Development of a Prediction Model to Estimate the Risk of Walking Limitations in Patients with Total Knee Arthroplasty

Fear of movement and other associated factors in older patients with total knee arthroplasty

BACKGROUND

Kinesiophobia is an important risk factor for functional activities of patients in the early stage following total knee arthroplasty (TKA).

AIMS

This study aimed to investigate the relationship between fear of movement and associated factors in older patients in the late stage after TKA.

METHODS

The study included 45 older patients (mean age:70.11 ± 0.90 years) with knee osteoarthritis who underwent TKA. Kinesiophobia was assessed with the Tampa Scale of Kinesiophobia (TSK). Pain and strength in the quadriceps femoris (QF) muscle were assessed by the Visual Analog Scale and hand-held dynamometer, respectively. Functional level was determined using the 30-s sit-to-stand test (STS) and 10-m walking test.

RESULTS

There were correlations between TSK and QF muscle strength (r =  -0.538, p < 0.001), activity pain level (r = 0.489, p = 0.001), and 30-s STS (r =  -0.306, p = 0.041). There were no correlations between TSK and age (r = 0.207, p = 0.172) and 10-m walking test (r = 0.112, p = 0.465).

CONCLUSIONS

Increased pain and decreased QF muscle strength and functional level on STS were related with fear of movement in TKA patients. It was concluded that kinesiophobia of older patients with TKA must be considered during the assessment and rehabilitation program in the late stage after TKA.

Current applications of gait analysis after total knee arthroplasty: A scoping review

Introduction

The biomechanics of the knee do not return to normal after knee replacement. The purpose of this scoping review is to summarize the current use of gait analysis in total knee arthroplasty and to identify the preoperative motion analysis parameters for which a systematic review aimed at determining the reliability and validity may be warranted.

Materials and methods

This IRB-exempt scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. The 279 articles from the five search engines underwent a title/abstract and full-text screening. Included articles were categorized as either: the role of gait analysis as a research tool for operative decisions, other research applications for motion analysis in total knee arthroplasty, gait analysis as a tool in predicting radiologic outcomes, or gait analysis as a tool in predicting clinical outcomes.

Results

Eleven articles studied gait analysis as a research tool in studying operative decisions. Five articles studied other research applications for motion analysis in total knee arthroplasty. Other research applications for motion analysis currently include studying the role of the unicompartmental knee arthroplasty and novel physical therapy protocols aimed at optimizing post-operative care. Two articles studied motion analysis as a tool for predicting radiographic outcomes. 15 articles studied motion analysis in conjunction with clinical scores.

Conclusions

There is a broad range of research applications for motion analysis in knee reconstruction. Current limitations include vague definitions of 'gait analysis' or 'motion analysis' and a limited number of articles with preoperative and postoperative outcomes. Knee adduction moment, knee adduction impulse, total knee range of motion, varus angle, cadence, stride length, and velocity have the potential for integration into composite clinical scores. A systematic review to determine the psychometric properties of these variables is warranted.

Artificial intelligence in knee arthroplasty: current concept of the available clinical applications

Background

Artificial intelligence (AI) is defined as the study of algorithms that allow machines to reason and perform cognitive functions such as problem-solving, objects, images, word recognition, and decision-making. This study aimed to review the published articles and the comprehensive clinical relevance of AI-based tools used before, during, and after knee arthroplasty.

Methods

The search was conducted through PubMed, EMBASE, and MEDLINE databases from 2000 to 2021 using the 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol (PRISMA).

Results

A total of 731 potential articles were reviewed, and 132 were included based on the inclusion criteria and exclusion criteria. Some steps of the knee arthroplasty procedure were assisted and improved by using AI-based tools. Before surgery, machine learning was used to aid surgeons in optimizing decision-making. During surgery, the robotic-assisted systems improved the accuracy of knee alignment, implant positioning, and ligamentous balance. After surgery, remote patient monitoring platforms helped to capture patients’ functional data.

