Correlations between patient-perceived outcome and objectively-measured biomechanical change following Total Knee Replacement

Performance of medial pivot, posterior stabilized and rotating platform total knee arthroplasty based on anteroposterior stability and patient-reported outcome measures; a multicentre double-blinded randomized controlled trial of 210 knees

BACKGROUND

Despite advancements in total knee arthroplasty (TKA), 10-20% of patients remain dissatisfied after surgery. Improved anteroposterior (AP) stability provided by medial pivot (MP) implants may theoretically lead to higher patient satisfaction.

METHODS

AP stability and patient-reported outcome measures (PROMs) at one-year postsurgery were compared between patients who underwent TKA with MP- (n = 121), posterior stabilized (PS; n = 53) and rotating platform (RP; n = 57) implants in a double-blind multicentre randomized controlled trial (Dutch Trial Register: NL6856, 21-02-2018). AP stability was assessed at 30°, 60° and 90° of knee flexion using a KT-2000 arthrometer. PROMs were measured preoperative and one-year postsurgery.

RESULTS

MP-TKA provided significant better AP stability at early flexion (30°) compared to PS- and RP-TKA (median [IQR]; 1.79 [1.14-2.77] mm vs. 3.31 [2.51-4.08] mm vs. 2.82 [1.80-4.03] mm, p < 0.001). Additionally, MP-TKA provided significant better AP stability at mid-flexion (60°) compared to PS-TKA (1.75 [1.23-2.36] mm vs. 2.14 [1.49-2.83] mm, p = 0.014). PROM improvements were comparable between implant designs. AP laxity of ≥ 4 mm at early flexion was independently of implant design associated with significantly worse Kujala scores. The incidence of ≥ 4 mm AP laxity at any knee angle was however not significantly different between implant designs.

CONCLUSION

MP-, PS- and RP-TKA all provide excellent and comparable results. Although MP-TKA provided better AP stability at early flexion compared to PS- and RP-TKA, this was found to be unrelated to improved PROMs in favour of MP-TKA. More studies focusing on early and mid-flexion performance based differences between MP and other TKA designs are required to confirm our findings. Other non-implant related factors may play a more important role in the performance of TKA and are potentially worthwhile examining.

Do Patient Reported Outcomes Reflect Objective Measures of Function? Implications for Total Knee Arthroplasty

BACKGROUND

Patient Reported Outcomes (PROs) are used in research, clinical practice, and by federal reimbursement models to assess outcomes for patients who have knee osteoarthritis (OA) and total knee arthroplasty (TKA). We examined a large cohort of patients to determine if commonly used PROs reflect observed evaluation as measured by standardized functional tests (SFTs).

METHODS

We used data from the Osteoarthritis Initiative, a ten-year observational study of knee osteoarthritis patients. Two cohorts were examined: 1) participants who received TKA (n=281) and 2) participants who have native OA (n=4,687). The PROs included Western Ontario and McMaster Osteoarthritis Index (WOMAC), Knee Injury and Osteoarthritis Outcome Score (KOOS), 12-Item Short Form Health Survey (SF-12), and Intermittent and Constant Pain Score (ICOAP). The SFTs included 20 and 400 meter (M) walks and chair stand pace. Repeated measures correlation coefficients were used to determine the relationship between PROs and SFTs.

RESULTS

The PROs and SFTs were not strongly correlated in either cohort. The magnitude of the repeated measures correlation (r_rm) between KOOS, WOMAC, SF-12, and ICOAP scores and SFT measurements in native knee OA patients ranged as follows: 400 M walk pace (0.08 to 0.20), chair stand pace (0.05 to 0.12), and 20 M pace (0.02 to 0.21), all with P<0.05. In the TKA cohort, values ranged as follows: 400 M walk pace (0.00 to 0.29), chair stand time (0.02 to 0.23), and 20 M pace (0.03 to 0.30). Due to the smaller cohort size, the majority, but not all had P values <0.05.

