Patient Specific Alignment, Anatomy, Recovery and Outcome in Total Knee Arthroplasty

Total knee arthroplasty (TKA), despite being an otherwise highly successful medical operation, has a recurrent problem of dissatisfaction and recurrent pain rates in the 15-20% range. A variety of factors contribute to this incidence of dissatisfaction which can broadly be considered to fall into one of three groups: factors driven by the surgical outcome, pre-existing factors relating to the patients psychology, appropriateness for surgery or expectation level, and factors driven by the patient’s recovery and their management during that recovery process. With consideration to the extensive variation between patients, it is reasonable to posit that addressing patient specific factors in selection for surgery, alignment of components during surgery and post-operative management may reduce the instance of post-operative dissatisfaction. The first goal of this thesis was to understand the variation of patient anatomy as it relates to standard practice in TKA. Following the finding of extensive variation, a bio-mechanical rigid body dynamics simulation of the knee joint was developed to determine the degree to which this variation was reflected in the kinematic behaviour of the implanted knees. Later studies showed extensive kinematic variation that was responsive to variation in the alignment of the components as well as well as significantly related to patient reported outcome. Later studies further investigated how outcome related to patient selection for surgery and recovery of the patient as measured with simple activity monitoring. From this work, a pre-operative simulation assessment tool has been developed, the Dynamic Knee Score (DKS), and paired with selection and recovery management tools forms the basis of 360 Knee Systems surgical planning and patient management, which has been used in over 3,000 primary TKA’s to date

Classification of biomechanical changes in gait following total knee replacement: an objective, multi-feature analysis

Incidence of osteoarthritis (OA) is steadily increasing amongst the developed world, with the knee being the most commonly affected joint. Knee OA is a complex, progressive and multifactorial disease which can result in severe disability, pain, and reduced quality of life. Numerous biomechanical changes have been associated with OA disease progression within both the affected and unaffected joints. Total knee replacement (TKR) is a common surgical intervention which aims to replace the degenerated articular surfaces. As longevity of the prostheses have improved, TKR surgery is being recommended to an increasingly younger population. There is, however, a growing body of evidence to suggest a proportion of patients exhibit several functional limitations following surgery. Measuring functional changes is challenging, and numerous studies suggest patient-reported changes in physical function aren’t reflective of objectively measured changes. This study builds upon techniques to objectively assess biomechanical function during level gait using three-dimensional stereophotogrammetry, with an aim to quantify biomechanical changes that occur as a result of late-stage OA, and measure and summarise functional changes following TKR surgery. Firstly, the appropriateness of principal component analysis (PCA) and the Cardiff Dempster-Shafer Theory (DST) classifier to reduce and summarise level gait biomechanics is investigated within a cohort of 85 OA and 38 non-pathological (NP) subjects. The validity of previously adopted rules for retaining principal components (PCs) is assessed; namely the application of Kaiser’s rule, and a factor loading threshold of ±0.71. Through the reconstruction of biomechanical waveforms using individual PCs, it is demonstrated that this rule discards biomechanical features which can accurately distinguish between OA and NP gait biomechanics. The currently accepted definitions of two control parameters of the DST classifier, which define the shape of the sigmoid activation function, are shown to introduce a bias under certain conditions. New definitions are proposed and tested, which result in an increase in classification accuracy. The robustness of the leave-one-out (LOO) cross-validation algorithm to assess the performance of the classification is investigated, and findings suggest little benefit of retaining larger cohorts within the cross-validation set. Training bodies of different sizes are investigated, and their ability to classify the remaining data is evaluated. Results indicated that a training body of ten subjects in each group resulted in high classification accuracy (92% ± 2.5%), and improvements in accuracy then began to steadily plateau. The techniques developed thus far are then adopted to classify the hip, knee and ankle biomechanics of 41 OA and 31 NP subjects, to describe the biomechanical characteristics of late-stage OA. There were numerous methodological changes within this section of the study, and it was proved necessary to recalculate new PCs using this cohort. These new PCs were contextualised and used to classify OA biomechanics, resulting in a LOO classification accuracy of 98.6%. Anecdotally, the single misclassified subject had late-stage OA, but reported only mild functional impairments. The biomechanical features which consistently distinguished OA gait are ranked and discussed. The trained DST classifier was used to quantify the biomechanical function of 22 subjects pre and 12-months post-TKR surgery. In contrast to previous findings using the DST technique, biomechanical improvements varied, with no clear group of improvers. Five subjects were classified as NP post-operatively, seven were classified as “non-dominant OA”, and ten as “dominant OA”. Objectively measured function was significantly correlated with two out of nine patient-reported outcome measures both before surgery, and in all nine post-operatively. This might explain discrepancies in the literature between patient-reported and objectively measured changes. A retrospective analysis explored pre-operative predictors highlighted knee and ankle coronal plane angulation at heel strike, ankle range of motion, and timing of peak knee flexion as potential predictors of post-operative function