Conclusion

In knee arthroplasty, the AI-based tools improve the decision-making process, surgical planning, accuracy, and repeatability of surgical procedures.

Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis

BACKGROUND

Osteoarthritis is the most common degenerative joint disease. It is associated with significant socioeconomic burden and poor quality of life, mainly due to knee osteoarthritis (KOA), and related total knee arthroplasty (TKA). Since early detection method and disease-modifying drug is lacking, the key of KOA treatment is shifting to disease prevention and progression slowing. The prognostic prediction models are called for to guide clinical decision-making. The aim of our review is to identify and characterize reported multivariable prognostic models for KOA about three clinical concerns: (1) the risk of developing KOA in the general population, (2) the risk of receiving TKA in KOA patients, and (3) the outcome of TKA in KOA patients who plan to receive TKA.

METHODS

The electronic datasets (PubMed, Embase, the Cochrane Library, Web of Science, Scopus, SportDiscus, and CINAHL) and gray literature sources (OpenGrey, British Library Inside, ProQuest Dissertations & Theses Global, and BIOSIS preview) will be searched from their inception onwards. Title and abstract screening and full-text review will be accomplished by two independent reviewers. The multivariable prognostic models that concern on (1) the risk of developing KOA in the general population, (2) the risk of receiving TKA in KOA patients, and (3) the outcome of TKA in KOA patients who plan to receive TKA will be included. Data extraction instrument and critical appraisal instrument will be developed before formal assessment and will be modified during a training phase in advance. Study reporting transparency, methodological quality, and risk of bias will be assessed according to the TRIPOD statement, CHARMS checklist, and PROBAST tool, respectively. Prognostic prediction models will be summarized qualitatively. Quantitative metrics on the predictive performance of these models will be synthesized with meta-analyses if appropriate.

DISCUSSION

Our systematic review will collate evidence from prognostic prediction models that can be used through the whole process of KOA. The review may identify models which are capable of allowing personalized preventative and therapeutic interventions to be precisely targeted at those individuals who are at the highest risk. To accomplish the prediction models to cross the translational gaps between an exploratory research method and a valued addition to precision medicine workflows, research recommendations relating to model development, validation, or impact assessment will be made.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42020203543.

Clinical Decision Support Tools for Predicting Outcomes in Patients Undergoing Total Knee Arthroplasty: A Systematic Review

BACKGROUND

Total knee arthroplasty is the standard surgical treatment for end-stage osteoarthritis. Although widely accepted as a successful procedure, approximately 30% of patients are not satisfied due to non-optimal postoperative outcomes. Clinical decision support tools that are able to accurately predict post-surgery outcomes would assist in providing individualized advice or services to help alleviate possible issues, resulting in significant benefits to both the healthcare system and individuals.

METHODS

Five databases (Ovid Medline, Ovid EMBASE, CINAHL complete, Cochrane Library, and Scopus) were searched for the key phrases "knee replacement" or "knee arthroplasty" and "decision support tool," "decision tool," "predict∗ tool," "predict∗ model," "algorithm" or "nomogram." Searches were limited to peer-reviewed journal articles published between January 2000 and June 2019. Reference lists of included articles were examined. Authors came to a consensus on the final list of included articles.

RESULTS

Eighteen articles were included for review. Most models reported low predictive success and inability to externally validate. Both candidate and final predictor variables were inconsistent between studies. Only 1 model was considered strongly predictive (AUROC >0.8), and only 2 studies were able to externally validate their developed model. In general, models that performed well used large patient numbers, were tested on similar demographics, and used either nonlinear input transformations or a completely nonlinear model.

CONCLUSION

Some models do show promise; however, there remains the question of whether the reported predictive success can continue to be replicated. Furthermore, clinical applicability and interpretation of predictive tools should be considered during development.

