Search Results (6,245)

Background: Skin and soft tissue infections (SSTIs) are a major cause of emergency room (ER) visits and hospitalizations. Long-acting lipoglycopeptides (LALs), such as dalbavancin and oritavancin, offer potential for early discharge and outpatient management, especially in patients at risk for methicillin-resistant Staphylococcus aureus (MRSA) or with comorbidities. Methods: We conducted a retrospective observational cohort study from March to December 2024 in an Italian tertiary-care hospital. Adult patients treated in the ER with a single dose of dalbavancin (1500 mg) or oritavancin (1200 mg) for SSTIs were included. Demographic, clinical, and laboratory data were collected. Follow-up evaluations were performed at 14 and 30 days post-treatment to assess outcomes. Results: Nineteen patients were enrolled (median age 59 years; 53% female). Most had lower limb involvement and elevated inflammatory markers. Three patients (16%) were septic. Fourteen patients (74%) were discharged without hospital admission; hospitalization in the remaining cases was due to comorbidities rather than SSTI severity. No adverse drug reactions were observed. At 14 days, 84% of patients had clinical resolution; only 10% had recurrence by day 30, with no mortality nor readmission reported. Conclusions: LALs appear effective and well-tolerated in the ER setting, supporting early discharge and reducing healthcare burden. Broader use may require structured care pathways and multidisciplinary coordination. Full article

Background: Drug–drug interactions (DDIs) may occur when two or more drugs are taken together, leading to undesired side effects or potential synergistic effects. Most clinical effects of drug combinations have not been assessed in clinical trials. Therefore, predicting DDIs can provide better patient management, avoid drug combinations that can negatively affect patient care, and exploit potential synergistic combinations to improve current therapies in women’s healthcare. Methods: A DDI prediction model was built to describe relevant drug combinations affecting reproductive treatments. Approved drug features (chemical structure of drugs, side effects, targets, enzymes, carriers and transporters, pathways, protein–protein interactions, and interaction profile fingerprints) were obtained. A unified predictive score revealed unknown DDIs between reproductive and commonly used drugs and their associated clinical effects on reproductive health. The performance of the prediction model was validated using known DDIs. Results: This prediction model accurately predicted known interactions (AUROC = 0.9876) and identified 2991 new DDIs between 192 drugs used in different female reproductive conditions and other drugs used to treat unrelated conditions. These DDIs included 836 between drugs used for in vitro fertilization. Most new DDIs involved estradiol, acetaminophen, bupivacaine, risperidone, and follitropin. Follitropin, bupivacaine, and gonadorelin had the highest discovery rate (42%, 32%, and 25%, respectively). Some were expected to improve current therapies (n = 23), while others would cause harmful effects (n = 11). We also predicted twelve DDIs between oral contraceptives and HIV drugs that could compromise their efficacy. Conclusions: These results show the importance of DDI studies aimed at identifying those that might compromise or improve their efficacy, which could lead to personalizing female reproductive therapies. Full article

Scrofuloderma, a cutaneous manifestation of tuberculosis, is a rare but clinically significant form of mycobacterial infection. It typically results from the local spread of Mycobacterium tuberculosis from an infected lymph node or bone area to the overlying skin. This disease is mainly characterized by chronic granulomatous inflammation, leading to skin ulcers and abscesses. Due to its nonspecific clinical presentation, scrofuloderma can mimic various dermatological conditions, making its diagnosis particularly challenging. This case report presents the clinical course of a patient who was positive for the Human Immunodeficiency Virus (HIV) with a diagnosis of scrofuloderma, managed at a tertiary healthcare center, with follow-up before and after treatment. A literature review was also made, highlighting the importance of maintaining a high index of clinical suspicion and utilizing appropriate diagnostic methods to ensure timely diagnosis. Full article

