Objective: To investigate the effect of abatacept (ABA) on preventing joint destruction in biological disease-modifying anti-rheumatic drug (bDMARD)-naïve rheumatoid arthritis (RA) patients in real-world clinical practice.
Patients And Methods: RA patients were collected from the ABROAD (ABatacept Research Outcomes as a First-line Biological Agent in the Real WorlD) study cohort. They had moderate or high disease activity and were treated with ABA as a first-line bDMARD. Radiographic change between baseline and 1 year after ABA treatment was assessed with the van der Heijde's modified Total Sharp Score (mTSS). Predictive factors for structural remission (St-REM), defined as ΔmTSS ≤0.5/year, were determined.
Results: Among 118 patients, 81 (67.5%) achieved St-REM. Disease duration <3 years (odds ratio (OR) = 3.152, p = .007) and slower radiographic progression (shown as 'baseline mTSS/year <3', OR = 3.727, p = .004) were independently significant baseline predictive factors for St-REM irrespective of age and sex. St-REM prevalence increased significantly if clinical remission based on the Simplified Disease Activity Index was achieved at least once until 24 weeks after ABA treatment.
Conclusion: Shorter disease duration, smaller radiographic progression at baseline, and rapid clinical response were predictive factors for sustained St-REM after ABA therapy in bDMARD-naïve RA patients.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1080/14397595.2018.1482609 | DOI Listing |
Sci Rep
December 2024
School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, Petaling Jaya, 47500, Selangor Darul Ehsan, Malaysia.
Cervical cancer is a deadly disease in women globally. There is a greater chance of getting rid of cervical cancer in case of earliest diagnosis. But for some patients, there is a chance of recurrence.
View Article and Find Full Text PDFSci Rep
December 2024
Imperial College London, London, UK.
Accurate estimation of the soil resilient modulus (M) is essential for designing and monitoring pavements. However, experimental methods tend to be time-consuming and costly; regression equations and constitutive models usually have limited applications, while the predictive accuracy of some machine learning studies still has room for improvement. To forecast M efficiently and accurately, a new model named black-winged kite algorithm-extreme gradient boosting (BKA-XGBOOST) is proposed.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Medical and Surgical Sciences, Institute of Cardiology, University of Bologna, Policlinico S.Orsola-Malpighi, via Massarenti 9, Bologna, 40138, Italy.
Cardiac implantable electronic devices infections (CIEDI) are associated with poor survival despite the improvement in transvenous lead extraction (TLE). Aetiology and systemic involvement are driving factors of clinical outcomes. The aim of this study was to explore their contribute on overall mortality.
View Article and Find Full Text PDFNat Commun
December 2024
School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!