Knee OA: Which patients are unlikely to benefit from manual PT and exercise?

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Original Research

Knee OA: Which patients are unlikely to benefit from manual PT and exercise?

J Fam Pract. 2012 January;61(1):E1-E8

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References

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CORRESPONDENCE Gail D. Deyle, PT, DSc, Orthopaedic Manual Physical Therapy Fellowship, 3551 Roger Brooke Drive, Brooke Army Medical Center, Ft. Sam Houston, TX 78234; gail.deyle@amedd.army.mil

Methods

Using a retrospective combined-cohort study design, we reviewed baseline patient examinations from 2 RCTs^1,2 to identify variables that indicate which individuals with knee OA are unlikely to benefit from manual physical therapy and exercise, and to thereby develop a preliminary CPR. We extracted data from the research folders of all study participants. The institutional review board of Brooke Army Medical Center determined that the study was exempt from review. From April to December 2008, we prepared an extensive database of examination findings and performed analyses to determine the variables that predict likely treatment nonsuccess with manual physical therapy and exercise. Improvement of <12% in the total WOMAC score after 4 weeks of treatment defined nonsuccess.⁴⁵

Data sets from the previously published trials contained 22 variables measured at baseline that were potential predictors of nonsuccess. We combined these variables with an additional 145 variables manually retrieved from standardized examination forms used for each subject, for a total of 167 potential predictors. We combined only data from treatment groups receiving manual therapy and exercise.

We limited the extent of some examination procedures in the earlier studies, due to the high level of symptoms experienced by some subjects at rest and during the initial examination. For example, if there was severe pain with active knee flexion, we did not perform passive manual overpressure to flexion; nor did we record a finding. Thus, the total number of data points for each subject varied somewhat.

Data analysis

We compared success and nonsuccess groups with 2-tailed unpaired t-tests for continuous variables, and chi-square tests for categorical variables. We additionally performed logistic regression analysis on potential predictors that yielded P values <.10, using a forward conditional stepwise procedure with probability levels set to .05 for entry and .10 for removal from the model. Predictors retained by the final logistic regression model comprised the CPR.

We coded each patient in the data set as positive or negative for each predictor in the CPR. To determine a cut score, we dichotomized the single retained continuous predictor variable using receiver-operator characteristic (ROC) curve analysis and the Youden index.⁴⁶ For each CPR level (ie, increasing number of predictors positive), we constructed a 2 × 2 contingency table with numbers of patients with true-positive test results, false-positive test results, true-negative test results, and false-negative test results. We characterized prognostic performance of the CPR by calculating sensitivity, specificity, and positive likelihood ratios for each level of positive predictors. To determine overall prognostic accuracy, we added true positives and true negatives and divided by the total number of patients in the cross tabulation.

For each CPR level, we derived posttest probabilities of nonsuccess from generalized pretest probability (incidence of treatment nonsuccess in the sample) and the positive likelihood ratios.⁴⁷ Finally, to determine how consistently the CPR performed with subjects in the original studies,^1,2 we generated separate cross-tabulations and prognostic accuracy statistics from each RCT.

Results

Baseline patient attributes are summarized in TABLE 1. Of the 101 subjects in the combined data set, 17 (16.8%) met the definition of nonsuccess. Among 47 continuous-scale variables available, 11 predictors significantly discriminated between those in the treatment success and nonsuccess groups. Among 120 categorical-scale variables, 15 predictors significantly discriminated between groups. We identified 6 potential predictors for entry into the final logistic regression analysis: height, assistive device type, prone knee bend degrees, baseline WOMAC visual analog scale (VAS) for difficulty descending stairs, anterior cruciate ligament (ACL) laxity, and pain with passive patellofemoral glide.

TABLE 1
Baseline descriptive summaries of patients (n=101)

Sex, n (%) Men Women	37 (36.6) 64 (63.4)
Age, y Mean±SD Range	62.5±10.4 39-85
Height, m Mean±SD Range	1.66±0.1041 1.42-1.91
Side(s) involved, n (%) Unilateral Bilateral	63 (62.4) 38 (37.6)
Weight, kg Mean±SD Range	84.5±17.8 48.6-132.7
Duration of symptoms, mo Mean±SD Range	76.1±87.9 1-480
WOMAC (VAS) total baseline, mm Mean±SD Range	1059.8±447.1 193-2289
6-minute walk test baseline, m Mean±SD Range	425.6±114.8 118.2-683.3
Physical activity relative to peers (self-report), n (%) Much more active Somewhat more active About the same Somewhat less active	26 (26) 33 (33) 20 (20) 21 (21)
Radiographic severity score, n (%) 0 1 2 3 4	6 (6.1) 25 (25.5) 33 (33.7) 25 (25.5) 9 (9.2)
*Baseline data were available for all 101 subjects except for duration of symptoms (n=98); physical activity (n=100); and radiographic severity (n=98). VAS, visual analog scale; WOMAC, Western Ontario MacMaster.

The final regression model retained 3 predictors comprising the CPR: height, ACL laxity, and pain with passive patellofemoral glides. We dichotomized height with a cut point of 1.71 m (5’7”), which corresponded with a deflection point at the upper left extent of the ROC curve (area under the curve=0.72; 95% CI, 0.57-0.87; P=.001). We thus deemed a patient 1.71 m or taller as positive for nonsuccess. We considered a patient with laxity of the ACL as positive for nonsuccess if a test result on the Lachman test (or the anterior drawer test) was positive (any grade other than 0). We regarded passive patellofemoral glide as positive for nonsuccess if a patient reported pain with any direction of passive gliding motion imposed by the therapist. The final regression model was a good fit to the data: Hosmer & Lemeshow test χ² = 2.90 (P=.940); Nagelkerke R²=0.680.