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Intraoperative Computer Navigation Parameters Are Poor Predictors of Function 1 Year After Total Knee Arthroplasty
The Journal of Arthroplasty Vol. 28 No. 1 2013
Intraoperative Computer Navigation Parameters Are Poor Predictors of Function 1 Year After Total Knee Arthroplasty Benjamin J. Widmer, MD,*y Corey J. Scholes, PhD,* Sébastien Lustig, MD, PhD,*z Leonard Conrad, MBBS,*§ Sam I. Oussedik, FRCS,* and David A. Parker, FRACS*§
Abstract: Intraoperative navigation data were collected prospectively for 134 knees undergoing cemented, posterior-stabilized total knee arthroplasty. Partial least squares regression analysis was used to test the association between patient demographics and intraoperative data collected with a computer-assisted navigation system (coronal alignment, ligament balance, range of motion, external tibiofemoral rotation) with 1-year outcomes (36-item Short-Form Health Survey, Oxford Knee Score, range of motion). Age at surgery displayed the largest coefficients of any other predictor. In contrast, navigation coefficients were variable in the strength and direction of their association with the outcome variables. Static knee alignment data obtained intraoperatively have limited capacity to explain the variance in functional outcome at 1 year. Although alignment and component position can be precisely measured intraoperatively, intrinsic patient factors remain dominant in determining the outcome. Keywords: total knee arthroplasty, function, computer navigation, alignment, predictors. © 2013 Elsevier Inc. All rights reserved.
because popliteal soft tissue redundancy provides a mechanical block to flexion [3]. Although obesity has been implicated, preoperative range of motion has been reported as the primary predictor of postoperative range of motion [4]. In addition to these patient factors, several biomechanical elements have been associated with knee function after TKA. Alignment of the surgically reconstructed limb has long been given a central role, both intraoperatively and postoperatively, by orthopedic surgeons for improvements in both function and longevity, even if other issues like balance might be just as critical. A recent development involves intraoperative computer navigation for positioning of instruments and prosthetic components. Surgery assisted by intraoperative navigation is recognized as a means of reducing the likelihood of significant deviation from the mechanical axis [5-7]. An association between appropriate coronal alignment and improved function at 1 year has also been demonstrated [8], but there have been limited attempts to link other biomechanical parameters recorded during intraoperative navigation with postoperative function. In addition to coronal alignment, rotational alignment, particularly when associated with component malrotation, has been associated with both inferior function and knee pain [9,10]. One study noted a relationship between femoral
Total knee arthroplasty (TKA) is an effective procedure for the treatment of end-stage degenerative and inflammatory arthropathies. Both patient factors and biomechanical factors have been correlated with functional outcome and range of motion after TKA. A recent prospective analysis reported that age greater than 70 years and body mass index (BMI) less than 27 kg/m 2 were associated with better function at 2 years after knee arthroplasty [1]. A joint registry review revealed that patients with metabolic syndrome achieved poorer function after lower-extremity arthroplasty at 1 year [2]. For those patients included in the joint registry review, obesity was identified as the most reliable negative predictor of function. Obesity has also been recognized as a factor negatively associated with range of motion
From the *Sydney Orthopaedic Research Institute, Chatswood, NSW, Australia; yIntermountain Medical Group, Orthopaedic Specialty Group, Murray, Utah; zAlbert Trillat Center, Lyon North Hospital, Lyon, France; and §Northern Clinical School, University of Sydney, Royal North Shore Hospital, St Leonards, NSW, Australia. Submitted November 9, 2011; accepted April 17, 2012. The Conflict of Interest statement associated with this article can be found at http://dx.doi.org/10.1016/j.arth.2012.04.018. Reprint requests: David A. Parker, FRACS, Level 1, The Gallery 445 Victoria Ave, Chatswood, NSW 2067 Australia. © 2013 Elsevier Inc. All rights reserved. 0883-5403/2801-0010$36.00/0 http://dx.doi.org/10.1016/j.arth.2012.04.018
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Intraoperative Computer Navigation as Predictors of Function Widmer et al
component malrotation and increasing postoperative pain, independent of instrumentation type [11]. The literature suggests that greater precision in alignment improves joint function. Although functional outcome is multifactorial and affected by both biomechanical and patient factors, these predictors have been typically analyzed independently. To that end, the purpose of this study was to determine the ability of intraoperative navigation parameters in conjunction with patient factors to predict postoperative functional outcomes of primary TKA.