CONCLUSION

There is not a strong association between PROs and SFTs among patients who have knee OA or among patients who received a TKA. Therefore, PROs should not be used as a simple proxy for observed evaluation of physical function. Rather, PROs and SFTs are complementary and should be used in combination for a more nuanced and complete characterization of outcome.

Gait biomechanics phenotypes among total knee arthroplasty candidates by machine learning cluster analysis

Knee osteoarthritis patient phenotyping is relevant to developing targeted treatments and assessing the treatment efficacy of total knee arthroplasty (TKA). This study aimed to identify clusters among TKA candidates based on demographic and knee mechanic features during gait, and compare gait changes between clusters postoperatively. TKA patients underwent 3D gait analysis 1-week pre (n = 134) and 1-year post-TKA (n = 105). Principal component analysis was applied to frontal and sagittal knee angle and moment waveforms, extracting major patterns of variability. Age, sex, body mass index, gait speed, and frontal and sagittal pre-TKA angle and moment PC scores previously identified as relevant to TKA outcomes were standardized (mean = 0, SD = 1, [134 × 15]). Multidimensional scaling and machine learning-based hierarchical clustering were applied. Final clusters were validated by examining intercluster differences pre-TKA and gait feature changes (Post_PCscore  - Pre_PCscore ) by k-way Χ² and ANOVA tests. Four TKA candidate phenotypes yielded optimum clustering metrics, interpreted as higher and lower functioning clusters that were predominantly male and female. Higher functioning clusters pre-TKA (clusters 1 and 4) had more dynamic sagittal flexion moment (p < 0.001) and frontal plane adduction moment (p < 0.001) loading/un-loading patterns during stance. Post-TKA, higher functioning clusters demonstrated less knee mechanic improvements during gait (flexion angle p < 0.001; flexion moment p < 0.001). TKA candidates can be characterized by four clusters, predominately separated by sex and knee joint biomechanics. Post-TKA knee kinematics and kinetics improvements were cluster-specific; lower functioning clusters experienced more improvement. Cluster-based patient profiling may aid in triaging and developing OA management and surgical strategies meeting group-level function needs.

Investigating and defining outcomes of suprapatellar versus infrapatellar intramedullary nailing of tibial shaft fractures: a protocol for a pilot randomised controlled trial

Background

Anterior knee pain is often reported following intramedullary nailing of tibial shaft fractures. The aetiology remains unclear, but the surgical approach may play an important role. To date, no biomechanically validated method exists to assess patient outcomes specific to anterior knee pain in this cohort. The central aims of this study are to (1) evaluate the feasibility of a full-scale randomised controlled trial (RCT) investigating the influence of surgical approach on intramedullary nailing of tibial shaft fractures (suprapatellar versus infrapatellar nailing), (2) explore differences in clinical outcomes between the approaches, and (3) explore the development of a biomechanically validated methodology for assessing post-operative anterior knee pain and knee function specific to intramedullary nailing of tibial shaft fractures.

Methods

This pilot study will follow a prospective randomised controlled design at the Royal Adelaide Hospital and The Queen Elizabeth Hospital (South Australia). This study aims to recruit 60 patients between 18 and 60 years old who will be randomly assigned to either the suprapatellar or infrapatellar approach following a decision for intramedullary surgical fixation by the treating surgeon. All nails in this study will be Stryker T2 Alpha nails. Patients will undergo standard radiograph, magnetic resonance imaging, and clinical assessments in-line with their standard operative care, and complete a number of patient-reported and performance-based outcome measures. Performance-based outcome measures will be assessed utilising three-dimensional motion capture techniques. Follow-up time points are 3, 6, 12, and 18 months. Feasibility outcomes include ability to meet enrolment and retention metrics, compliance with all questionnaires and assessment procedures, and the occurrence of any adverse events. The primary clinical outcome is the incidence of anterior knee pain at 12 months after surgery.

Discussion

This study will establish the feasibility and inform the design of a large-scale RCT. Evaluation of all clinical data and patient outcomes will lead to the development of a new tool for assessing patient outcomes in this cohort. Limitations of the study include an unpredictable enrolment rate and loss to follow-up, small sample size, and the unknown ability of three-dimensional motion analysis to pick up the effects of anterior knee pain after tibial nailing.

