Assessing changes in subjective and objective function from pre- to post-knee arthroplasty using the Cardiff Dempster-Shafer theory classifier

The effect of harvesting the anterior half of the peroneus longus tendon on foot morphology and gait

BACKGROUND AND OBJECTIVES

In anterior cruciate ligament reconstruction, the strength of the graft was found to be unsatisfactory usually the anterior half of the peroneus longus tendon was taken for supplementation, but the effect on foot and ankle function and gait in the donor area is unclear. This study aims to explore the changes in the ankle and gait after using the harvested anterior half of the peroneus longus tendon as a reconstruction graft for the anterior cruciate ligament.

METHODS

A total of 20 patients, 6 males and 14 females, aged 18 to 44 years, with unilateral anterior cruciate ligament injuries, underwent reconstruction using the harvested anterior half of the peroneus longus tendon as a graft between June 2021 and December 2021. The part on which the anterior half of the peroneus longus tendon was harvested was considered the experimental group, while the contralateral foot was the control group. At the 6-month follow-up, the Lysholm knee score, AOFAS ankle score, and gait-related data (foot length, arch index, arch volume, arch volume index, and gait cycle parameters: percentage of time in each gait phase, step frequency, step length, foot strike angle, and push-off angle) were assessed using a 3D foot scanner and wearable sensors for both groups.

RESULTS

All 20 patients completed the six-month follow-up. There were no statistically significant differences between the experimental and control groups regarding knee scores, ankle scores, foot length, arch index, arch volume, arch volume index, step frequency, and step length (P > 0.05). However, there were statistically significant differences between the experimental and control groups in terms of the gait cycle parameters, including the percentage of time in the stance, mid-stance, and push-off phases, as well as foot strike angle and push-off angle (P < 0.05).

CONCLUSION

Through our study of the surgical experimental group we have shown that harvesting the anterior half of the peroneus longus tendon does not affect foot morphology and gait parameters; however, it does impact the gait cycle.

Can the Output of a Learned Classification Model Monitor a Person's Functional Recovery Status Post-Total Knee Arthroplasty?

Osteoarthritis is a common musculoskeletal disorder. Classification models can discriminate an osteoarthritic gait pattern from that of control subjects. However, whether the output of learned models (probability of belonging to a class) is usable for monitoring a person’s functional recovery status post-total knee arthroplasty (TKA) is largely unexplored. The research question is two-fold: (I) Can a learned classification model’s output be used to monitor a person’s recovery status post-TKA? (II) Is the output related to patient-reported functioning? We constructed a logistic regression model based on (1) pre-operative IMU-data of level walking, ascending, and descending stairs and (2) 6-week post-operative data of walking, ascending-, and descending stairs. Trained models were deployed on subjects at three, six, and 12 months post-TKA. Patient-reported functioning was assessed by the KOOS-ADL section. We found that the model trained on 6-weeks post-TKA walking data showed a decrease in the probability of belonging to the TKA class over time, with moderate to strong correlations between the model’s output and patient-reported functioning. Thus, the LR-model’s output can be used as a screening tool to follow-up a person’s recovery status post-TKA. Person-specific relationships between the probabilities and patient-reported functioning show that the recovery process varies, favouring individual approaches in rehabilitation.

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.

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.

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.

A Parametric Identification Method of Human Gait Differences and its Application in Rehabilitation

In order to understand the regularity of human motion, characteristic description is widely used in gait analysis. For completely expressing gait information and providing more concise indicators, parametric description is also particularly significant as a means of analysis. Therefore, in this paper, the mathematical models of gait curves based on the generalized extension-Bézier curve were investigated, of which the shape parameters were used as individual gait characteristics to distinguish whether the gait is normal or not and to assist in judging rehabilitation. To evaluate the models, angle data from three joints (hip, knee, and ankle) were recorded with motion capture system when participants (10 healthy males and 6 male patients with ankle fracture) were walking at comfortable velocity along a walkway. Then, the shape parameters of each subject were obtained by applying the mathematical models, and the parameter range of the normal group was further summarized. Through comparison, it could be found that most shape parameters of patients exceed the normal ranges in varying degrees, and are concentrated on specific parameters. The results can not only help to judge the recovery stages of patients but also figure out the corresponding abnormal postures, so as to provide guidance for rehabilitation training.

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

BACKGROUND

Total Knee Replacement (TKR) surgery is being utilised in a younger, more active population with greater functional expectations. Understanding whether patient-perceived measures of function reflect objective biomechanical measures is critical in understanding whether functional limitations can be adequately captured within a clinical setting.

RESEARCH QUESTION

Do changes in objective gait biomechanics measures reflect patient-reported outcome measures at approximately 12 months following TKR surgery?

METHODS

Three-dimensional gait analysis was performed on 41 patients with OA who were scheduled for TKR surgery, 22 of which have returned for a (9-24 month) follow-up assessment. Principal Component Analysis was used to define features of variation between OA subjects and an additional 31 non-pathological control subjects. These were used to train the Cardiff Classifier, an objective classification technique, and subsequently quantify changes following TKR surgery. Patient-perceived changes were also assessed using the Oxford Knee Score (OKS), Knee Outcome Survey (KOS), and Pain Audit Collection System scores (PACS). Pearson and Spearman correlation coefficients were calculated to establish the relationship between changes in objectively-measured and perceived outcome.