A clinical prediction rule for predicting a delay in quality of life recovery at 1 month after total knee arthroplasty: A decision tree model

BACKGROUND

There is no clinical prediction rule for predicting the prognosis of quality of life after total knee arthroplasty and for assessing its accuracy. The study aimed to develop and assess a clinical prediction rule to predict decline in quality of life at 1 month after total knee arthroplasty.

METHODS

This study included 116 patients with total knee arthroplasty in Japan. Potential predictors such as sociodemographic factors, medical information, and motor functions were measured. Quality of life was measured using the Japanese Knee Osteoarthritis Measure at 1 day before surgery and 1 month after total knee arthroplasty. The classification and regression tree methodology was used for developing a clinical prediction rule.

RESULTS

The Japanese Knee Osteoarthritis Measure score pre-total knee arthroplasty (≦34.0 or >34.0) was the best single discriminator. Among those with the Japanese Knee Osteoarthritis Measure score pre-total knee arthroplasty ≦34.0, the next best predictor was knee flexor muscle strength on the affected side (≦0.45 or >0.45 N m/kg). Among those with knee flexor muscle strength on the affected side >0.45, the next predictor was knee flexion range of motion on the affected side (≦132.5°or >132.5°). The area under the receiver operating characteristic curves of the model was 0.805 (95% confidence interval, 0.701-0.909).

CONCLUSIONS

In this study, 4 variables were selected as the significant predictor. However, the results of knee flexor muscle strength and knee flexion range of motion were paradoxical. This result suggests that it should be careful to perform surgery to the patients with good preoperative knee function. The clinical prediction rule was developed for predicting quality of life decline 1 month after total knee arthroplasty, and the accuracy was moderate. This clinical prediction rule can be used for screening of patients with total knee arthroplasty.

Does body mass index influence quality-of-life recovery in individuals who underwent total knee arthroplasty: A prospective study

Background/Purpose:

To clarify the relationship between body mass index (BMI) and quality-of-life (QOL) recovery in individuals who underwent total knee arthroplasty (TKA).

Methods:

This prospective cohort study included 80 individuals who underwent TKA. The dependent variable was the Japanese Knee Osteoarthritis Measure used for assessing the QOL, and the independent variables were age, sex, BMI, and the Kellgren–Lawrence grade. A hierarchical multiple regression analysis was used to clarify whether BMI was a significant independent variable after accounting for other factors.

Results:

Sex was found to be the only significant predictor ( β = 0.29, p < 0.05), and BMI was not related to QOL recovery in individuals who underwent TKA.

Conclusion:

This result suggests that sex was related to QOL recovery and should be assessed and that BMI was not related to QOL recovery in individuals who underwent TKA. These results may help health-care providers to identify individuals who might struggle with QOL recovery.

How to predict early clinical outcomes and evaluate the quality of primary total knee arthroplasty: a new scoring system based on lower-extremity angles of alignment

Background

A method that can accurately predict the outcome of surgery can give patients timely feedback. In addition, to some extent, an objective evaluation method can help the surgeon quickly summarize the patient’s surgical experience and lessen dependence on the long wait for follow-up results. However, there was still no precise tool to predict clinical outcomes of total knee arthroplasty (TKA). This study aimed to develop a scoring system to predict clinical results of TKA and then grade the quality of TKA.

Methods

We retrospectively reviewed 98 primary TKAs performed between April 2013 and March 2017 to determine predictors of clinical outcomes among lower-extremity angles of alignment. Applying multivariable linear-regression analysis, we built Models (i) and (ii) to predict detailed clinical outcomes which were evaluated using the Knee Society Score (KSS). Multivariable logistic-regression analysis was used to establish Model (iii) to predict probability of getting a good clinical outcome (PGGCO) which was evaluated by Knee Injury and Osteoarthritis Outcome Score (KOOS) score. Finally, we designed a new scoring system consisting of 3 prediction models and presented a method of grading TKA quality. Thirty primary TKAs between April and December 2017 were enrolled for external validation.

