Technological innovations that reduce polyethylene wear and decrease joint reaction forces in total knee arthroplasty have the potential to increase the durability of the reconstructed knee.14 Unfortunately, new technology cannot be evaluated with long-term outcome studies before implementation, and not all innovations succeed. Furthermore, new technologies often are introduced for clinical problems associated with those patients with the most challenging problems such as young, active, high-demand and/or heavy patients. After total knee arthroplasty, these patients may wish to maintain active lifestyles that include athletic activities.2,3,6,7,11,16 Guidelines for who should receive new implants based upon new technology have rarely been reported.8
Preoperative demand matching or implant standardization for total knee arthroplasty was developed to provide objective guidelines for knee implant selection.5,9 Guidelines for knee implant selection are based on the demand a patient is expected to place on the knee prosthesis after total knee arthroplasty. The process of demand matching also has been used by hospitals to control and reduce the cost of joint replacement implants.1,5,9 In our implant standardization program, higher priced knee implants that include new technological innovations have been recommended for high-demand patients who would theoretically benefit from implants manufactured with new technology. We used lower priced implants produced with older technology for lower demand patients.9 Using this method of implant standardization, implant cost can be reduced without sacrificing durability of the surgical reconstruction.5
Prospective preoperative prediction of patient activity after total knee arthroplasty has been difficult, but recent literature suggests increasing interest.2,3,6,10,13,16-22 Hip- joint bearing surface wear has been correlated with patient activity rather than longevity of implantation.19 Patients with more physically demanding jobs and who walk more have higher rates of polyethylene wear.8,13,18,19 Preoperative demand is poorly correlated with postoperative demand.1,8,10 Body weight and age do not predict polyethylene wear in postoperative patient activity.8,10,18 The literature contains no validated tool that can accurately and reproducibly predict patient activity after total knee arthroplasty. However, if validated, demand categories could be used to stratify patient demand and select those patients who would likely benefit from new (and often costly) technological innovations in total knee replacement and who will do well with less expensive implants.
We therefore sought to validate the hypothesis that our institution's demand categories would effectively predict postoperative patient reported activity.
MATERIALS AND METHODS
From our prospective database we identified 511 patients who had a unilateral, primary total knee arthroplasty from 1994 to 1999 and who preoperatively had been evaluated with our institution's demand-matching or implant standardization system (I, high demand; II, high intermediate demand; III, low intermediate demand; IV, low demand) using five patient characteristics: age, weight, surgeon's prediction of expected postoperative activity, health status, and bone stock (Appendix 1).5,9 The 511 total knee arthroplasties were distributed into four categories of decreasing demand: 39 (7.6%) demand I (high); 156 (30.5%) demand II; 294 (57.5%) demand III; and 22 (4.3%) demand IV (low). The power analysis yielded a value of 0.99 (alpha set at 0.05), which supported the validity of the study design to distinguish individual patient variables from demand category.
Additional patient characteristics that could potentially predict patient reported postoperative activity including gender, operative side, preoperative activity level, visual analog pain score, Hospital for Special Surgery Knee Score, Knee Society Knee Score, Knee Society Function Score (KSFS), SF-36 bodily pain score, SF-36 physical function score, outcome and activity measures, and the specific demand category (Table 1) were recorded to a comprehensive, prospective total knee replacement database. The median length of followup for these patients was 4.24 years (range, 3-7.2 years).
Postoperative questionnaires regarding patient activity were administered at 1-year followup and biannually thereafter. Minimum 3-year followup was required to assign a maximum postoperative activity level achieved by each patient. The questionnaires asked each patient to choose one of the following activity categories: (1) I am bedridden or confined to a wheelchair; (2) I am sedentary with minimal capacity for walking or other activity; (3) I perform light labor such as housecleaning, yard work, assembly line work, or light sports; (4) I perform moderate manual labor with lifting heavy weights and participate in moderate sports such as walking or bicycling; or (5) I participate in heavy manual labor, I frequently lift heavy weights, and participate in vigorous sports such as tennis and racquetball (Table 2).
Each individual preoperative patient activity characteristic and the multivariable demand category were correlated with patient-reported maximum postoperative activity for each patient. Univariate regression analysis was used to compare the independent preoperative patient characteristics and outcome measures with patient-reported maximum postoperative activity. A multivariate regression analysis was used to compare the multivariable demand category (age, weight, expected postoperative activity, health status, and bone stock) with patient-reported maximum postoperative activity.
Certain preoperative patient characteristics correlated with patient reported postoperative activity levels. Age, physician-predicted postoperative patient activity, bone stock, health status, preoperative patient-reported activity level, gender, Hospital for Special Surgery Knee Score, Knee Society Knee Score, Knee Society Function Score, and preoperative SF-6 physical function scores correlated with (p < 0.01) patient-reported postoperative activity level (Table 3). Other preoperative patient variables (weight, visual analog pain score, and preoperative SF-36 bodily pain score) were not correlated to patient-reported postoperative activity level.