Methods A retrospective analysis of prospectively collected data was performed on patients who underwent knee arthroplasty during a 1-year period. Patients who underwent primary TKA with Stryker precisioN Navigation (Stryker, Kalamazoo, Michigan) between ages 50 and 85 years at the time of surgery were eligible for inclusion in the analysis. A single, fellowship-trained knee surgeon performed all arthroplasties in a fully cemented, posterior-stabilized fashion. Exclusion criteria included inflammatory arthropathy, prior arthroplasty, and inability to comply with the postoperative follow-up requirements. The data collection protocol was approved by the local human research ethics committee. All participation was voluntary, and patients provided written informed consent before measurement. Data collection was conducted on 134 knees, comprising 76 female knees and 58 male knees, which were included in the final analysis. Of the 134 knees analyzed, 62 were bilateral arthroplasties. The patients had a mean (±) age of 67.7 (±7.8) years and were defined as obese according to the World Health Organization classification with a mean (±SD) BMI of 30.8 (±6.3) kg/m 2. Before surgery, the patients were enrolled into a prospective observational database, and demographic data were collected including date of birth, height, and weight. The patients also completed the Oxford Knee Score (OKS) and the 36-item Short-Form Health Survey (SF-36) questionnaire during the visit, either at a computer workstation or on a paper (Table 1). At the time of surgery, navigated TKA was undertaken by the senior author according to his standard technique. Intraoperative knee alignment was measured with the Stryker precisioN computer-assisted surgical (CAS) system
57
(Stryker) by attaching clusters of infrared-emitting markers to the distal femoral metaphysis and tibial diaphysis via percutaneous pins in accordance with the manufacturer's instructions. The hip's center of rotation was calculated by the CAS software before medial arthrotomy and registration of femoral and tibial anatomical landmarks using a handheld digitizer [12]. Static knee alignment in the coronal (hip-knee-ankle angle), sagittal, and transverse planes was captured before arthroplasty as the surgeon lifted the lower limb off the table by the heel and allowed gravity to provide full limb extension. The operated limb was then flexed to 90° at the hip to allow the knee to flex with the heel unsupported to record maximum knee flexion. The surgeon then returned the knee to full extension, and maximum varus and valgus angulation was captured as the surgeon applied a manual varus and valgus force, respectively. Cemented posterior-stabilized TKA was then undertaken in a standard fashion. Knee alignment was measured again after the final implant had been cemented into place but before wound closure. Alignment measurements recorded by the CAS system were saved into a Portable Document Format file (Fig. 1). The recorded data were then transferred manually to an Excel (Microsoft, Redmond, WA) spreadsheet in preparation for statistical analysis. Clinical Follow-Up During routine follow-up at 1 year from the date of the TKA (±3 months), the patients were assessed by a trained research associate who measured the knee range of motion using a handheld goniometer. The goniometer was placed on the lateral aspect of the limb, with the hinge centered over the lateral joint line. The proximal arm was aligned with the greater trochanter and the distal arm with the distal tibia. The resulting angle was recorded in degrees. Sagittal knee range of motion was calculated by subtracting any fixed flexion deformity from the maximum passive flexion measurement. Passive flexion was achieved by asking the patient to flex their hip to 90º and allowing gravity to flex the knee while the research associate performed the measurement. Patients also completed the OKS and the SF-36 questionnaire during the visit, either directly at a computer workstation or on a paper. Paper-based responses were later transferred by
Table 1. Parameters Acquired Preoperatively, Intraoperatively, and at 1-Year Follow-Up Preoperatively BMI SF-36 (PCS) OKS Knee Society flexion Knee Society extension Knee Society range of motion
Intraoperatively
1-y Follow-Up
Coronal alignment Maximum flexion Maximum extension Maximum valgus Maximum varus External tibiofemoral rotation
SF-36 (PCS) OKS Knee Society flexion Knee Society extension Knee Society range of motion
58 The Journal of Arthroplasty Vol. 28 No. 1 January 2013
Fig. 1. Example of knee alignment, extension, and rotational position graphic produced by the Stryker precisioN navigation system.
data entry personnel and collated with the rest of the study information into a data management system (SOCRATES, Ortholink, Balmain, NSW, Australia). Data and Statistical Analysis Patient demographics (age and BMI) were recorded in preparation for statistical analysis. Knee alignment measures recorded with the CAS system before and after the arthroplasty comprised maximum passive flexion and extension, external tibiofemoral rotation, coronal alignment, and maximum active varus and valgus. Patient outcomes compared at the 1-year postoperative follow-up with preoperative measurements consisted of the SF-36 physical component score (PCS), OKS, the maximum passive flexion and extension recorded with handheld goniometry, and the calculated passive range of motion (Table 1). Changes in CAS measurements and patient outcomes between prearthroplasty and postarthroplasty measurements were assessed using Mann-Whitney U tests. Partial least squares (PLS) regression was used with cross-validation (leave one out) to associate the
patient demographics and CAS measurements with the patient outcome data. The raw measurements for the predictor and outcome variables were transformed into delta scores, denoted by Δ, by subtracting the prearthroplasty and postarthroplasty measurements for each patient. Partial least squares is an appropriate regression approach when there is a high number of predictor variables relative to the number of samples and the independent variables display considerable collinearity. Partial least squares regression reduces the predictors to a set of uncorrelated components and then performs least squares regression using these components. The regression design for this study, using the Δ scores for the predictor and outcome variables, is illustrated in Fig. 2. The PLS regression process outputs a series of standardized model coefficients for each predictor variable with respect to each outcome variable. These standardized coefficients indicate the strength and direction of the relationship between variables. Model goodness of fit was assessed using the R 2 statistic, and the significance of the model was set a priori at P b .05. All statistical analyses were performed
Fig. 2. Partial least squares regression model. Δ denotes a change in parameter prearthroplasty and postarthroplasty.
Intraoperative Computer Navigation as Predictors of Function Widmer et al
using Minitab statistical software (version 16; Minitab Inc, Boston, Massachusetts).
Results Alignment measures changed significantly after the total knee arthroplasty procedure (Table 2). The knees were significantly less varus, with greater maximum flexion and reduced flexion contracture and external tibiofemoral rotation. In addition, the between-patient variability of the alignment measures was reduced postoperatively (Table 2). In addition, the TKA procedure significantly improved all patient outcomes at the 1-year follow-up compared with the preoperative measurements (Table 3). A 4-component model was selected that explained 79% of the variance in the predictor variables. The model was significantly related to ΔSF-36 PCS (F2, 71 = 6.7, P b .01), ΔOKS (F4, 71 = 3.2, P = .02), Δgoniometer flexion (F4, 71 = 6.5, P b .01), Δgoniometer extension (F4, 71 = 5.2, P b .01), and Δgoniometer range of motion (F4, 71 = 8.9, P b .01). The model explained the greatest proportion of variance in Δgoniometer range of motion (R 2 = 35%), followed by ΔSF-36 PCS (R 2 = 29%), Δgoniometer knee flexion (R 2 = 28%), Δgoniometer knee extension (R 2 = 24%), and ΔOKS (R 2 = 16%). Age at surgery displayed the largest coefficients of any other predictor for ΔSF-36 PCS, ΔOKS, Δgoniometer knee flexion, and Δgoniometer range of motion. In contrast, the CAS coefficients were more variable in strength and direction of their association with the outcome variables (Table 4). The variable that displayed the next largest coefficients after age at surgery in relation to ΔSF-36 and ΔOKS is ΔCAS maximum varus and valgus.
