outcome measures
- Rates of total hip replacement, total knee replacement and unicompartmental knee replacement in the UK
- Regional and national variation for hip and knee replacement surgery in the UK
- Describing the revision rates for hip and knee arthroplasty, and quantifying the rates in the UK
- Association of body mass index with time of revision
- Projected future trends for total hip replacement/total knee replacement accounting for projected changes in age and obesity
- Discussion
- Conclusion
- Chapter 3. Work package 2: designing the statistical tool to predict surgery outcome
- Predictors of outcome
- Aims and objectives
- Design and setting
- Outcome measures
- Main exposure
- Sample size
- Variables
- Statistical methods
- Defining good and bad patient-reported outcome measures
- Identifying the role of univariable as well as multivariable risk factors in patient-reported outcomes
- Conclusion
- Developing a predictive model
- Chapter 4. Work package 3: economic evaluation
- Chapter 5. Work package 4: external validation of the tool
- Aim
- Background
- Description of the study
- Study population
- Patient pathway
- Ethics
- Data, samples and radiographic variables
- Data storage and database
- Patients and scientific community
- Statistical analysis (methods) and end points
- Results
- Absolute differences between baseline and the 12-month follow-up Oxford scores
- Patient expectation and outcome
- Discussion
- Conclusion
- Chapter 6. Summary of the programme
- Acknowledgements
- References
- Appendix 1. Read codes
- Appendix 2. Statistical methods
- Appendix 3. Variable list
- Appendix 4. Hip replacement model by Briggs et al.
- Appendix 5. Full total hip replacement economic model schema
- Appendix 6. Expert elicitation
- Appendix 7. Estimating 2-year revision rates by Kalairajah outcome classification using data from the New Zealand Joint Registry
- Appendix 8. Estimating primary care costs attributable to hip pain
- Appendix 9. Logit model to predict surgery outcome
- Appendix 10. Mapping the Oxford Hip Score onto the EuroQol-5 Dimensions
- Appendix 11. Model assumptions and limitations
- Appendix 12. Primary care costs for total knee replacements
- Appendix 13. External validation of the model
- List of abbreviations
About the Series
Article history
The research reported in this issue of the journal was funded by PGfAR as project number RP-PG-0407-10064. The contractual start date was in September 2008. The final report began editorial review in August 2015 and was accepted for publication in March 2016. As the funder, the PGfAR programme agreed the research questions and study designs in advance with the investigators. The authors have been wholly responsible for all data collection, analysis and interpretation, and for writing up their work. The PGfAR editors and production house have tried to ensure the accuracy of the authors’ report and would like to thank the reviewers for their constructive comments on the final report document. However, they do not accept liability for damages or losses arising from material published in this report.
Declared competing interests of authors
Nigel Arden reports personal fees from Bioventus, Flexion, Merck Sharp & Dohme, Regeneron, Smith & Nephew, Freshfields Bruckhaus Deringer, and grants from Bioibérica and Novartis outside the submitted work. Daniel Prieto-Alhambra reports grants from Servier Laboratories, AMGEN and Bioibérica S.A., and advisory board fees from AMGEN outside the submitted work. Andrew Judge reports personal fees from Anthera Pharmaceuticals, Inc., Servier, the UK Renal Registry, Blood Journal and Freshfields Bruckhaus Deringer, and grants and personal fees from the Oxford Craniofacial Unit, and grants from Roche-Chugai outside the submitted work. Jeremy Latham reports personal fees from Zimmer Biomet, Lima Corporate, MatOrtho and DePuy Synthes outside the submitted work. Rafael Pinedo-Villanueva reports personal fees from Freshfields Bruckhaus Deringer outside the submitted work. David Murray reports grants and personal fees from Zimmer Biomet and grants from Stryker and Health Technology Assessment (HTA) outside the submitted work. James Raftery reports that he is an editor on the National Institute for Health Research HTA and EME editorial board. In addition, James Raftery was previously a Director of the Wessex Institute and Head of the National Institute for Health Research (NIHR) Evaluation, Trials and Studies Coordinating Centre (NETSCC).
Last reviewed: August 2015; Accepted: March 2016.
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