RESULTS

Objective measures of biomechanical change were strongly correlated to changes in OKS(r=-0.695, p < 0.001) and KOS(r=-.810, p < 0.001) assessed outcomes. Pain (PACS) was only related to biomechanical function post-operatively (r=-.623, p = 0.003).

SIGNIFICANCE

In this biomechanics study, the relationship between changes in objective function and patient-reported measures pre to post TKR surgery is stronger than in studies which did not include biomechanics metrics. Quality of movement may hold more significance for a patient's perception of improvement than functional measures which consider only the time taken or distance travelled during functional activities.

Objectively measured 24-hour activity profiles before and after total hip arthroplasty

AIMS

The purpose of this exploratory study was to investigate if the 24-hour activity profile (i.e. waking activities and sleep) objectively measured using wrist-worn accelerometry of patients scheduled for total hip arthroplasty (THA) improves postoperatively.

PATIENTS AND METHODS

A total of 51 THA patients with a mean age of 64 years (24 to 87) were recruited from a single public hospital. All patients underwent THA using the same surgical approach with the same prosthesis type. The 24-hour activity profiles were captured using wrist-worn accelerometers preoperatively and at 2, 6, 12, and 26 weeks postoperatively. Patient-reported outcomes (Hip Disability and Osteoarthritis Outcome Score (HOOS)) were collected at all timepoints except two weeks postoperatively. Accelerometry data were used to quantify the intensity (sedentary, light, moderate, and vigorous activities) and frequency (bouts) of activity during the day and sleep efficiency. The analysis investigated changes with time and differences between Charnley class.

RESULTS

Patients slept or were sedentary for a mean of 19.5 hours/day preoperatively and the 24-hour activity pattern did not improve significantly postoperatively. Outside of sleep, the patients spent their time in sedentary activities for a mean of 620 minutes/day (sd 143) preoperatively and 641 minutes/day (sd 133) six months postoperatively. No significant improvements were observed for light, moderate, and vigorous intensity activities (p = 0.140, p = 0.531, and p = 0.407, respectively). Sleep efficiency was poor (< 85%) at all timepoints. There was no postoperative improvement in sleep efficiency when adjusted for medications (p > 0.05). Patient-reported outcome measures showed a significant improvement with time in all domains when compared with preoperative levels. There were no differences with Charnley class at six months postoperatively. However, Charnley class C patients were more sedentary at two weeks postoperatively when compared with Charnley class A patients (p < 0.05). There were no further differences between Charnley classifications.

CONCLUSION

This study describes the 24-hour activity profile of THA patients for the first time. Prior to THA, patients in this cohort were inactive and slept poorly. This cohort shows no improvement in 24-hour activity profiles at six months postoperative. Cite this article: Bone Joint J 2019;101-B:415-425.

Which osteoarthritic gait features recover following total knee replacement surgery?

Background

Gait analysis can be used to measure variations in joint function in patients with knee osteoarthritis (OA), and is useful when observing longitudinal biomechanical changes following Total Knee Replacement (TKR) surgery. The Cardiff Classifier is an objective classification tool applied previously to examine the extent of biomechanical recovery following TKR. In this study, it is further developed to reveal the salient features that contribute to recovery towards healthy function.

Methods

Gait analysis was performed on 30 patients before and after TKR surgery, and 30 healthy controls. Median TKR follow-up time was 13 months. The combined application of principal component analysis (PCA) and the Cardiff Classifier defined 18 biomechanical features that discriminated OA from healthy gait. Statistical analysis tested whether these features were affected by TKR surgery and, if so, whether they recovered to values found for the controls.

Results

The Cardiff Classifier successfully discriminated between OA and healthy gait in all 60 cases. Of the 18 discriminatory features, only six (33%) were significantly affected by surgery, including features in all three planes of the ground reaction force (p<0.001), ankle dorsiflexion moment (p<0.001), hip adduction moment (p = 0.003), and transverse hip angle (p = 0.007). All but two (89%) of these features remained significantly different to those of the control group after surgery.

Conclusions

This approach was able to discriminate gait biomechanics associated with knee OA. The ground reaction force provided the strongest discriminatory features. Despite increased gait velocity and improvements in self-reported pain and function, which would normally be clinical indicators of recovery, the majority of features were not affected by TKR surgery. This TKR cohort retained pre-operative gait patterns; reduced sagittal hip and knee moments, decreased knee flexion, increased hip flexion, and reduced hip adduction. The changes that were associated with surgery were predominantly found at the ankle and hip, rather than at the knee.

A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms

The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects.

Application and Exploration of Big Data Mining in Clinical Medicine

OBJECTIVE

To review theories and technologies of big data mining and their application in clinical medicine.

DATA SOURCES

Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015.

STUDY SELECTION

Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected.

RESULTS

This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster-Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine.

CONCLUSION

Big data mining has the potential to play an important role in clinical medicine.