Individual items versus summary scores in comparing patient outcomes among different types of total hip and knee arthroplasty designs

Individual items versus summary scores in comparing patient outcomes among different types of total hip and knee arthroplasty designs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Individual items versus summary scores in comparing patient outcomes among different types of total hip and knee arthroplasty designs Marys Revaz, Thomas Perneger, Christophe Barea, Hermes H. Miozzari, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8593555/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose This study aimed to evaluate the discriminative ability of summary scores and individual items in comparing patient reported outcomes (PROs) across two total hip arthroplasty (THA) and two total knee arthroplasty (TKA) designs. Methods Primary elective THAs and TKAs performed between January 2012 and June 2022 from the Geneva Arthroplasty Registry were included. Two cup-stem combinations in THA and two prostheses with different stability designs in TKA were compared using WOMAC Pain and Function summary scores and individual items one year after surgery. Linear and ordinal logistic regression models were used to compare the ability of summary scores and items in differentiating between prostheses. Results A total of 773 THA and 624 TKA patients were included. Overall, differences between prostheses were detected using either WOMAC summary scores or items. Nevertheless, analyses revealed variability in the discriminative ability of WOMAC summary scores and items, with variations depending on the population. Additionally, in both THA and TKA, differences between prostheses were larger in the function domain than in the pain domain. Conclusion PRO summary scores are valuable for evaluating and comparing prostheses performance in THA and TKA. In addition, individual items may, in certain contexts, provide enhanced sensitivity to identify implant-related differences and offer a more detailed understanding of these differences. Total Hip Arthroplasty Total Knee Arthroplasty Patient Reported Outcomes WOMAC Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Patient reported outcomes (PROs) play an important role in evaluating outcomes following total hip and knee arthroplasties (THAs & TKAs). In recent years, they have been increasingly used alongside revision rates to evaluate prosthesis benefits or compare prostheses[ 1 , 2 ]. To date, studies have exclusively used PRO summary scores for such comparisons, which often failed to capture differences[ 3 , 4 ]. Indeed, comparisons of prostheses or techniques using PROs may lack sensitivity, a problem well known and referred to in the literature as the ceiling effect. With improvements in surgical techniques and prostheses, most patients achieve good results after THA or TKA, making it difficult to distinguish between good outcomes[ 5 , 6 ]. Alternatively, PRO scales may lack discrimination because they average multiple aspects of patient experience. Typically, PRO scales combine answers from several items to capture a single latent variable (e.g. degree of pain) which cannot be measured directly. This is the classic psychometric approach. The latent variable is deemed to influence all item answers to some extent, explaining why item responses are correlated. The sum of item responses approaches the true value of the latent variable better than any single item, if considered in isolation. But the specific content of items is not of interest, items only serve to reveal the latent variable. In fact, any information that is specific to a given item (i.e. information uncorrelated to other item responses) is considered to represent measurement error[ 7 ]. Here, we propose a "non-psychometric" approach, which aims to use the specific content of each scale item. Items are selected because they represent the most salient situations where the latent variable can be perceived or influence behavior, e.g. various situations where the respondent is likely to experience pain. When comparing prostheses or surgical techniques, their impact on patient outcomes may be heterogeneous, or situation dependent. For example, some prostheses may be accompanied by pain when climbing stairs, but not when walking on flat ground, and conversely for others. Whether the use of such item-specific information has any utility in assessing PROs of arthroplasty surgery is currently unknown, as little research has compared the use of items with summary scores. However, most studies highlighted the advantage of using items or a subset of items to classify patients and take therapeutic decisions[ 8 – 11 ]. The present study aims at contrasting full-scale to item-specific scores when comparing outcomes of two prostheses in THA and TKA. Specifically, it evaluates and compares the ability of WOMAC Pain and Function summary scores and individual items to differentiate between two cup-stem combinations with the same surgical approach and fixation type in THA, and two prostheses with different stability designs in TKA. Methods Study Design and Population This cohort study used data from the Geneva Arthroplasty Registry[ 12 ], which prospectively collects data from THAs and TKAs performed at a tertiary care, university hospital. It was approved by the local ethics committee (CCER Geneva, Switzerland). For this study, all primary elective THAs and TKAs performed between January 2012 and June 2022 were selected. In THA, patients who underwent an anterior surgical approach and were implanted with either Versafit cup and Quadra-H stem (Versafit/Quadra-H; Medacta Int., Castel San Pietro, Switzerland) or Pinnacle cup and Corail-H stem (Pinnacle/Corail-H; DePuy Synthes, Warsaw, IN, US), using an uncemented fixation, were eligible. The Pinnacle/Corail-H group generally received larger head sizes, with 1.9% of patients receiving a 28mm, 55.3% a 32mm and 42.7% a 36mm head. In the Versafit/Quadra-H group, 24.9% of patients received a 28mm, 63.1% a 32mm and 12% a 36mm head. In TKA, patients who were implanted with either a PFC Sigma prosthetic design in its posterior stabilized version (PFC-PS; DePuy Synthes, Warsaw, IN, US) or a GMK Sphere design (Medacta Int., Castel San Pietro, Switzerland) with medial pivot, were eligible. The PFC-PS group had a higher patella resurfacing rate (67.2% vs 52.8%) and underwent predominantly mechanical alignment (98.5% mechanical vs 1.5% kinematic), whereas the GMK Sphere group showed a more balanced distribution (58.2% mechanical vs 41.8% kinematic). From these groups, patients who did not consent to research, who died, were lost to follow-up, or underwent revision within one year after surgery were excluded. Finally, only patients with a complete short-form (12-item scale) Western Ontario and McMasters Universities Osteoarthritis Index (WOMAC) questionnaire preoperatively and at one year follow-up were included in the analysis[ 13 ]. The prostheses selected for this study were chosen because they were the most frequently implanted at our institution during the study period. Additionally, to ensure that the focus remains on the study’s objective, i.e. assessing whether WOMAC items discriminate better between prostheses than summary scores, rather than comparing specific prostheses, prosthesis names were coded throughout the manuscript. For THA, Versafit/Quadra-H will be referred to as prosthesis A and Pinnacle/Corail-H as prosthesis B. For TKA, PFC-PS will be referred to as prosthesis A and GMK Sphere as prosthesis B. Study Variables The main independent variables were the prosthesis types for both populations. In THA, two cup-stem combinations were studied, while in TKA, two prostheses with different stability designs were studied. The main dependent variables included the WOMAC Pain and Function summary scores and 12 individual items assessed by the short-form WOMAC questionnaire, one year after surgery. The WOMAC questionnaire used is a reduced version of the original, composed of the 5 original items for pain and 7 items for function[ 13 ]. It is a patient self-assessed questionnaire developed for patients with hip and knee osteoarthritis. Items are assessed on a 5-point Likert scale and both pain and function scores range from 0 to 100 (= best)[ 13 ]. Descriptive variables were assessed preoperatively and included for both populations: sex, age at surgery, body mass index (BMI), American Society of Anesthesiologists (ASA) score[ 14 ], number of comorbidities, smoking status (never, former or current), education level (≤ 8, 9–12 or ≥ 13 years), diagnosis (primary or secondary osteoarthritis), University of California Los Angeles (UCLA) activity scale[ 15 ], Mental and Physical Component Score (MCS & PCS) of the 12-item Short Form (SF-12) questionnaire[ 16 ], self-rated health assessed with the first SF-12 question, and pain and function summary scores of the short-form WOMAC questionnaire[ 13 ]. ASA score assesses a patient’s pre-anesthesia medical comorbidities (1: healthy, 2: mild systemic disease, 3: severe systemic disease and 4: severe systemic disease that is constant threat to life)[ 14 ]. UCLA activity scale assesses a patient’s activity level on a scale from 1 (wholly inactive) to 10 (regularly participates in impact sports)[ 15 ]. It was grouped into three categories - low (1–4), moderate (5–7) and high activity (8–10). SF-12 is a patient self-assessed questionnaire assessing a patient’s general health status and comprising two summary scores, MCS and PCS[ 16 ]. Statistical Analysis In both populations, baseline characteristics were compared between prostheses. Continuous variables were reported as mean and standard deviation (SD), while categorical variables were reported as proportions (in %). To compare WOMAC Pain and Function summary scores and items one year after surgery between prostheses, mean and SD were calculated, even though items are ordinal variables. WOMAC items were coded from 1 to 5 (= best). The density distribution (kernel smoothing of the frequency distribution) of WOMAC Pain and Function summary scores pre-surgery and one-year post-surgery was also calculated. To analyze the ability of WOMAC Pain and Function summary scores and items in discriminating between prostheses, univariate linear and ordinal logistic regression models were applied. Linear regression was used for continuous summary scores, whereas ordinal logistic regression was used for items. In all models, prosthesis type was used as the explanatory variable and was coded as 0 for prosthesis A and 1 for prosthesis B for both THA and TKA. In linear models, WOMAC Pain and Function summary scores were alternately used as response variables. In ordinal logistic models, the 5 pain and 7 function items were alternately used as response variables. WOMAC items were coded from 1 to 5 (= best). In total, 14 models were built for each population. All models were first adjusted for the corresponding preoperative summary score or item. In a second step, they were additionally adjusted for age at surgery (continuous), ASA score (1–2 vs 3–4) and education (< 13 vs ≥ 13 years) to account for confounding. For TKA, models were additionally adjusted for patella resurfacing in both adjustment steps. Results from linear models were reported as differences with 95% confidence intervals (CIs) and those from ordinal logistic models as odds ratios with 95% CIs, odds ratios representing the relative odds of a 1-point higher rating on the scale between 1 and 5 points for prosthesis B versus prosthesis A. Data were analyzed using R, version 4.2.2 (R Foundation for Statistical Computing, Austria). Results THA population Among the 3278 patients who underwent a primary elective THA between January 2012 and June 2022, 1232 were eligible for the study. Of those, 773 patients had a complete WOMAC questionnaire preoperatively and at one-year follow-up and were included in the analysis (Fig. 1 ). All baseline characteristics were similar between the eligible and included groups. Sex (44.7% vs 45.0%), age (64.8 vs 65.9 years) and BMI (27.5 vs 27.2 kg/m 2 ) were comparable, as well as ASA score, number of comorbidities, smoking status, education level, diagnosis, activity level, SF-12 MCS & PCS