Heart failure (HF) is a major global health challenge, characterized by high morbidity, mortality, and frequent hospital readmissions. Despite the advent of guideline-directed medical therapies (GDMTs), the burden of HF continues to grow, necessitating a shift toward comprehensive, multidisciplinary care models. Heart Failure Disease Management Programs (HF-DMPs) have emerged as structured frameworks that integrate evidence-based medical therapy, patient education, telemonitoring, and support for social determinants of health to optimize outcomes and reduce healthcare costs. This review outlines the key components of HF-DMPs, including patient identification and risk stratification, pharmacologic optimization, team-based care, transitional follow-up, remote monitoring, performance metrics, and social support systems. Incorporating tools such as artificial intelligence, pharmacist-led titration, and community health worker support, HF-DMPs represent a scalable approach to improving care delivery. The success of these programs depends on tailored interventions, interdisciplinary collaboration, and health equity-driven strategies. Full article

Cardiovascular disease (CVD) remains Europe’s leading cause of mortality, responsible for >45% of deaths. Beyond established risk factors (hypertension, diabetes, dyslipidaemia, smoking, obesity), psychosocial elements—depression, anxiety, financial stress, personality traits, and trauma—significantly influence CVD development and progression. Behavioural Cardiology addresses this connection by systematically incorporating psychosocial factors into prevention and rehabilitation protocols. This review examines the HEARTBEAT model, developed by Greece’s first Behavioural Cardiology Unit, which aligns with current European guidelines. The model serves dual purposes: primary prevention (targeting at-risk individuals) and secondary prevention (treating established CVD patients). It is a personalised medicine approach that integrates psychosocial profiling with traditional risk assessment, utilising tailored evaluation tools, caregiver input, and multidisciplinary collaboration to address personality traits, emotional states, socioeconomic circumstances, and cultural contexts. The model emphasises three critical implementation aspects: (1) digital health integration, (2) cost-effectiveness analysis, and (3) healthcare system adaptability. Compared to international approaches, it highlights research gaps in psychosocial interventions and advocates for culturally sensitive adaptations, particularly in resource-limited settings. Special consideration is given to older populations requiring tailored care strategies. Ultimately, Behavioural Cardiology represents a transformative systems-based approach bridging psychology, lifestyle medicine, and cardiovascular treatment. This integration may prove pivotal for optimising chronic disease management through personalised interventions that address both biological and psychosocial determinants of cardiovascular health. Full article

Background: Brain health is a growing public health priority due to the high global burden of neurological and mental disorders. Promoting brain health across the lifespan supports individual and societal well-being, creativity, and productivity. Objective: To address the need for specialized education in this field, the University of Bern developed a Certificate of Advanced Studies (CAS) in Brain Health. This article outlines the program’s rationale, structure, and goals. Program Description: The one-year, 15 ECTS-credit program is primarily online and consists of four modules: (1) Introduction to Brain Health, (2) Brain Disorders, (3) Risk Factors, Protective Factors and Interventions, and (4) Brain Health Implementation. It offers a multidisciplinary, interprofessional, life-course approach, integrating theory with practice through case studies and interactive sessions. Designed for healthcare and allied professionals, the CAS equips participants with skills to promote brain health in clinical, research, and public health contexts. Given the shortage of trained professionals in Europe and globally, the program seeks to build a new generation of brain health advocates. It aims to inspire action and initiatives that support the prevention, early detection, and management of brain disorders. Conclusions: The CAS in Brain Health is an innovative educational response to a pressing global need. By fostering interdisciplinary expertise and practical skills, it enhances professional development and supports improved brain health outcomes at individual and population levels. Full article

Neurodevelopmental disorders (NDDs) significantly impact children’s health and development. They pose a substantial burden to families and the healthcare system. Challenges in early identification, accurate and timely diagnosis, and effective treatment persist due to overlapping symptoms, lack of appropriate diagnostic biomarkers, significant stigma and discrimination, and systemic barriers. Generative Artificial Intelligence (GenAI) offers promising solutions to these challenges by enhancing screening, diagnosis, personalized treatment, and research. Although GenAI is already in use in some aspects of NDD management, effective and strategic leveraging of evolving AI tools and resources will enhance early identification and screening, reduce diagnostic processing by up to 90%, and improve clinical decision support. Proper use of GenAI will ensure individualized therapy regimens with demonstrated 36% improvement in at least one objective attention measure compared to baseline and 81–84% accuracy relative to clinician-generated plans, customize learning materials, and deliver better treatment monitoring. GenAI will also accelerate NDD-specific research and innovation with significant time savings, as well as provide tailored family support systems. Finally, it will significantly reduce the mortality and morbidity associated with NDDs. This article explores the potential of GenAI in transforming NDD management and calls for policy initiatives to integrate GenAI into NDD management systems. Full article