Discussion Given the importance of patient factors, biomechanical factors, and the ability of computer navigation to reliably measure multiple parameters intraoperatively, this study aimed to identify intraoperative and patientspecific parameters associated with functional outcomes at 1 year. The main finding of this investigation is that static knee alignment data measured intraoperatively played a limited role in explaining the variance in selfassessed outcome on both SF-36 and Oxford scores. Several studies have confirmed the contribution of static limb alignment to knee function. Kamat et al [13]
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suggested that the coronal alignment achieved was more important to knee function than the surgical method used. They demonstrated no significant difference between the mean OKS comparing navigated vs conventional TKA within the 5-year period of follow-up. Seon and Song [14] observed superior functional results over conventionally performed TKA at 1 year after surgery using the Hospital for Special Surgery score, Western Ontario and McMaster Universities subset pain and function score, and the flexion range of the knee. Unfortunately, this study was limited by a comparison of navigated, minimally invasive TKA and nonnavigated, conventional TKA. The current study did not support these prior reports because patient-specific factors were so dominant that change in alignment did not appear as a predictor of functional outcome. Comparisons of preoperative and postoperative parameters (Table 2) demonstrated significantly improved range of motion as well as significantly improved coronal, sagittal, and rotational alignment. These findings were consistent with prior reports regarding computer-assisted surgery [5,15,16]. Despite the intuitive value of alignment in predicting longevity, a recent long-term study showed that limbs with greater than 3º of variation from neutral did not display significantly different rates of revision [17]. This counterintuitive result could be attributed to the fact that other, more dynamic factors such as ligament balance are just as critical. The findings of the present study concur, in that ΔCAS maximum varus and valgus displayed the next largest coefficients after age at surgery in relation to ΔSF-36 and ΔOKS. The negative association of increasing BMI with postoperative function is intuitive and has been advanced recently [1,18]. This finding was not confirmed by our report, with BMI appearing with a coefficient close to 0 in our model. Age has been associated both negatively and positively with functional outcome. Greater age at time of surgery was identified as a negative prognostic factor for functional outcome after TKA in a retrospective study of 551 patients [19]. Perhaps, counterintuitively age greater than 70 years was associated with an improved functional outcome in a prospective study of patient outcomes 2 years after TKA [1]. Such results have been attributed to the lower demands placed on prosthetic joints by older patients. The analysis in our
Table 2. Differences in CAS Measurements Prearthroplasty (Pre) and Postarthroplasty (Post) Tested With Mann-Whitney U Tests (Median and Interquartile Range) Maximum varus Maximum valgus Coronal alignment Maximum flexion Maximum extension External tibiofemoral rotation
Pre
Post
Change
P
5.3 (2.5, 7.5) 0 (−1.5, 1.5) 4.0 (1.5, 6.5) 119.8 (114.9, 125.6) 4.0 (1.0, 7.0) 8.0 (4.5, 10.5)
1.5 (1.0, 2.5) 0.5 (0, 1.0) 1.0 (0.5, 1.5) 125.5 (120.6, 129.5) 1.0 (0, 2.0) 2.5 (−1.5, 6.0)
−3.5 (−5.5,1.0) 0.5 (−1.3, 2.5) −3.0 (−5.5, −0.5) 4.5 (2.6, 7.4) −2.5 (−6.5, −0.5) −5.0 (−7.5, −2.5)
b.01 .031 b.01 b.01 b.01 b.01
All units are degrees, unless stated otherwise. Coronal alignment (varus: positive).
60 The Journal of Arthroplasty Vol. 28 No. 1 January 2013 Table 3. Change in Subjective Scores and Knee Motion After Arthroplasty Knee Society flexion Knee Society extension Knee Society range of motion OKS SF-36 (PCS)
Prearthroplasty
Postarthroplasty
Change
P
115 (105, 120) 5 (2, 9) 109.5 (100, 115) 26 (21, 31) 33.8 (27.6, 40.8)
120 (114, 125) 2 (0, 5) 116 (110, 123) 41 (36, 44) 47.3 (41.5, 54.0)
5 (−3.5, 13) −0.5 (−7.8, 0) 7.5 (4, 12.5) 13 (7.3, 19.8) 11.7 (3.5,19.4)
b.01 b.01 b.01 b.01 b.01
report did not support this latter finding but, rather, found advancing age at surgery as a significant negative predictor of functional outcome. It must be noted, however, that physical function deteriorates generally with age and associated mental illness [20,21]. Several explanations for the lack of independent biomechanical predictors were entertained. First, given the nonparametric distribution of the outcome data, identifying trends was difficult. Perhaps, orthopedic surgeons have been asymptotically approaching the extent of improvement achievable with intraoperative technique improvements. In that case, future efforts should focus on improving patient-specific factors outside the operating theater. Second, it is possible that surgeons have nearly reached the maximum benefit available from the optimization of static measures, albeit in real time. The use of any instrumentation method to optimize alignment in the coronal, sagittal, and axial planes may not be the most beneficial use of intraoperative data. Much of the intraoperative input for surgeons has been based on optimization of mechanical alignment in full extension and balanced soft tissue tension in both extension and 90º of flexion. The functional envelope of a given knee certainly encompasses a greater kinematic variability than the passive extension, midflexion, and flexion of the unloaded knee tested in theater [22]. Furthermore, older adults use variable knee kinematics even in simple tasks such as stair descent; therefore, soft tissue balancing and implant position should be considered relative to these tasks [23]. Intraoperative imageless navigation provides large amounts of data for the surgeon throughout the range of motion and through varus and valgus stresses at given points of flexion. The real benefit of these navigation techniques will likely