scores, and self-rated health. Among included patients, 546 received prosthesis A, while 227 received prosthesis B. Preoperatively, baseline characteristics were similar between prosthesis groups, with approximately 45.0% female, a mean age of 66.1 years and BMI of 27.1 kg/m 2 . However, differences were observed in age (65.6 vs 66.5 years), ASA score (13.7% vs 18.9% classified as ASA 3–4) and education level (35.8% vs 41.4% with ≥ 13 years of schooling) (Table 1 ). Table 1 Baseline characteristics of the THA population by prosthesis. Included Prosthesis A (N = 546) Prosthesis B (N = 227) Female, N (%) 246 (45.1%) 102 (44.9%) Age at Surgery, mean (SD) 65.6 (11.0) 66.5 (9.5) BMI, mean (SD) 27.2 (4.6) 27.0 (4.8) ASA Score, N (%) 1 50 (9.2%) 23 (10.1%) 2 421 (77.1%) 161 (70.9%) 3–4 75 (13.7%) 43 (18.9%) Number of Comorbidities, N (%) 0 71 (13.0%) 33 (14.5%) 1–3 376 (68.9%) 149 (65.6%) ≥ 4 99 (18.1%) 45 (19.8%) Smoking Status, N (%) Never 282 (51.6%) 106 (47.1%) Former 163 (29.9%) 73 (32.4%) Current 101 (18.5%) 46 (20.4%) Education (Years of Schooling), N (%) ≤ 8 years 170 (31.7%) 63 (29.3%) 9–12 years 175 (32.6%) 63 (29.3%) ≥ 13 years 192 (35.8%) 89 (41.4%) Diagnosis, N (%) Primary osteoarthritis 496 (90.8%) 210 (92.5%) Secondary osteoarthritis 50 (9.2%) 17 (7.5%) UCLA Presurgery, N (%) Low activity 391 (76.5%) 170 (80.6%) Moderate activity 103 (20.2%) 31 (14.7%) High activity 17 (3.3%) 10 (4.7%) SF-12 MCS Presurgery, mean (SD) 43.4 (10.7) 43.8 (11.9) SF-12 PCS Presurgery, mean (SD) 33.6 (6.9) 33.7 (7.3) Self-Rated Health Presurgery, N (%) Poor - Fair 91 (16.7%) 45 (20.0%) Good 296 (54.4%) 114 (50.7%) Very good - Excellent 157 (28.9%) 66 (29.3%) Note: Missing information on contralateral hip status (A: n = 1, B: n = 1), education (A: n = 9, B: n = 12), UCLA presurgery (A: n = 35, B: n = 16), SF-12 MCS presurgery (A: n = 10, B: n = 3), SF-12 PCS presurgery (A: n = 10, B: n = 3), self-rated health presurgery (A: n = 2, B: n = 2). Density distribution analysis revealed a large improvement in WOMAC Pain and Function summary scores from pre-surgery to one-year post-surgery in both prosthesis groups. For both scores, preoperative and one-year postoperative distributions for prosthesis B were shifted toward higher values, suggesting that regression models might need to be adjusted for WOMAC summary scores pre-surgery (Fig. 2 ). Before surgery, all WOMAC Pain and Function summary scores and items were higher in the prosthesis B group, indicating a better preoperative state in terms of pain and functional abilities. This confirmed the need to adjust regression models for summary scores and items pre-surgery. One year after surgery, the same pattern was observed, with prosthesis B group showing higher values for all summary scores and items. Also, the difference between prostheses was more pronounced for WOMAC Function than WOMAC Pain summary score. Regression analyses revealed differences between prosthesis groups. After adjusting for the corresponding preoperative WOMAC summary score or item, as well as age, ASA score and education level, differences in pain and functional abilities between prostheses one year after surgery were attenuated. Two function item models - “walking on flat” and “getting in/out of car” - showed statistically significant differences in favor of prosthesis B, with odds ratios between 1.16 and 1.57 per 1-point change in response, whereas none of the pain item models did. Considering summary score models, WOMAC Function showed a statistically significant difference in favor of prosthesis B, whereas no difference was observed for WOMAC Pain (Table 2 & Table 3 ). Table 2 WOMAC Pain & Function summary scores before and one-year after surgery by prosthesis, as well as linear regression models for the THA population. THA Pre-surgery mean (SD) 1 Year follow-up mean (SD) Unadjusted models Adjusted models Additional adjusted models A (N = 546) B (N = 227) A (N = 546) B (N = 227) Difference favoring B over A (95% CI) Difference favoring B over A (95% CI) Difference favoring B over A (95% CI) WOMAC Pain 39.2 (17.7) 43.3 (17.1) 86.2 (18.0) 88.3 (17.6) 2.1 (-0.7 to 4.9) 0.8 (-1.9 to 3.4) 0.8 (-1.9 to 3.5) WOMAC Function 40.3 (18.7) 45.6 (17.6) 83.0 (19.4) 87.3 (17.4) 4.3 (1.4 to 7.2) 2.5 (-0.3 to 5.3) 2.9 (0.1 to 5.7) * 95% CI = 95% Confidence Interval * Adjusted models for the corresponding summary score pre-surgery & additional adjusted models for the corresponding summary score, age, ASA score and education level pre-surgery Table 3 WOMAC Pain & Function items before and one-year after surgery by prosthesis, as well as ordinal logistic regression models for the THA population. THA Pre-surgery mean (SD) 1 Year follow-up mean (SD) Unadjusted models Adjusted models A (N = 546) B (N = 227) A (N = 546) B (N = 227) Odds ratio (95% CI) Odds ratio (95% CI) Pain - Walking on Flat 2.51 (0.85) 2.63 (0.81) 4.53 (0.76) 4.61 (0.76) 1.39 (0.99 to 1.98) 1.30 (0.92 to 1.85) Pain - Going Up or Down Stairs 2.24 (0.89) 2.43 (0.87) 4.27 (0.93) 4.41 (0.90) 1.40 (1.03 to 1.91) 1.26 (0.92 to 1.73) Pain - At Night While in Bed 2.81 (1.03) 3.00 (0.99) 4.57 (0.79) 4.59 (0.75) 1.07 (0.76 to 1.51) 0.93 (0.66 to 1.33) Pain - Rising or Sitting on Chair 2.51 (0.94) 2.65 (0.89) 4.41 (0.87) 4.48 (0.82) 1.18 (0.86 to 1.63) 1.08 (0.79 to 1.50) Pain - Standing Upright 2.77 (0.87) 2.97 (0.98) 4.46 (0.85) 4.56 (0.78) 1.36 (0.98 to 1.90) 1.21 (0.86 to 1.70) Function - Ascending Stairs 2.37 (0.95) 2.60 (0.91) 4.14 (1.05) 4.32 (0.97) 1.43 (1.07 to 1.94) 1.27 (0.94 to 1.73) Function - Rising from Sitting 2.56 (0.95) 2.75 (0.89) 4.28 (0.94) 4.44 (0.80) 1.36 (1.00 to 1.84) 1.23 (0.90 to 1.68) Function - Walking on Flat 2.71 (0.87) 2.94 (0.84) 4.49 (0.82) 4.66 (0.71) 1.72 (1.21 to 2.46) 1.57 (1.10 to 2.26) Function - Getting In/Out of Car 2.43 (0.87) 2.70 (0.86) 4.15 (0.97) 4.44 (0.84) 1.83 (1.36 to 2.49) 1.56 (1.15 to 2.14) Function - Putting on Socks 2.25 (1.04) 2.39 (1.06) 4.11 (1.08) 4.32 (0.93) 1.39 (1.04 to 1.87) 1.31 (0.97 to 1.78) Function - Rising from Bed 2.89 (0.93) 3.10 (0.94) 4.49 (0.82) 4.58 (0.76) 1.31 (0.94 to 1.83) 1.16 (0.83 to 1.64) Function - Sitting 3.07 (0.97) 3.30 (0.86) 4.58 (0.75) 4.69 (0.68) 1.43 (1.00 to 2.08) 1.19 (0.82 to 1.75) * 95% CI = 95% Confidence Interval * Odds ratios for a higher rating on the scale between 1 and 5 for prosthesis B versus A * Adjusted models for the corresponding item pre-surgery * Additional adjustment for age, ASA score and education level did not change the results (not presented here) TKA population Among the 2478 patients who underwent a primary TKA between January 2012 and June 2022, 1057 were eligible for the study. Of those, 624 patients had a complete WOMAC questionnaire preoperatively and at one-year follow-up and were included in the analysis (Fig. 3 ). All baseline characteristics were similar between the eligible and included groups. Sex (63.9% vs 61.9%), age (70.7 vs 71.5 years) and BMI (30.3 vs 29.9 kg/m 2 ) were comparable, as well as ASA score, number of comorbidities, smoking status, education level, diagnosis, activity level, SF-12 MCS & PCS scores, and self-rated health. Among included patients, 361 received prosthesis A and 263 received prosthesis B. Preoperatively, baseline characteristics were similar between prosthesis groups, with approximately 61.8% female, a mean age of 71.3 years and BMI of 30.0 kg/m 2 . However, differences were observed in age (72.9 vs 69.7 years), ASA score (25.8% vs 20.9% classified as ASA 3–4) and education level (25.7% vs 30.4% with ≥ 13 years of schooling) (Table 4 ). Table 4 Baseline characteristics of the TKA population by prosthesis. Included Prosthesis A (N = 361) Prosthesis B (N = 263) Female, N (%) 224 (62.0%) 162 (61.6%) Age at Surgery, mean (SD) 72.9 (9.3) 69.7 (9.2) BMI, mean (SD) 29.8 (5.4) 30.1 (5.5) ASA Score, N (%) 1 10 (2.8%) 7 (2.7%) 2 258 (71.5%) 201 (76.4%) 3–4 93 (25.8%) 55 (20.9%) Number of Comorbidities, N (%) 0 22 (6.1%) 18 (6.8%) 1–3 223 (61.8%) 157 (59.7%) ≥ 4 116 (32.1%) 88 (33.5%) Smoking Status, N (%) Never 220 (61.5%) 155 (59.4%) Former 100 (27.9%) 67 (25.7%) Current 38 (10.6%) 39 (14.9%) Education (Years of Schooling), N (%) ≤ 8 years 131 (38.6%) 88 (35.6%) 9–12 years 121 (35.7%) 84 (34.0%) ≥ 13 years 87 (25.7%) 75 (30.4%) Diagnosis, N (%) Primary osteoarthritis 319 (88.4%) 223 (84.8%) Secondary osteoarthritis 42 (11.6%) 40 (15.2%) UCLA Presurgery, N (%) Low activity 265 (81.3%) 194 (80.5%) Moderate activity 56 (17.2%) 45 (18.7%) High activity 5 (1.5%) 2 (0.8%) SF-12 MCS Presurgery, mean (SD) 44.7 (11.7) 42.8 (10.8) SF-12 PCS Presurgery, mean (SD) 33.7 (7.0) 32.8 (6.9) Self-Rated Health Presurgery, N (%) Poor - Fair 65 (18.1%) 63 (24.0%) Good 228 (63.3%) 151 (57.6%) Very good - Excellent 67 (18.6%) 48 (18.3%) Note: Missing information on education (A: n = 22, B: n = 16), UCLA presurgery (A: n = 35, B: n = 22), SF-12 MCS presurgery (A: n = 9, B: n = 4), SF-12 PCS presurgery (A: n = 9, B: n = 4), self-rated health presurgery (A: n = 1, B: n = 1). Density distribution analysis revealed a large improvement in WOMAC Pain and Function summary scores from pre-surgery to one-year post-surgery in both prosthesis groups. Preoperative summary score distributions were similar between prostheses. However, one-year postoperative distributions for prosthesis B were shifted toward higher values. This suggests that patients receiving prosthesis B may present better pain and functional ability results at one-year follow-up (Fig. 4 ). Before surgery, WOMAC Pain and Function summary scores and items were similar between prosthesis groups. However, for consistency with THA analyses, preoperative WOMAC scores and items were used to adjust regression models. One year after surgery, all scores and items were higher in patients implanted with prosthesis B, confirming the trend observed in the density distribution analysis. Furthermore, the difference between prostheses was more pronounced for WOMAC Function than WOMAC Pain summary score. Regression analyses revealed differences between prosthesis groups. One pain item model - “going up or down stairs” - and four function item models - “rising from sitting”, “getting in/out of car”, “rising from bed” and “sitting” - as well as pain and function summary scores showed statistically significant differences in favor of prosthesis B, with odds ratios between 1.06 and 1.75 per 1-point change in response. In addition, both summary scores were significant, with WOMAC Function showing a larger difference than WOMAC Pain (Table 5 & Table 6 ). Table 5 WOMAC Pain & Function summary scores before and one-year after surgery by prosthesis, as well as linear regression models for the TKA population. TKA Pre-surgery mean (SD) 1 Year follow-up mean (SD) Unadjusted models Adjusted models Additional adjusted models A (N = 361) B (N = 263) A (N = 361) B (N = 263) Difference favoring B over A (95% CI) Difference favoring B over A (95% CI) Difference favoring B over A (95% CI) WOMAC Pain 39.4 (17.2) 39.4 (16.6) 75.8 (21.4) 78.4 (20.2) 2.6 (-0.7 to 5.9) 3.1 (0.0 to 6.3) 3.3 (0.1 to 6.6) WOMAC Function 44.1 (19.6) 44.3 (18.9) 72.5 (21.6) 76.9 (20.2) 4.5 (1.1 to 7.8) 4.8 (1.7 to 7.8) 4.4 (1.2 to 7.5) * 95% CI = 95% Confidence Interval * Adjusted models for patella resurfacing and the corresponding summary score pre-surgery & additional adjusted models for patella resurfacing, corresponding summary score, age, ASA score and education level pre-surgery Table 6 WOMAC Pain & Function items before and one-year after surgery by prosthesis, as well as ordinal logistic regression models for the TKA population. TKA Pre-surgery mean (SD) 1 Year follow-up mean (SD) Unadjusted models Adjusted models A (N = 361) B (N = 263) A (N = 361) B (N = 263) Odds ratio (95% CI) Odds ratio (95% CI) Pain - Walking on Flat 2.57 (0.79) 2.59 (0.79) 4.29 (0.90) 4.35 (0.83) 1.10 (0.81 to 1.49) 1.17 (0.86 to 1.59) Pain - Going Up or Down Stairs 1.98 (0.81) 2.03 (0.82) 3.60 (1.11) 3.79 (1.04) 1.36 (1.02 to 1.81) 1.43 (1.07 to 1.91) Pain - At Night While in Bed 3.18 (1.06) 3.08 (1.02) 4.25 (0.97) 4.37 (0.88) 1.20 (0.89 to 1.63) 1.31 (0.95 to 1.81) Pain - Rising or Sitting on Chair 2.55 (0.98) 2.48 (0.92) 3.92 (1.09) 4.04 (0.99) 1.18 (0.89 to 1.58) 1.31 (0.98 to 1.77) Pain - Standing Upright 2.62 (0.94) 2.69 (0.87) 4.09 (1.00) 4.14 (0.96) 