Background and Aim: Type 2 diabetes (T2D) continues to pose a significant public health challenge worldwide. Among therapeutic options, glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have proven effective in optimizing glycemic control and improving cardiometabolic profiles. Semaglutide, now available in an oral formulation, represents a modern strategy to improve patient adherence while supporting glucose and weight regulation. This study primarily investigated the effects of oral semaglutide on key metabolic indicators and secondary endpoints included cardiovascular risk markers (blood pressure and lipid profile) and patient-reported quality of life (QoL). Study Design and Methods: A longitudinal, prospective observational study was conducted involving patients with T2D across two Italian healthcare facilities. Participants were assessed at baseline (T0) and at three subsequent intervals—6 months (T1), 12 months (T2), and 18 months (T3)—following the initiation of oral semaglutide use. Key Findings: Out of 116 participants enrolled, 97 had complete and analyzable data. Across the 18-month follow-up, significant improvements were observed in glycemic parameters, with a notable reduction in HbA1c levels (T0 vs. T3, p = 0.0028; p ≤ 0.05, statistically significant). Self-reported outcomes showed enhanced quality of life, especially in treatment satisfaction and perceived flexibility (T0 vs. T3, p < 0.001). Conclusions: Daily administration of 14 mg oral semaglutide in individuals with T2D resulted in substantial benefits in glycemic regulation, weight reduction, cardiovascular risk management, and overall patient satisfaction. These findings reinforce its potential role as a sustainable and effective option in long-term diabetes care from both a clinical and public health perspective. Full article

Although technological innovations are often intended to improve quality and efficiency, they can exacerbate systemic challenges when not aligned with the principles of value-based care. As a result, healthcare systems in many countries face persistent inefficiencies stemming from the overuse, underuse, misuse, and waste associated with the adoption of health technology. This narrative review examines the dual impact of healthcare technology and evaluates how contract mechanisms can serve as strategic tools for promoting cost-effective, outcome-oriented integration. Drawing from healthcare management, and supply chain literature, this paper analyzes various payment and contract models, including performance-based, bundled, cost-sharing, and revenue-sharing agreements, through the lens of stakeholder alignment. It explores how these mechanisms influence provider behavior, patient access, and system sustainability. The study contends that well-designed contract mechanisms can align stakeholder incentives, reduce inefficiencies, and support the delivery of high-value care across diverse healthcare settings. We provide concrete examples to illustrate how various contract mechanisms impact the integration of health technologies in practice. Full article

Background/Objectives: Targeting individuals with certain characteristics provides improved precision in many healthcare applications. An alert mechanism for COPD exacerbations has recently been validated. It has been argued that its efficacy improves considerably with stratification. This paper provides an in-depth analysis of the cough data of the stratified cohort to identify options for and the feasibility of improved precision in the alert mechanism for the intended patient group. Methods: The alert system was extended using a system complementary to the existing one to accommodate observed rapid changes in cough trends. The designed system was tested in a post hoc analysis of the data. The trend data were inspected to consider their meaningfulness for patients and caregivers. Results: While stratification was effective in reducing misses, the augmented alert system improved the sensitivity and number of early alerts for the acute exacerbation of COPD (AE-COPD). The combination of stratification and the augmented mechanism led to sensitivity of 86%, with a false alert rate in the order of 1.5 per year in the target group. The alert system is rule-based, operating on interpretable signals that may provide patients or their caregivers with better insights into the respiratory condition. Conclusions: The augmented alert system operating based on cough trends has the promise of increased precision in detecting AE-COPD in the target group. Since the design and testing of the augmented system were based on the same data, the system needs to be validated. Signals within the alert system are potentially useful for improved self-management in the target group. Full article