stem from the formulation, characterization, and validation of real-time, intraoperative dynamic tests to assess and optimize knee function. Finally, it should be recognized that nonbiomechanical factors influence a patient's knee function. Age, as previously discussed, has been found in this study to factor heavily in the prediction of outcome. The future search for independent, purely biomechanical predictors must be undertaken with an appreciation for the importance of patient-specific factors. As with any regression analysis, this study has limitations. A post hoc power analysis conducted using a previously published sample size calculator for PLS regression [24] revealed that the present sample size (n = 134) was inadequate to detect the minimum correlation coefficient observed. Indeed, the sample size required was more than 550 patients to detect a correlation coefficient of 0.15, considering the number of variables included and the latent structures identified by the model, with 80% power and α of 5%. The limited nature of the present sample, without significant outliers or unsatisfactory outcomes, restricted the variability in the data and potentially contributed to the small correlation coefficients observed. This is obviously beneficial for patient function, but it reduced the ability of the regression analysis to answer the aims of the study. Nevertheless, this initial analysis was necessary to identify the parameters for future studies that will include a sufficient sample size in the analysis, considering the lack of information in the existing literature. In a larger sample, one might expect a wider variation in outcomes, but these were not available. In addition OKS and SF-36 PCS are blunt instruments with known limitations, but patient-reported outcome measures are clearly necessary [25,26]. Preoperative and postoperative mental health
Table 4. Standardized Coefficients Between Predictor Variables and Patient Outcomes Generated by PLS Regression Model Predictors Age at surgery BMI ΔCAS flexion ΔCAS extension ΔCAS range of motion ΔCAS external rotation ΔCAS coronal alignment ΔCAS maximum varus ΔCAS maximum valgus
ΔSF-36 PCS −0.49 0.07 −0.03 0.02 −0.04 0.11 −0.02 −0.12 −0.16
ΔOKS −0.37 0.05 −0.04 0.02 −0.04 0.07 −0.03 −0.10 −0.12
ΔKSS Flexion −0.26 0.18 −0.15 −0.22 0.03 −0.08 0.06 0.07 −0.16
Δ denotes a change in variable between postarthroplasty and prearthroplasty measurements. Boldface type indicates three highest coefficients.
ΔKSS Extension 0.09 −0.14 0.03 0.27 −0.14 0.12 −0.01 −0.08 0.03
ΔKSS Range of Motion −0.23 0.19 −0.12 −0.29 0.09 −0.12 0.05 0.09 −0.13
Intraoperative Computer Navigation as Predictors of Function Widmer et al
has been shown to play a role in the arena of physical functional score and activities of daily living by other authors, and it was not possible to control for this potential crossover effect [20,21]. Certainly, more reliable and robust self-reported scoring measures would be appreciated in the future, but those currently available have served as recognized standards and can be readily compared with other published data. Finally, as with most preintervention and postintervention analyses, we have analyzed these patients as pooled groups in the regression. Perhaps, a different approach to handle each patient separately using a within-subject control model would have provided different results.
Conclusions Navigation is an instrument in the surgeons' armamentarium, but data obtained at the time of surgery only have limited capacity to explain the variance in functional outcome at 1 year. This is not surprising, given the large effect of other patient factors including age and BMI on self-reported patient outcome. Intraoperative flexion has a greater ability to explain range of motion at 1 year than other patient self-reported outcome measures. Intraoperative data in this study provide some small but significant explanation of the variability in functional outcomes. Although alignment and component position can be precisely measured intraoperatively, they have not been shown here to independently predict functional outcome because intrinsic patient factors are understandably difficult to overcome.
Acknowledgments The authors wish to acknowledge the secretarial staff at the Sydney Orthopaedic Arthritis and Sports Medicine Clinic for their assistance with data collection and patient recruitment.
References 1. Merle-Vincent F, Couris CM, Schott AM, et al. Factors predicting patient satisfaction 2 years after total knee arthroplasty for osteoarthritis. Joint Bone Spine 2011;78:383. 2. Gandhi R, Razak F, Davey JR, et al. Metabolic syndrome and the functional outcomes of hip and knee arthroplasty. J Rheumatol 2010;37:1917. 3. Laskin RS, Beksac B. Stiffness after total knee arthroplasty. J Arthroplasty 2004;19:41. 4. Gatha NM, Clarke HD, Fuchs R, et al. Factors affecting postoperative range of motion after total knee arthroplasty. J Knee Surg 2004;17:196. 5. Victor J, Hoste D. Image-based computer-assisted total knee arthroplasty leads to lower variability in coronal alignment. Clin Orthop Relat Res 2004;428:131. 6. Bauwens K, Matthes G, Wich M, et al. Navigated total knee replacement: a meta-analysis. J Bone Joint Surg [Am] 2007;89:261. 7. Mason JB, Fehring TK, Estok R, et al. Meta-analysis of alignment outcomes in computer-assisted total knee arthroplasty surgery. J Arthroplasty 2007;22:1097.
