Objective: Although the Computer Assisted Management in Early Rheumatoid Arthritis Trial-II (CAMERA-II) showed favorable clinical effects in the most intensive methotrexate (MTX)-based strategy with prednisone (MTX ± prednisone) compared to that with placebo (MTX + placebo), this beneficial difference was only seen in 1 of the 3 analyses of remission. Our objective was to investigate whether the Continuity Rewarded (ConRew) score and a simple sum score would better reveal differences regarding remission between the 2 treatment arms of CAMERA-II. Furthermore, we investigated whether the patient vector graph, which plots on patient level, would add visual information on remission compared to a conventional box plot only, which displays data on the group level.
Methods: The ConRew method, which awards continuous periods of remission with a higher score, was applied, in addition to a simple sum score of remission periods of 4 weeks. A patient vector graph was compared with box plots.
Results: Both the mean ± SD simple sum score and the ConRew score of remission were significantly higher (favorable) in the MTX + prednisone strategy group versus the MTX + placebo group, respectively: 9 ± 7 versus 12 ± 8; P = 0.003, and 23 ± 16 versus 17 ± 14; P = 0.004. The patient vector graphs show a visual pattern of more and longer periods of remission in the MTX + prednisone strategy and visually add information to the box plots.
Conclusion: The simple sum of remission periods, the ConRew score, and the patient vector graph add understanding and discrimination to the analysis of the remission outcome in CAMERA-II.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/acr.22565 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Suppression of METTL3 expression attenuated matrix stiffness-induced vaginal fibroblast-to-myofibroblast differentiation and abnormal modulation of the extracellular matrix in pelvic organ prolapse.
Chin Med J (Engl)
January 2025
Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing 100730, China.
Background: Fibrosis of the connective tissue in the vaginal wall predominates in pelvic organ prolapse (POP), which is characterized by excessive fibroblast-to-myofibroblast differentiation and abnormal deposition of the extracellular matrix (ECM). Our study aimed to investigate the effect of ECM stiffness on vaginal fibroblasts and to explore the role of methyltransferase 3 (METTL3) in the development of POP.
Methods: Polyacrylamide hydrogels were applied to create an ECM microenvironment with variable stiffness to evaluate the effects of ECM stiffness on the proliferation, differentiation, and expression of ECM components in vaginal fibroblasts.
ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis.
J Biomed Inform
January 2025
Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, 02115, MA, USA; VA Boston Healthcare System, 150 S Huntington Ave, Boston, 02130, MA, USA. Electronic address:
Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. To address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features.
View Article and Find Full Text PDFMachine-learning based risk prediction of outcomes in patients hospitalised with COVID-19 in Australia: the AUS-COVID score.
J Am Med Inform Assoc
January 2025
Department of Cardiology, Royal North Shore Hospital, Sydney, NSW, Australia.
Objective: We aimed to develop a highly interpretable and effective, machine-learning based risk prediction algorithm to predict in-hospital mortality, intubation and adverse cardiovascular events in patients hospitalised with COVID-19 in Australia (AUS-COVID Score).
Materials And Methods: This prospective study across 21 hospitals included 1714 consecutive patients aged ≥ 18 in their index hospitalization with COVID-19. The dataset was separated into training (80%) and test sets (20%).
Objective: The objective of this research was to devise and authenticate a predictive model that employs CT radiomics and deep learning methodologies for the accurate prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC).
Methods: A total of 143 ccRCC patients were included in the training cohort, and 62 ccRCC patients were included in the validation cohort. The CT images from all patients were normalized, and the tumor regions were manually segmented via ITK-SNAP software.
Identification of Novel Biomarkers for Ischemic Stroke Through Integrated Bioinformatics Analysis and Machine Learning.
J Mol Neurosci
January 2025
Lanzhou University Second Hospital, The Second Medical College of Lanzhou University, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.
Ischemic stroke leads to permanent damage to the affected brain tissue, with strict time constraints for effective treatment. Predictive biomarkers demonstrate great potential in the clinical diagnosis of ischemic stroke, significantly enhancing the accuracy of early identification, thereby enabling clinicians to intervene promptly and reduce patient disability and mortality rates. Furthermore, the application of predictive biomarkers facilitates the development of personalized treatment plans tailored to the specific conditions of individual patients, optimizing treatment outcomes and improving prognoses.
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!