Results

We set up a scoring system consisting of 3 models. The interpretations of Model (i) and (ii) were good (R² = 0.756 and 0.764, respectively). Model (iii) displayed good discrimination, with an area under the curve (AUC) of 0.936, and good calibration according to the calibration curve. Quality of surgery was stratified as follows: “A” = PGGCO ≥0.8, “B” = PGGCO ≤0.6 but < 0.8, and “C” = PGGCO < 0.6. The scoring system performed well in external validation.

Conclusions

This study first developed a validated, evidence-based scoring system based on lower-extremity angles of alignment to predict early clinical outcomes and to objectively evaluate the quality of TKA.

Development of Preoperative Prediction Models for Pain and Functional Outcome After Total Knee Arthroplasty Using The Dutch Arthroplasty Register Data

BACKGROUND

One of the main determinants of treatment satisfaction after total knee arthroplasty (TKA) is the fulfillment of preoperative expectations. For optimal expectation management, it is useful to accurately predict the treatment result. Multiple patient factors registered in the Dutch Arthroplasty Register (LROI) can potentially be utilized to estimate the most likely treatment result. The aim of the present study is to create and validate models that predict residual symptoms for patients undergoing primary TKA for knee osteoarthritis.

METHODS

Data were extracted from the LROI of all TKA patients who had preoperative and postoperative patient-reported outcome measures registered. Multivariable logistic regression analyses were performed to construct predictive algorithms for satisfaction, treatment success, and residual symptoms concerning pain at rest and during activity, sit-to-stand movement, stair negotiation, walking, performance of activities of daily living, kneeling, and squatting. We assessed predictive performance by examining measures of calibration and discrimination.

RESULTS

Data of 7071 patients could be included for data analysis. Residual complaints on kneeling (female 72%/male 59%) and squatting (female 71%/male 56%) were reported most frequently, and least residual complaints were scored for walking (female 16%/male 12%) and pain at rest (female 18%/male 14%). The predictive algorithms were presented as clinical calculators that present the probability of residual symptoms for an individual patient. The models for residual symptoms concerning sit-to-stand movement, stair negotiation, walking, activities of daily living, and treatment success showed acceptable discriminative values (area under the curve 0.68-0.74). The algorithms for residual complaints regarding kneeling, squatting, pain, and satisfaction showed less favorable results (area under the curve 0.58-0.64). The calibration curves showed adequate calibration for most of the models.

CONCLUSION

A considerable proportion of patients have residual complaints after TKA. The present study showed that demographic and patient-reported outcome measure data collected in the LROI can be used to predict the probability of residual symptoms after TKA. The models developed in the present study predict the chance of residual symptoms for an individual patient on 10 specific items concerning treatment success, functional outcome, and pain relief. This prediction can be useful for individualized expectation management in patients planned for TKA.

Limited predictive value of pre-surgical level of functioning for functioning at 3 and 12 months after TKA

PURPOSE

A total knee arthroplasty (TKA) is a cost-effective option to relieve pain and improve knee function in patients suffering from osteoarthritis. However, results differ among patients. The predictive value of pre-surgically assessed factors on the level of functioning after 3 and 12 months was investigated in this study.

METHODS

This study used an inception cohort design and a follow-up of 12 months. One hundred and fifty patients who were to receive a TKA were assessed pre-surgically with an International Classification of Functioning, Disability and Health (ICF) core assessment set: Knee Society Score (KSS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Short-Form 12 (SF12), Patient-Specific Function Scale (PSFS), range of motion (ROM), quadriceps and hamstring strength and gait parameters. The main outcome measure was WOMAC-Function at 3 and 12 months after surgery.

RESULTS

Pre-surgical physical and mental health on the SF12 and functioning and stiffness on the WOMAC explained 23% of the variance in the level of functioning 3 months after surgery. Pre-surgical knee function measured with the KSS-Knee, and functioning as assessed by WOMAC-Function explained 16% of the variance of the level of functioning 12 months after surgery.

