Development of an international Core Outcome Set (COS) for best care for the dying person: study protocol

Background: In contrast to typical measures employed to assess outcomes in healthcare such as mortality or recovery rates, it is difficult to define which specific outcomes of care are the most important in caring for dying individuals. Despite a variety of tools employed to assess different dimensions of palliative care, there is no consensus on a set of core outcomes to be measured in the last days of life. In order to optimise decision making in clinical practice and comparability of interventional studies, we aim to identify and propose a set of core outcomes for the care of the dying person. Methods: Following the COMET initiative approach, the proposed study will proceed through four stages to develop a set of core outcomes: In stage 1, a systematic review of the literature will identify outcomes measured in existing peer reviewed literature, as well as outcomes derived through qualitative studies. Grey literature, will also be included. Stage 2 will allow for the identification and determination of patient and proxy defined outcomes of care at the end of life via quantitative and qualitative methods at an international level. In stage 3, from a list of salient outcomes identified through stages 1 and 2, international experts, family members, patients, and patient advocates will be asked to score the importance of the preselected outcomes through a Delphi process. Stage 4 consists of a face-to-face consensus meeting of in

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Non-surgical management of knee osteoarthritis:comparison of ESCEO and OARSI 2019 guidelines

Abstract Knee osteoarthritis (OA) is a heterogeneous disease associated with substantial effects on quality of life, and its clinical management is difficult. Among the several available guidelines for the management of knee OA, those from OARSI and ESCEO were updated in 2019. Here, we examine the similarities and differences between these two guidelines and provide a narrative to help guide health-care providers through the complexities of non-surgical management of knee OA. OARSI and ESCEO both recommend education, structured exercise and weight loss as core treatments, topical NSAIDs as first-line treatments and oral NSAIDs and intra-articular injections for persistent pain. Low-dose, short-term acetaminophen, pharmaceutical grade glucosamine and chondroitin sulfate are recommended by ESCEO whereas OARSI strongly recommends against their use (including all glucosamine and chondroitin formulations). Despite this difference, the two guidelines are consistent in the majority of their recommendations and provide useful treatment recommendations for individuals with OA and health-care providers

Neuropathic pain in the IMI-APPROACH knee osteoarthritis cohort:prevalence and phenotyping

Abstract Objectives:Osteoarthritis (OA) patients with a neuropathic pain (NP) component may represent a specific phenotype. This study compares joint damage, pain and functional disability between knee OA patients with a likely NP component, and those without a likely NP component. Methods:Baseline data from the Innovative Medicines Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway knee OA cohort study were used. Patients with a painDETECT score ≥19 (with likely NP component, n=24) were matched on a 1:2 ratio to patients with a painDETECT score ≤12 (without likely NP component), and similar knee and general pain (Knee Injury and Osteoarthritis Outcome Score pain and Short Form 36 pain). Pain, physical function and radiographic joint damage of multiple joints were determined and compared between OA patients with and without a likely NP component. Results:OA patients with painDETECT scores ≥19 had statistically significant less radiographic joint damage (p≤0.04 for Knee Images Digital Analysis parameters and Kellgren and Lawrence grade), but an impaired physical function (p<0.003 for all tests) compared with patients with a painDETECT score ≤12. In addition, more severe pain was found in joints other than the index knee (p≤0.001 for hips and hands), while joint damage throughout the body was not different. Conclusions: OA patients with a likely NP component, as determined with the painDETECT questionnaire, may represent a specific OA phenotype, where local and overall joint damage is not the main cause of pain and disability. Patients with this NP component will likely not benefit from general pain medication and/or disease-modifying OA drug (DMOAD) therapy. Reserved inclusion of these patients in DMOAD trials is advised in the quest for successful OA treatments. Trial registration number. The study is registered under clinicaltrials.gov nr: NCT03883568

Predicted and actual 2-year structural and pain progression in the IMI-APPROACH knee osteoarthritis cohort

