Technologie en innovatie in Vlaanderen: Prioriteiten.

Vlaanderen;

Common data elements for clinical research in mitochondrial disease: a National Institute for Neurological Disorders and Stroke project

Objectives The common data elements (CDE) project was developed by the National Institute of Neurological Disorders and Stroke (NINDS) to provide clinical researchers with tools to improve data quality and allow for harmonization of data collected in different research studies. CDEs have been created for several neurological diseases; the aim of this project was to develop CDEs specifically curated for mitochondrial disease (Mito) to enhance clinical research. Methods Nine working groups (WGs), composed of international mitochondrial disease experts, provided recommendations for Mito clinical research. They initially reviewed existing NINDS CDEs and instruments, and developed new data elements or instruments when needed. Recommendations were organized, internally reviewed by the Mito WGs, and posted online for external public comment for a period of eight weeks. The final version was again reviewed by all WGs and the NINDS CDE team prior to posting for public use

Patients' acceptance of new procedures in healthcare

The dissertation "Patients' Acceptance of New Procedures in Healthcare" is based on four different research articles that thematically investigate acceptance via intention to use new procedures in healthcare from the patient's perspective. Both digital and analog procedures are used. As a basis, an Integrated Theoretical Framework (ITF) is developed, which includes theories from healthcare, information systems, and psychology perspectives to study the intention to use. The ITF is applied to study individual decision-making behavior for different decisions and different levels of risk.Die Dissertation "Patients' Acceptance of New Procedures in Healthcare" basiert auf 4 Beiträgen, die thematisch die Akzeptanz über die Nutzungsabsicht neuer Verfahren im Gesundheitswesen aus Patientensicht untersuchen. Dabei werden digitale und analoge Verfahren eingesetzt. Als Basis wird ein Integriertes Theoretisches Framework (ITF) entwickelt, das Theorien aus Gesundheitswesen, Informationssystemen und Psychologie zur Untersuchung der Nutzungsabsicht umfasst. Das ITF wird angewandt, um das individuelle Entscheidungsverhalten für verschiedene Entscheidungen und Risikoniveaus zu untersuchen

The Use of Technology in the Subcategorisation of Osteoarthritis: a Delphi Study Approach

Objective This UK-wide OATech Network + consensus study utilised a Delphi approach to discern levels of awareness across an expert panel regarding the role of existing and novel technologies in osteoarthritis research. To direct future cross-disciplinary research it aimed to identify which could be adopted to subcategorise patients with osteoarthritis (OA). Design An online questionnaire was formulated based on technologies which might aid OA research and subcategorisation. During a two-day face-to-face meeting concordance of expert opinion was established with surveys (23 questions) before, during and at the end of the meeting (Rounds 1, 2 and 3, respectively). Experts spoke on current evidence for imaging, genomics, epigenomics, proteomics, metabolomics, biomarkers, activity monitoring, clinical engineering and machine learning relating to subcategorisation. For each round of voting, ≥80% votes led to consensus and ≤20% to exclusion of a statement. Results Panel members were unanimous that a combination of novel technological advances have potential to improve OA diagnostics and treatment through subcategorisation, agreeing in Rounds 1 and 2 that epigenetics, genetics, MRI, proteomics, wet biomarkers and machine learning could aid subcategorisation. Expert presentations changed participants’ opinions on the value of metabolomics, activity monitoring and clinical engineering, all reaching consensus in Round 2. X-rays lost consensus between Rounds 1 and 2; clinical X-rays reached consensus in Round 3. Conclusion Consensus identified that 9 of the 11 technologies should be targeted towards OA subcategorisation to address existing OA research technology and knowledge gaps. These novel, rapidly evolving technologies are recommended as a focus for emergent, cross-disciplinary osteoarthritis research programmes

Temporomandibularni poremećaji – problemi u dijagnostici

Temporomandibular disorders (TMD) is a group of conditions affecting the temporomandibular joint, masticatory muscles and the adjacent structures, and most clinicians and researchers believe that it is multifactorial etiology. There are multiple risk factors associated with TMD. The etiology of TMD has not been yet completely understood. Likewise with other chronic musculoskeletal pain disorders, TMD seems to be best explained from a biopsychosocial perspective, in which it is viewed as a psychophysiological disorder involving changes in endogenous regulatory pain pathways, resulting in maladaptive emotional, physiological and neuroendocrine responses to physical and psychological stressors. In adolescents with TMD, psychosocial factors such as increased levels of stress, somatic complaints, and emotional problems seem to play a more prominent role than dental factors. Multiple pains in the body and fatigue are significantly more common with TMD group than with the controls. Several studies have categorized TMD into subgroups. So far, studies which examined psychological differences between subgroups generally pointed to the fact that patients with myogenic diagnoses had more pain and distress than those with joint-related diagnoses. It seems that muscular pain may overshadow joint pain. However, subjects in the myogenous group more often reported parafunction, depression, and worrying. Recent studies suggest that subjects with muscular diagnoses have more severe pain and psychological distress than those with joint diagnoses. Further studies are needed to clarify the temporal sequence of risk factors, as well as the mechanisms accounting for the association between TMD pain and gender.Temporomandibularnim poremećajem (TMP) uglavnom su zahvaćeni temporomandibularni zglobovi (TMZ) i žvačni mišići s okolnim strukturama. Smatra se da su uzročnici mnogobrojni te neki izravno utječu na pojavu i razvoj TMP-a, dok se drugi pojavljuju kao čimbenici koji mogu doprinijeti nastanku bolesti. Kao i ostali kronični mišićno-koštani bolni poremećaji, i TMP bi se mogao opisati kao psihofizički poremećaj s promjenama u endogenim regulacijskim putovima boli i posljedično otežanom prilagodbom na emocionalne, fiziološke i neuroendokrine odgovore na fizičke i psihičke stresore. Čini se da kod adolescenata s TMP-om važniju ulogu imaju psihosocijalni čimbenici, kao što su povećana razina stresa, somatske tegobe i emocionalni problem, nego sami dentalni čimbenici. U pacijenata s TMP-om znatno je prisutnija pojava multiplih boli u tijelu i općenito umora nego u kontrolnih ispitanika. Postoje različite klasifikacije TMP-a; neki ih dijele u podgrupe. Pacijenti s mišićnim dijagnozama imaju izraženije pritužbe na bol i distres nego oni koji imaju poremećaje vezane uz zglob. Čini se da mišićna bol može zasjeniti zglobnu, a pacijenti iz miogene skupine više se žale na parafunkcije, depresiju i zabrinutost. Smatra se da pacijenti s mišićnim dijagnozama imaju više bolnih simptoma od onih sa zglobnim dijagnozama. Bit će potrebna daljnja istraživanja kako bi se razjasnila uloga i utjecaj čimbenika rizika i mehanizama koji su odgovorni za povezanost TMP-a i boli te utjecaj spola

Machine Learning in Chronic Pain Research: A Scoping Review

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care