Aim: To investigate potential predictors of response to conventional DMARDs in RA.
Methods: Study design - 6-month follow-up prospective study.
Participants: RA patients with active disease. INTERVENTION AND FOLLOW-UP: Introduction of one DMARD. Response to treatment evaluated at 6 months (ACR20 criteria).
Analysis: Potential predictors of response, patients' demographics, disease activity, percentages of PBMC subsets expressing P-gp, serum IL-1β, IL-6, IL-8, IL-10, IL-12, TNF-α levels, were evaluated using univariate and multivariate logistic regression analysis. ROC curve analyses were performed in order to obtain thresholds allowing the prediction of response.
Results: Forty-two patients (mean age = 57 ± 13 years, mean disease duration = 5.4 ± 7.2 years) were included. MTX was given to 30. The response to therapy was predicted by the baseline serum level of TNF-α (mean = 30.2 pg/ml ± 18 in non-responders vs. 11.9 pg/ml ± 11.2 in responders). The threshold, which predicted with the best accuracy the response to treatment, was 20.1 pg/ml (sensitivity, specificity, positive and negative predictive values of 75, 78.9, 83.3, and 69.2%, respectively; AUC = 80.3%, 95% CI = 62.8-97.7%). Similar results were obtained in the subgroups of patients treated with MTX and patients with early RA of less than 3 years duration.
Conclusion: In the present work, the serum concentration of TNF-α was related to further response to DMARDs. Other works are needed for confirmation and to assess whether such biomarker could be used to predict the response to DMARDs at the individual level.
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http://dx.doi.org/10.1016/j.jbspin.2010.02.018 | DOI Listing |
Front Psychol
January 2025
Indiana University Indianapolis, School of Health and Human Sciences, Indianapolis, IN, United States.
Background: College students significantly decrease physical activity (PA) over the course of a four-year degree, increasing the risk for chronic disease. Research shows that psychological constructs impact behavior and goal attainment. However, little is known regarding the effect of psychological variables on PA levels in students.
View Article and Find Full Text PDFJ Inflamm Res
January 2025
Department of Hematology, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, Henan Province, People's Republic of China.
Background: Sepsis is a severe complication in leukemia patients, contributing to high mortality rates. Identifying early predictors of sepsis is crucial for timely intervention. This study aimed to develop and validate a predictive model for sepsis risk in leukemia patients using machine learning techniques.
View Article and Find Full Text PDFFront Med (Lausanne)
January 2025
Department of Critical Care Medicine, First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Background: Rhabdomyolysis (RM) frequently gives rise to diverse complications, ultimately leading to an unfavorable prognosis for patients. Consequently, there is a pressing need for early prediction of survival rates among RM patients, yet reliable and effective predictive models are currently scarce.
Methods: All data utilized in this study were sourced from the MIMIC-IV database.
Front Immunol
January 2025
Department of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.
Objective: Although pegylated interferon α-2b (PEG-IFN α-2b) therapy for chronic hepatitis B has received increasing attention, determining the optimal treatment course remains challenging. This research aimed to develop an efficient model for predicting interferon (IFN) treatment course.
Methods: Patients with chronic hepatitis B, undergoing PEG-IFN α-2b monotherapy or combined with NAs (Nucleoside Analogs), were recruited from January 2018 to December 2023 at Tianjin Third Central Hospital.
Front Immunol
January 2025
Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China.
Purpose: The α-FAtE score, composed of alpha-fetoprotein, alkaline phosphatase, and eosinophil levels, has been reported as a predictor of prognosis in hepatocellular carcinoma (HCC) patients treated with atezolizumab plus bevacizumab. This study aimed to investigate the predictive ability of α-FAtE score for the efficacy and safety of locoregional immunotherapy as the treatment of HCC patients.
Methods And Patients: We conducted a retrospective study of 446 HCC patients at Sun Yat-sen University Cancer Center from January 1 2019 to January 1 2023.
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