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8. Longstaff LM, Sloan K, Stamp N, et al. Good alignment after total knee arthroplasty leads to faster rehabilitation and better function. J Arthroplasty 2009;24:570. 9. Barrack RL, Schrader T, Bertot AJ, et al. Component rotation and anterior knee pain after total knee arthroplasty. Clin Orthop Relat Res 2001;392:46. 10. Nicoll D, Rowley DI. Internal rotational error of the tibial component is a major cause of pain after total knee replacement. J Bone Joint Surg [Br] 2010;92:1238. 11. Czurda T, Fennema P, Baumgartner M, et al. The association between component malalignment and postoperative pain following navigation-assisted total knee arthroplasty: results of a cohort/nested case-control study. Knee Surg Sports Traumatol Arthrosc 2010;18:863. 12. Doro LC, Hughes RE, Miller JD, et al. The reproducibility of a kinematically-derived axis of the knee versus digitized anatomical landmarks using a knee navigation system. Open Biomed Eng J 2008;2:52. 13. Kamat YD, Aurakzai KM, Adhikari AR, et al. Does computer navigation in total knee arthroplasty improve patient outcome at midterm follow-up? Int Orthop 2009;33:1567. 14. Seon JK, Song EK. Functional impact of navigationassisted minimally invasive total knee arthroplasty. Orthopedics 2005;28:s1251. 15. Bolognesi M, Hofmann A. Computer navigation versus standard instrumentation for TKA: a single-surgeon experience. Clin Orthop Relat Res 2005;440:162. 16. Stockl B, Nogler M, Rosiek R, et al. Navigation improves accuracy of rotational alignment in total knee arthroplasty. Clin Orthop Relat Res 2004;426:180. 17. Parratte S, Pagnano MW, Trousdale RT, et al. Effect of postoperative mechanical axis alignment on the fifteenyear survival of modern, cemented total knee replacements. J Bone Joint Surg [Am] 2010;92:2143. 18. Gadinsky NE, Ehrhardt JK, Urband C, et al. Effect of body mass index on range of motion and manipulation after total knee arthroplasty. J Arthroplasty 2011;26:1194. 19. Gandhi R, Dhotar H, Razak F, et al. Predicting the longer term outcomes of total knee arthroplasty. Knee 2010;17:15. 20. Russo A, Cesari M, Onder G, et al. Depression and physical function: results from the aging and longevity study in the Sirente geographic area (ilSIRENTE Study). J Geriatr Psychiatry Neurol 2007;20:131. 21. Escobar A, Quintana JM, Bilbao A, et al. Effect of patient characteristics on reported outcomes after total knee replacement. Rheumatology 2007;46:112. 22. Wilson DR, Feikes JD, Zavatsky AB, et al. The components of passive knee movement are coupled to flexion angle. J Biomech 2000;33:465. 23. Mian OS, Thom JM, Narici MV, et al. Kinematics of stair descent in young and older adults and the impact of exercise training. Gait Posture 2007;25:9. 24. Westland JC. Lower bounds on sample size in structural equation modelling. Electron Commerce Res Applic 2010; 9:476. 25. Escobar A, Quintana JM, Bilbao A, et al. Responsiveness and clinically important differences for the WOMAC and SF-36 after total knee replacement. Osteoarthritis Cartilage 2007;15:273. 26. Whitehouse SL, Blom AW, Taylor AH, et al. The Oxford Knee Score; problems and pitfalls. Knee 2005;12:287.
Intraoperative Computer Navigation Parameters Are Poor Predictors of Function 1 Year After Total Knee Arthroplasty Benjamin J. Widmer, MD,*y Corey J. Scholes, PhD,* Sébastien Lustig, MD, PhD,*z Leonard Conrad, MBBS,*§ Sam I. Oussedik, FRCS,* and David A. Parker, FRACS*§
Abstract: Intraoperative navigation data were collected prospectively for 134 knees undergoing cemented, posterior-stabilized total knee arthroplasty. Partial least squares regression analysis was used to test the association between patient demographics and intraoperative data collected with a computer-assisted navigation system (coronal alignment, ligament balance, range of motion, external tibiofemoral rotation) with 1-year outcomes (36-item Short-Form Health Survey, Oxford Knee Score, range of motion). Age at surgery displayed the largest coefficients of any other predictor. In contrast, navigation coefficients were variable in the strength and direction of their association with the outcome variables. Static knee alignment data obtained intraoperatively have limited capacity to explain the variance in functional outcome at 1 year. Although alignment and component position can be precisely measured intraoperatively, intrinsic patient factors remain dominant in determining the outcome. Keywords: total knee arthroplasty, function, computer navigation, alignment, predictors. © 2013 Elsevier Inc. All rights reserved.
because popliteal soft tissue redundancy provides a mechanical block to flexion [3]. Although obesity has been implicated, preoperative range of motion has been reported as the primary predictor of postoperative range of motion [4]. In addition to these patient factors, several biomechanical elements have been associated with knee function after TKA. Alignment of the surgically reconstructed limb has long been given a central role, both intraoperatively and postoperatively, by orthopedic surgeons for improvements in both function and longevity, even if other issues like balance might be just as critical. A recent development involves intraoperative computer navigation for positioning of instruments and prosthetic components. Surgery assisted by intraoperative navigation is recognized as a means of reducing the likelihood of significant deviation from the mechanical axis [5-7]. An association between appropriate coronal alignment and improved function at 1 year has also been demonstrated [8], but there have been limited attempts to link other biomechanical parameters recorded during intraoperative navigation with postoperative function. In addition to coronal alignment, rotational alignment, particularly when associated with component malrotation, has been associated with both inferior function and knee pain [9,10]. One study noted a relationship between femoral
Total knee arthroplasty (TKA) is an effective procedure for the treatment of end-stage degenerative and inflammatory arthropathies. Both patient factors and biomechanical factors have been correlated with functional outcome and range of motion after TKA. A recent prospective analysis reported that age greater than 70 years and body mass index (BMI) less than 27 kg/m 2 were associated with better function at 2 years after knee arthroplasty [1]. A joint registry review revealed that patients with metabolic syndrome achieved poorer function after lower-extremity arthroplasty at 1 year [2]. For those patients included in the joint registry review, obesity was identified as the most reliable negative predictor of function. Obesity has also been recognized as a factor negatively associated with range of motion
From the *Sydney Orthopaedic Research Institute, Chatswood, NSW, Australia; yIntermountain Medical Group, Orthopaedic Specialty Group, Murray, Utah; zAlbert Trillat Center, Lyon North Hospital, Lyon, France; and §Northern Clinical School, University of Sydney, Royal North Shore Hospital, St Leonards, NSW, Australia. Submitted November 9, 2011; accepted April 17, 2012. The Conflict of Interest statement associated with this article can be found at http://dx.doi.org/10.1016/j.arth.2012.04.018. Reprint requests: David A. Parker, FRACS, Level 1, The Gallery 445 Victoria Ave, Chatswood, NSW 2067 Australia. © 2013 Elsevier Inc. All rights reserved. 0883-5403/2801-0010$36.00/0 http://dx.doi.org/10.1016/j.arth.2012.04.018
56
Intraoperative Computer Navigation as Predictors of Function Widmer et al
component malrotation and increasing postoperative pain, independent of instrumentation type [11]. The literature suggests that greater precision in alignment improves joint function. Although functional outcome is multifactorial and affected by both biomechanical and patient factors, these predictors have been typically analyzed independently. To that end, the purpose of this study was to determine the ability of intraoperative navigation parameters in conjunction with patient factors to predict postoperative functional outcomes of primary TKA.