1.06 (0.79 to 1.42) 1.06 (0.79 to 1.44) Function - Ascending Stairs 2.09 (0.89) 2.19 (0.90) 3.49 (1.12) 3.66 (1.11) 1.34 (1.01 to 1.79) 1.33 (0.99 to 1.78) Function - Rising from Sitting 2.53 (0.99) 2.52 (0.90) 3.70 (1.07) 3.94 (0.99) 1.52 (1.14 to 2.02) 1.61 (1.20 to 2.17) Function - Walking on Flat 2.74 (0.81) 2.78 (0.84) 4.22 (0.93) 4.34 (0.86) 1.25 (0.93 to 1.70) 1.31 (0.96 to 1.79) Function - Getting In/Out of Car 2.65 (1.00) 2.59 (0.94) 3.73 (1.03) 3.94 (0.97) 1.45 (1.09 to 1.94) 1.64 (1.22 to 2.21) Function - Putting on Socks 2.94 (1.16) 2.94 (1.15) 3.75 (1.12) 3.89 (1.12) 1.27 (0.96 to 1.70) 1.32 (0.98 to 1.78) Function - Rising from Bed 3.15 (1.10) 3.11 (0.99) 4.16 (1.00) 4.38 (0.87) 1.54 (1.14 to 2.10) 1.75 (1.27 to 2.43) Function - Sitting 3.26 (1.03) 3.30 (0.96) 4.23 (0.91) 4.39 (0.82) 1.37 (1.01 to 1.86) 1.44 (1.05 to 1.98) * 95% CI = 95% Confidence Interval * Odds ratios for a higher rating on the scale between 1 and 5 for prosthesis B versus A * Adjusted models for patella resurfacing and the corresponding item pre-surgery * Additional adjustment for age, ASA score and education level did not change the results (not presented here) Discussion Our findings revealed that differences between prostheses were captured using either WOMAC summary scores or items, with results favoring prosthesis B over prosthesis A. Overall, differences between both cup-stem combinations and knee prostheses were larger for patient-reported functional abilities than for patient-reported pain, indicating a greater influence on function. The observed results suggest that summary scores may be sufficient in identifying prosthesis-related differences. However, considering items alongside summary scores may offer additional insight in certain situations. Indeed, the item-level analysis showed that certain items were more sensitive to differences between prostheses one year after THA and TKA than others, and in relation to WOMAC Pain and Function summary scores. These discriminative items varied by comparison, between cup-stem combinations in THA or among prostheses with different stability designs in TKA, and were clinically relevant, with functional abilities related to vertical movement playing a critical role in comparing knee prostheses. To the authors’ knowledge, no previous studies have explored PRO individual items within the field of total joint arthroplasty, making comparisons with other studies impossible. However, some studies have highlighted the importance of considering individual items. As an example, Perneger et al.[ 17 ] reported that self-rated health, the first SF-12 question, behaves very differently from other SF-12 items when assessing health changes following THA or TKA. This highlights the potential benefit of examining items rather than relying solely on summary scores. Additionally, Hinds et al.[ 18 ] warned against the risk of inaccurately assessing patients relying solely on summary scores, which may have serious implications for patient’s clinical care. Moreover, a few studies in other fields have compared the use of PRO individual items with summary scores, all demonstrating the added value of considering items. For instance, Kalron et al.[ 8 ] investigated the association between PRO items and mobility in patients with multiple sclerosis and found that only two items were sufficient to assess patient mobility capabilities. Houben-Wilke et al.[ 9 ] studied the impact of respiratory and non-respiratory items on summary score in patients with chronic obstructive pulmonary disease. They reported that a considerable number of patients were classified as highly symptomatic, therefore being candidates for respiratory pharmacological treatments, based mostly on non-respiratory symptoms. Nielsen et al.[ 10 ] compared two methods to identify subgroups of patients with low back pain, latent class analysis using summary scores and items of several PRO tools. Both strategies yielded clinically satisfying results, with the items approach allowing for more detailed subgrouping. Lastly, Floden et al.[ 11 ] used items to compare changes in depressive symptoms following two treatments, treatment and control, in patients with treatment-resistant depression. It revealed significant improvements in specific items, offering insight into which depressive symptoms improved most after treatment. This study has several limitations. First, since the comparison of WOMAC summary scores and items in primary THA and TKA was limited to one-year results, results cannot be generalized to later time points. One-year post-surgery is the recommended time point for PROs assessment[ 19 ], as recovery is considered complete for most patients, with maximal pain reduction and improvement in function[ 20 ]. After one year, some additional improvement has been reported only in TKA patients[ 21 ]. Second, not all patients completed the WOMAC questionnaire at one-year follow-up. However, PROs were available for 74.2% of THA patients and 71.6% of TKA, exceeding the 60% threshold deemed sufficient[ 19 ]. Third, other external factors, such as variations in surgical techniques or perioperative management, may have influenced the results and were not controlled for in this study. Nevertheless, the study objective was to compare the discriminative ability of WOMAC items to summary scores, rather than comparing specific prostheses. Lastly, this study focused on specific prostheses. Therefore, the findings and conclusions need to be tested with other prosthesis models. In conclusion, the findings indicate that WOMAC summary scores were sufficient to detect differences between prostheses, as both scores and items captured differences. The results highlight however the potential added value of considering individual items alongside summary scores when evaluating and comparing prostheses performance in THA and TKA, especially for detecting implant-related differences in functional abilities. Indeed, item-level analysis may provide additional information or a more nuanced understanding of differences. Furthermore, the findings do not justify selecting or prioritizing specific items, since discriminative items varied across comparisons. Future research should further investigate the combined use of summary scores and items across various PRO tools, time points, and populations to better understand when item-level analysis adds meaningful information and to draw more robust conclusions. Declarations Funding: Institutional financial support was received for the registry from the “Fondation pour la recherche ostéoarticulaire”. Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Author Contributions: All authors contributed to the conception of the study. Methodology was developed by Marys Revaz, Thomas Perneger, and Anne Lübbeke. Data curation was performed by Marys Revaz, Christophe Barea, and Anne Lübbeke. Formal analysis and visualization were conducted by Marys Revaz, Thomas Perneger, and Anne Lübbeke. All authors contributed to the investigation and validation. Supervision was provided by Thomas Perneger, Hermes H. Miozzari, Didier Hannouche, and Anne Lübbeke. The original draft of the manuscript was written by Marys Revaz, Thomas Perneger, and Anne Lübbeke, and all authors contributed to the review and editing of the manuscript. All authors read and approved the final manuscript. Ethics approval: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the local ethics committee (CCER Geneva, Switzerland). Consent to participate: Written informed consent was obtained from all patients included in the study. Consent to publish: Not applicable. Acknowledgments: The authors thank all patients and orthopedic surgeons from the Division of Orthopedic Surgery and Musculoskeletal Trauma Care who have contributed information to the registry since 1996. They also express their gratitude to Carole Bandi, Flavia Renevey, and Lamia Blatter-Sellak for their invaluable assistance with data entry. References Wilson, I., Bohm, E., Lübbeke, A., Lyman, S., Overgaard, S., Rolfson, O., & Dunbar, M. (2019). Orthopaedic registries with patient-reported outcome measures. EFORT open reviews , 4 (6), 357–367. https://doi.org/10.1302/2058-5241.4.180080 Nieuwenhuijse, M. J., Randsborg, P. H., Hyde, J. H., Xi, W., Franklin, P., Sun, L., & Sedrakyan, A. (2023). Evidence-based objective performance criteria for the evaluation of hip and knee replacement devices and technologies. International Journal of Surgery (London England) , 109 (5), 1125–1135. https://doi.org/10.1097/JS9.0000000000000169 Babu, S., Singh, P., Wiik, A., Shastri, O., Malik, K., Bailey, J., & Cobb, J. (2020). A comparison of patient-reported outcome measures (PROMs) between short and conventional stem hip replacements: a systematic review and meta-analysis. Hip International: The Journal of Clinical and Experimental Research on Hip Pathology and Therapy , 30 (5), 513–522. https://doi.org/10.1177/1120700019888210 Migliorini, F., Maffulli, N., Cuozzo, F., Pilone, M., Elsner, K., & Eschweiler, J. (2022). No difference between mobile and fixed bearing in primary total knee arthroplasty: a meta-analysis. Knee surgery, sports traumatology, arthroscopy: official journal of the ESSKA , 30 (9), 3138–3154. https://doi.org/10.1007/s00167-022-07065-5 Eckhard, L., Munir, S., Wood, D., Talbot, S., Brighton, R., Walter, B., & Baré, J. (2021). The ceiling effects of patient reported outcome measures for total knee arthroplasty. Orthopaedics & traumatology surgery & research: OTSR , 107 (3), 102758. https://doi.org/10.1016/j.otsr.2020.102758 Nisar, S., Ahmad, K., Palan, J., Pandit, H., & van Duren, B. (2022). Medial stabilised total knee arthroplasty achieves comparable clinical outcomes when compared to other TKA designs: a systematic review and meta-analysis of the current literature. Knee surgery, sports traumatology, arthroscopy: official journal of the ESSKA , 30 (2), 638–651. https://doi.org/10.1007/s00167-020-06358-x McDowell, I. (2006). Measuring health: a guide to rating scales and questionnaires (3rd ed.). Oxford University Press. Kalron, A., Ehling, R., Baert, I., Smedal, T., Rasova, K., Heric-Mansrud, A., … Feys,P. (2020). Improving our understanding of the most important items of the Multiple Sclerosis Walking Scale-12 indicating mobility dysfunction: Secondary results from a RIMS multicenter study. Multiple Sclerosis and Related Disorders , 46 , 102511. https://doi.org/10.1016/j.msard.2020.102511. Houben-Wilke, S., Janssen, D. J. A., Franssen, F. M. E., Vanfleteren, L. E. G. W., Wouters, E. F. M., & Spruit, M. A. (2018). Contribution of individual COPD assessment test (CAT) items to CAT total score and effects of pulmonary rehabilitation on CAT scores. Health and Quality of Life Outcomes , 16 (1), 205. https://doi.org/10.1186/s12955-018-1034-4 Nielsen, A. M., Vach, W., Kent, P., Hestbaek, L., & Kongsted, A. (2016). Using existing questionnaires in latent class analysis: should we use summary scores or single items as input? A methodological study using a cohort of patients with low back pain. Clinical Epidemiology , 8 , 73–89. https://doi.org/10.2147/CLEP.S103330 Floden, L., Hudgens, S., Jamieson, C., Popova, V., Drevets, W. C., Cooper, K., & Singh, J. (2022). Evaluation of Individual Items of the Patient Health Questionnaire (PHQ-9) and Montgomery-Asberg Depression Rating Scale (MADRS) in Adults with Treatment-Resistant Depression Treated with Esketamine Nasal Spray Combined with a New Oral Antidepressant. CNS drugs , 36 (6), 649–658. https://doi.org/10.1007/s40263-022-00916-2 Lübbeke, A., Baréa, C., Miozzari, H., Garavaglia, G., Gonzalez, A., Zingg, M., …Hannouche, D. (2021). [Lessons learned from 25 years of an institutional hip and knee arthroplasty registry]. Revue Medicale Suisse , 17 (763), 2161–2165. Whitehouse, S. L., Lingard, E. A., Katz, J. N., & Learmonth, I. D. (2003). Development and testing of a reduced WOMAC function scale. The Journal of Bone and Joint Surgery British Volume , 85 (5), 706–711. ​Statement on ASA Physical Status Classification System. (n.d.). Retrieved January 30 (2024). from https://www.asahq.org/standards-and-practice-parameters/statement-on-asa-physical-status-classification-system Zahiri, C. A., Schmalzried, T. P., Szuszczewicz, E. S., & Amstutz, H. C. (1998). Assessing activity in joint replacement patients. The Journal of Arthroplasty , 13 (8), 890–895. https://doi.org/10.1016/s0883-5403(98)90195-4 Ware, J., Kosinski, M., & Keller, S. D. (1996). A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Medical Care , 34 (3), 220–233. https://doi.org/10.1097/00005650-199603000-00003 Perneger, T., & Lübbeke, A. (2019). The paradox of self-rated health following joint replacement surgery. