Development of an outcome prediction tool for patients considering a total knee replacement – the Knee Outcome Prediction Study (KOPS)

Primary osteoarthritis (OA) of the knee is a condition that can lead to loss of knee function. This in turn can lead to difficulty working, performing activities of daily living, stress, and depression [1]. Ten percent of the U.K. population over the age of 55 suffers from pain as a result of knee OA [2]. With an ageing population, this condition will present an ever increasing health burden. Surgery in the form of total knee replacement has reliably been shown to have a beneficial effect [3], and 95,454 knee replacements were performed in England and Wales in 2013, with over 90% of these for OA [4]. However a sub group of patients exist who have poor outcomes following knee replacement. Some studies show chronic pain rates as high as 17% [5]. Most previous work has focused on the effect of different prostheses and treatment protocols on outcome. However these factors have proved insufficient to account for all the variability in the outcome, and there is growing evidence that patient factors may significantly affect outcome [6]. A patient factor can be defined as being any factor that is intrinsic to the patient and that is not rapidly changed by a change in environment. These factors may include demographic data, functional and general health scores as well as psychological attributes and radiographic appearances.

Several patient factors have been identified as accounting for some of the variance in outcome after knee replacement; however which patient factors are important, and how much they affect outcome remains uncertain.

Pre-operative functional scores have been shown in several studies to have a consistent correlation to post operative function scores [7–10]. This would suggest that patients who are operated on earlier in their clinical course have a better outcome.

Medical co-morbidity has also been shown to have an effect on postoperative function [5, 9, 11–13], although some studies of reasonable size have not found a correlation [6, 7, 10, 14].

Psychosocial factors have been examined by several authors. Sharma [10] included the role function, emotional, social function and motivation subscales of the SF-36 in their hierarchical regression model, where an additional 19% of the variance of three-month post-op SF-36 PF was explained. Heck [7] and Lingard [9] used slightly different measures of mental health but both showed a significant association with postoperative physical function.

There is some evidence to suggest that an increased body mass index (BMI) is correlated with a worse absolute physical outcome. However, Spicer [14] noticed that there was no significant difference in the absolute improvement in physical outcome. Fortin [8] reported information on the change scores for preoperatively determined high function and low function groups. The lower function groups, although having lower absolute six month function scores, showed greater improvement than the high functioning group.

Age, gender and education had either non-significant results, or produced correlations that were too small to be of clinical significance in the above papers. However, it may be that these patient factors were, at least in part, confounded with one or other of the factors that showed positive associations with outcome.

Stratification of patient risk is currently the most important question in knee replacement surgery, and has been identified as a research priority by the U.K. National Institute of Health and Care Excellence [13].

Previous pilot work at out institution has identified measurable pre-operative patient factors that may affect outcome. The pilot work has allowed the development of a protocol that will enable measurement of the effect of different patient factors.

The primary aim of this study is to measure the effect of patient factors on outcome after knee replacementand to develop an outcome prediction tool for patients considering a total knee replacement. This tool could be used to empower patients to make a more informed decision about having a knee replacement, by allowing an accurate, personalised prediction of what they can expect.

We plan to undertake a prospective multi-centre cohort study including all six hospitals (NHS and private) that provide total knee replacements to the population of Coventry and Warwickshire. This represents a diverse population and basic demographic data will be compared to data from the National Joint Registry (NJR) [4] to allow comparison between the study population and the U.K. as a whole.

This study will use methodologies tested in a pilot study undertaken at our institution. This study tested the feasibility of the recruitment procedure and the type and presentation of questionnaires used to measure patient factors.

A consecutive series of patients presenting at clinics at each centre will be approached and if deemed eligible will be asked to consent to take part in the study. Our pilot study showed that this procedure is feasible and that 80% of eligible patients give consent to participate. Based on this figure, we expect to recruit about 60 patients per month, over a recruitment period from April 2013 to July 2014. This should provide a pool of over 1000 patients who are eligible for inclusion in the study, and over 800 who would consent to participate. We have set our recruitment target to be somewhat lower than this to allow for any unexpected recruitment problems. We do not anticipate any serious problems in reaching this target; however, the pilot study took place at one institution, and there may be a drop in recruitment rate when extending the study over multiple sites.

Patients will be recruited from their pre-operative clinic appointment where a baseline assessment of patient factors will take place. Factors assessed include: age; BMI; social support (measured by living alone); deprivation (postcode); knee status and knee pain (Oxford Knee Score - OKS); general health (Short Form-36 - SF-36, which has eight domains across two subscales (mental and physical functioning)); medical co-morbidity (Co-morbidity questionnaire); joint co-morbidity (Joint Co-morbidity questionnaire); psychological co-morbidity (Hospital Depression and Anxiety Score); helplessness and coping style (Arthritis Helplessness Index); expectation (Knee Expectation Questionnaire); and radiographic status (Ahlback Score).

Patients will then be followed up at six and twelve months by postal questionnaire using the OKS, the SF-36, and a satisfaction score.

This paper describes the justification for conducting a multi-centre cohort study using patient factors to predict outcome in patients after knee replacement surgery. By measuring the patient factors that are associated with better or worse functional outcome, we plan to predict outcome in individual patients, and support clinical decision making, hence improving quality of care. It will also facilitate interpretation of evidence from published observational studies of different interventions by determining whether groups of patients were similar, and allow post-hoc adjustment of clinical effectiveness studies for risk profile. The ability to adjust samples for case mix is particularly topical in view of the recent National Joint Registry introduction of Patient Reported Outcome Measures for total knee replacement. This data is in line with Department of Health recommendations and is used in service allocation.

The main strength of this study is the breadth and comprehensiveness of the patient factors selected for inclusion in the model building process; many of these factors have previously been shown to correlate with important outcome after surgery. Another strength is the size and demographic of the patient population sampled, which includes both NHS and private patients.