Minimal clinically important differences in health-related quality of life after total hip or knee replacement

Article focus

We hypothesised that the precision of minimal clinically important differences (MCID) estimates could be enhanced by means of a meta-analysis

Key messages

Meta-analyses enhance the precision of an estimate by pooling data from different studies

Meta-analyses are not limited to results from randomised controlled trials. Any estimate from any type of study can be enhanced using the point estimate and its standard error

Strengths and limitations

This is the first study that sets out to enhance the precision of the estimate of a minimal clinically important difference

We could not perform a meta-analysis due to the lack of published MCID estimates

Introduction

Total hip (THR) and knee replacement (TKR) are effective surgical interventions, which alleviate pain and improve function and health-related quality of life (HRQoL) in patients with end-stage degeneration of the hip or knee joint, respectively.¹ Typically, studies report the mean improvement in HRQoL at the population level, which provides information for the average patient in a population. However, this information may not be meaningful for individual patients encountered in clinical practice, who will be concerned with the likelihood that they will experience a meaningful improvement in return for the risk undertaken when undergoing an intervention.² More relevant for the individual patient therefore is the minimal clinically important difference (MCID), defined as the minimal difference in scores of an outcome measure that is perceived by patients as beneficial or harmful.^3,4 The MCID enables patients to be classified as either a responder or a non-responder to a particular therapy, based on their own assessment of their pre- and post-operative HRQoL. Additionally, the MCID allows an estimation of the probability of a relevant improvement in HRQoL of a particular therapy.

Expected benefits of treatment must be weighed against its adverse effects, inconvenience and costs.⁵ Therefore, there is not necessarily a single MCID value for any one outcome measure of HRQoL, which can be used for all applications and patient samples.⁶ For instance, the benefits of treatment in patients suffering from end-stage osteoarthritis are considerably larger for THR and TKR compared with rehabilitational interventions. On the other hand, the risk of adverse effects is also considerably higher. These differences complicate the direct use of MCIDs in HRQoL as established for rehabilitational interventions,⁷ in THR or TKR patients. The use of specific MCIDs in HRQoL after THR or TKR should be encouraged.

MCIDs can be established using two different methods. Anchor-based approaches use an external indicator to assign patients into several groups reflecting different amounts of change in health status.⁶ The within-person global change rating is often used as an anchor, which is measured using Likert scales, ranging from five to 15 options.⁶ Positive MCIDs are usually estimated by the mean difference between pre- and post-operative scores of patients, who indicate that their condition is ‘somewhat better’; negative MCIDs are usually estimated by the mean difference between pre- and post-operative scores of patients who indicate that their condition is ‘somewhat worse’.^5,8 Distribution-based methods offer another approach in the estimation of MCIDs, which interpret results in terms of the relation between the magnitude of effect and some measure of variability in results.⁵ Individual effect size standards are often used to estimate the MCID, which is defined as the difference between a patient’s pre- and post-operative HRQoL scores, normed to the standard deviation of the pre-operative scores.⁸ Generally accepted individual effect size standards are equal to the group effect size standards, as defined by Cohen.⁹ Therefore, the MCID is calculated by multiplying the standard deviation of patients at baseline by 0.5.

Recently Quintana et al¹⁰ and Escobar et al¹¹ have estimated MCIDs for the SF-36 after THR and TKR. However, these authors have advised against using the found estimates of MCIDs as absolute thresholds, due to the imprecision of these estimates caused by small sample sizes. The precision of an estimate can be enhanced by pooling results of multiple studies in a meta-analysis. Therefore, the purpose of our study was to enhance the precision of the MCIDs after THR and TKR, by means of a systematic review and meta-analysis.

Materials and Methods

This systematic review was performed in November 2011, using the PRISMA-Statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) as a guideline in the development of the study protocol and the report of the current study.¹² The inclusion criteria and methods of analysis were specified in advance and documented in a protocol.

Results

Study selection

The search strategy revealed a total of 126 results (Fig. 1). After removal of duplicate entries, 114 unique papers remained. Screening of titles and abstracts revealed 29 papers eligible for inclusion. Further assessment of eligibility, based on full-text papers, led to the exclusion of 26 papers: two did not address THR or TKR and 24 presented no estimation of an MCID. This left three papers, describing three studies, for further analysis.^10,11,16

Fig. 1

Flow-chart of study inclusion (THR, total hip replacement; TKR, total knee replacement; MCID, minimal clinically important difference).

Synthesis of results

All studies have described a distinct study population, precluding any meaningful synthesis of study results. An overview of the results of all individual studies is presented in Table III and Figures 2 and 3. The MCIDs are presented with 95% confidence intervals for each of the SF-36 domains in primary TKR and primary and revision THR at six months (Fig. 2, Table III)^10,11,16 and for primary TKR and THR at two years post-operatively (Fig. 3, Table III).^10,11

Table III

Minimal clinically important differences (MCIDs) in Short-Form 36 (SF-36) domains after primary and revision total hip replacement (THR) and primary total knee replacement (TKR)