Abstract Objectives: The IMI-APPROACH knee osteoarthritis study used machine learning (ML) to predict structural and/or pain progression, expressed by a structural (S) and pain (P) predicted-progression score, to select patients from existing cohorts. This study evaluates the actual 2-year progression within the IMI-APPROACH, in relation to the predicted-progression scores. Methods: Actual structural progression was measured using minimum joint space width (minJSW). Actual pain (progression) was evaluated using the Knee injury and Osteoarthritis Outcomes Score (KOOS) pain questionnaire. Progression was presented as actual change (Δ) after 2 years, and as progression over 2 years based on a per patient fitted regression line using 0, 0.5, 1 and 2-year values. Differences in predicted-progression scores between actual progressors and non-progressors were evaluated. Receiver operating characteristic (ROC) curves were constructed and corresponding area under the curve (AUC) reported. Using Youden’s index, optimal cut-offs were chosen to enable evaluation of both predicted-progression scores to identify actual progressors. Results: Actual structural progressors were initially assigned higher S predicted-progression scores compared with structural non-progressors. Likewise, actual pain progressors were assigned higher P predicted-progression scores compared with pain non-progressors. The AUC-ROC for the S predicted-progression score to identify actual structural progressors was poor (0.612 and 0.599 for Δ and regression minJSW, respectively). The AUC-ROC for the P predicted-progression score to identify actual pain progressors were good (0.817 and 0.830 for Δ and regression KOOS pain, respectively). Conclusion: The S and P predicted-progression scores as provided by the ML models developed and used for the selection of IMI-APPROACH patients were to some degree able to distinguish between actual progressors and non-progressors. Trial registration: ClinicalTrials.gov, https://clinicaltrials.gov, NCT03883568

Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials:the IMI-APPROACH study

Abstract Objectives: To efficiently assess the disease-modifying potential of new osteoarthritis treatments, clinical trials need progression-enriched patient populations. To assess whether the application of machine learning results in patient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study. Design: We designed a two-stage recruitment process supported by machine learning models trained to rank candidates by the likelihood of progression. First stage models used data from pre-existing cohorts to select patients for a screening visit. The second stage model used screening data to inform the final inclusion. The effectiveness of this process was evaluated using the actual 24-month progression. Results: From 3500 candidate patients, 433 with knee osteoarthritis were screened, 297 were enrolled, and 247 completed the 2-year follow-up visit. We observed progression related to pain (P, 30%), structure (S, 13%), and combined pain and structure (P ​+ ​S, 5%), and a proportion of non-progressors (N, 52%) ∼15% lower vs an unenriched population. Our model predicted these outcomes with AUC of 0.86 [95% CI, 0.81–0.90] for pain-related progression and AUC of 0.61 [95% CI, 0.52–0.70] for structure-related progression. Progressors were ranked higher than non-progressors for P ​+ ​S (median rank 65 vs 143, AUC = 0.75), P (median rank 77 vs 143, AUC = 0.71), and S patients (median rank 107 vs 143, AUC = 0.57). Conclusions: The machine learning-supported recruitment resulted in enriched selection of progressive patients. Further research is needed to improve structural progression prediction and assess this strategy in an interventional trial

Management of hand osteoarthritis:from an US evidence-based medicine guideline to a European patient-centric approach

Abstract Hand osteoarthritis is the most common joint condition and is associated with significant morbidity. It is of paramount importance that patients are thoroughly assessed and examined when complaining of hand stiffness, pain, deformity or disability and that the patient’s concerns and expectations are addressed by the healthcare professional. In 2019 the American College of Rheumatology and Arthritis Foundation (ACR/AF) produced guidelines which included recommendations for the treatment of hand osteoarthritis. An ESCEO expert working group (including patients) was convened and composed this paper with the aim to assess whether these guidelines were appropriate for the treatment of hand osteoarthritis therapy in Europe and whether they met with the ESCEO patient-centered approach. Indeed, patients are the key stakeholders in healthcare and eliciting the patient’s preference is vital in the context of an individual consultation but also for informing research and policy-making. The patients involved in this working group emphasised the often-neglected area of aesthetic changes in hand osteoarthritis, importance of developing pharmacological therapies which can alleviate pain and disability and the need of the freedom to choose which approach (out of pharmacological, surgical or non-pharmacological) they wished to pursue. Following robust appraisal, it was recommended that the ACR/AF guidelines were suitable for a European context (as described within the body of the manuscript) and it was emphasised that patient preferences are key to the success of individual consultations, future research and future policy-making