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment Care and Rehabilitation , 28 (2), 503–508. https://doi.org/10.1007/s11136-018-2018-x Hinds, P. S., Schum, L., & Srivastava, D. K. (2002). Is clinical relevance sometimes lost in summative scores? Western Journal of Nursing Research , 24 (4), 345–353. https://doi.org/10.1177/01945902024004004 Rolfson, O., Bohm, E., Franklin, P., Lyman, S., Denissen, G., Dawson, J., … Patient-Reported Outcome Measures Working Group of the International Society of Arthroplasty Registries.(2016). Patient-reported outcome measures in arthroplasty registries Report of the Patient-Reported Outcome Measures Working Group of the International Society of Arthroplasty Registries Part II. Recommendations for selection, administration, and analysis. Acta Orthopaedica , 87 Suppl 1 (Suppl 1), 9–23. https://doi.org/10.1080/17453674.2016.1181816. Galea, V. P., Rojanasopondist, P., Ingelsrud, L. H., Rubash, H. E., Bragdon, C.,Huddleston Iii, J. I., … Troelsen, A. (2019). Longitudinal changes in patient-reported outcome measures following total hip arthroplasty and predictors of deterioration during follow-up: a seven-year prospective international multicentre study. The Bone & Joint Journal , 101-B (7), 768–778. https://doi.org/10.1302/0301-620X.101B7.BJJ-2018-1491.R1. Williams, D. P., Blakey, C. M., Hadfield, S. G., Murray, D. W., Price, A. J., & Field, R. E. (2013). Long-term trends in the Oxford knee score following total knee replacement. The Bone & Joint Journal , 95-B (1), 45–51. https://doi.org/10.1302/0301-620X.95B1.28573 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8593555","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580949088,"identity":"a925dbfc-3e92-4767-a256-1e7b9d41195a","order_by":0,"name":"Marys Revaz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYJADxsdgSoIELczGJGthkyZKi2772WeSPxjuyRkcP/6surCtloF/dgN+LWZn0s2keRiKjQ3OJKTdntl2nEHizgECWg6kgdyTkDizIeHYbd62YwwGEgkEtJx/xgZ0WEL9zP6HbcXEabmRxibBw5CQwC+RzMbM21ZDjJZnzNY8BgmG/RLPmKV5zh3gkbhB0GFpjDd/VCTIs/GnP/zMU1Ynxz+DgBYgYJFgMIBzDvMQVA8EzB+QOHXE6BgFo2AUjIIRBgB0fDwS/A17VAAAAABJRU5ErkJggg==","orcid":"","institution":"University Hospital of Geneva","correspondingAuthor":true,"prefix":"","firstName":"Marys","middleName":"","lastName":"Revaz","suffix":""},{"id":580949094,"identity":"f37141c9-fcaa-4f45-a9a5-61fcffe02f9b","order_by":1,"name":"Thomas Perneger","email":"","orcid":"","institution":"University Hospital of Geneva","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Perneger","suffix":""},{"id":580949095,"identity":"9fd52164-c630-4203-8d49-333cee707942","order_by":2,"name":"Christophe Barea","email":"","orcid":"","institution":"University Hospital of Geneva","correspondingAuthor":false,"prefix":"","firstName":"Christophe","middleName":"","lastName":"Barea","suffix":""},{"id":580949096,"identity":"9d6fcc5c-861f-469d-9a6e-9c3acd2638c9","order_by":3,"name":"Hermes H. Miozzari","email":"","orcid":"","institution":"University Hospital of Geneva","correspondingAuthor":false,"prefix":"","firstName":"Hermes","middleName":"H.","lastName":"Miozzari","suffix":""},{"id":580949098,"identity":"2cdc9898-be66-4097-b4db-0dd97cb79fa0","order_by":4,"name":"Didier Hannouche","email":"","orcid":"","institution":"University Hospital of Geneva","correspondingAuthor":false,"prefix":"","firstName":"Didier","middleName":"","lastName":"Hannouche","suffix":""},{"id":580949100,"identity":"b4625caf-273c-4ba0-99e1-ba88d9737e78","order_by":5,"name":"Anne Lübbeke","email":"","orcid":"","institution":"University Hospital of Geneva","correspondingAuthor":false,"prefix":"","firstName":"Anne","middleName":"","lastName":"Lübbeke","suffix":""}],"badges":[],"createdAt":"2026-01-13 15:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8593555/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8593555/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101434071,"identity":"7cc3a754-76d2-429c-9789-607257444303","added_by":"auto","created_at":"2026-01-29 16:03:44","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":315764,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for the THA population\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8593555/v1/f72a29a3cbeee0254f3f51a3.jpg"},{"id":101434067,"identity":"ee1d33c4-c968-4ef3-a8e9-2ff3d4b33bd4","added_by":"auto","created_at":"2026-01-29 16:03:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":247369,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of preoperative and 1-year postoperative WOMAC Pain \u0026amp; Function summary scores by prosthesis for the THA population\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8593555/v1/8e3a83d021f5e3bc1e9d6dea.jpg"},{"id":101434069,"identity":"7bb8fd61-4f97-4aa6-8738-1601aa3a5bff","added_by":"auto","created_at":"2026-01-29 16:03:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":276511,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for the TKA population\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8593555/v1/ae3edbe684e86119765e85ac.jpg"},{"id":101434068,"identity":"b0ef4c18-6c38-447d-b016-37fc290f387e","added_by":"auto","created_at":"2026-01-29 16:03:40","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":271787,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of preoperative and 1-year postoperative WOMAC Pain \u0026amp; Function summary scores by prosthesis for the TKA population\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8593555/v1/b6d608089694bd1b50eb375d.jpg"},{"id":109527828,"identity":"6ea4de4c-5ec7-4c0d-bc15-ac83d33d7ff0","added_by":"auto","created_at":"2026-05-19 07:26:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1547453,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8593555/v1/35286ede-c06a-46e3-a8f9-e0c0aa041c7a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Individual items versus summary scores in comparing patient outcomes among different types of total hip and knee arthroplasty designs","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePatient reported outcomes (PROs) play an important role in evaluating outcomes following total hip and knee arthroplasties (THAs \u0026amp; TKAs). In recent years, they have been increasingly used alongside revision rates to evaluate prosthesis benefits or compare prostheses[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. To date, studies have exclusively used PRO summary scores for such comparisons, which often failed to capture differences[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Indeed, comparisons of prostheses or techniques using PROs may lack sensitivity, a problem well known and referred to in the literature as the ceiling effect. With improvements in surgical techniques and prostheses, most patients achieve good results after THA or TKA, making it difficult to distinguish between good outcomes[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Alternatively, PRO scales may lack discrimination because they average multiple aspects of patient experience.\u003c/p\u003e \u003cp\u003eTypically, PRO scales combine answers from several items to capture a single latent variable (e.g. degree of pain) which cannot be measured directly. This is the classic psychometric approach. The latent variable is deemed to influence all item answers to some extent, explaining why item responses are correlated. The sum of item responses approaches the true value of the latent variable better than any single item, if considered in isolation. But the specific content of items is not of interest, items only serve to reveal the latent variable. In fact, any information that is specific to a given item (i.e. information uncorrelated to other item responses) is considered to represent measurement error[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHere, we propose a \"non-psychometric\" approach, which aims to use the specific content of each scale item. Items are selected because they represent the most salient situations where the latent variable can be perceived or influence behavior, e.g. various situations where the respondent is likely to experience pain. When comparing prostheses or surgical techniques, their impact on patient outcomes may be heterogeneous, or situation dependent. For example, some prostheses may be accompanied by pain when climbing stairs, but not when walking on flat ground, and conversely for others. Whether the use of such item-specific information has any utility in assessing PROs of arthroplasty surgery is currently unknown, as little research has compared the use of items with summary scores. However, most studies highlighted the advantage of using items or a subset of items to classify patients and take therapeutic decisions[\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study aims at contrasting full-scale to item-specific scores when comparing outcomes of two prostheses in THA and TKA. Specifically, it evaluates and compares the ability of WOMAC Pain and Function summary scores and individual items to differentiate between two cup-stem combinations with the same surgical approach and fixation type in THA, and two prostheses with different stability designs in TKA.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eThis cohort study used data from the Geneva Arthroplasty Registry[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which prospectively collects data from THAs and TKAs performed at a tertiary care, university hospital. It was approved by the local ethics committee (CCER Geneva, Switzerland). For this study, all primary elective THAs and TKAs performed between January 2012 and June 2022 were selected.\u003c/p\u003e \u003cp\u003eIn THA, patients who underwent an anterior surgical approach and were implanted with either Versafit cup and Quadra-H stem (Versafit/Quadra-H; Medacta Int., Castel San Pietro, Switzerland) or Pinnacle cup and Corail-H stem (Pinnacle/Corail-H; DePuy Synthes, Warsaw, IN, US), using an uncemented fixation, were eligible. The Pinnacle/Corail-H group generally received larger head sizes, with 1.9% of patients receiving a 28mm, 55.3% a 32mm and 42.7% a 36mm head. In the Versafit/Quadra-H group, 24.9% of patients received a 28mm, 63.1% a 32mm and 12% a 36mm head.\u003c/p\u003e \u003cp\u003eIn TKA, patients who were implanted with either a PFC Sigma prosthetic design in its posterior stabilized version (PFC-PS; DePuy Synthes, Warsaw, IN, US) or a GMK Sphere design (Medacta Int., Castel San Pietro, Switzerland) with medial pivot, were eligible. The PFC-PS group had a higher patella resurfacing rate (67.2% vs 52.8%) and underwent predominantly mechanical alignment (98.5% mechanical vs 1.5% kinematic), whereas the GMK Sphere group showed a more balanced distribution (58.2% mechanical vs 41.8% kinematic).\u003c/p\u003e \u003cp\u003eFrom these groups, patients who did not consent to research, who died, were lost to follow-up, or underwent revision within one year after surgery were excluded. Finally, only patients with a complete short-form (12-item scale) Western Ontario and McMasters Universities Osteoarthritis Index (WOMAC) questionnaire preoperatively and at one year follow-up were included in the analysis[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe prostheses selected for this study were chosen because they were the most frequently implanted at our institution during the study period. Additionally, to ensure that the focus remains on the study\u0026rsquo;s objective, i.e. assessing whether WOMAC items discriminate better between prostheses than summary scores, rather than comparing specific prostheses, prosthesis names were coded throughout the manuscript. For THA, Versafit/Quadra-H will be referred to as prosthesis A and Pinnacle/Corail-H as prosthesis B. For TKA, PFC-PS will be referred to as prosthesis A and GMK Sphere as prosthesis B.