Bearings in Hip Arthroplasty:Joint Registries vs Precision Medicine: Review Article

Background: Precision medicine has been adopted in a range of clinical settings where omics data have led to greater characterisation of disease and stratification of patients into subcategories of phenotypes and pathologies. However, in orthopaedics, precision medicine lags behind other disciplines such as cancer. Joint registries have now amassed a huge body of data pertaining to implant performance which can be broken down into performance statistics for different material types in different cohorts of patients. The National Joint Registry of England, Wales and Northern Ireland (NJR) is now one of the largest datasets available. Other registries such as those from Sweden and Australia however contain longer follow-up. Together, these registries can provide a wealth of informative for the orthopaedics community when considering which implant to give to any particular patient. Questions/Purposes: We aim to explore the benefits of combining multiple large data streams including joint registries, published data on osteoarthritis (OA) pathogenesis and pathology and data concerning performance of each implant material combination in terms of biocompatibility. We believe that this analysis will provide a comprehensive overview of implant performance hopefully aiding surgeons in making more informed choices about which implant should be used in which patient. Methods: Data from three joint registries were combined with established literature to highlight the heterogeneity of OA disease and the different clinical outcomes following arthroplasty with a range of material types. Results: This review confirms that joint registries are unable to consider differences in arthritis presentation or underlying drivers of pathology. OA is now recognised to present with varying pathology with differing morbidity in different patient populations. Equally, just as OA is a heterogeneous disease, there are disparate responses to wear debris from different material combinations used in joint replacement surgery. This has been highlighted by recent high-profile scrutiny of early failure of metal-on-metal total hip replacement (THR) implants. Conclusions: Bringing together data from joint registries, biomarker analysis, phenotyping of OA patients and knowledge of how different patients respond to implant debris will lead to a truly personalised approach to treating OA patients, ensuring that the correct implant is given to the correct patient at the correct time

Radiographs and low field MRI (0.2T) as predictors of efficacy in a weight loss trial in obese women with knee osteoarthritis

<p>Abstract</p> <p>Background</p> <p>To study the predictive value of baseline radiographs and low-field (0.2T) MRI scans for the symptomatic outcome of clinically significant weight loss in obese patients with knee osteoarthritis.</p> <p>Methods</p> <p>In this study we hypothesize that imaging variables assessed with radiographs and MRI scans pre-treatment can predict the symptomatic changes following a recommended clinically significant weight reduction Patients were recruited from the Department of Rheumatology, Frederiksberg Hospital, Denmark. Eligibility criteria were: age >18 years; primary osteoarthritis according to ACR; BMI > 28 kg/m2; motivation for weight loss. Subjects were randomly assigned to either intervention by low-energy diet (LED) for 8 weeks followed by another 24 weeks of dietary instruction or control-group. MRI scans and radiographs were scored for structural changes and these parameters were examined as independent predictors of changes in osteoarthritis symptoms after 32 weeks. The outcome assessor and statistician were blinded to group allocation.</p> <p>Results</p> <p>No significant correlations were found between imaging variables and changes in Western Ontario and McMaster Universities Index of Osteoarthritis (Spearman's test, r < 0.33 and P > 0.07).</p> <p>Only the LED group achieved a weight loss, with a mean difference of 16.3 kg (95%CI: 13.4-19.2;P < 0.0001) compared to the control group. The total WOMAC index showed a significant difference favouring LED, with a group mean difference of - 321.3 mm (95%CI: -577.5 to -65.1 mm; P = 0.01). No significant adverse events were reported.</p> <p>Conclusion</p> <p>Stage of joint destruction, assessed on either radiographs or low-field MRI (0.2T), does not preclude a symptoms relief following a clinically relevant weight loss in elderly obese female patients with knee osteoarthritis.</p

Automatic radiographic quantification of hand osteoarthritis; accuracy and sensitivity to change in joint space width in a phantom and cadaver study

This is the final version of the article. Available from Springer Verlag via the DOI in this record.OBJECTIVE: To validate a newly developed quantification method that automatically detects and quantifies the joint space width (JSW) in hand radiographs. Repeatability, accuracy and sensitivity to changes in JSW were determined. The influence of joint location and joint shape on the measurements was tested. METHODS: A mechanical micrometer set-up was developed to define and adjust the true JSW in an acrylic phantom joint and in human cadaver-derived phalangeal joints. Radiographic measurements of the JSW were compared to the true JSW. Repeatability, systematic error (accuracy) and sensitivity (defined as the smallest detectable difference (SDD)) were determined. The influence of joint position on the JSW measurement was assessed by varying the location of the acrylic phantom on the X-ray detector with respect to the X-ray beam and the influence of joint shape was determined by using morphologically different human cadaver joints. RESULTS: The mean systematic error was 0.052 mm in the phantom joint and 0.210 mm in the cadaver experiment. In the phantom experiments, the repeatability was high (SDD = 0.028 mm), but differed slightly between joint locations (p = 0.046), and a change in JSW of 0.037 mm could be detected. Dependent of the joint shape in the cadaver hand, a change in JSW between 0.018 and 0.047 mm could be detected. CONCLUSIONS: The automatic quantification method is sensitive to small changes in JSW. Considering the published data of JSW decline in the normal and osteoarthritic population, the first signs of OA progression with this method can be detected within 1 or 2 years.This work was funded by the Dutch Arthritis Association (Reumafonds). The study sponsor had no involvement in study design, data collection, data analysis, or interpretation of the results