Advanced Magnetic Resonance Imaging of Osteoarthritis

This thesis examines the potential utility of magnetic resonance (MR) quantitative imaging biomarkers (QIBs) of knee osteoarthritis (OA) for rapid assessment of treatment efficacy in experimental medicine studies. The development of treatments able to modify disease in OA is hampered by an inability to evaluate treatment response over a timeframe relevant to clinical trials. There are particular challenges in the experimental medicine setting due to the small numbers of participants and short follow-up duration relative to the expected time course of OA development and progression. Multiple MR QIBs of OA exist which may help address the problem of early evaluation of treatment response. However, their use in early phase studies has remained limited. Possible reasons for this include incomplete characterisation of the performance of QIBs in this setting and lack of head-to-head comparison of candidate QIBs to determine which would be optimal. This thesis aims to address these shortcomings and provide new information on the likely utility of MR QIBs in the setting of experimental medicine studies, as well as their potential for improving our general understanding of OA pathophysiology. I start by examining the reliability and ability to discriminate between OA and healthy knees of cartilage compositional MR imaging in a systematic review and meta-analysis. I then describe the development and validation of a novel semi-automatic surface-based method for analysing articular cartilage composition and morphology at the knee which may offer improved responsiveness and spatial localisation of change. Moving to QIBs of subchondral bone, I evaluate the association between measures of subchondral bone architecture derived from MR texture analysis and OA progression in the Osteoarthritis Initiative. The remainder of the thesis describes a prospective observational study where the utility of MR QIBs of synovium, subchondral bone and cartilage in experimental medicine studies is assessed. In summary, this thesis will inform decisions regarding the use of MR-based QIBs in future longitudinal and interventional studies. Their inclusion in experimental medicine studies may allow early assessment of treatment efficacy at a structural level and improve efficiency of treatment development pipelines.Funding: Addenbrooke's Charitable Trust, Experimental Medicine Initiative, non-investigator sponsored study grant from GlaxoSmithKlin

Exploring the Application of Wearable Movement Sensors in People with Knee Osteoarthritis

People with knee osteoarthritis have difficulty with functional activities, such as walking or get into/out of a chair. This thesis explored the clinical relevance of biomechanics and how wearable sensor technology may be used to assess how people move when their clinician is unable to directly observe them, such as at home or work. The findings of this thesis suggest that artificial intelligence can be used to process data from sensors to provide clinically important information about how people perform troublesome activities

Towards Remote Gait Analysis: Combining Physics and Probabilistic Models for Estimating Human Joint Mechanics

The connected health movement and remote patient monitoring promise to revolutionize patient care in multiple clinical contexts. In orthopedics, continuous monitoring of human joint and muscle tissue loading in free-living conditions will enable novel insight concerning musculoskeletal disease etiology. These developments are necessary for comprehensive patient characterization, progression monitoring, and personalized therapy. This vision has motivated many recent advances in wearable sensor-based algorithm development that aim to perform biomechanical analyses traditionally restricted to confined laboratory spaces. However, these techniques have not translated to practical deployment for remote monitoring. Several barriers to translation have been identified including complex sensor arrays. Thus, the aim of this work was to lay the foundation for remote gait analysis and techniques for estimating clinically relevant biomechanics with a reduced sensor array. The first step in this process was to develop an open-source platform that generalized the processing pipeline for automated remote biomechanical analysis. The clinical utility of the platform was demonstrated for monitoring patient gait following knee surgery using continuous recordings of thighworn accelerometer data and rectus femoris electromyograms (EMG) during free-living conditions. Individual walking bouts were identified from which strides were extracted and characterized for patient evaluation. A novel, multifactorial asymmetry index was proposed based on temporal, EMG, and kinematic descriptors of gait that was able to differentiate between patients at different stages of recovery and that was more sensitive to recovery time than were indices of cumulative physical activity. The remainder of the work focused on algorithms for estimating joint moment and simulating muscle contraction dynamics using a reduced sensor array. A hybrid technique was proposed that combined both physics and probabilistic models in a complementary fashion. Specifically, the notion of a muscle synergy function was introduced that describes the mapping between excitations from a subset of muscles and excitations from other synergistic muscles. A novel model of these synergy functions was developed that enabled estimation of unmeasured muscle excitations using a measured subset. Data from thigh- and shank-worn inertial sensors were used to estimate segment kinematics and muscle-tendon unit (MTU) lengths using physics-based techniques and a model of the musculoskeletal geometry. These estimates of muscle excitation and MTU length were used as inputs for EMG-driven simulation of muscle contraction. Estimates of muscle force, power, and work as well as net joint moment from the proposed hybrid technique were compared to estimates from laboratory-based techniques. This presents the first sensor-only (four EMG and two inertial sensors) simulation of muscle contraction dynamics and joint moment estimation using machine learning only for estimating unmeasured muscle excitations. This work provides the basis for automated remote biomechanical analysis with reduced sensor arrays; from raw sensor recordings to estimates of muscle moment, force, and power. The proposed hybrid technique requires data from only four EMG and two inertial sensors and work has begun to seamlessly integrate these sensors into a knee brace for monitoring patients following knee surgery. Future work should build on these developments including further validation and design of methods utilizing remotely and longitudinally observed biomechanics for prognosis and optimizing patient-specific interventions

