Therapie最新文献

Introduction: Acute musculoskeletal injuries are a common reason for primary care consultations. Nonsteroidal anti-inflammatory drugs (NSAIDs) are currently used in this context. Recent meta-analyses (M-A) showed a better benefit--risk profile for topical NSAIDs and no superiority of oral NSAIDs over paracetamol alone or in combination in terms of pain relief. It seems appropriate to evaluate rigorously NSAIDs' benefit-risk balance to support more consistent recommendations and promote appropriate evidence-based use of this drug class in trauma care.

Objective: This study's objective is to assess the efficacy and benefit-risk balance of oral and topical NSAIDs in the management of acute pain related to acute musculoskeletal injuries.

Materials and methods: A systematic review was conducted using public databases Medline (PubMed), CENTRAL (Cochrane) and Embase. Controlled randomised trials (RCTs) evaluating oral and topical NSAIDs (diclofenac, ibuprofen, ketoprofen and naproxen) for the treatment of acute pain from minor soft tissue injuries were included. These RCTs are from 2020 meta-analysis by Busse et al. bibliographic references and from complementary research from January 2020 to December 2023. Primary outcome is short-term pain (from 30minutes to 7days). Secondary outcomes are pain at rest, pain on movement (preferably on day 3), and safety analysis for oral and topical forms. Risk of bias was assessed using the Risk of Bias version 2 (RoB2) tool. Meta-analyses were done with Review Manager (RevMan) software. Levels of evidence were evaluated using both the REB and GRADE methods.

Results: The REB analysis concluded to a "solid evidence" for topical diclofenac (based on 10 RCTs and 7 confirmatory studies), to an "evidence requiring confirmation" evidence for topical ibuprofen (2 RCTs) and topical ketoprofen (2 RCTs). REB evaluation also concluded to a lack of proof for naproxen and for oral NSAIDs. GRADE analysis showed a low quality of evidence for topical NSAIDs efficacy and a high quality of evidence for their safety. It showed a very low quality of evidence for oral NSAIDs efficacy and safety.

Conclusion: According to REB methodology, topical NSAIDs, unlike oral formulations, have demonstrated efficacy in managing acute pain related to musculoskeletal injuries. These results reinforce recent meta-analyses favoring topical NSAIDs and recommending to limit the use of oral NSAIDs due to a lack of proof in this indication. Finally, NSAIDs impact on tissue healing has not been objectively studied by RCTs in humans, so further research is warranted. The use of NSAIDs in trauma care may potentially evolve as new data and knowledge emerge.

Objective: To assess the level of evidence supporting antibiotic therapy recommendations in primary care.

Method: After collecting the clinical practice guidelines (CPGs) listed on the Antibioclic® website (https://antibioclic.com), each recommendation regarding whether or not to prescribe antibiotics was evaluated based on whether it included a GRADE classification by the French National Authority for Health (HAS): A, B, C, or Expert Opinion (EO). The sources cited by the CPGs were reviewed. The recommendations and studies had to answer the question: "Should antibiotic therapy be prescribed in this situation?" The possible answers were "yes," "no," or "conditional yes," meaning delayed prescription. The primary outcome was the percentage of recommendations for which supporting studies were cited, in comparison to the stated GRADE.

Results: A total of 152 recommendations from 49 CPGs on antibiotic prescription were analyzed. In all, 71.7% did not mention a level of evidence, 3.3% were classified as GRADE A, and 9.2% as GRADE B. Upon reviewing the studies used to justify the recommendations, high-quality evidence was identified for only 7.9% of the recommendations, while 80.9% had no referenced studies.

Conclusion: Most French recommendations regarding antibiotic prescription in primary care do not provide the level of evidence supporting them.

Introduction: Precision medicine aims to tailor healthcare decisions and interventions to the unique biological and clinical characteristics of each patient. The recent convergence of artificial intelligence (AI) with advances in digital health, omics, and big data analytics has accelerated progress toward this goal. AI technologies - particularly machine learning, deep learning, natural language processing and generative large language models - enable the rapid and meaningful analysis of complex biomedical datasets, supporting more individualized care.

Purpose of review: In this narrative review, we provide an accessible overview of the core principles of AI for healthcare professionals and explore its practical applications across the spectrum of precision medicine. Real-world examples highlight how AI is being used to enhance early diagnosis, guide treatment selection, support disease prevention, and even contribute directly to therapeutic interventions. Alongside these advances, we discuss critical limitations and challenges, including ethical considerations, algorithmic bias, data privacy concerns, environmental impact, and practical barriers to clinical implementation.

Conclusion: This review offers both an introduction to AI and a practical overview of how it is being used, and where its limitations lie, in precision medicine, with the goal of helping healthcare professionals understand these evolving tools and use them efficiently and responsibly in clinical practice.

The advent of digital twins in pharmacology presents transformative potential for precision medicine, enabling personalized treatment optimization through dynamic computational simulations of drug interactions at molecular, cellular, and patient levels. These advanced virtual replicas of a patient's biological system are designed to predict individual therapeutic responses with high fidelity, thereby moving beyond the one-size-fits-all paradigm. This paper explores the concept of digital pharmacological twins, detailing how they can integrate heterogeneous data, including multi-omic, pharmacokinetic, pharmacodynamic, clinical, and environmental information, and employing a synergy of advanced mechanistic and machine learning models. Using illustrative examples from ongoing international initiatives, this work highlights the methodological frameworks necessary for developing and validating such comprehensive predictive tools. We underscore the critical importance of model interoperability, robust data integration strategies, and rigorous validation to ensure clinical utility. Ultimately, digital pharmacological twins promise to enhance therapeutic efficacy, minimize adverse drug reactions, and accelerate the translation of pharmacological science into tangible patient benefits.