Our multivariable demand categories (age, weight, physician-predicted expected patient postoperative activity after total knee arthroplasty, bone stock, and health status) correlated with patient-reported postoperative activity at the highest R2 value (R2 = 0.178, p < 0.001). The five demand category variables were more predictive as a group for postoperative patient-reported activity level than individual patient variables.
New technology in total knee arthroplasty is generally, but not always, associated with improvement in the treatment of osteoarthritis of the knee. However, the outcomes from new technology are not always well understood before introduction to the market.14 As the prevalence of total knee arthroplasty increases because of demographic changes, and as the demands of young, active patients with knee osteoarthritis increase, the demand for new technology will likely increase. A “high function knee” will evolve with potential for greater range of motion, and reduced bearing surface wear under high loads. Evaluating new technology for appropriate use will be a challenge. Stratification of patients with different activity or demand is important for implementing and evaluating new technologies. Because new technology will likely be used for the patient groups already most at risk for failure with traditional technologies, it will be difficult to show superiority of new implants over traditional models. This is because a higher failure rate might be anticipated in these groups and since past outcomes of traditional models have been reported in lower demand groups. Orthopaedic surgeons will need an easily implemented patient activity measure for stratification of patient groups and results of treatment.
The principle limitation in predicting patient activity as defined by patient self-assessment is the reliability of the patient. Although the patient reported activity level correlates well with a number of outcome measures, it has not been independently correlated with a quantifiable measure of activity in this study. Although patient self-assessment of activity is not as accurate as objective pedometer data, it is readily available. In our study, self-assessment correlated with other measured activity indicators such as pre- operative patient reported activity, surgeon's expected postoperative patient activity, and SF-36 physical function score.19,20,22 Our institution's demand-matching system uses objective measures whenever possible (age, weight, bone stock, health status) to minimize reliance on subjective variables. The next step in assessing the validity of demand categories as a predictor of postoperative patient activity is to correlate patient-reported activity levels with pedometer or other objective measurements.
Surgeons performing knee reconstruction should be able to counsel patients on the relationships of knee implant designs and materials, postoperative activity, and knee implant survivorship. The literature has few reports concerning the relationship of physical activity and the risk of revision total knee arthroplasty.12 Young active patients have shown reasonable survivorship at intermediate followup,4,12,15 and patients with preoperative functional limitations, severe pain, low mental health scores, and other comorbid conditions are more likely to have a poor outcome after TKA than young, active, high-demand patients.15 Many patients with knee replacements wish to participate in high-demand activities and enjoy an active lifestyle. However, increasing age, concerns about the endurance of their joint replacement, and the advice of their surgeon contribute to caution among patients concerning physical activity after total joint replacement.7 Although several surveys have made recommendations concerning activity and athletics after total knee replacement, there is little consensus in the literature regarding the advisability of high-level patient activity after total knee arthroplasty.2,3,6,7,11
Our study shows multivariable demand categories accurately reflect self-reported, postoperative patient activity. The five patient variables used in our institution's implant standardization program5,9 (age, weight, surgeon- expected postoperative patient activity, health status, and bone stock) correlate with self-reported, postoperative patient activity after knee replacement surgery. Furthermore, our demand categories were more indicative of self- reported, postoperative patient activity than any individual preoperative patient outcome characteristic.
Knee-bearing surface wear in total knee arthroplasty has been correlated with patient activity.13 Polyethylene wear has been identified as an important risk factor affecting aseptic fixation failure in total knee arthroplasty.4,10,13 Current innovations in knee replacement surgery have been designed to minimize the effects of wear on the endurance of total knee arthroplasty survivorship. Mobile- bearing knee replacements, alternative-bearing surface knee replacements, and minimally invasive knee arthroplasty techniques have been introduced to improve the results of total knee arthroplasty. Whether these technological innovations will improve implant survivorship is unknown. Our demand-matching system can be used to stratify demand for the reporting of results concerning these new technologies. These new technologies are more expensive than conventional metal on polyethylene knee replacement. Demand matching can provide an objective determination concerning which patients will have high demand activity and which patients may benefit from presumed technological improvements in knee implant design.
This study shows preoperative demand matching can be an appropriate tool to predict patient reported activity after total knee arthroplasty. Because patient activity is correlated with wear, implant selection and resource allocation can be targeted to patients who may benefit from presumed improvements in implant technology.
The authors thank Rosemary Immig, Orthopaedic Research Coordinator, for her help in preparing this manuscript for publication.
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