Preoperative demand matching is a valid indicator of patient activity after total hip arthroplasty

The Journal of Arthroplasty Vol. 19 No. 7 2004

Preoperative Demand Matching Is a Valid Indicator of Patient Activity After Total Hip Arthroplasty Richard Iorio, MD, William L. Healy, MD, and David Appleby, MPH

Abstract: The validity of preoperative demand matching as an indicator of patient activity following total hip arthroplasty (THA) was evaluated by studying 518 patients (mean age, 67 years; range, 26 –92 years) who were assigned to 4 categories of decreasing demand: I (high demand), 68 patients (13%); II, 144 patients (28%); III, 281 patients (54%); and IV (low demand), 25 patients (5%). Preoperative Lahey Clinic Demand Category (as defined by age, weight, expected activity, health, and bone stock) was significantly correlated with self-reported, postoperative patient activity (P ⫽ .0038, R2 ⫽ .2024) and was much more predictive than any individual variable. Because patient activity is related to hip joint bearing surface wear, implant selection and resource allocation could be influenced by using demand matching to identify patients with high postoperative demand who may benefit from improvements in implant technology. Demand matching may also be used to stratify patient activity in long-term outcomes studies of THA. Key words: total hip arthroplasty, demand matching, postoperative activity. © 2004 Elsevier Inc. All rights reserved.

hip prosthesis after THA. Demand matching has also been used as a cost-containment tool. Higher-priced implants manufactured with newer technology have been recommended for higher-demand patients who may realize clinical benefits from the new developments, and lower-priced implants manufactured with less advanced technology have been recommended for low-demand patients [4]. Cost savings can be realized with these programs without compromising the quality of the surgical result [5]. The purpose of this study was to investigate the relationship of preoperative demand matching with patient-reported postoperative activity. The authors’ hypothesis was that the Lahey Clinic Demand Categories could effectively predict postoperative patient activity, and that the demand categories could be used to stratify patient demand in long-term evaluations of THA [6].

Polyethylene wear at the hip joint bearing surface in total hip arthroplasty (THA) has been correlated with patient activity [1]. Furthermore, polyethylene wear has been identified as the most important risk factor affecting aseptic fixation failure in THA [2]. Recent technological developments in hip implants have been specifically designed to prevent or diminish bearing surface wear [3]. However, patient-selection indications for these new technologies have not been objectively defined. Preoperative demand matching or implant standardization for THA was developed to provide objective guidelines for hip implant selection [4,5]. The hip implant selection guidelines are based on the demand that a patient is expected to place on the From the Department of Orthopaedic Surgery, Lahey Clinic Medical Center, Burlington, Massachusetts. Submitted September 30, 2003; accepted March 12, 2004. No benefits or funds were received in support of this study. Reprint requests: Richard Iorio, MD, Lahey Clinic Medical Center, 41 Mall Road, Burlington, MA 01805. © 2004 Elsevier Inc. All rights reserved. 0883-5403/04/1907-0003$30.00/0 doi:10.1016/j.arth.2004.03.014

Materials and Methods From 1993 to 1998, 518 primary, unilateral THA patients were preoperatively evaluated with the

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826 The Journal of Arthroplasty Vol. 19 No. 7 October 2004 Table 1. Hip Implant Demand-Matching Program Age* (y)

Weight

Expected Activity†

1. ⬎75 2. 70–75 3. 65–69 4. 60–64 5. ⬍60

1. ⬍120 2. 120–149 3. 150–179 4. 180–200 5. ⬎200

1. Sedentary 2. Household ambulatory 3. Community ambulatory 4. No walking limit 5. Sports/heavy work

Health‡

Bone Stock§ 1. ⱖ.63 2. .56–.62 3. .49–.55 4. .42–.48 5. ⱕ.41

1. Poor 2. Fair 3. Moderate 4. Good 5. Excellent Age: Weight: Activity: Health: Bone Stock: Total:

(patient type score)

Demand Category

Patient Type Score

Category Category Category Category

ⱖ22 (high demand) 18–21 12–17 ⱕ11 (low demand)

I II III IV

*Add 5 points for ⬍55 years old. †Preoperative surgeon’s prediction of expected activity after THA. ‡Based on ASA classification. §Femoral index (inner diameter/outer diameter of femoral cortex, 8 cm distal to lesser trochanter).