Trial registration

This trial was prospectively registered on the 7 February 2020 on ANZCTR, ACTRN12620000109909.

Gait function improvements, using Cardiff Classifier, are related to patient-reported function and pain following hip arthroplasty

Summarizing results of three-dimensional (3D) gait analysis into a comprehensive measure of overall gait function is valuable to discern to what extent gait function is affected, and later recovered after surgery and rehabilitation. This study aimed to investigate whether preoperative gait function, quantified and summarized using the Cardiff Classifier, can predict improvements in postoperative patient-reported activities of daily living, and overall gait function 1 year after total hip arthroplasty (THA). Secondly, to explore relationships between pre-to-post surgical change in gait function versus changes in patient-reported and performance-based function. Thirty-two patients scheduled for THA and 25 nonpathological individuals were included in this prospective cohort study. Patients were evaluated before THA and 1 year postoperatively using 3D gait analysis, patient-reported outcomes, and performance-based tests. Kinematic and kinetic gait parameters, derived from 3D gait analysis, were quantified using the Cardiff Classifier. Linear regressions investigated the predictive value of preoperative gait function on postoperative outcomes of function, and univariate correlations explored relationships between pre-to-post surgical changes in outcome measures. Preoperative gait function, by means of Cardiff Classifier, explained 35% and 30% of the total variance in change in patient-reported activities of daily living, and in gait function, respectively. Moderate-to-strong correlations were found between change in gait function and change in patient-reported function and pain, while no correlations were found between change in gait function and performance-based function. Clinical significance: Preoperative gait function predicts postsurgical function to a moderate degree, while improvements in gait function after surgery are more closely related to how patients perceive function than their maximal performance of functional tests.

Assessing functional recovery following total knee replacement surgery using objective classification of level gait data and patient-reported outcome measures

BACKGROUND

Patient recovery can be quantified objectively, via gait analysis, or subjectively, using patient reported outcome measures. Association between these measures would explain the level of disability reported in patient reported outcome measures and could assist with therapeutic decisions.

METHODS

Total knee replacement outcome was assessed using objective classification and patient-reported outcome measures (Knee Outcome Survey and Oxford Knee Scores). A classifier was trained to distinguish between healthy and osteoarthritic characteristics using knee kinematics, ground reaction force and temporal gait data, combined with anthropometric data from 32 healthy and 32 osteoarthritis knees. For the osteoarthritic cohort, classification of 20 subjects quantified changes at up to 3 timepoints post-surgery.

FINDINGS

Osteoarthritic classification was reduced for 17 subjects when comparing pre- to post-operative assessments, however only 6 participants achieved non-pathological classification and only 4 of these were classified as non-pathological at 12 months. In 15 cases, the level of osteoarthritic classification did not decrease between every post-operative assessment. For an individual's recovery, classification outputs correlated (r > 0.5) with knee outcome survey for 75% of patients and oxford knee score for 78% of patients (based on 20 and 9 subjects respectively). Classifier outputs from all visits of the combined total knee replacement sample correlated moderately with knee outcome survey (r > 0.4) and strongly with oxford knee score (r > 0.6).

INTERPRETATION

Biomechanical deficits existed in most subjects despite improvements in Patient Reported Outcome Measures, with larger changes reported subjectively as compared to measured objectively. Objective Classification provides additional insight alongside Patient Reported Outcomes when reporting recovered outcomes.

GaitSmart motion analysis compared to commonly used function outcome measures in the IMI-APPROACH knee osteoarthritis cohort

Background

There are multiple measures for assessment of physical function in knee osteoarthritis (OA), but each has its strengths and limitations. The GaitSmart® system, which uses inertial measurement units (IMUs), might be a user-friendly and objective method to assess function. This study evaluates the validity and responsiveness of GaitSmart® motion analysis as a function measurement in knee OA and compares this to Knee Injury and Osteoarthritis Outcome Score (KOOS), Short Form 36 Health Survey (SF-36), 30s chair stand test, and 40m self-paced walk test.