Background and Objectives: Diabetes is a global public health challenge, with increasing prevalence worldwide. The implementation of artificial intelligence (AI) in the management of this condition offers potential benefits in improving healthcare outcomes. This study primarily investigates the barriers and facilitators perceived by healthcare professionals in the adoption of AI. Secondarily, by analyzing both quantitative and qualitative data collected, it aims to support the potential development of AI-based programs for diabetes management, with particular focus on a possible bottom-up approach. Materials and Methods: A scoping review was conducted following PRISMA-ScR guidelines for reporting and registered in the Open Science Framework (OSF) database. The study selection process was conducted in two phases—title/abstract screening and full-text review—independently by three researchers, with a fourth resolving conflicts. Data were extracted and assessed using Joanna Briggs Institute (JBI) tools. The included studies were synthesized narratively, combining both quantitative and qualitative analyses to ensure methodological rigor and contextual depth. Results: The adoption of AI tools in diabetes management is influenced by several barriers, including perceived unsatisfactory clinical performance, high costs, issues related to data security and decision-making transparency, as well as limited training among healthcare workers. Key facilitators include improved clinical efficiency, ease of use, time-saving, and organizational support, which contribute to broader acceptance of the technology. Conclusions: The active and continuous involvement of healthcare workers represents a valuable opportunity to develop more effective, reliable, and well-integrated AI solutions in clinical practice. Our findings emphasize the importance of a bottom-up approach and highlight how adequate training and organizational support can help overcome existing barriers, promoting sustainable and equitable innovation aligned with public health priorities. Full article

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation. Full article

The COVID-19 pandemic catalyzed the rapid adoption of remote patient monitoring (RPM) technologies such as telemedicine and wearable devices (WDs), significantly transforming healthcare delivery. Telemedicine made virtual consultations possible, reducing in-person visits and infection risks, particularly for the management of chronic diseases. Wearable devices enabled the real-time continuous monitoring of health that assisted in condition prediction and management, such as for COVID-19. This narrative review addresses these transformations by uniquely synthesizing findings from 13 diverse studies (sourced from PubMed and Google Scholar, 2020–2024) to analyze the parallel evolution of telemedicine and WDs as interconnected RPM components. It highlights the pandemic’s dual impact, as follows: accelerating RPM innovation and adoption while simultaneously unmasking systemic challenges such as inequities in access and a need for robust integration approaches; while telemedicine usage soared during the pandemic, consumption post-pandemic, as indicated by the reviewed studies, suggests continued barriers to adoption among older adults. Likewise, wearable devices demonstrated significant potential in early disease detection and long-term health management, with promising applications extending beyond COVID-19, including long COVID conditions. Addressing the identified challenges is crucial for healthcare providers and systems to fully embrace these technologies and this would improve efficiency and patient outcomes. Full article

The prevalence of early non-infectious peritoneal dialysis (PD) catheter complications makes performing PD challenging for patients and difficult for the healthcare team to manage. Three common patient scenarios are presented: catheter flow dysfunction, peri-catheter leaks, and catheter-related abdominal pain. Practice recommendations are integrated into each scenario and tailored to clinical presentation, patient need, and resource availability. The importance of including patients in the decision-making process is emphasized, and examples of how contextual factors modify the proposed approach to complications are given. Full article

Background: The education process for newly diagnosed Type 1 diabetes mellitus (T1D) patients and their families, primarily led by diabetes specialist nurses, is essential for gaining knowledge about the disease and its management. However, few assessment tools have been employed to evaluate long-term knowledge retention among T1D patients years after diagnosis. Methods: We developed a 20-question test to assess the knowledge of patients and their families at the conclusion of the initial education process and again 6–12 months later. Demographic and clinical data were also collected. Statistical analyses included comparisons between the first and second test results, as well as evaluation of potential contributing factors. The internal consistency and construct validity of the questionnaire were evaluated. Results: Forty-four patients completed both assessments, with a median interval of 11.5 months between them. The average score on the first test was 88.6, which declined to 82.7 on the second assessment (p < 0.001). In univariate analysis, factors positively associated with higher scores included Jewish ethnicity, lower HbA1c levels, and shorter hospitalization duration. Multivariate analysis revealed that parents had lower odds of experiencing a significant score decline compared to patients. Cronbach’s alpha was 0.69, and Principal Component Analysis (PCA) identified eight components accounting for 67.1% of the total variance. Conclusions: Healthcare providers should consider offering re-education to patients and their families approximately one year after diagnosis, with particular attention to high-risk populations during the initial education phase. Further studies are needed to examine this tool’s performance in larger cohorts. Full article