CONCLUSIONS

The results of this study show that better functioning before surgery, less knee stiffness and a better physical and mental health to some extent predict better functioning 3 months after surgery. This effect is less evident at 12 months. This study is clinically relevant since it provides benchmark data for health care providers who want to compare their individual patients.

LEVEL OF EVIDENCE

II.

Validity of a low-cost laser with freely available software for improving measurement of walking and running speed

OBJECTIVES

Accurately measuring speed and acceleration during walking, running and sprinting has important implications for rehabilitation, planning training and talent identification in sporting and clinical populations. Light detection and ranging laser technology provides a continuous stream of distance data. It has the potential to allow rapid and precise measurement and may be advantageous compared with discrete methods of assessment, such as stopwatches and timing gates, which may be inaccurate over short distances. Therefore, the aim of this study was to assess the validity of a novel, low-cost and easy to implement laser-based system during walking and running trials.

DESIGN

Cross-sectional study.

METHODS

Thirty-two healthy adults performed walking and running trials from flying and static starts while monitored concurrently with reference standard three-dimensional motion analysis and laser systems. Velocity was calculated over short (0.5m) and longer (3m) intervals using both systems. Validity was assessed using absolute agreement intraclass correlation coefficients (ICC_2,1), mean absolute errors, Pearson's correlations and regressions and Bland-Altman plots.

RESULTS

All intraclass correlation coefficients and correlations were excellent (ICC>0.88, R>0.89). For the longer interval, all mean absolute errors were <0.03m/s (0.24-1.31%). Slightly higher mean absolute error values were reported for the shorter interval (3.16-5.10%), with the highest error of 0.184m/s evident for the flying start running trial.

CONCLUSIONS

These results indicate that a low-cost and accessible laser system can be used to accurately assess walking and running speed. To aid implementation and further research, freely available hardware design descriptions and downloadable software can be accessed at www.rehabtools.org/LIDAR.

Development and validation of a clinical prediction model for patient-reported pain and function after primary total knee replacement surgery

To develop and validate a clinical prediction model of patient-reported pain and function after undergoing total knee replacement (TKR). We used data of 1,649 patients from the Knee Arthroplasty Trial who received primary TKR across 34 centres in the UK. The external validation included 595 patients from Southampton University Hospital, and Nuffield Orthopaedic Centre (Oxford). The outcome was the Oxford Knee Score (OKS) 12-month after TKR. Pre-operative predictors including patient characteristics and clinical factors were considered. Bootstrap backward linear regression analysis was used. Low pre-operative OKS, living in poor areas, high body mass index, and patient-reported anxiety or depression were associated with worse outcome. The clinical factors associated with worse outcome were worse pre-operative physical status, presence of other conditions affecting mobility and previous knee arthroscopy. Presence of fixed flexion deformity and an absent or damaged pre-operative anterior cruciate ligament (compared with intact) were associated with better outcome. Discrimination and calibration statistics were satisfactory. External validation predicted 21.1% of the variance of outcome. This is the first clinical prediction model for predicting self-reported pain and function 12 months after TKR to be externally validated. It will help to inform to patients regarding expectations of the outcome after knee replacement surgery.

Preoperative factors related to the ambulatory status at 1 year after total knee arthroplasty

PURPOSE

The purpose of this study is to investigate whether preoperative factors can predict the ambulatory status at 1 year after primary total knee arthroplasty (TKA).

METHODS

The subjects were 115 patients who had undergone TKA. Isometric lower limb muscle strength was measured and the Timed Up and Go (TUG) test and the 2011 knee society scoring were conducted preoperatively. Then, the patients were divided into two groups after surgery: a cane-assisted walking group (n = 42) and independent walking group (n = 73). Unpaired t-test, chi-square test, Mann-Whitney U-test, logistic regression analysis and the receiver-operating characteristic curve analysis were used in this study.

