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Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

Objective

To investigate whether variation exists in the preoperative age, pain, stiffness, and physical function of people undergoing total knee replacement (TKR) and total hip replacement (THR) at several centers in Australia and Europe.

Methods

Individual Western Ontario and McMaster Universities Osteoarthritis Index data (range 0–100, where 0 = best and 100 = worst) collected within 6 weeks prior to primary TKR and THR were extracted from 16 centers (n = 2,835) according to specified eligibility criteria. Analysis of covariance was used to evaluate differences in pain, stiffness, and physical function between centers, with adjustment for age and sex.

Results

There was marked variation in the age of people undergoing surgery between the centers (TKR mean age 67–73 years; F[6,1004] = 4.21, P < 0.01, and THR mean age 63–72 years; F[14,1807] = 7.27, P < 0.01). Large differences in preoperative status were observed between centers, most notably for pain (TKR adjusted mean pain 52.5–61.1; F[6,1002] = 4.26, P < 0.01, and THR adjusted mean pain 49.2–65.7; F[14,1802] = 8.44, P < 0.01) and physical function (TKR adjusted mean function 52.7–61.4; F[6,1002] = 5.27, P < 0.01, and THR adjusted mean function 53.3–71.0; F[14,1802] = 6.71, P < 0.01). Large effect sizes (up to 0.98) reflect the magnitude of variation between centers and highlight the clinical relevance of these findings.

Conclusion

The large variations in age and preoperative status indicate substantial differences in the timing of joint replacement across the centers studied, with potential for compromised surgical outcomes due to premature or delayed surgery. Possible contributing factors include patient preferences, the absence of concrete indications for surgery, and the capacity of the health care systems.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

Joint replacement surgery is an effective and increasingly common intervention for end-stage knee and hip osteoarthritis (OA). However, the specific indications for total knee replacement (TKR) and total hip replacement (THR) surgery are neither clearly defined nor evidence based (1), and they are known to vary substantially between health professionals (2, 3). This lack of consensus could introduce discrepancies into the provision of joint replacement surgery both within and between countries. Because early provision of surgery (for example, to younger people with less severe joint disease) or delayed surgery (undertaken in older people with more severe arthritis) can have major health consequences (4, 5), systematic differences between centers have important implications for clinical practice and health policy.

To our knowledge, there has been little international research to date comparing the preoperative characteristics of people undergoing joint replacement; however, incidental findings from earlier studies suggest that preoperative physical status may indeed vary between countries. People undergoing TKR or THR in Canada had higher levels of pain and poorer physical function before surgery compared with those in the US (6), and those undergoing TKR in the UK had poorer preoperative physical function than their counterparts in Australia and the US (7). Because preoperative pain and function are important predictors of outcome from joint replacement surgery (8–10), further research is essential to verify these findings and explore the extent of variation across a broad range of centers. This study investigates whether variation exists in the preoperative age, pain, stiffness, and physical function of people undergoing TKR and THR surgery at centers in Australia and Europe.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

Study design.

A cross-sectional analysis of available individual data extracted from research and clinical databases in Australia and Europe was performed. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was used as the outcome. The WOMAC is a widely used, disease-specific measure for knee and hip OA (11) that contains 24 items: 5 pain, 2 stiffness, and 17 physical function.

Available data sets.

Through consultation with international musculoskeletal researchers and clinicians, it was identified that preoperative WOMAC data for inclusion in this comparison were available from sites in Australia (Geelong: clinical data set; Melbourne: research data set; Perth: clinical data set; and Sydney: combined research data set from 3 centers, containing both public and private patients) and Europe (Dundee, UK: research data set; the European collaborative database of cost and practice patterns of THR [EUROHIP]: combined research data set from 20 European centers; Lund, Sweden: research data set; Halmstad, Sweden: research data set; and Hassleholm, Sweden: research data set). Potential collaborators at each site were invited to contribute preoperative data for this analysis and were sent a study protocol and data dictionary.

Data extraction.

All of the data sets were made available in a de-identified format. Individual age, sex, and WOMAC item responses were extracted from each data set. Information on waiting time and socioeconomic status was either not routinely recorded or was recorded in a way that precluded concatenation, and therefore was not available for this study.

Eligibility criteria.