Relationship between radiographic changes and symptoms or physical examination findings in subjects with symptomatic medial knee osteoarthritis: a three-year prospective study

<p>Abstract</p> <p>Background</p> <p>Although osteoarthritis (OA) of the knee joints is the most common and debilitating joint disease in developed countries, the factors that determine the severity of symptoms are not yet understood well. Subjects with symptomatic medial knee OA were followed up prospectively to explore the relationship between radiographic changes and symptoms or physical examination findings.</p> <p>Methods</p> <p>One-hundred six OA knees in 68 subjects (mean age 71.1 years; 85% women) were followed up at 6-month intervals over 36 months. At each visit, knee radiographs were obtained, symptoms were assessed by a validated questionnaire, and the result of physical examination was recorded systematically using a specific chart. Correlations between the change of radiographs and clinical data were investigated in a longitudinal manner.</p> <p>Results</p> <p>During the study period, the narrowing of joint space width (JSW) was observed in 34 joints (32%). Although those knees were clinically or radiographically indistinguishable at baseline from those without JSW narrowing, differences became apparent at later visits during the follow-up. The subjects with knees that underwent JSW narrowing had severer symptoms, and the symptoms tended to be worse for those with higher rates of narrowing. A significant correlation was not found between the severity of symptoms and the growth of osteophytes. For the knees that did not undergo radiographic progression, the range of motion improved during the follow-up period, possibly due to the reduction of knee pain. Such improvement was not observed with the knees that underwent JSW narrowing or osteophyte growth.</p> <p>Conclusion</p> <p>The result of this study indicates that the symptoms of knee OA patients tend to be worse when JSW narrowing is underway. This finding may explain, at least partly, a known dissociation between the radiographic stage of OA and the severity of symptoms.</p

Feature learning to automatically assess radiographic knee osteoarthritis severity

Feature learning refers to techniques that learn to transform raw data input into an effective representation for further higher-level processing in many computer vision tasks. This chapter presents the investigations and the results of feature learning using convolutional neural networks to automatically assess knee osteoarthritis (OA) severity and the associated clinical and diagnostic features of knee OA from radiographs (X-ray images). Also, this chapter demonstrates that feature learning in a supervised manner is more effective than using conventional handcrafted features for automatic detection of knee joints and fine-grained knee OA image classification. In the general machine learning approach to automatically assess knee OA severity, the first step is to localize the region of interest that is to detect and extract the knee joint regions from the radiographs, and the next step is to classify the localized knee joints based on a radiographic classification scheme such as Kellgren and Lawrence grades. First, the existing approaches for detecting (or localizing) the knee joint regions based on handcrafted features are reviewed and outlined in this chapter. Next, three new approaches are introduced: 1) to automatically detect the knee joint region using a fully convolutional network, 2) to automatically assess the radiographic knee OA using CNNs trained from scratch for classification and regression of knee joint images to predict KL grades in ordinal and continuous scales, and 3) to quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross entropy and mean-squared error using multi-objective convolutional learning. The results from these methods show progressive improvement in the overall quantification of the knee OA severity. Two public datasets: the OAI and the MOST are used to evaluate the approaches with promising results that outperform existing approaches. In summary, this work primarily contributes to the field of automated methods for localization (automatic detection) and quantification (image classification) of radiographic knee OA

Differential associations of APOE-ε2 and APOE-ε4 alleles with PET-measured amyloid-β and tau deposition in older individuals without dementia

Data de publicació electrònica: 01-02-2021Purpose: To examine associations between the APOE-ε2 and APOE-ε4 alleles and core Alzheimer's disease (AD) pathological hallmarks as measured by amyloid-β (Aβ) and tau PET in older individuals without dementia. Methods: We analyzed data from 462 ADNI participants without dementia who underwent Aβ ([18F]florbetapir or [18F]florbetaben) and tau ([18F]flortaucipir) PET, structural MRI, and cognitive testing. Employing APOE-ε3 homozygotes as the reference group, associations between APOE-ε2 and APOE-ε4 carriership with global Aβ PET and regional tau PET measures (entorhinal cortex (ERC), inferior temporal cortex, and Braak-V/VI neocortical composite regions) were investigated using linear regression models. In a subset of 156 participants, we also investigated associations between APOE genotype and regional tau accumulation over time using linear mixed models. Finally, we assessed whether Aβ mediated the cross-sectional and longitudinal associations between APOE genotype and tau. Results: Compared to APOE-ε3 homozygotes, APOE-ε2 carriers had lower global Aβ burden (βstd [95% confidence interval (CI)]: - 0.31 [- 0.45, - 0.16], p = 0.034) but did not differ on regional tau burden or tau accumulation over time. APOE-ε4 participants showed higher Aβ (βstd [95%CI]: 0.64 [0.42, 0.82], p < 0.001) and tau burden (βstd range: 0.27-0.51, all p < 0.006). In mediation analyses, APOE-ε4 only retained an Aβ-independent effect on tau in the ERC. APOE-ε4 showed a trend towards increased tau accumulation over time in Braak-V/VI compared to APOE-ε3 homozygotes (βstd [95%CI]: 0.10 [- 0.02, 0.18], p = 0.11), and this association was fully mediated by baseline Aβ. Conclusion: Our data suggest that the established protective effect of the APOE-ε2 allele against developing clinical AD is primarily linked to resistance against Aβ deposition rather than tau pathology