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Variables\u003c/h3\u003e\n\u003cp\u003eThe main independent variables were the prosthesis types for both populations. In THA, two cup-stem combinations were studied, while in TKA, two prostheses with different stability designs were studied. The main dependent variables included the WOMAC Pain and Function summary scores and 12 individual items assessed by the short-form WOMAC questionnaire, one year after surgery. The WOMAC questionnaire used is a reduced version of the original, composed of the 5 original items for pain and 7 items for function[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It is a patient self-assessed questionnaire developed for patients with hip and knee osteoarthritis. Items are assessed on a 5-point Likert scale and both pain and function scores range from 0 to 100 (=\u0026thinsp;best)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDescriptive variables were assessed preoperatively and included for both populations: sex, age at surgery, body mass index (BMI), American Society of Anesthesiologists (ASA) score[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], number of comorbidities, smoking status (never, former or current), education level (\u0026le;\u0026thinsp;8, 9\u0026ndash;12 or \u0026ge;\u0026thinsp;13 years), diagnosis (primary or secondary osteoarthritis), University of California Los Angeles (UCLA) activity scale[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], Mental and Physical Component Score (MCS \u0026amp; PCS) of the 12-item Short Form (SF-12) questionnaire[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], self-rated health assessed with the first SF-12 question, and pain and function summary scores of the short-form WOMAC questionnaire[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. ASA score assesses a patient\u0026rsquo;s pre-anesthesia medical comorbidities (1: healthy, 2: mild systemic disease, 3: severe systemic disease and 4: severe systemic disease that is constant threat to life)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. UCLA activity scale assesses a patient\u0026rsquo;s activity level on a scale from 1 (wholly inactive) to 10 (regularly participates in impact sports)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It was grouped into three categories - low (1\u0026ndash;4), moderate (5\u0026ndash;7) and high activity (8\u0026ndash;10). SF-12 is a patient self-assessed questionnaire assessing a patient\u0026rsquo;s general health status and comprising two summary scores, MCS and PCS[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eIn both populations, baseline characteristics were compared between prostheses. Continuous variables were reported as mean and standard deviation (SD), while categorical variables were reported as proportions (in %). To compare WOMAC Pain and Function summary scores and items one year after surgery between prostheses, mean and SD were calculated, even though items are ordinal variables. WOMAC items were coded from 1 to 5 (=\u0026thinsp;best). The density distribution (kernel smoothing of the frequency distribution) of WOMAC Pain and Function summary scores pre-surgery and one-year post-surgery was also calculated.\u003c/p\u003e \u003cp\u003eTo analyze the ability of WOMAC Pain and Function summary scores and items in discriminating between prostheses, univariate linear and ordinal logistic regression models were applied. Linear regression was used for continuous summary scores, whereas ordinal logistic regression was used for items. In all models, prosthesis type was used as the explanatory variable and was coded as 0 for prosthesis A and 1 for prosthesis B for both THA and TKA. In linear models, WOMAC Pain and Function summary scores were alternately used as response variables. In ordinal logistic models, the 5 pain and 7 function items were alternately used as response variables. WOMAC items were coded from 1 to 5 (=\u0026thinsp;best). In total, 14 models were built for each population. All models were first adjusted for the corresponding preoperative summary score or item. In a second step, they were additionally adjusted for age at surgery (continuous), ASA score (1\u0026ndash;2 vs 3\u0026ndash;4) and education (\u0026lt;\u0026thinsp;13 vs\u0026thinsp;\u0026ge;\u0026thinsp;13 years) to account for confounding. For TKA, models were additionally adjusted for patella resurfacing in both adjustment steps. Results from linear models were reported as differences with 95% confidence intervals (CIs) and those from ordinal logistic models as odds ratios with 95% CIs, odds ratios representing the relative odds of a 1-point higher rating on the scale between 1 and 5 points for prosthesis B versus prosthesis A. Data were analyzed using R, version 4.2.2 (R Foundation for Statistical Computing, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTHA population\u003c/h2\u003e \u003cp\u003eAmong the 3278 patients who underwent a primary elective THA between January 2012 and June 2022, 1232 were eligible for the study. Of those, 773 patients had a complete WOMAC questionnaire preoperatively and at one-year follow-up and were included in the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All baseline characteristics were similar between the eligible and included groups. Sex (44.7% vs 45.0%), age (64.8 vs 65.9 years) and BMI (27.5 vs 27.2 kg/m\u003csup\u003e2\u003c/sup\u003e) were comparable, as well as ASA score, number of comorbidities, smoking status, education level, diagnosis, activity level, SF-12 MCS \u0026amp; PCS scores, and self-rated health.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong included patients, 546 received prosthesis A, while 227 received prosthesis B. Preoperatively, baseline characteristics were similar between prosthesis groups, with approximately 45.0% female, a mean age of 66.1 years and BMI of 27.1 kg/m\u003csup\u003e2\u003c/sup\u003e. However, differences were observed in age (65.6 vs 66.5 years), ASA score (13.7% vs 18.9% classified as ASA 3\u0026ndash;4) and education level (35.8% vs 41.4% with \u0026ge;\u0026thinsp;13 years of schooling) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the THA population by prosthesis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eIncluded\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProsthesis A\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;546)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProsthesis B\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;227)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale, N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e246 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102 (44.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at Surgery, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.6 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.5 (9.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.2 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.0 (4.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASA Score, N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e421 (77.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161 (70.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75 (13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43 (18.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Comorbidities, N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e376 (68.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e149 (65.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99 (18.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking Status, N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e282 (51.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106 (47.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163 (29.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation (Years of Schooling), N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;8 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026ndash;12 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175 (32.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;13 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e192 (35.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89 (41.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiagnosis, N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary osteoarthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e496 (90.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e210 (92.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary osteoarthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUCLA Presurgery, N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e391 (76.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e170 (80.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (14.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSF-12 MCS Presurgery, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.4 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.8 (11.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSF-12 PCS Presurgery, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.6 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.7 (7.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-Rated Health Presurgery, N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor - Fair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e296 (54.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114 (50.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good - Excellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e157 (28.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Missing information on contralateral hip status (A: n\u0026thinsp;=\u0026thinsp;1, B: n\u0026thinsp;=\u0026thinsp;1), education (A: n\u0026thinsp;=\u0026thinsp;9, B: n\u0026thinsp;=\u0026thinsp;12), UCLA presurgery (A: n\u0026thinsp;=\u0026thinsp;35, B: n\u0026thinsp;=\u0026thinsp;16), SF-12 MCS presurgery (A: n\u0026thinsp;=\u0026thinsp;10, B: n\u0026thinsp;=\u0026thinsp;3), SF-12 PCS presurgery (A: n\u0026thinsp;=\u0026thinsp;10, B: n\u0026thinsp;=\u0026thinsp;3), self-rated health presurgery (A: n\u0026thinsp;=\u0026thinsp;2, B: n\u0026thinsp;=\u0026thinsp;2).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDensity distribution analysis revealed a large improvement in WOMAC Pain and Function summary scores from pre-surgery to one-year post-surgery in both prosthesis groups. For both scores, preoperative and one-year postoperative distributions for prosthesis B were shifted toward higher values, suggesting that regression models might need to be adjusted for WOMAC summary scores pre-surgery (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBefore surgery, all WOMAC Pain and Function summary scores and items were higher in the prosthesis B group, indicating a better preoperative state in terms of pain and functional abilities. This confirmed the need to adjust regression models for summary scores and items pre-surgery. One year after surgery, the same pattern was observed, with prosthesis B group showing higher values for all summary scores and items. Also, the difference between prostheses was more pronounced for WOMAC Function than WOMAC Pain summary score.