Because the data sets were not collected according to a single study protocol, data from each center were screened according to a set of eligibility criteria (specified a priori). For inclusion in the analyses, data were required to have been collected from people undergoing primary unilateral TKR or THR, collected using the Likert version of the WOMAC, and collected within 6 weeks prior to surgery, because minimal waiting-related deterioration was anticipated to occur beyond this time point. Additionally, individual data were excluded if a WOMAC score could not be calculated due to an insufficient proportion of item responses (11).

After applying these eligibility criteria, it was identified that a number of centers had very small samples (combined TKR and THR samples of n <50). To minimize the likelihood of spurious results, these centers were excluded from the analyses. Details of the excluded data and reasons for exclusion are presented in Appendix A (available in the online version of this article at http://www3.interscience.wiley.com/journal/77005015/home).

Statistical analyses.

Analyses were undertaken using Statistical Package for the Social Sciences software, version 14.0 (SPSS, Chicago, IL). WOMAC pain, stiffness, and physical function subscale scores were calculated according to published scoring guidelines (11), and were transformed to a 0–100 scale (where 0 = best and 100 = worst). Separate statistical analyses were undertaken for TKR and THR because the EUROHIP, Halmstad, and Lund databases contained only one operation type (either TKR or THR) (Table 1). Because differences in age and sex were observed between the centers (Table 2), an analysis of covariance (ANCOVA) was used to evaluate whether there were differences in pain, stiffness, and physical function between centers while adjusting for age and sex. Individual age and sex data were used for each ANCOVA. To explore whether there were systematic differences in the age and physical status of people undergoing surgery (for example, surgery being consistently performed on younger patients with less severe joint disease or on older patients with more severe disease), Pearson's correlation coefficient was used to assess the relationship between age, pain, stiffness, and physical function.

Table 1. Characteristics of participating centers*
LocationSetting typeTKR, nTHR, nYears of surgeryDiagnosisRecruitment method
  • *

    TKR = total knee replacement; THR = total hip replacement; OA = osteoarthritis.

  • Combined sample collected from 3 hospitals (1 hospital was public only, 2 were both public and private).

Australia      
GeelongHospital791102002–2005Mixed (97% OA)Consecutive
MelbourneHospital41432002–2005Mixed (90% OA)Consecutive
PerthHospital3132402002–2005Mixed (91% OA)Consecutive
SydneyHospitals125871994–2005OAConsecutive
Europe      
Akureyri, IcelandHospital0562004–2005OAConsecutive
Dresden, GermanyHospital01142003–2004OAConsecutive
Dundee, UKHospital1231572003–2005Mixed (91% OA)Consecutive
Halmstad, SwedenHospital0871997–1998OAConsecutive
Hamburg, GermanyHospital01552003OAConsecutive
Hassleholm, SwedenHospital2523902004OAConsecutive
Innsbruck, AustriaHospital0842003–2004OAConsecutive
Karlshamm, SwedenHospital0502003–2004OAConsecutive
Lund, SwedenHospital7901999–2001OAConsecutive
Szeged, HungaryHospital0582003–2004OAConsecutive
Ulm, GermanyHospital01392003–2004OAConsecutive
Zurich, SwitzerlandHospital0532003–2005OAConsecutive
Total 1,0121,823   
Table 2. Age and sex by center*
Operation typeCenterNMean age, years (95% CI)Women, %
  • *

    95% CI = 95% confidence interval; TKR = total knee replacement; THR = total hip replacement.

TKRDundee, UK12369 (67–70)52
 Geelong, Australia7973 (72–75)63
 Hassleholm, Sweden25271 (70–72)55
 Lund, Sweden7971 (69–73)63
 Melbourne, Australia4167 (64–70)56
 Perth, Australia31370 (69–71)63
 Sydney, Australia12571 (70–72)42
THRAkureyri, Iceland5669 (67–72)46
 Dresden, Germany11465 (63–66)56
 Dundee, UK15767 (65–69)57
 Geelong, Australia11071 (69–72)57
 Halmstad, Sweden8771 (69–73)54
 Hamburg, Germany15566 (65–68)60
 Hassleholm, Sweden39070 (69–71)56
 Innsbruck, Austria8463 (61–65)54
 Karlshamm, Sweden5066 (63–69)50
 Melbourne, Australia4367 (63–70)61
 Perth, Australia24068 (66–69)55
 Sydney, Australia8765 (62–67)51
 Szeged, Hungary5866 (63–68)59
 Ulm, Germany13966 (64–68)53
 Zurich, Switzerland5372 (69–75)55