Information technologies for pain management

Millions of people around the world suffer from pain, acute or chronic and this raises the importance of its screening, assessment and treatment. The importance of pain is attested by the fact that it is considered the fifth vital sign for indicating basic bodily functions, health and quality of life, together with the four other vital signs: blood pressure, body temperature, pulse rate and respiratory rate. However, while these four signals represent an objective physical parameter, the occurrence of pain expresses an emotional status that happens inside the mind of each individual and therefore, is highly subjective that makes difficult its management and evaluation. For this reason, the self-report of pain is considered the most accurate pain assessment method wherein patients should be asked to periodically rate their pain severity and related symptoms. Thus, in the last years computerised systems based on mobile and web technologies are becoming increasingly used to enable patients to report their pain which lead to the development of electronic pain diaries (ED). This approach may provide to health care professionals (HCP) and patients the ability to interact with the system anywhere and at anytime thoroughly changes the coordinates of time and place and offers invaluable opportunities to the healthcare delivery. However, most of these systems were designed to interact directly to patients without presence of a healthcare professional or without evidence of reliability and accuracy. In fact, the observation of the existing systems revealed lack of integration with mobile devices, limited use of web-based interfaces and reduced interaction with patients in terms of obtaining and viewing information. In addition, the reliability and accuracy of computerised systems for pain management are rarely proved or their effects on HCP and patients outcomes remain understudied. This thesis is focused on technology for pain management and aims to propose a monitoring system which includes ubiquitous interfaces specifically oriented to either patients or HCP using mobile devices and Internet so as to allow decisions based on the knowledge obtained from the analysis of the collected data. With the interoperability and cloud computing technologies in mind this system uses web services (WS) to manage data which are stored in a Personal Health Record (PHR). A Randomised Controlled Trial (RCT) was implemented so as to determine the effectiveness of the proposed computerised monitoring system. The six weeks RCT evidenced the advantages provided by the ubiquitous access to HCP and patients so as to they were able to interact with the system anywhere and at anytime using WS to send and receive data. In addition, the collected data were stored in a PHR which offers integrity and security as well as permanent on line accessibility to both patients and HCP. The study evidenced not only that the majority of participants recommend the system, but also that they recognize it suitability for pain management without the requirement of advanced skills or experienced users. Furthermore, the system enabled the definition and management of patient-oriented treatments with reduced therapist time. The study also revealed that the guidance of HCP at the beginning of the monitoring is crucial to patients' satisfaction and experience stemming from the usage of the system as evidenced by the high correlation between the recommendation of the application, and it suitability to improve pain management and to provide medical information. There were no significant differences regarding to improvements in the quality of pain treatment between intervention group and control group. Based on the data collected during the RCT a clinical decision support system (CDSS) was developed so as to offer capabilities of tailored alarms, reports, and clinical guidance. This CDSS, called Patient Oriented Method of Pain Evaluation System (POMPES), is based on the combination of several statistical models (one-way ANOVA, Kruskal-Wallis and Tukey-Kramer) with an imputation model based on linear regression. This system resulted in fully accuracy related to decisions suggested by the system compared with the medical diagnosis, and therefore, revealed it suitability to manage the pain. At last, based on the aerospace systems capability to deal with different complex data sources with varied complexities and accuracies, an innovative model was proposed. This model is characterized by a qualitative analysis stemming from the data fusion method combined with a quantitative model based on the comparison of the standard deviation together with the values of mathematical expectations. This model aimed to compare the effects of technological and pen-and-paper systems when applied to different dimension of pain, such as: pain intensity, anxiety, catastrophizing, depression, disability and interference. It was observed that pen-and-paper and technology produced equivalent effects in anxiety, depression, interference and pain intensity. On the contrary, technology evidenced favourable effects in terms of catastrophizing and disability. The proposed method revealed to be suitable, intelligible, easy to implement and low time and resources consuming. Further work is needed to evaluate the proposed system to follow up participants for longer periods of time which includes a complementary RCT encompassing patients with chronic pain symptoms. Finally, additional studies should be addressed to determine the economic effects not only to patients but also to the healthcare system