Lahey Clinic demand-matching or implant standardization system (I, high demand; II, high intermediate demand; III, low intermediate demand; and IV, low demand) using 5 patient variables: age, weight, surgeon’s prediction of expected postoperative activity, health, and bone stock (Table 1) [4]. Easily obtainable patient-associated information was used to ensure that the demand-matching system could be quickly and easily implemented in the outpatient office or in the operating room. The demand-matching system was used as a guideline for implant selection: I, cementless femur and acetabulum; II, cemented femur and cementless acetabulum; III, cemented femur and if ⬍70 years old, cementless acetabulum, ⬎70 years old cemented acetabulum; IV, cemented femur and cemented acetabulum [5]. In addition to the 5 patient characteristics used for demand matching, a comprehensive, prospective THA database [7] recorded many more patient variables that could potentially predict patient-re-

ported postoperative activity: gender, employment, patient-reported preoperative activity level, visual analog pain score, Harris Hip Score, modified Merle d’Aubigne and Postel score, SF-36 Bodily Pain Scale, SF-36 Physical Function Scale, outcome and activity measures, and the specific demand category (Table 2). The mean length of follow-up for these patients was 3.94 years (range, 3.0 – 6.98 years). All patients assessed their postoperative activity level in their routine postoperative questionnaires. Patients were asked to choose 1 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, light sports; 4) I perform moderate manual labor with lifting heavy weights, and participate in moderate sports such as walking or bicycling; and 5) I participate in heavy manual labor, I frequently lift heavy weights, participate in vigorous sports, such as tennis and racquetball (Table 3). Using all postoperative follow-up questionnaires, the patient’s maximum postoperative activity level was determined. Each individual preoperative patient variable and the multivariable demand category were correlated with patient-reported maximum postoperative patient activity for each patient. Univariate regression analysis was used to compare the independent preoperative patient variables 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,

Table 2. Preoperative Assessment Number of hips/patients Age in years, mean (range) Weight in pounds, mean (range) Sex Male Female Operative side Right Left Visual analog pain score (0–10), mean (range) Harris Hip Score (0–100), mean (range) Merle d’Aubigne and Postel score (0–18), mean (range) Demand category I II III IV

518 67.4 176.9

(26–92) (98–288)

247 271

(47.7%) (52.3%)

289 229 7.60

(55.8%) (44.4%) (4–10)

39.62 7.48

(5–87) (3–12)

68 144 281 25

(13.1%) (27.8%) (54.2%) (4.8%)

Preoperative Demand Matching • Iorio et al. Table 3. Postoperative Assessment Number of hips Duration of follow-up in years, mean (range) Visual analog pain score (1–10), mean (range) Harris Hip Score, mean (range) Merle d’Aubigne and Postel scores Patient-reported postoperative activity level Bedridden Sedentary Light labor Moderate labor Heavy labor

518 3.94 (3–6.98) 1.17 (0–6) 87.19 (32–100) 11.76 (5–18) 1 (0.2%) 55 (10.6%) 308 (59.5%) 145 (28.0%) 8 (1.5%)

health, and bone stock) with patient-reported maximum postoperative activity level (Table 1) [4].

Results Individual preoperative patient variables of age, gender, surgeon prediction of expected postoperative activity following THA, health, bone stock, Harris Hip Score, patient-reported preoperative activity level, and SF-36 Physical Function Score were independent variables with significant correlation (P⬍.01) to patient-reported postoperative activity level (Table 3). Other preoperative patient variables (weight, visual analog pain score, modified Merle d’Aubigne and Postel score, and SF-36 Bodily Pain score) were not significantly correlated to patientreported postoperative activity level (Table 4). Multivariate linear regression analysis correlated the multivariable Lahey Clinic Demand Category (age, weight, surgeon’s prediction of expected patient activity following THA, bone stock, and health) with patient-reported postoperative activity level at the highest R2 value (R2 ⫽ .2024, P ⫽ .0038). The power analysis yielded a value of 0.99 (␣ set at 0.05). The 5 demand category variables, as a group, were more predictive of postoperative activity level than any individual patient variable.