Methods A retrospective analysis of prospectively collected data was performed on patients who underwent knee arthroplasty during a 1-year period. Patients who underwent primary TKA with Stryker precisioN Navigation (Stryker, Kalamazoo, Michigan) between ages 50 and 85 years at the time of surgery were eligible for inclusion in the analysis. A single, fellowship-trained knee surgeon performed all arthroplasties in a fully cemented, posterior-stabilized fashion. Exclusion criteria included inflammatory arthropathy, prior arthroplasty, and inability to comply with the postoperative follow-up requirements. The data collection protocol was approved by the local human research ethics committee. All participation was voluntary, and patients provided written informed consent before measurement. Data collection was conducted on 134 knees, comprising 76 female knees and 58 male knees, which were included in the final analysis. Of the 134 knees analyzed, 62 were bilateral arthroplasties. The patients had a mean (±) age of 67.7 (±7.8) years and were defined as obese according to the World Health Organization classification with a mean (±SD) BMI of 30.8 (±6.3) kg/m 2. Before surgery, the patients were enrolled into a prospective observational database, and demographic data were collected including date of birth, height, and weight. The patients also completed the Oxford Knee Score (OKS) and the 36-item Short-Form Health Survey (SF-36) questionnaire during the visit, either at a computer workstation or on a paper (Table 1). At the time of surgery, navigated TKA was undertaken by the senior author according to his standard technique. Intraoperative knee alignment was measured with the Stryker precisioN computer-assisted surgical (CAS) system
57
(Stryker) by attaching clusters of infrared-emitting markers to the distal femoral metaphysis and tibial diaphysis via percutaneous pins in accordance with the manufacturer's instructions. The hip's center of rotation was calculated by the CAS software before medial arthrotomy and registration of femoral and tibial anatomical landmarks using a handheld digitizer [12]. Static knee alignment in the coronal (hip-knee-ankle angle), sagittal, and transverse planes was captured before arthroplasty as the surgeon lifted the lower limb off the table by the heel and allowed gravity to provide full limb extension. The operated limb was then flexed to 90° at the hip to allow the knee to flex with the heel unsupported to record maximum knee flexion. The surgeon then returned the knee to full extension, and maximum varus and valgus angulation was captured as the surgeon applied a manual varus and valgus force, respectively. Cemented posterior-stabilized TKA was then undertaken in a standard fashion. Knee alignment was measured again after the final implant had been cemented into place but before wound closure. Alignment measurements recorded by the CAS system were saved into a Portable Document Format file (Fig. 1). The recorded data were then transferred manually to an Excel (Microsoft, Redmond, WA) spreadsheet in preparation for statistical analysis. Clinical Follow-Up During routine follow-up at 1 year from the date of the TKA (±3 months), the patients were assessed by a trained research associate who measured the knee range of motion using a handheld goniometer. The goniometer was placed on the lateral aspect of the limb, with the hinge centered over the lateral joint line. The proximal arm was aligned with the greater trochanter and the distal arm with the distal tibia. The resulting angle was recorded in degrees. Sagittal knee range of motion was calculated by subtracting any fixed flexion deformity from the maximum passive flexion measurement. Passive flexion was achieved by asking the patient to flex their hip to 90º and allowing gravity to flex the knee while the research associate performed the measurement. Patients also completed the OKS and the SF-36 questionnaire during the visit, either directly at a computer workstation or on a paper. Paper-based responses were later transferred by
Table 1. Parameters Acquired Preoperatively, Intraoperatively, and at 1-Year Follow-Up Preoperatively BMI SF-36 (PCS) OKS Knee Society flexion Knee Society extension Knee Society range of motion
Intraoperatively
1-y Follow-Up
Coronal alignment Maximum flexion Maximum extension Maximum valgus Maximum varus External tibiofemoral rotation
SF-36 (PCS) OKS Knee Society flexion Knee Society extension Knee Society range of motion
58 The Journal of Arthroplasty Vol. 28 No. 1 January 2013
Fig. 1. Example of knee alignment, extension, and rotational position graphic produced by the Stryker precisioN navigation system.
data entry personnel and collated with the rest of the study information into a data management system (SOCRATES, Ortholink, Balmain, NSW, Australia). Data and Statistical Analysis Patient demographics (age and BMI) were recorded in preparation for statistical analysis. Knee alignment measures recorded with the CAS system before and after the arthroplasty comprised maximum passive flexion and extension, external tibiofemoral rotation, coronal alignment, and maximum active varus and valgus. Patient outcomes compared at the 1-year postoperative follow-up with preoperative measurements consisted of the SF-36 physical component score (PCS), OKS, the maximum passive flexion and extension recorded with handheld goniometry, and the calculated passive range of motion (Table 1). Changes in CAS measurements and patient outcomes between prearthroplasty and postarthroplasty measurements were assessed using Mann-Whitney U tests. Partial least squares (PLS) regression was used with cross-validation (leave one out) to associate the
patient demographics and CAS measurements with the patient outcome data. The raw measurements for the predictor and outcome variables were transformed into delta scores, denoted by Δ, by subtracting the prearthroplasty and postarthroplasty measurements for each patient. Partial least squares is an appropriate regression approach when there is a high number of predictor variables relative to the number of samples and the independent variables display considerable collinearity. Partial least squares regression reduces the predictors to a set of uncorrelated components and then performs least squares regression using these components. The regression design for this study, using the Δ scores for the predictor and outcome variables, is illustrated in Fig. 2. The PLS regression process outputs a series of standardized model coefficients for each predictor variable with respect to each outcome variable. These standardized coefficients indicate the strength and direction of the relationship between variables. Model goodness of fit was assessed using the R 2 statistic, and the significance of the model was set a priori at P b .05. All statistical analyses were performed
Fig. 2. Partial least squares regression model. Δ denotes a change in parameter prearthroplasty and postarthroplasty.