To gain further insight into the data, the magnitude of variation in preoperative pain, stiffness, and physical function across the centers was estimated using standardized effect sizes. The effect size statistic has been described by Cohen for use with independent samples, and effect sizes can be categorized as small (0.20–0.49), medium (0.50–0.79), or large (≥0.8) (12). Data from the center with the highest mean adjusted WOMAC score for each subscale and from the center with the lowest mean adjusted WOMAC score were used for the calculations. Effect sizes were calculated by dividing the difference between the mean WOMAC subscale scores of the 2 centers by the pooled SD, which was used to account for differences in sample size between centers (13). To view the observed between-center variation in a clinical context, calculated effect sizes were compared with effect sizes for a range of commonly used conservative OA interventions, as reported in meta-analyses and Cochrane systematic reviews. Comparisons were also made with reported effect sizes from studies investigating outcomes from joint replacement surgery.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

Demographics.

Information about the number of eligible participants, surgery dates, diagnoses, and recruitment methods used at each of the centers is provided in Table 1. Most participants at each center had a diagnosis of OA (range 91–100%).

Age and sex data for each center are presented in Table 2. Analysis of variance revealed marked variation in the mean age before TKR (mean age range 67–73 years; F[6,1004] = 4.21, P < 0.01) and THR (mean age range 63–72 years; F[14,1807] = 7.27, P < 0.01). The ratio of women to men also varied considerably between centers (range 42–63% women for TKR and 46–61% women for THR).

Comparison of physical status across the centers.

Table 3 shows substantial variation in preoperative pain and physical function across centers for people undergoing TKR (F[6,1002] = 4.26, P < 0.01 for pain and F[6,1002] = 5.27, P < 0.01 for physical function) and THR (F[14,1802] = 8.44, P < 0.01 for pain and F[14,1802] = 6.71, P < 0.01 for physical function). There was little variation in preoperative stiffness across centers for people undergoing TKR (F[6,1002] = 0.94, P = 0.46); however, greater variation in stiffness was seen among those undergoing THR (F[14,1802] = 2.81, P < 0.01). The extent of the observed variation in pain, stiffness, and physical function is described in the following sections. Because the unadjusted mean (95% confidence interval) values were comparable to the adjusted values, only the latter have been reported in Table 3.

Table 3. Preoperative WOMAC pain, stiffness, and physical function scores by center*
Operation type by centerNPainStiffnessPhysical function
  • *

    Values are the adjusted mean (95% confidence interval). Means adjusted for age and sex. WOMAC = Western Ontario and McMaster Universities Osteoarthritis Index; TKR = total knee replacement; THR = total hip replacement.

  • Missing age or sex data not able to be included in the analyses of covariance (ANCOVAs) for 3 participants in Halmstad (THR), 1 in Hamburg (THR), and 1 in Hassleholm (TKR).

  • Range: 0–100 for each subscale score; higher score indicates worse pain, stiffness, or physical function.

  • §

    ANCOVA with adjustment for age and sex.