Discussion As the population increases and ages, the prevalence of THA is increasing [8]. Many patients who have hip arthroplasty surgeries choose to pursue vigorous activity following reconstruction. Presumably, reconstructive hip surgeons should be able to counsel patients regarding the relationships of hip implant designs and materials, postoperative activity, and implant survivorship. However, there is

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little information available to develop consensus recommendations regarding patient activity following THA [9]. Schmalzried et al [1] showed that polyethylene wear in THA is a function of use, not time. Patients who walk more and patients who work in more demanding jobs have higher polyethylene wear rates [1,10]. Body weight alone is a controversial variable for predicting polyethylene wear following THA [1,11], and age alone is a poor predictor of activity level [12]. Physical-activity questionnaires have been validated as reproducible and useful measures of physical activity [13]. However, in a clinical setting that assessed activity in joint arthroplasty patients, an activity score based solely on activity level ranking did not always accurately predict patient demand [14]. The orthopedic-surgery literature does not have a validated tool that can accurately and reproducibly predict patient activity following hip arthroplasty surgery. This study demonstrates that a multivariable, demand-matching system can be a valid and significant indicator of self-reported postoperative patient activity (Table 4). The 5 patient variables used in the Lahey Clinic demand-matching or implant standardization program [4] (age, weight, expected postoperative activity, health, and bone stock) are strongly correlated with self-reported postoperative

Table 4. Regression Analysis

Univariate regression analysis independent variable Age Weight Expected activity Bone stock Health Preoperative activity level Sex Harris Hip Score Modified Merle d’Aubigne and Postel score Visual analog pain score Preoperative SF-36 Bodily Pain Score Preoperative SF-36 Physical Function Score Multivariate regression analysis independent variable Demand Category Age Weight Expected Postoperative activity Bone stock Health

R2

P Value

.0939 .0023 .1037 .0158 .1202 .1447 .0390 .0809 .0029

⬍.001 .2823 ⬍.0001 .0046 ⬍.0001 ⬍.0001 ⬍.0001 ⬍.0001 .2345

.0082 .0148 .1160

.0414 .2473 .0009

.2024

.0038

NOTE. Number of THA patients, 518. Dependent variable— Patient-reported postoperative activity level.

828 The Journal of Arthroplasty Vol. 19 No. 7 October 2004 patient activity. Furthermore, the demand category was more strongly indicative of self-reported postoperative patient activity than any individual preoperative patient characteristic. Implant demand matching or standardization has been used and validated as a cost-containment tool for THA [5]. Our data demonstrate that the Lahey Clinic Demand Categories may also be used as an indicator of postoperative patient activity. We believe that these demand categories, serving as an indicator of postoperative activity, may be used to select patients who may benefit from new technology. Alternative bearing surfaces are being introduced as a potential solution for polyethylene wear in high-demand patients. Highly cross-linked polyethylene has been presented as a bearing surface with improved wear characteristics that will reduce polyethylene wear when compared with conventional polyethylene [3]. Metal-on-metal, ceramicon-polyethylene, and ceramic-on-ceramic articulations have been introduced as bearing surfaces with less wear and particulate debris [3]. However, these innovations are more expensive than metal– on– conventional polyethylene articulations, and the indications for these new bearing surfaces with potential, but unproven, clinical benefits are not well defined. Demand matching can provide an objective determination about which patients will have high-demand activity and which patients may benefit from presumed technological improvements in hip implants. As a valid indicator of patient activity following hip arthroplasty surgery, the Lahey Clinic Demand Categories may also be useful in evaluating longterm results of hip arthroplasty surgeries with different hip implants. To develop more clinically useful information regarding hip implant survivorship in high-demand populations, patients should be stratified according to levels of activity. The implant demand-matching program used in this study can be used to stratify patients based on probable activity following THA. The principal weakness of this method of predicting postoperative patient activity is the reliance on patient self-assessment. Although patient self-assessment of activity is not as accurate as pedometer data, it is readily available and was highly correlated with the other measured activity indicators such as preoperative patient-reported activity, surgeon’s expected postoperative patient activity, and SF-36 physical function score. The weakness of all patientoriented outcome information is the accuracy of the transmitted data. When possible, objective mea-

sures (age, weight, bone stock, health) were included in the demand-matching category determination to minimize reliance on subjective variables.

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