Rheumatoid Arthritis (RA) is an autoimmune disease that mainly affects joints in the wrist and hands. It typically results in inflamed and painful joints. MRI is one of the most common imaging...Show moreRheumatoid Arthritis (RA) is an autoimmune disease that mainly affects joints in the wrist and hands. It typically results in inflamed and painful joints. MRI is one of the most common imaging modalities to detect and monitor possible inflamed RA-related areas, enabling rheumatologists to treat patients more timely and efficiently. Despite the importance of finding and tracking inflamed areas associated with RA in MRI, there is no previously published work on finding pixel-by-pixel changes related to RA between baseline and follow-up MRIs. Therefore, this paper proposes a hypothesis-free deep learning-based model to discover changes in wrist MRIs on a pixel level to detect changes in inflamed areas related to RA without using prior anatomical information. To do this, a combination of a U-Net-based network and image thresholding was utilised to find pixel-level non-trivial changes between baseline and follow-up MRI images. A wrist MRI dataset including 99 individual pairs of MRI images (each pair constructed of baseline and follow-up images) was used to evaluate the proposed model. Data were collected from patients with clinically suspected arthralgia (CSA), defined as patients at risk of developing RA according to their rheumatologist and already had subclinical inflammation on MRI but could not be diagnosed with RA (yet) since they had not developed clinically detectable arthritis. The obtained results were evaluated using an observer study. The evaluation showed that our proposed model is a promising first step toward developing an automatic model to find RA-related inflammatory changes. Show less
Background and Aims: The Pharmacovigilance Risk Assessment Committee (PRAC) proposed measures to address severe side effects linked to Janus kinase inhibitors (JAKi) in immune-mediated inflammatory... Show moreBackground and Aims: The Pharmacovigilance Risk Assessment Committee (PRAC) proposed measures to address severe side effects linked to Janus kinase inhibitors (JAKi) in immune-mediated inflammatory diseases (IMID). Use of these medications in individuals aged 65 and older, those at high cardiovascular risk, active or former long-term smokers, and those with increased cancer risk should be considered only if no alternatives exist. Caution is advised when administering JAKi to patients at risk of venous thromboembolism. We aim to implement recommendations from regulatory guidelines based on areas of uncertainty identified. Methods: A two-round modified Research and Development/University of California Los Angeles appropriateness methodology study was conducted. A panel of 21 gastroenterologists, dermatologists and rheumatologists used a 9-point Likert scale to rate the appropriateness of administering a JAKi for each proposed clinical scenario. Scores for appropriateness were categorized as appropriate, uncertain, or inappropriate. Two rounds were performed, each with online surveys and a virtual meeting to enable discussion and rating of each best practice. Results: Round 1 involved participants rating JAKi appropriateness and suggesting descriptors to reduce uncertainty. Survey results were discussed in a virtual meeting, identifying areas of disagreement. In round 2, participants rated their agreement with descriptors from round 1, and the level of uncertainty and disagreement reduced. Age flexibility is recommended in the absence of other risk factors. Active counseling on modifiable risks (e.g., overweight, mild hyperlipidemia and hypertension) and smoking cessation is advised. Uncertainty persists regarding cancer risk due to various factors. Conclusions: We outlined regulatory guidance without a personalized evaluation of the patient's risk profile might lead to uncertainty and become an arid technicality. Therefore, we identified gaps and implemented PRAC recommendations to help health professionals in clinical practice. Show less