		MCID (95% confidence interval)
	SF-36 domain	At six months	At two years
Primary THR	Physical functioning	20.40 (14.4 to 26.4)	8.29 (-1.8 to 18.4)
	Role physical	10.78 (1.5 to 20.0)	11.00 (-1.3 to 23.3)
	Bodily pain	14.67 (6.8 to 22.6)	18.34 (9.1 to 27.6)
	General health	0.40 (-5.2 to 6.0)	-6.37 (-10.9 to -1.9)
	Vitality	10.14 (3.1 to 17.2)	14.51 (6.4 to 22.6)
	Social functioning	8.63 (0.9 to 16.4)	17.97 (7.8 to 28.1)
	Role emotional	-6.45 (-24.5 to 11.6)	20.83 (-0.6 to 42.3)
	Mental health	8.99 (2.3 to 15.7)	16.15 (9.0 to 23.3)
Primary TKR	Physical functioning	11.57 (6.5 to 16.7)	11.07 (5.8 to 16.3)
	Role physical	11.69 (3.8 to 19.6)	13.16 (3.5 to 22.8)
	Bodily pain	16.86 (9.7 to 24.0)	6.69 (-0.4 to 13.8)
	General health	0.85 (-3.2 to 4.9)	-7.30 (-11.3 to -3.3)
	Vitality	3.86 (-1.7 to 9.4)	3.44 (-2.2 to 9.1)
	Social functioning	11.66 (3.7 to 19.6)	6.15 (-1.7 to 14.0)
	Role emotional	7.65 (-4.5 to 19.8)	2.42 (-9.2 to 14.1)
	Mental health	-0.32 (-5.5 to 4.9)	4.02 (-1.7 to 9.7)
Revision THR	Physical functioning	3.25 (2.8 to 3.9)	-
	Role physical	4.78 (4.1 to 5.8)	-
	Bodily pain	14.91 (12.7 to 18.0)	-
	General health	14.12 (12.1 to 17.0)	-
	Vitality	22.81 (19.5 to 27.5)	-
	Social functioning	15.83 (13.5 to 19.1)	-
	Role emotional	19.98 (17.1 to 24.1)	-
	Mental health	12.37 (10.6 to 14.9)	-

Fig. 2

Graph showing the minimal clinically important differences (MCIDs) in the domains of the Short-Form 36 (SF-36) at six months after primary total hip (THR)¹⁰ and total knee replacement (TKR)¹¹ and revision THR.¹⁶ The size of the coloured circles represents the sample sizes used to estimate the MCID, and the error bars denote the 95% confidence intervals (PF, physical functioning; RP, role physical; BP, bodily pain; GH, general health; VT, vitality; SF, social functioning; RE, role emotional; MH, mental health).

Fig. 3

Graph showing the minimal clinically important differences (MCIDs) in the domains of the Short-Form 36 (SF-36) at two years after primary total hip (THR)¹⁰ and total knee replacement (TKR).¹¹ The size of the coloured circles represents the sample sizes used to estimate the MCID, and the error bars denote the 95% confidence intervals (PF, physical functioning; RP, role physical; BP, bodily pain; GH, general health; VT, vitality; SF, social functioning; RE, role emotional; MH, mental health).

Discussion

We have found one study describing MCIDs in SF-36 after primary THR,¹⁰ one after primary TKR¹¹ and one after revision THR¹⁶; we did not find any studies describing MCIDs after revision TKR. As all studies have described a distinct study population, no synthesis of study results can be given. Therefore, we were unable to improve the precision of each MCID estimate.

However, in order to visualise the precision of all MCID estimates, we calculated 95% confidence intervals of all MCID estimates, which were not presented in the original studies. These confidence intervals are presented in Figures 2 and 3.

The findings of this systematic review underline the need to identify MCIDs for each specific population. As can be seen from Figures 2 and 3, MCIDs differ both between SF-36 subscales and patient populations. The use of a ‘one-size-fits-all’ MCID does not appear justified, as patients suffering from osteoarthritis of the hip and knee, which are regarded as similar disease entities, have different MCIDs in HRQoL.^17-19 In order to study patient-relevant improvements in HRQoL at the individual level in revision TKR patients, MCIDs need to be established in this particular population as well.

Limitations of the included studies include imprecision as a result of small sample sizes, the lack of validation of the MCID estimates and the rates of loss to follow-up. Anchor-based approaches in particular suffer from imprecision due to small sample sizes, as this approach uses only a part of all data to estimate the MCID. A precise estimation of the MCID is further hampered by the clinical success of joint replacement: typically, one expects a large effect of THR or TKR.²⁰ The group sizes of patients who indicate that their condition has “somewhat improved” are therefore expected to be small, which contributes to an imprecise estimation of MCIDs. Unfortunately, there are only two ways to improve the precision of anchor-based MCID estimates: one can either perform larger studies, or pool study results in a meta-analysis. To date, the only studies that have established anchor-based MCIDs in HRQoL after primary THR or TKR were those of Quintana et al¹⁰ and Escobar et al.¹¹ More research is required to improve the precision of MCIDs in HRQoL. Estimates with higher precision are generated by distribution-based approaches, which use data from the entire population to estimate the MCID. However, these approaches are criticised for the arbitrariness of the individual effect size standards.⁵

A strongly recommended method of determining MCIDs is by triangulation of multiple approaches.⁶ None of the included studies has applied any form of additional validation, such as secondary anchor questions; all used a single approach. Besides a limited precision, caused by small group sizes, the accuracy of the MCID estimates might be limited as well due to this lack of additional validation.²¹ Therefore, further research is needed to provide external validation of the established MCIDs in HRQoL. However, until further research is performed, the MCID estimates of these three studies^10,11,16 are the best available estimates. Cautious use of these estimates should be encouraged in order to study improvement in HRQoL at the individual level, the most relevant outcome measure for individual patients encountered in clinical practice.