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Background: Long-term outcome after COVID-19 in patients with multiple sclerosis (pwMS) is scarcely studied and controlled data are lacking.
Objective: To compare long-term outcome after COVID-19 in pwMS to a matched control group of pwMS without COVID-19.
Methods: We included pwMS with PCR-confirmed diagnosis of COVID-19 and ≥6 months of follow-up available and, as a control group, pwMS matched 1:1 for age, sex, disability level and disease-modifying treatment type.
Results: Of 211 pwMS with COVID-19 (mean age 42.6 years [SD 12.2], 69% female, median EDSS 1.5 [range: 0-7.5], 16% antiCD20), 90.5% initially had a mild COVID-19 course. At follow-up, 70% had recovered completely 3 months (M3) after COVID-19, 83% after 6 months (M6) and 94% after 12 months (M12). Mild initial COVID-19 course was the only significant predictor of complete recovery (odds ratio [OR]: 10.5; p<0.001). Most frequent residual symptoms were fatigue (M3: 18.5%, M6: 13.7%, M12: 7.3%), hyposmia (M3: 13.7%, M6: 5.2%, M12: 1.7%) and dyspnea (M3: 7.1%, M6: 6.6%, M12: 2.8%). Compared to matched controls, fatigue, hyposmia and dyspnea were significantly more frequent at M3 and still slightly at M6, while there was no difference at M12. PwMS with COVID-19 had neither a significantly increased risk for relapses (OR 1.1; p=0.70) nor disability worsening (OR 0.96; p=0.60).
Discussion: Long-term outcome of COVID-19 is favourable in a large majority of pwMS with only a small proportion of patients suffering from persistent symptoms usually resolving after 3-6 months. COVID-19 is not associated with increased risk of relapse or disability.
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http://dx.doi.org/10.1111/ene.15477 | DOI Listing |
J Orthop Res
September 2025
Department of Kinesiology, College of Health Sciences, University of Rhode Island, Kingston, Rhode Island, USA.
Arthroplasty surgery is a common and successful end-stage intervention for advanced osteoarthritis. Yet, postoperative outcomes vary significantly among patients, leading to a plethora of measures and associated measurement approaches to monitor patient outcomes. Traditional approaches rely heavily on patient-reported outcome measures (PROMs), which are widely used, but often lack sensitivity to detect function changes (e.
View Article and Find Full Text PDFClin Rheumatol
September 2025
Division of Rheumatology, Department of Internal Medicine, Mayo Clinic, 200 First St SW, Rochester, MN, 55906, USA.
Objectives: IgG4-related disease (IgG4-RD) can affect multiple organ systems, with coronary artery involvement being rare. Coronary periarteritis may lead to complications such as myocardial infarction and ischemic cardiomyopathy. This case series characterizes the clinical and radiological features, complications, and treatment strategies in patients with IgG4-RD-associated coronary periarteritis.
View Article and Find Full Text PDFClin Transl Oncol
September 2025
Department of Radiation Oncology, Vithas La Milagrosa University Hospital, Madrid, 28010, Spain.
This narrative review analyzes current evidence comparing single-session and two-session approaches in Stereotactic Body Radiation Therapy (SBRT) and high-dose-rate (HDR) brachytherapy for localized prostate cancer. These ultra-hypofractionated strategies deliver high-precision ablative doses while minimizing exposure to normal tissues. SBRT regimens with fewer than five fractions show tumor control comparable to conventional treatments, offering reduced treatment burden and increased convenience.
View Article and Find Full Text PDFObes Surg
September 2025
Clinique Mutualiste de Pessac, Pessac, France.
Background: Preoperative treatment with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) before bariatric surgery has not been studied. Therefore, we investigated the impact of neoadjuvant treatment with GLP-1 RAs on weight loss and postoperative outcomes in patients who underwent sleeve gastrectomy for severe obesity.
Method: A retrospective single-center study was conducted between January 2022 and December 2023.
Bariatric surgery is an effective treatment for morbid obesity, but patient outcomes differ greatly because of a variety of phenotypes, comorbidities, and postoperative adherence. In bariatric care, artificial intelligence (AI) and machine learning (ML) are becoming revolutionary tools because traditional predictive models based on BMI and demographic variables are unable to account for these complexities. To put it simply, AI is a branch of computer science that enables machines to perform tasks that typically require human intelligence.
View Article and Find Full Text PDF