Methods

The 2-year Innovative Medicines Initiative—Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) knee OA cohort was conducted between January 2018 and April 2021. For this study, available baseline and 6 months follow-up data (n = 262) was used. Principal component analysis was used to investigate whether above mentioned function instruments could represent one or more function domains. Subsequently, linear regression was used to explore the association between GaitSmart® parameters and those function domains. In addition, standardized response means, effect sizes and t-tests were calculated to evaluate the ability of GaitSmart® to differentiate between good and poor general health (based on SF-36). Lastly, the responsiveness of GaitSmart® to detect changes in function was determined.

Results

KOOS, SF-36, 30s chair test and 40m self-paced walk test were first combined into one function domain (total function). Thereafter, two function domains were substracted related to either performance based (objective function) or self-reported (subjective function) function. Linear regression resulted in the highest R² for the total function domain: 0.314 (R² for objective and subjective function were 0.252 and 0.142, respectively.). Furthermore, GaitSmart® was able to distinguish a difference in general health status, and is responsive to changes in the different aspects of objective function (Standardized response mean (SRMs) up to 0.74).

Conclusion

GaitSmart® analysis can reflect performance based and self-reported function and may be of value in the evaluation of function in knee OA. Future studies are warranted to validate whether GaitSmart® can be used as clinical outcome measure in OA research and clinical practice.

Osteoarthritis year in review 2020: mechanics

The mechanical environment of the joint during dynamic activity plays a significant role in osteoarthritis processes. Understanding how the magnitude, pattern and duration of joint-specific loading features contribute to osteoarthritis progression and response to treatment is a topic of on-going relevance. This narrative review synthesizes evidence from recent papers that have contributed to knowledge related to three identified emerging subthemes: 1) the role of the joint mechanical environment in osteoarthritis pathogenesis, 2) joint biomechanics as an outcome to arthroplasty treatment of osteoarthritis, and 3) methodological trends for advancing our knowledge of the role of biomechanics in osteoarthritis. Rather than provide an exhaustive review of a broad area of research, we have focused on evidence this year related to these subthemes. New research this year has indicated significant interest in using biomechanics investigations to understand structural vs clinical progression of osteoarthritis, the role and interaction in the three-dimensional loading environment of the joint, and the contribution of muscle activation and forces to osteoarthritis progression. There is ongoing interest in understanding how patient variability with respect to gait biomechanics influences arthroplasty surgery outcomes, and subgroup analyses have provided evidence for the potential utility in tailored treatment approaches. Finally, we are seeing a growing trend in the application of translational biomechanics tools such as wearable inertial measurement units for improved integration of biomechanics into clinical decision-making and outcomes assessment for osteoarthritis.

Improving Patient Outcomes Following Total Knee Arthroplasty: Identifying Rehabilitation Pathways Based on Modifiable Psychological Risk and Resilience Factors

Total knee arthroplasty (TKA) is a commonly implemented elective surgical treatment for end-stage osteoarthritis of the knee, demonstrating high success rates when assessed by objective medical outcomes. However, a considerable proportion of TKA patients report significant dissatisfaction postoperatively, related to enduring pain, functional limitations, and diminished quality of life. In this conceptual analysis, we highlight the importance of assessing patient-centered outcomes routinely in clinical practice, as these measures provide important information regarding whether surgery and postoperative rehabilitation interventions have effectively remediated patients’ real-world “quality of life” experiences. We propose a novel precision medicine approach to improving patient-centered TKA outcomes through the development of a multivariate machine-learning model. The primary aim of this model is to predict individual postoperative recovery trajectories. Uniquely, this model will be developed using an interdisciplinary methodology involving non-linear analysis of the unique contributions of a range of preoperative risk and resilience factors to patient-centered TKA outcomes. Of particular importance to the model’s predictive power is the inclusion of a comprehensive assessment of modifiable psychological risk and resilience factors that have demonstrated relationships with TKA and other conditions in some studies. Despite the potential for patient psychological factors to limit recovery, they are typically not routinely assessed preoperatively in this patient group, and thus can be overlooked in rehabilitative referral and intervention decision-making. This represents a research-to-practice gap that may contribute to adverse patient-centered outcomes. Incorporating psychological risk and resilience factors into a multivariate prediction model could improve the detection of patients at risk of sub-optimal outcomes following TKA. This could provide surgeons and rehabilitation providers with a simplified tool to inform postoperative referral and intervention decision-making related to a range of interdisciplinary domains outside their usual purview. The proposed approach could facilitate the development and provision of more targeted rehabilitative interventions on the basis of identified individual needs. The roles of several modifiable psychological risk and resilience factors in recovery are summarized, and intervention options are briefly presented. While focusing on rehabilitation following TKA, we advocate for the broader utilization of multivariate prediction models to inform individually tailored interventions targeting a range of health conditions.