Intraoperative Computer Navigation as Predictors of Function Widmer et al
using Minitab statistical software (version 16; Minitab Inc, Boston, Massachusetts).
Results Alignment measures changed significantly after the total knee arthroplasty procedure (Table 2). The knees were significantly less varus, with greater maximum flexion and reduced flexion contracture and external tibiofemoral rotation. In addition, the between-patient variability of the alignment measures was reduced postoperatively (Table 2). In addition, the TKA procedure significantly improved all patient outcomes at the 1-year follow-up compared with the preoperative measurements (Table 3). A 4-component model was selected that explained 79% of the variance in the predictor variables. The model was significantly related to ΔSF-36 PCS (F2, 71 = 6.7, P b .01), ΔOKS (F4, 71 = 3.2, P = .02), Δgoniometer flexion (F4, 71 = 6.5, P b .01), Δgoniometer extension (F4, 71 = 5.2, P b .01), and Δgoniometer range of motion (F4, 71 = 8.9, P b .01). The model explained the greatest proportion of variance in Δgoniometer range of motion (R 2 = 35%), followed by ΔSF-36 PCS (R 2 = 29%), Δgoniometer knee flexion (R 2 = 28%), Δgoniometer knee extension (R 2 = 24%), and ΔOKS (R 2 = 16%). Age at surgery displayed the largest coefficients of any other predictor for ΔSF-36 PCS, ΔOKS, Δgoniometer knee flexion, and Δgoniometer range of motion. In contrast, the CAS coefficients were more variable in strength and direction of their association with the outcome variables (Table 4). The variable that displayed the next largest coefficients after age at surgery in relation to ΔSF-36 and ΔOKS is ΔCAS maximum varus and valgus.
Discussion Given the importance of patient factors, biomechanical factors, and the ability of computer navigation to reliably measure multiple parameters intraoperatively, this study aimed to identify intraoperative and patientspecific parameters associated with functional outcomes at 1 year. The main finding of this investigation is that static knee alignment data measured intraoperatively played a limited role in explaining the variance in selfassessed outcome on both SF-36 and Oxford scores. Several studies have confirmed the contribution of static limb alignment to knee function. Kamat et al [13]
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suggested that the coronal alignment achieved was more important to knee function than the surgical method used. They demonstrated no significant difference between the mean OKS comparing navigated vs conventional TKA within the 5-year period of follow-up. Seon and Song [14] observed superior functional results over conventionally performed TKA at 1 year after surgery using the Hospital for Special Surgery score, Western Ontario and McMaster Universities subset pain and function score, and the flexion range of the knee. Unfortunately, this study was limited by a comparison of navigated, minimally invasive TKA and nonnavigated, conventional TKA. The current study did not support these prior reports because patient-specific factors were so dominant that change in alignment did not appear as a predictor of functional outcome. Comparisons of preoperative and postoperative parameters (Table 2) demonstrated significantly improved range of motion as well as significantly improved coronal, sagittal, and rotational alignment. These findings were consistent with prior reports regarding computer-assisted surgery [5,15,16]. Despite the intuitive value of alignment in predicting longevity, a recent long-term study showed that limbs with greater than 3º of variation from neutral did not display significantly different rates of revision [17]. This counterintuitive result could be attributed to the fact that other, more dynamic factors such as ligament balance are just as critical. The findings of the present study concur, in that ΔCAS maximum varus and valgus displayed the next largest coefficients after age at surgery in relation to ΔSF-36 and ΔOKS. The negative association of increasing BMI with postoperative function is intuitive and has been advanced recently [1,18]. This finding was not confirmed by our report, with BMI appearing with a coefficient close to 0 in our model. Age has been associated both negatively and positively with functional outcome. Greater age at time of surgery was identified as a negative prognostic factor for functional outcome after TKA in a retrospective study of 551 patients [19]. Perhaps, counterintuitively age greater than 70 years was associated with an improved functional outcome in a prospective study of patient outcomes 2 years after TKA [1]. Such results have been attributed to the lower demands placed on prosthetic joints by older patients. The analysis in our
Table 2. Differences in CAS Measurements Prearthroplasty (Pre) and Postarthroplasty (Post) Tested With Mann-Whitney U Tests (Median and Interquartile Range) Maximum varus Maximum valgus Coronal alignment Maximum flexion Maximum extension External tibiofemoral rotation
Pre
Post
Change
P
5.3 (2.5, 7.5) 0 (−1.5, 1.5) 4.0 (1.5, 6.5) 119.8 (114.9, 125.6) 4.0 (1.0, 7.0) 8.0 (4.5, 10.5)
1.5 (1.0, 2.5) 0.5 (0, 1.0) 1.0 (0.5, 1.5) 125.5 (120.6, 129.5) 1.0 (0, 2.0) 2.5 (−1.5, 6.0)
−3.5 (−5.5,1.0) 0.5 (−1.3, 2.5) −3.0 (−5.5, −0.5) 4.5 (2.6, 7.4) −2.5 (−6.5, −0.5) −5.0 (−7.5, −2.5)
b.01 .031 b.01 b.01 b.01 b.01
All units are degrees, unless stated otherwise. Coronal alignment (varus: positive).
