Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1010194 | PMC |
| http://dx.doi.org/10.1136/ard.55.6.403-a | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Classifying Self-Reported Rheumatoid Arthritis Flares Using Daily Patient-Generated Data From a Smartphone App: Exploratory Analysis Applying Machine Learning Approaches.
JMIR Form Res
May 2024
Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom.
Background: The ability to predict rheumatoid arthritis (RA) flares between clinic visits based on real-time, longitudinal patient-generated data could potentially allow for timely interventions to avoid disease worsening.
Objective: This exploratory study aims to investigate the feasibility of using machine learning methods to classify self-reported RA flares based on a small data set of daily symptom data collected on a smartphone app.
Methods: Daily symptoms and weekly flares reported on the Remote Monitoring of Rheumatoid Arthritis (REMORA) smartphone app from 20 patients with RA over 3 months were used.
Assessment of interactions and dosage recommendations of synthetic DMARDs-Evidence-based and consensus-based recommendations based on a systematic literature search.
Z Rheumatol
February 2024
Kommission Pharmakotherapie, Deutsche Gesellschaft für Rheumatologie (DGRh), Berlin, Germany.
Conventional synthetic (cs) and targeted synthetic (ts) disease-modifying antirheumatic drugs (DMARD) have potential interactions with a multitude of drugs. Furthermore, they sometimes have a lower therapeutic index, particularly in cases of limited organ functions. The aim of this work was to establish evidence-based recommendations on the therapeutic use of DMARDs in the context of drug interactions and dosage recommendations.
View Article and Find Full Text PDFArticle Synopsis
- Rheumatoid arthritis (RA) is a chronic inflammatory condition causing joint pain, with biologics being effective treatments now approved for those with moderately active disease, thanks to recent guideline changes (NICE TA715).
- Discussions among UK medical teams highlighted challenges in educating doctors and patients about the new biologic treatments, along with identifying suitable patients who would benefit from them.
- Limitations of traditional assessment methods, such as the Disease Activity Score in 28 Joints, were acknowledged, suggesting the need for complementary approaches like ultrasound and better use of telemedicine and patient-reported outcomes.
[Assessment of interactions and dosage recommendations of synthetic DMARDs-Evidence-based and consensus-based recommendations based on a systematic literature search].
Z Rheumatol
March 2023
Kommission Pharmakotherapie, Deutsche Gesellschaft für Rheumatologie (DGRh), Berlin, Deutschland.
Conventional synthetic (cs) and targeted synthetic (ts) disease-modifying antirheumatic drugs (DMARD) have potential interactions with a multitude of drugs. Furthermore, they sometimes have a lower therapeutic index, particularly in cases of limited organ functions. The aim of this work was to establish evidence-based recommendations on the therapeutic use of DMARDs in the context of drug interactions and dosage recommendations.
View Article and Find Full Text PDFMatching researchers' needs and patients' contributions: practical tips for meaningful patient engagement from the field of rheumatology.
Ann Rheum Dis
March 2023
Stichting Tools, Amsterdam, The Netherlands.
There is an increasing recognition of the importance of patient engagement and involvement in health research, specifically within the field of rheumatology. In general, researchers in this specialty appreciate the value of patients as partners in research. In practice, however, the majority of researchers does not involve patients on their research teams.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!