Gait Abnormality Detection in People with Cerebral Palsy Using an Uncertainty-Based State-Space Model

Assessment and quantification of feature uncertainty in modeling gait pattern is crucial in clinical decision making. Automatic diagnostic systems for Cerebral Palsy gait often ignored the uncertainty factor while recognizing the gait pattern. In addition, they also suffer from limited clinical interpretability. This study establishes a low-cost data acquisition set up and proposes a state-space model where the temporal evolution of gait pattern was recognized by analyzing the feature uncertainty using Dempster-Shafer theory of evidence. An attempt was also made to quantify the degree of abnormality by proposing gait deviation indexes. Results indicate that our proposed model outperformed state-of-the-art with an overall \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$87.5\%$$\end{document} of detection accuracy (sensitivity \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$80.00\%$$\end{document}, and specificity \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100\%$$\end{document}). In a gait cycle of a Cerebral Palsy patient, first double limb support and left single limb support were observed to be affected mainly. Incorporation of feature uncertainty in quantifying the degree of abnormality is demonstrated to be promising. Larger value of feature uncertainty was observed for the patients having higher degree of abnormality. Sub-phase wise assessment of gait pattern improves the interpretability of the results which is crucial in clinical decision making.

Identifying non-specific low back pain clinical subgroups from sitting and standing repositioning posture tasks using a novel Cardiff Dempster-Shafer Theory Classifier

BACKGROUND

Low back pain (LBP) classification systems are used to deliver targeted treatments matched to an individual profile, however, distinguishing between different subsets of LBP remains a clinical challenge.

METHODS

A novel application of the Cardiff Dempster-Shafer Theory Classifier was employed to identify clinical subgroups of LBP on the basis of repositioning accuracy for subjects performing a sitting and standing posture task. 87 LBP subjects, clinically subclassified into flexion (n = 50), passive extension (n = 14), and active extension (n = 23) motor control impairment subgroups and 31 subjects with no LBP were recruited. Thoracic, lumbar and pelvic repositioning errors were quantified. The Classifier then transformed the error variables from each subject into a set of three belief values: (i) consistent with no LBP, (ii) consistent with LBP, (iii) indicating either LBP or no LBP.

FINDINGS

In discriminating LBP from no LBP the Classifier accuracy was 96.61%. From no-LBP, subsets of flexion LBP, active extension and passive extension achieved 93.83, 98.15% and 97.62% accuracy, respectively. Classification accuracies of 96.8%, 87.7% and 70.27% were found when discriminating flexion from passive extension, flexion from active extension and active from passive extension subsets, respectively. Sitting lumbar error magnitude best discriminated LBP from no LBP (92.4% accuracy) and the flexion subset from no-LBP (90.1% accuracy). Standing lumbar error best discriminated active and passive extension from no LBP (94.4% and 95.2% accuracy, respectively).

INTERPRETATION

Using repositioning accuracy, the Cardiff Dempster-Shafer Theory Classifier distinguishes between subsets of LBP and could assist decision making for targeted exercise in LBP management.