60 The Journal of Arthroplasty Vol. 28 No. 1 January 2013 Table 3. Change in Subjective Scores and Knee Motion After Arthroplasty Knee Society flexion Knee Society extension Knee Society range of motion OKS SF-36 (PCS)
Prearthroplasty
Postarthroplasty
Change
P
115 (105, 120) 5 (2, 9) 109.5 (100, 115) 26 (21, 31) 33.8 (27.6, 40.8)
120 (114, 125) 2 (0, 5) 116 (110, 123) 41 (36, 44) 47.3 (41.5, 54.0)
5 (−3.5, 13) −0.5 (−7.8, 0) 7.5 (4, 12.5) 13 (7.3, 19.8) 11.7 (3.5,19.4)
b.01 b.01 b.01 b.01 b.01
report did not support this latter finding but, rather, found advancing age at surgery as a significant negative predictor of functional outcome. It must be noted, however, that physical function deteriorates generally with age and associated mental illness [20,21]. Several explanations for the lack of independent biomechanical predictors were entertained. First, given the nonparametric distribution of the outcome data, identifying trends was difficult. Perhaps, orthopedic surgeons have been asymptotically approaching the extent of improvement achievable with intraoperative technique improvements. In that case, future efforts should focus on improving patient-specific factors outside the operating theater. Second, it is possible that surgeons have nearly reached the maximum benefit available from the optimization of static measures, albeit in real time. The use of any instrumentation method to optimize alignment in the coronal, sagittal, and axial planes may not be the most beneficial use of intraoperative data. Much of the intraoperative input for surgeons has been based on optimization of mechanical alignment in full extension and balanced soft tissue tension in both extension and 90º of flexion. The functional envelope of a given knee certainly encompasses a greater kinematic variability than the passive extension, midflexion, and flexion of the unloaded knee tested in theater [22]. Furthermore, older adults use variable knee kinematics even in simple tasks such as stair descent; therefore, soft tissue balancing and implant position should be considered relative to these tasks [23]. Intraoperative imageless navigation provides large amounts of data for the surgeon throughout the range of motion and through varus and valgus stresses at given points of flexion. The real benefit of these navigation techniques will likely
stem from the formulation, characterization, and validation of real-time, intraoperative dynamic tests to assess and optimize knee function. Finally, it should be recognized that nonbiomechanical factors influence a patient's knee function. Age, as previously discussed, has been found in this study to factor heavily in the prediction of outcome. The future search for independent, purely biomechanical predictors must be undertaken with an appreciation for the importance of patient-specific factors. As with any regression analysis, this study has limitations. A post hoc power analysis conducted using a previously published sample size calculator for PLS regression [24] revealed that the present sample size (n = 134) was inadequate to detect the minimum correlation coefficient observed. Indeed, the sample size required was more than 550 patients to detect a correlation coefficient of 0.15, considering the number of variables included and the latent structures identified by the model, with 80% power and α of 5%. The limited nature of the present sample, without significant outliers or unsatisfactory outcomes, restricted the variability in the data and potentially contributed to the small correlation coefficients observed. This is obviously beneficial for patient function, but it reduced the ability of the regression analysis to answer the aims of the study. Nevertheless, this initial analysis was necessary to identify the parameters for future studies that will include a sufficient sample size in the analysis, considering the lack of information in the existing literature. In a larger sample, one might expect a wider variation in outcomes, but these were not available. In addition OKS and SF-36 PCS are blunt instruments with known limitations, but patient-reported outcome measures are clearly necessary [25,26]. Preoperative and postoperative mental health
Table 4. Standardized Coefficients Between Predictor Variables and Patient Outcomes Generated by PLS Regression Model Predictors Age at surgery BMI ΔCAS flexion ΔCAS extension ΔCAS range of motion ΔCAS external rotation ΔCAS coronal alignment ΔCAS maximum varus ΔCAS maximum valgus
ΔSF-36 PCS −0.49 0.07 −0.03 0.02 −0.04 0.11 −0.02 −0.12 −0.16
ΔOKS −0.37 0.05 −0.04 0.02 −0.04 0.07 −0.03 −0.10 −0.12
ΔKSS Flexion −0.26 0.18 −0.15 −0.22 0.03 −0.08 0.06 0.07 −0.16
Δ denotes a change in variable between postarthroplasty and prearthroplasty measurements. Boldface type indicates three highest coefficients.
ΔKSS Extension 0.09 −0.14 0.03 0.27 −0.14 0.12 −0.01 −0.08 0.03
ΔKSS Range of Motion −0.23 0.19 −0.12 −0.29 0.09 −0.12 0.05 0.09 −0.13
Intraoperative Computer Navigation as Predictors of Function Widmer et al
has been shown to play a role in the arena of physical functional score and activities of daily living by other authors, and it was not possible to control for this potential crossover effect [20,21]. Certainly, more reliable and robust self-reported scoring measures would be appreciated in the future, but those currently available have served as recognized standards and can be readily compared with other published data. Finally, as with most preintervention and postintervention analyses, we have analyzed these patients as pooled groups in the regression. Perhaps, a different approach to handle each patient separately using a within-subject control model would have provided different results.
Conclusions Navigation is an instrument in the surgeons' armamentarium, but data obtained at the time of surgery only have limited capacity to explain the variance in functional outcome at 1 year. This is not surprising, given the large effect of other patient factors including age and BMI on self-reported patient outcome. Intraoperative flexion has a greater ability to explain range of motion at 1 year than other patient self-reported outcome measures. Intraoperative data in this study provide some small but significant explanation of the variability in functional outcomes. Although alignment and component position can be precisely measured intraoperatively, they have not been shown here to independently predict functional outcome because intrinsic patient factors are understandably difficult to overcome.
Acknowledgments The authors wish to acknowledge the secretarial staff at the Sydney Orthopaedic Arthritis and Sports Medicine Clinic for their assistance with data collection and patient recruitment.
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