\u003c/p\u003e \u003cp\u003eRegression analyses revealed differences between prosthesis groups. After adjusting for the corresponding preoperative WOMAC summary score or item, as well as age, ASA score and education level, differences in pain and functional abilities between prostheses one year after surgery were attenuated. Two function item models - \u0026ldquo;walking on flat\u0026rdquo; and \u0026ldquo;getting in/out of car\u0026rdquo; - showed statistically significant differences in favor of prosthesis B, with odds ratios between 1.16 and 1.57 per 1-point change in response, whereas none of the pain item models did. Considering summary score models, WOMAC Function showed a statistically significant difference in favor of prosthesis B, whereas no difference was observed for WOMAC Pain (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWOMAC Pain \u0026amp; Function summary scores before and one-year after surgery by prosthesis, as well as linear regression models for the THA population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePre-surgery\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1 Year follow-up\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003cp\u003emodels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003emodels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdditional\u003c/p\u003e \u003cp\u003eadjusted models\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;546)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;227)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;546)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;227)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDifference\u003c/p\u003e \u003cp\u003efavoring\u003c/p\u003e \u003cp\u003eB over A\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDifference\u003c/p\u003e \u003cp\u003efavoring\u003c/p\u003e \u003cp\u003eB over A\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDifference\u003c/p\u003e \u003cp\u003efavoring\u003c/p\u003e \u003cp\u003eB over A\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWOMAC Pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.2\u003c/p\u003e \u003cp\u003e(17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.3\u003c/p\u003e \u003cp\u003e(17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.2\u003c/p\u003e \u003cp\u003e(18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.3\u003c/p\u003e \u003cp\u003e(17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003cp\u003e(-0.7 to 4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003cp\u003e(-1.9 to 3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003cp\u003e(-1.9 to 3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWOMAC Function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.3\u003c/p\u003e \u003cp\u003e(18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003cp\u003e(17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.0\u003c/p\u003e \u003cp\u003e(19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.3\u003c/p\u003e \u003cp\u003e(17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003cp\u003e(1.4 to 7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003cp\u003e(-0.3 to 5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003cp\u003e(0.1 to 5.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e* 95% CI\u0026thinsp;=\u0026thinsp;95% Confidence Interval\u003c/h2\u003e \u003cp\u003e* Adjusted models for the corresponding summary score pre-surgery \u0026amp; additional adjusted models for the corresponding summary score, age, ASA score and education level pre-surgery\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWOMAC Pain \u0026amp; Function items before and one-year after surgery by prosthesis, as well as ordinal logistic regression models for the THA population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePre-surgery\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1 Year follow-up\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnadjusted models\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdjusted models\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;546)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;227)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;546)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;227)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain - Walking on Flat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.51 (0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.63 (0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.53 (0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.61 (0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003cp\u003e(0.99 to 1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003cp\u003e(0.92 to 1.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain - Going Up or Down Stairs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.24 (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.43 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.27 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.41 (0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003cp\u003e(1.03 to 1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003cp\u003e(0.92 to 1.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain - At Night While in Bed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.81 (1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.57 (0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.59 (0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003cp\u003e(0.76 to 1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003cp\u003e(0.66 to 1.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain - Rising or Sitting on Chair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.51 (0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.65 (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.41 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.48 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003cp\u003e(0.86 to 1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003cp\u003e(0.79 to 1.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain - Standing Upright\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.77 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.97 (0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.46 (0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.56 (0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003cp\u003e(0.98 to 1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003cp\u003e(0.86 to 1.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunction - Ascending Stairs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.37 (0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.60 (0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.14 (1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.32 (0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003cp\u003e(1.07 to 1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003cp\u003e(0.94 to 1.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunction - Rising from Sitting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.56 (0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.75 (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.28 (0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.44 (0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003cp\u003e(1.00 to 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003cp\u003e(0.90 to 1.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunction - Walking on Flat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.71 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.94 (0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.49 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.66 (0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003cp\u003e(1.21 to 2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003cp\u003e(1.10 to 2.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunction - Getting In/Out of Car\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.43 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.70 (0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.15 (0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.44 (0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003cp\u003e(1.36 to 2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003cp\u003e(1.15 to 2.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunction - Putting on Socks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.25 (1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.39 (1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.11 (1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.32 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003cp\u003e(1.04 to 1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003cp\u003e(0.97 to 1.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunction - Rising from Bed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.89 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.10 (0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.49 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.58 (0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003cp\u003e(0.94 to 1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003cp\u003e(0.83 to 1.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunction - Sitting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.07 (0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.30 (0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.58 (0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.69 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003cp\u003e(1.00 to 2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003cp\u003e(0.82 to 1.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e* 95% CI = 95% Confidence Interval\u003c/h3\u003e\n\u003cp\u003e* Odds ratios for a higher rating on the scale between 1 and 5 for prosthesis B versus A\u003c/p\u003e \u003cp\u003e* Adjusted models for the corresponding item pre-surgery\u003c/p\u003e \u003cp\u003e* Additional adjustment for age, ASA score and education level did not change the results (not presented here)\u003c/p\u003e\n\u003ch3\u003eTKA population\u003c/h3\u003e\n\u003cp\u003eAmong the 2478 patients who underwent a primary TKA between January 2012 and June 2022, 1057 were eligible for the study. Of those, 624 patients had a complete WOMAC questionnaire preoperatively and at one-year follow-up and were included in the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). All baseline characteristics were similar between the eligible and included groups. Sex (63.9% vs 61.9%), age (70.7 vs 71.5 years) and BMI (30.3 vs 29.9 kg/m\u003csup\u003e2\u003c/sup\u003e) were comparable, as well as ASA score, number of comorbidities, smoking status, education level, diagnosis, activity level, SF-12 MCS \u0026amp; PCS scores, and self-rated health.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong included patients, 361 received prosthesis A and 263 received prosthesis B. Preoperatively, baseline characteristics were similar between prosthesis groups, with approximately 61.8% female, a mean age of 71.3 years and BMI of 30.0 kg/m\u003csup\u003e2\u003c/sup\u003e. However, differences were observed in age (72.9 vs 69.7 years), ASA score (25.8% vs 20.9% classified as ASA 3\u0026ndash;4) and education level (25.7% vs 30.4% with \u0026ge;\u0026thinsp;13 years of schooling) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the TKA population by prosthesis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eIncluded\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProsthesis A\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;361)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProsthesis B \u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;263)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale, N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e224 (62.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162 (61.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at Surgery, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.9 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.7 (9.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.8 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.1 (5.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASA Score, N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e258 (71.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201 (76.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Comorbidities, N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e223 (61.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e157 (59.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116 (32.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking Status, N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e220 (61.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155 (59.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100 (27.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67 (25.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation (Years of Schooling), N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;8 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131 (38.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88 (35.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026ndash;12 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84 (34.