TKR    
Dundee, UK12358.1 (55.1–61.1)58.1 (54.5–61.8)53.2 (50.2–56.1)
Geelong, Australia7961.1 (57.4–64.9)63.3 (58.7–67.8)61.4 (57.6–65.1)
Hassleholm, Sweden25155.4 (53.3–57.5)59.0 (56.5–61.6)57.8 (55.8–59.9)
Lund, Sweden7958.0 (54.2–61.8)61.3 (56.8–65.8)58.8 (55.1–62.5)
Melbourne, Australia4157.4 (52.1–62.6)62.2 (55.9–68.4)60.4 (55.3–65.6)
Perth, Australia31352.5 (50.6–54.4)58.3 (56.1–60.6)52.7 (50.8–54.5)
Sydney, Australia12553.3 (50.3–56.3)59.4 (55.8–63.0)55.6 (52.7–58.6)
TKR summary F statistic F[6,1002] = 4.26F[6,1002] = 0.94F[6,1002] = 5.27
TKR summary P < 0.01§0.46§< 0.01§
THR    
Akureyri, Iceland5662.7 (58.1–67.2)68.1 (62.9–73.3)62.7 (58.3–67.0)
Dresden, Germany11458.6 (55.5–61.8)63.2 (59.6–66.9)62.4 (59.3–65.4)
Dundee, UK15760.1 (57.4–62.8)58.7 (55.6–61.8)59.2 (56.6–61.8)
Geelong, Australia11065.7 (62.5–69.0)67.4 (63.6–71.1)68.5 (65.4–71.6)
Halmstad, Sweden8455.6 (51.8–59.3)61.8 (57.5–66.0)60.9 (57.4–64.5)
Hamburg, Germany15450.9 (48.1–53.6)59.1 (56.0–62.2)57.4 (54.8–60.0)
Hassleholm, Sweden39059.2 (57.5–60.9)60.7 (58.7–62.7)60.9 (59.2–62.5)
Innsbruck, Austria8450.3 (46.6–54.0)58.8 (54.5–63.1)55.0 (51.5–58.6)
Karlshamm, Sweden5057.0 (52.2–61.8)60.8 (55.3–66.3)63.7 (59.1–68.3)
Melbourne, Australia4362.4 (57.3–67.6)64.2 (58.2–70.1)61.8 (56.9–66.8)
Perth, Australia24058.3 (56.1–60.4)63.1 (60.6–65.6)62.1 (60.0–64.2)
Sydney, Australia8749.2 (45.5–52.8)57.1 (52.9–61.3)54.4 (51.0–57.9)
Szeged, Hungary5864.5 (60.1–69.0)55.4 (50.3–60.5)71.0 (66.7–75.2)
Ulm, Germany13956.7 (53.8–59.6)64.3 (61.0–67.6)62.4 (59.6–65.1)
Zurich, Switzerland5350.3 (45.7–55.0)59.7 (54.4–65.1)53.3 (48.9–57.8)
THR summary F statistic F[14,1802] = 8.44F[14,1802] = 2.81F[14,1802] = 6.71
THR summary P < 0.01§< 0.01§< 0.01§
TKR.

The mean adjusted pain scores for people undergoing TKR ranged from 52.5 to 61.1 (Table 3). People in Geelong had, on average, the worst preoperative pain, after adjusting for age and sex (mean adjusted physical function score 61.1). People in Perth and Sydney had the least pain, after adjusting for age and sex (mean adjusted physical function scores of 52.5 and 53.3, respectively). There was little variation in preoperative stiffness for people undergoing TKR, with mean adjusted scores ranging from 58.1 in Dundee to 63.3 in Geelong.

The mean adjusted physical function scores for people undergoing TKR ranged from 52.7 to 61.4. People in Geelong and Melbourne had the worst physical function before surgery, after adjusting for age and sex (mean adjusted physical function scores of 61.4 and 60.4, respectively). Those in Perth and Dundee had the best physical function (mean adjusted physical function scores of 52.7 and 53.2, respectively).

THR.

The mean adjusted pain scores for people undergoing THR ranged from 49.2 to 65.7, as shown in Figure 1. People in Geelong and Szeged (Hungary) had the worst pain prior to surgery (mean adjusted pain scores of 65.7 and 64.5, respectively). People in Sydney, Innsbruck (Austria), and Zurich (Switzerland) had the least preoperative pain (mean adjusted pain scores of 49.2, 50.3, and 50.3, respectively).

thumbnail image

Figure 1. Preoperative pain scores by center for participants undergoing total hip replacement. Scores, adjusted for age and sex, range 0–100 (where 0 = least pain and 100 = worst pain). Solid vertical line indicates pooled sample mean, dashed vertical line indicates 95% confidence interval (95% CI) for pooled sample mean. * = n differs from the data in Table 1 due to missing age or sex data (n = 3 for Halmstad, n = 1 for Hamburg). WOMAC = Western Ontario and McMaster Universities Osteoarthritis Index.

Download figure to PowerPoint

The mean adjusted stiffness scores for people undergoing THR ranged from 55.4 to 68.1. People in Akureyri (Iceland) and Geelong had the worst stiffness before surgery (mean adjusted stiffness scores of 68.1 and 67.4, respectively), and those in Szeged had the least stiffness (mean adjusted stiffness score 55.4).

The mean adjusted physical function scores for people undergoing THR at each center (range 53.3–71.0) are shown in Figure 2. People in Szeged and Geelong had the worst physical function before THR (mean adjusted physical function scores of 71.0 and 68.5, respectively), and those in Zurich and Sydney had the best function (mean adjusted physical function scores of 53.3 and 54.4, respectively).

thumbnail image

Figure 2. Preoperative physical function scores by center for participants undergoing total hip replacement. Scores, adjusted for age and sex, range 0–100 (where 0 = best function and 100 = worst function). Solid vertical line indicates pooled sample mean, dashed vertical line indicates 95% confidence interval (95% CI) for pooled sample mean. * = n differs from the data in Table 1 due to missing age or sex data (n = 3 for Halmstad, n = 1 for Hamburg). WOMAC = Western Ontario and McMaster Universities Osteoarthritis Index.