RESULTS

A multiple logistic regression analysis selected age, TUG test and functional activities as significant variables estimating the use of a cane after surgery. Receiver-operating characteristic curve analyses revealed that the cut-off score for TUG test was 10.8 s (sensitivity = 69%, specificity = 67%, area under curve = 0.81) and the cut-off score for functional activities was 39 points (sensitivity = 83%, specificity = 63%, area under curve = 0.83) in predicting the ambulatory status.

CONCLUSIONS

Preoperative TUG test with a cut-off score of 10.8 s and functional activities with a cut-off score of 39 points are reliable assessment tools for predicting the use of walking aid following TKA. Implications for Rehabilitation An accurate prediction of the ambulatory status after total knee arthroplasty can aid patients in understanding their own goals of the activities of daily living. Preoperative timed up and go test of <10.8 s and a preoperative functional activities functional activities score in the 2011 knee society scoring >39 points are useful for predicting the ambulatory status after total knee arthroplasty.

Does kinesiophobia affect the early functional outcomes following total knee arthroplasty?

The purpose of this study was to investigate the effects of kinesiophobia on early functional outcomes in patients following total knee arthroplasty (TKA) and how kinesiophobia is related to functional outcomes and pain. The Tampa Scale for Kinesiophobia (TSK), 2-minute walk test (2-MWT), and the timed up and go test (TUG) were used to assess 46 TKA patients on discharge day. The pain levels and active knee flexion range of motion (ROM) were recorded. Patients were divided into two groups as high kinesiophobia (Group I, n = 22) and low kinesiophobia (Group II, n = 24) based on the TSK levels. The TUG results were similar between groups (p = 0.826). 2-MWT results (p < 0.001), pain levels (p = 0.003), and knee flexion ROM (p = 0.025) scores were better in Group II when compared to Group I. The TSK scores were significantly correlated with 2-MWT results (r = -0.40; p = 0.003), pain levels (r = 0.80; p < 0.001), and knee flexion ROM (r = -0.47; p = 0.001). The regression analysis revealed that 41% of 2-MWT score, 47% of knee flexion ROM, and 60% of pain level changes could be explained by kinesiophobia level. The results suggest that early outcomes following TKA were affected by the pain-related fear of movement. The clinicians need to consider the interrelationships between fear of movement and functional outcomes when designing, implementing, and monitoring daily therapeutic exercise programs.

The relationship between pain with walking and self-rated health 12 months following total knee arthroplasty: a longitudinal study

BACKGROUND

A subgroup of patients continue to report pain with walking 12 months after total knee arthroplasty (TKA). The association between walking pain and self-rated health (SRH) after TKA is not known. This prospective longitudinal study aimed to investigate the association between a comprehensive list of preoperative factors, postoperative pain with walking, and SRH 12 months after TKA.

METHODS

Patients (N = 156) scheduled for TKA completed questionnaires that evaluated demographic and clinical characteristics, symptoms, psychological factors, and SRH. SRH was re-assessed 12 months after TKA. Clinical variables were retrieved from medical records. Pain with walking was assessed before surgery, at 6 weeks, 3, and 12 months after TKA. Subgroups with distinct trajectories of pain with walking over time were identified using growth mixture modeling. Multiple linear regression was used to investigate the relationships between pain with walking and other factors on SRH.

RESULTS

Higher body mass index, a higher number of painful sites at 12 months, recurrent pain with walking group membership, ketamine use, higher depression scores, and poorer preoperative self-rated health were associated with poorer SRH 12 months after TKA. The final model was statistically significant (p = 0.005) and explained 56.1% of the variance in SRH 12 months after surgery. SRH improved significantly over time. Higher C-reactive protein levels, higher number of painful sites before surgery, higher fatigue severity, and more illness concern was associated with poorer preoperative SRH.

CONCLUSIONS

In patients whose walking ability decreases over time, clinicians need to assess for unreleaved pain and decreases in SRH. Additional research is needed on interventions to improve walking ability and SRH.