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;13 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87 (25.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiagnosis, N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary osteoarthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e319 (88.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e223 (84.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary osteoarthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUCLA Presurgery, N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e265 (81.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194 (80.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (18.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSF-12 MCS Presurgery, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.7 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.8 (10.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSF-12 PCS Presurgery, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.7 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.8 (6.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-Rated Health Presurgery, N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor - Fair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65 (18.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63 (24.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e228 (63.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151 (57.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good - Excellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Missing information on education (A: n\u0026thinsp;=\u0026thinsp;22, B: n\u0026thinsp;=\u0026thinsp;16), UCLA presurgery (A: n\u0026thinsp;=\u0026thinsp;35, B: n\u0026thinsp;=\u0026thinsp;22), SF-12 MCS presurgery (A: n\u0026thinsp;=\u0026thinsp;9, B: n\u0026thinsp;=\u0026thinsp;4), SF-12 PCS presurgery (A: n\u0026thinsp;=\u0026thinsp;9, B: n\u0026thinsp;=\u0026thinsp;4), self-rated health presurgery (A: n\u0026thinsp;=\u0026thinsp;1, B: n\u0026thinsp;=\u0026thinsp;1).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDensity distribution analysis revealed a large improvement in WOMAC Pain and Function summary scores from pre-surgery to one-year post-surgery in both prosthesis groups. Preoperative summary score distributions were similar between prostheses. However, one-year postoperative distributions for prosthesis B were shifted toward higher values. This suggests that patients receiving prosthesis B may present better pain and functional ability results at one-year follow-up (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBefore surgery, WOMAC Pain and Function summary scores and items were similar between prosthesis groups. However, for consistency with THA analyses, preoperative WOMAC scores and items were used to adjust regression models. One year after surgery, all scores and items were higher in patients implanted with prosthesis B, confirming the trend observed in the density distribution analysis. Furthermore, the difference between prostheses was more pronounced for WOMAC Function than WOMAC Pain summary score.\u003c/p\u003e \u003cp\u003eRegression analyses revealed differences between prosthesis groups. One pain item model - \u0026ldquo;going up or down stairs\u0026rdquo; - and four function item models - \u0026ldquo;rising from sitting\u0026rdquo;, \u0026ldquo;getting in/out of car\u0026rdquo;, \u0026ldquo;rising from bed\u0026rdquo; and \u0026ldquo;sitting\u0026rdquo; - as well as pain and function summary scores showed statistically significant differences in favor of prosthesis B, with odds ratios between 1.06 and 1.75 per 1-point change in response. In addition, both summary scores were significant, with WOMAC Function showing a larger difference than WOMAC Pain (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWOMAC Pain \u0026amp; Function summary scores before and one-year after surgery by prosthesis, as well as linear regression models for the TKA population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTKA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePre-surgery\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1 Year follow-up\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003cp\u003emodels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003emodels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdditional adjusted models\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;361)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;263)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;361)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;263)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDifference\u003c/p\u003e \u003cp\u003efavoring\u003c/p\u003e \u003cp\u003eB over A\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDifference\u003c/p\u003e \u003cp\u003efavoring\u003c/p\u003e \u003cp\u003eB over A\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDifference\u003c/p\u003e \u003cp\u003efavoring\u003c/p\u003e \u003cp\u003eB over A\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWOMAC Pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.4\u003c/p\u003e \u003cp\u003e(17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.4\u003c/p\u003e \u003cp\u003e(16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.8\u003c/p\u003e \u003cp\u003e(21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.4\u003c/p\u003e \u003cp\u003e(20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003cp\u003e(-0.7 to 5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003cp\u003e(0.0 to 6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003cp\u003e(0.1 to 6.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWOMAC Function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.1\u003c/p\u003e \u003cp\u003e(19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.3\u003c/p\u003e \u003cp\u003e(18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003cp\u003e(21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.9\u003c/p\u003e \u003cp\u003e(20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003cp\u003e(1.1 to 7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003cp\u003e(1.7 to 7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003cp\u003e(1.2 to 7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e* 95% CI\u0026thinsp;=\u0026thinsp;95% Confidence Interval\u003c/h2\u003e \u003cp\u003e* Adjusted models for patella resurfacing and the corresponding summary score pre-surgery \u0026amp; additional adjusted models for patella resurfacing, corresponding summary score, age, ASA score and education level pre-surgery\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWOMAC Pain \u0026amp; Function items before and one-year after surgery by prosthesis, as well as ordinal logistic regression models for the TKA population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTKA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePre-surgery\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1 Year follow-up\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnadjusted models\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdjusted models\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA (N\u0026thinsp;=\u0026thinsp;361)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB (N\u0026thinsp;=\u0026thinsp;263)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA (N\u0026thinsp;=\u0026thinsp;361)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB (N\u0026thinsp;=\u0026thinsp;263)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain - Walking on Flat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.57 (0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.59 (0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.29 (0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.35 (0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003cp\u003e(0.81 to 1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003cp\u003e(0.86 to 1.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain - Going Up or Down Stairs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.98 (0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.03 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.60 (1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.79 (1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003cp\u003e(1.02 to 1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003cp\u003e(1.07 to 1.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain - At Night While in Bed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.18 (1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.08 (1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.25 (0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.37 (0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003cp\u003e(0.89 to 1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003cp\u003e(0.95 to 1.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain - Rising or Sitting on Chair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.55 (0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.48 (0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.92 (1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.04 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003cp\u003e(0.89 to 1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003cp\u003e(0.98 to 1.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain - Standing Upright\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.62 (0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.69 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.09 (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.14 (0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003cp\u003e(0.79 to 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003cp\u003e(0.79 to 1.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunction - Ascending Stairs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.09 (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.19 (0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.49 (1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.66 (1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003cp\u003e(1.01 to 1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003cp\u003e(0.99 to 1.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunction - Rising from Sitting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.53 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.52 (0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.70 (1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.94 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003cp\u003e(1.14 to 2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003cp\u003e(1.20 to 2.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunction - Walking on Flat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.74 (0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.78 (0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.22 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.34 (0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003cp\u003e(0.93 to 1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003cp\u003e(0.96 to 1.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunction - Getting In/Out of Car\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.65 (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.59 (0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.73 (1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.94 (0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003cp\u003e(1.09 to 1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003cp\u003e(1.22 to 2.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunction - Putting on Socks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.94 (1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.94 (1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.75 (1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.89 (1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003cp\u003e(0.96 to 1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003cp\u003e(0.98 to 1.