Download figure to PowerPoint

Relationship between age, pain, stiffness, and physical function.

For participants undergoing TKR there was no substantive relationship between age and pain (r = −0.15, P < 0.01), age and stiffness (r = −0.10, P < 0.01), or age and physical function (r = −0.03, P = 0.36) (see Supplemental Figure 1, available in the online version of this article at http://www3.interscience.wiley.com/journal/77005015/home). Similarly, for participants undergoing THR there was no relationship between age and pain (r = 0.00, P = 0.89), age and stiffness (r = −0.03, P = 0.15), or age and physical function (r = 0.08, P < 0.01) (see Supplemental Figure 2, available in the online version of this article at http://www3.interscience.wiley.com/journal/77005015/home). These data suggest that at the centers studied there was no systematic trend toward performing joint replacement surgery on younger patients with less severe joint disease symptoms or on older patients with more severe symptoms.

Estimation of the magnitude of preoperative variation between centers.

Effect sizes were calculated to estimate the magnitude of variation in preoperative status across the range of centers using data from centers that had the lowest and highest WOMAC subscale scores, as shown in Table 3. These analyses revealed small to medium effect sizes for TKR (pain 0.44, stiffness 0.21, and physical function 0.47). For THR, medium to large effect sizes were found (pain 0.93, stiffness 0.50, and physical function 0.98).

To gauge the clinical relevance of these data, the largest effect sizes for TKR and THR (representing the between-center variation in physical function) were compared with published effect sizes for commonly used OA interventions. For TKR, the calculated effect size for physical function of 0.47 exceeds the reported effect sizes for physical function following interventions such as intraarticular corticosteroid injection (0.24) (14), nonsteroidal antiinflammatory drugs (0.29) (15), and land-based exercise (0.31) (16). To interpret these data in a different clinical context, comparison with recent research (17) shows that the calculated effect size of 0.47 represents ∼30% of the improvement in WOMAC physical function scores 6 months after TKR (effect size 1.58).

For THR, the calculated effect size for physical function of 0.98 exceeds the effect size for physical function associated with therapies such as opioid analgesics (0.31) (18) and aquatic exercise (0.76) (19). Additionally, compared with the reported improvement in WOMAC physical function scores 6 and 12 months after THR (6-month effect size 2.34 [20], 12-month effect size 2.40 [21]), the calculated effect size of 0.98 represents >40% of the reported benefit from surgery. These comparisons indicate that the magnitude of observed variation between centers can be considered substantial and clinically important.

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

This research has revealed substantial variation in the preoperative age, pain, stiffness, and physical function of people undergoing joint replacement surgery at a range of centers in Australia and Europe. The magnitude of the variability in preoperative pain and physical function was striking (more than 8 WOMAC units before TKR and more than 17 WOMAC units before THR) and can be regarded as clinically meaningful, as highlighted by the calculated effect sizes. The differences in physical status were evident even after adjusting for differences in age and sex between centers. These results indicate that the timing of hip and knee replacement surgery is highly variable with respect to severity of joint disease symptoms.

The most important clinical implication is that current variations in care could lead to compromised patient outcomes for those at the extremes of the distribution. Earlier research has shown that provision of joint replacement surgery to people with greater preoperative pain and functional impairment is associated with poorer postoperative outcomes (8, 10, 22). Conversely, early provision of surgery to people with less severe symptoms results in smaller postoperative improvements (10, 23), and could represent an improper use of health resources. In almost all settings, systems are not available to determine the need or urgency for joint replacement and, consequently, access to surgery may be highly dependent on local customs. The absence of clear indications for joint replacement surgery is particularly relevant for countries such as the UK and Australia, where general practitioners and orthopedic surgeons act as gatekeepers (1) to orthopedic waiting lists and, ultimately, to surgery.