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunction - Rising from Bed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.15 (1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.11 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.16 (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.38 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003cp\u003e(1.14 to 2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003cp\u003e(1.27 to 2.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunction - Sitting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.26 (1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.30 (0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.23 (0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.39 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003cp\u003e(1.01 to 1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003cp\u003e(1.05 to 1.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e* 95% CI\u0026thinsp;=\u0026thinsp;95% Confidence Interval\u003c/h2\u003e \u003cp\u003e* Odds ratios for a higher rating on the scale between 1 and 5 for prosthesis B versus A\u003c/p\u003e \u003cp\u003e* Adjusted models for patella resurfacing and the corresponding item pre-surgery\u003c/p\u003e \u003cp\u003e* Additional adjustment for age, ASA score and education level did not change the results (not presented here)\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings revealed that differences between prostheses were captured using either WOMAC summary scores or items, with results favoring prosthesis B over prosthesis A. Overall, differences between both cup-stem combinations and knee prostheses were larger for patient-reported functional abilities than for patient-reported pain, indicating a greater influence on function. The observed results suggest that summary scores may be sufficient in identifying prosthesis-related differences. However, considering items alongside summary scores may offer additional insight in certain situations. Indeed, the item-level analysis showed that certain items were more sensitive to differences between prostheses one year after THA and TKA than others, and in relation to WOMAC Pain and Function summary scores. These discriminative items varied by comparison, between cup-stem combinations in THA or among prostheses with different stability designs in TKA, and were clinically relevant, with functional abilities related to vertical movement playing a critical role in comparing knee prostheses.\u003c/p\u003e \u003cp\u003eTo the authors\u0026rsquo; knowledge, no previous studies have explored PRO individual items within the field of total joint arthroplasty, making comparisons with other studies impossible. However, some studies have highlighted the importance of considering individual items. As an example, Perneger et al.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] reported that self-rated health, the first SF-12 question, behaves very differently from other SF-12 items when assessing health changes following THA or TKA. This highlights the potential benefit of examining items rather than relying solely on summary scores. Additionally, Hinds et al.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] warned against the risk of inaccurately assessing patients relying solely on summary scores, which may have serious implications for patient\u0026rsquo;s clinical care.\u003c/p\u003e \u003cp\u003eMoreover, a few studies in other fields have compared the use of PRO individual items with summary scores, all demonstrating the added value of considering items. For instance, Kalron et al.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] investigated the association between PRO items and mobility in patients with multiple sclerosis and found that only two items were sufficient to assess patient mobility capabilities. Houben-Wilke et al.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] studied the impact of respiratory and non-respiratory items on summary score in patients with chronic obstructive pulmonary disease. They reported that a considerable number of patients were classified as highly symptomatic, therefore being candidates for respiratory pharmacological treatments, based mostly on non-respiratory symptoms. Nielsen et al.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] compared two methods to identify subgroups of patients with low back pain, latent class analysis using summary scores and items of several PRO tools. Both strategies yielded clinically satisfying results, with the items approach allowing for more detailed subgrouping. Lastly, Floden et al.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] used items to compare changes in depressive symptoms following two treatments, treatment and control, in patients with treatment-resistant depression. It revealed significant improvements in specific items, offering insight into which depressive symptoms improved most after treatment.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, since the comparison of WOMAC summary scores and items in primary THA and TKA was limited to one-year results, results cannot be generalized to later time points. One-year post-surgery is the recommended time point for PROs assessment[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], as recovery is considered complete for most patients, with maximal pain reduction and improvement in function[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. After one year, some additional improvement has been reported only in TKA patients[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Second, not all patients completed the WOMAC questionnaire at one-year follow-up. However, PROs were available for 74.2% of THA patients and 71.6% of TKA, exceeding the 60% threshold deemed sufficient[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Third, other external factors, such as variations in surgical techniques or perioperative management, may have influenced the results and were not controlled for in this study. Nevertheless, the study objective was to compare the discriminative ability of WOMAC items to summary scores, rather than comparing specific prostheses. Lastly, this study focused on specific prostheses. Therefore, the findings and conclusions need to be tested with other prosthesis models.\u003c/p\u003e \u003cp\u003eIn conclusion, the findings indicate that WOMAC summary scores were sufficient to detect differences between prostheses, as both scores and items captured differences. The results highlight however the potential added value of considering individual items alongside summary scores when evaluating and comparing prostheses performance in THA and TKA, especially for detecting implant-related differences in functional abilities. Indeed, item-level analysis may provide additional information or a more nuanced understanding of differences. Furthermore, the findings do not justify selecting or prioritizing specific items, since discriminative items varied across comparisons. Future research should further investigate the combined use of summary scores and items across various PRO tools, time points, and populations to better understand when item-level analysis adds meaningful information and to draw more robust conclusions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eInstitutional financial support was received for the registry from the \u0026ldquo;Fondation pour la recherche ost\u0026eacute;oarticulaire\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eAll authors contributed to the conception of the study. Methodology was developed by Marys Revaz, Thomas Perneger, and Anne L\u0026uuml;bbeke. Data curation was performed by Marys Revaz, Christophe Barea, and Anne L\u0026uuml;bbeke. Formal analysis and visualization were conducted by Marys Revaz, Thomas Perneger, and Anne L\u0026uuml;bbeke. All authors contributed to the investigation and validation. Supervision was provided by Thomas Perneger, Hermes H. Miozzari, Didier Hannouche, and Anne L\u0026uuml;bbeke. The original draft of the manuscript was written by Marys Revaz, Thomas Perneger, and Anne L\u0026uuml;bbeke, and all authors contributed to the review and editing of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the local ethics committee (CCER Geneva, Switzerland).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eWritten informed consent was obtained from all patients included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors thank all patients and orthopedic surgeons from the Division of Orthopedic Surgery and Musculoskeletal Trauma Care who have contributed information to the registry since 1996. They also express their gratitude to Carole Bandi, Flavia Renevey, and Lamia Blatter-Sellak for their invaluable assistance with data entry.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWilson, I., Bohm, E., L\u0026uuml;bbeke, A., Lyman, S., Overgaard, S., Rolfson, O., \u0026amp; Dunbar, M. (2019). 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Long-term trends in the Oxford knee score following total knee replacement. \u003cem\u003eThe Bone \u0026amp; Joint Journal\u003c/em\u003e, \u003cem\u003e95-B\u003c/em\u003e(1), 45\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1302/0301-620X.95B1.28573\u003c/span\u003e\u003cspan address=\"10.1302/0301-620X.95B1.28573\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Total Hip Arthroplasty, Total Knee Arthroplasty, Patient Reported Outcomes, WOMAC","lastPublishedDoi":"10.21203/rs.3.rs-8593555/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8593555/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study aimed to evaluate the discriminative ability of summary scores and individual items in comparing patient reported outcomes (PROs) across two total hip arthroplasty (THA) and two total knee arthroplasty (TKA) designs.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePrimary elective THAs and TKAs performed between January 2012 and June 2022 from the Geneva Arthroplasty Registry were included. Two cup-stem combinations in THA and two prostheses with different stability designs in TKA were compared using WOMAC Pain and Function summary scores and individual items one year after surgery. Linear and ordinal logistic regression models were used to compare the ability of summary scores and items in differentiating between prostheses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 773 THA and 624 TKA patients were included. Overall, differences between prostheses were detected using either WOMAC summary scores or items. Nevertheless, analyses revealed variability in the discriminative ability of WOMAC summary scores and items, with variations depending on the population. Additionally, in both THA and TKA, differences between prostheses were larger in the function domain than in the pain domain.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePRO summary scores are valuable for evaluating and comparing prostheses performance in THA and TKA. In addition, individual items may, in certain contexts, provide enhanced sensitivity to identify implant-related differences and offer a more detailed understanding of these differences.\u003c/p\u003e","manuscriptTitle":"Individual items versus summary scores in comparing patient outcomes among different types of total hip and knee arthroplasty designs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 16:03:35","doi":"10.21203/rs.3.rs-8593555/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af85c0a5-dbb1-44d1-a02e-8f4dd881edda","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-19T07:16:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-18T08:46:59+00:00","index":32,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T08:37:11+00:00","index":31,"fulltext":""},{"type":"reviewerAgreed","content":"143231745948947078578776152288698220029","date":"2026-05-04T05:12:13+00:00","index":30,"fulltext":""},{"type":"reviewerAgreed","content":"55262239096137324487693609924913432877","date":"2026-04-30T22:05:09+00:00","index":29,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T07:25:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 16:03:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8593555","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8593555","identity":"rs-8593555","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}