The length of time waited for joint replacement surgery may also have contributed to the present findings, with delayed access to surgery manifesting as worse pain and poorer function (10, 24). This is highlighted by the substantial variation in pain and physical function observed between the Australian centers (Table 3). It is known that centers such as Geelong have protracted waiting times for surgery (up to 19 months for an initial appointment to see an orthopedic surgeon and up to 27 months for surgery), whereas some of the Sydney participants were recruited from private hospitals with considerably shorter waits (in the order of days or weeks). However, waiting times are unlikely to be the only contributing factor; participants undergoing TKR in another Australian public hospital (Perth) also had considerably less pain and better physical function preoperatively compared with participants in Geelong (Table 3). Although it could not be explored in the present study, the capacity of individual centers to undertake joint replacement surgery may have contributed to the observed variation, with likely differences in factors such as health care payment systems. Local rationing of surgical services due to limited resources may also have an impact on an individual's access to surgery, so that only people with the greatest pain and functional impairment can gain access to waiting lists.

Delayed access to joint replacement surgery for particular demographic groups may lead to worse preoperative pain and functional impairment. Disparities in access to joint replacement surgery have been identified across a range of characteristics, including sex (25), age (26), racial background (27, 28), and socioeconomic status (26, 29, 30), and could explain the wide variation across centers in the average age and sex ratios of people undergoing surgery in the present study. Such disparities could occur at several points in the health care process, from access to referring practitioners and orthopedic surgeons through to entering the waiting list and progression along the list. Delayed access to surgery may also relate to an individual's willingness to undergo surgery. A number of studies have reported differences in patient preferences for joint replacement according to sex, ethnic group, and socioeconomic status (31–33).

Cultural differences in the understanding and reporting of symptoms may have also contributed to the observed variation. Although the WOMAC is available in a range of languages (34), it was not primarily designed for cross-cultural application, and its cross-cultural stability has not been reported. However, although cultural differences in the interpretation of some of the concepts (e.g., pain severity) may have had an impact on the findings, this is unlikely to have been a substantial contributor to the variation observed across the Australian centers.

Because the international data were collected from a convenience sample of available centers rather than according to a standardized prospective protocol, it is possible that sampling variation may have contributed to differences in physical status between centers. Consecutive recruitment methods were used at each site with the expectation that the samples were broadly representative of their population base; however, it is possible that differences in recruitment rates (data not available) may have influenced this. How representative each center is of their region or country is not known, and it is possible that people with greater disease severity were recruited in some centers but not others, introducing systematic bias.

Despite these limitations, this study has a number of strengths that enabled new information to be obtained. The pooling of individual data permitted WOMAC scores to be adjusted for potential confounders such as age and sex. Additionally, although a range of research and clinical data sets were used, the application of eligibility criteria allowed for some standardization. Finally, this study extends the work by Lingard et al (7), which involved Australian participants recruited predominantly from a private hospital with short waiting times of less than 2 months (35); the current findings are expected to be more generalizable to people undergoing joint replacement in the Australian public health care system.

In conclusion, large between-center variation in preoperative age, pain, stiffness, and physical function was observed in people undergoing joint replacement surgery at a range of centers in Australia and Europe. These findings have highlighted the lack of consistency between the centers studied with respect to the timing of joint replacement surgery, and raise concerns regarding the quality of care and the appropriateness of services provided. Although this study did not investigate possible contributing factors, a range of patient-related, medical professional–related, and health system–related factors have been proposed, including patient preferences, the absence of concrete indications for joint replacement, and the capacity of individual health care systems. Given the role of preoperative physical status as a key predictor of joint replacement outcomes, future prospective research is warranted to uncover the origins and impact of the observed variation.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

Dr. Osborne had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study design. Ackerman, Dieppe, March, Brown, Osborne.

Acquisition of data. Ackerman, March, Roos, Nilsdotter, Brown, Sloan, Osborne.

Analysis and interpretation of data. Ackerman, Dieppe, March, Brown, Osborne.

Manuscript preparation. Ackerman, Dieppe, March, Roos, Nilsdotter, Brown, Sloan, Osborne.

Statistical analysis. Ackerman, Osborne.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

We wish to thank the EUROHIP study group; Beth Pollard, Scotland; the National Health and Medical Research Council Arthritis Costs and Outcomes of Surgery and Other Treatments Study Team, Australia; and the Royal Perth Hospital Joint Replacement Assessment Clinic, Australia for contributing preoperative data for these analyses. We are also grateful to Marita Cross, The University of Sydney, and Sue Williams, University of Bristol, for their assistance with data extraction.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

Additional Supporting Information may be found in the online version of this article.

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acr_24215_sm_appendix.doc106KSupplemental Appendix
acr_24215_sm_suppfigs.doc177KSupplemental Figures

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