Internal and Emergency Medicine最新文献

Vitamin K antagonists (VKAs) for 3-6 months are the recommended treatment for left-ventricular thrombosis (LVT) to achieve thrombus resolution and reduce the risk of cardioembolic events. The aim of this study was to evaluate the association between quality of VKAs and early-6 months-thrombus resolution in patients with LVT. In this retrospective study, patients with LVT referred to three Italian Anticoagulation Clinics between January 2011 and December 2023 were enrolled if they received VKAs and data on LVT resolution up to 6 months of treatment were available. Primary outcome was the effect of time-in-therapeutic range and of INR variability defined according to validated formulas on early thrombus resolution. Fifty-four patients were included. Median age was 68 years and 87% were male. Overall, 41.7% and 35.4% patients had a reduced or moderately reduced left-ventricle ejection fraction and apical aneurysm. Twenty-three patients (42.6%) had early thrombus resolution. For the prediction of early LVT resolution, the best value of TTR was 56% (sensitivity, 57%; specificity, 71%; AUC, 0.657), that of INR variability, formula A was 1.65 (sensitivity, 0%; specificity, 96%; AUC, 0.478) and that of formula B 0.40 (sensitivity, 4%; specificity, 100%; AUC, 0.599). The cumulative incidence of early LVT resolution was higher in patients with TTR values ≥ 56% (57.1%; 95% confidence interval-CI 32.8-75.5%) than < 56% (30.3%; 95% CI 15.6-46.4%).The quality of VKAs anticoagulation may be a relevant driver for early LVT resolution. TTR evaluation appears to have a slightly greater accuracy compared to INR variability.

Direct oral anticoagulants (DOAC) have similar efficacy for stroke prevention in patients with non-valvular atrial fibrillation (AF) with improved safety. Recently, a higher bleeding risk was reported when elderly AF patients were switched from VKAs to DOACs. AF patients aged ≥ 75 years who were maintained on VKA treatment or who were switched from VKAs to DOACs at the time of enrollment in the START-AF Register were followed. Bleedings, thrombotic events, and deaths that occurred during follow-up were collected. We included 4230 AF patients, 2930 (69.3%) on VKAs and 1300 patients (30.7%) shifted to DOACs. Low-dose DOACs were prescribed to 732 (56.3%) patients, 257 (35.1%) of whom received off-label low-dose. Thrombotic events were more frequent among patients switched to DOACs compared to patients continuing VKAs (1.0 and 0.6 × 100pt-yrs p = 0.05). The rate of bleeding events was similar between the two groups (3.0 and 2.5 × 100pt-yrs; p = 0.2), and was higher in the subgroup treated with on-label low-dose DOACs (3.4 × 100pt/yrs) compared to patients on VKAs (2.5 × 100pt-yrs)(RR1.4, 95%CI 0.9-2.0; p = 0.1). Conversely, patients on off-label low-dose DOACs showed higher rates of thrombotic events compared to patients on VKAs (1.4 vs 0.6 × 100pt/yrs, p = 0.05). Mortality was significantly higher in VKA patients than in DOACs patients independently from the dosage (6.7 and 2.9 × 100pt-years; p = 0.05). Elderly AF patients switched from VKAs to DOACs are at increased risk of thrombotic events than patients who remain on VKAs but with a lower risk of mortality. Switching to low-dose DOACs may expose patients to a higher risk of adverse outcomes.

Artificial Intelligence (AI) represents an innovative technological support for clinical practice. The Italian Society of Internal Medicine (SIMI) emphasizes the need for clear guidance on the use of AI in medicine, recognizing that knowledge in this field is continuously evolving. This position paper presents a comprehensive vision for the responsible integration of AI into clinical practice. AI should serve as a support tool-not a replacement-for clinicians. It has the potential to improve diagnostic accuracy, reduce administrative workload, and strengthen the physician-patient relationship. In the light of these characteristics, SIMI advocates for transparency, data privacy, equity, and sustainability in the development and implementation of AI systems. SIMI also highlights several ethical, legal, and methodological challenges that must be addressed, including algorithmic bias, environmental impact, and disparities in access. Ultimately, SIMI envisions a future in which AI augments human expertise, enabling more efficient, personalized, and compassionate care. SIMI calls for active clinician participation in the co-design and validation of AI tools to ensure alignment with real-world clinical needs. Key recommendations include the preferential use of certified AI systems, the integration of AI education into medical training, and continuous monitoring after deployment.

The 2025 focus update of the European society of cardiology (ESC) and European atherosclerosis society (EAS) guidelines for dyslipidemia management highlight a significant evolution in patient care, shifting from an intensive, target-driven approach to earlier and more personalized strategies. The transition from SCORE to SCORE2 and SCORE2-OP has improved risk stratification accuracy by including non-fatal cardiovascular events and expanding the evaluable population, particularly among older adults. The introduction of the "extreme risk" category and the early adoption of combination therapies allow previously theoretical LDL targets (< 40 mg/dL) to be achieved through the synergistic use of non-statin lipid-lowering agents. Additionally, the 2025 update emphasizes the role of biomarkers such as lipoprotein(a) and elevated triglycerides, as well as traditionally underrepresented populations, including HIV-positive and oncology patients, with recommendations grounded in recent clinical evidence. At the same time, the lack of efficacy of supplements and nutraceuticals without scientific support is reaffirmed. This update consolidates the paradigm of "earlier, more intensive, more personalized" management, underscoring that the current challenge lies not only in scientific knowledge but also in its clinical implementation, with the goal of optimizing cardiovascular prevention and improving patient prognosis and quality of life.

Emergency departments (EDs) are high-pressure environments where healthcare workers face ongoing acute and chronic stressors, increasing the risk of burnout, anxiety, and depression. These challenges affect staff well-being, job satisfaction, and patient care. Although various interventions have been developed to improve well-being, their effectiveness remains insufficiently understood. This systematic review aims to examine interventions targeting the well-being of ED healthcare workers, focusing on individual, group, and organizational-level strategies. Following the PRISMA guidelines, a comprehensive search was conducted across four databases (PubMed, PsycINFO, Scopus, Web of Science) to identify peer-reviewed studies published between 2021 and 2024. A total of 26 articles met inclusion criteria, each evaluating well-being interventions for ED staff. Studies were categorized by intervention type-individual, group, organizational, or multilevel-and outcomes assessed. Most interventions targeted the individual level (n = 21), including mindfulness training, resilience programs, and educational approaches. Fewer studies addressed group (n = 1), organizational (n = 2), or multilevel (n = 2) interventions. Eighteen studies reported improvements in at least one well-being outcome, most commonly reductions in stress and burnout. However, only six included long-term follow-up. Overall, interventions in ED settings primarily focus on individual-level strategies and demonstrate short-term benefits. Less frequent multilevel approaches may provide more sustainable improvements. Future research should emphasize longer follow-up periods, robust study designs, and context-specific implementation to better assess and enhance the effectiveness of well-being interventions for ED healthcare workers.

With a rapidly aging population, accurately predicting rehabilitation outcomes in older adults is increasingly important. As the impact of age on recovery trajectories in geriatric rehabilitation remains unclear, we conducted a retrospective observational study to evaluate the effect of older age on improvements in rehabilitation functional outcomes. We included 158 patients (median age: 84 years; IQR: 77.0-88.0) admitted in 2024 to a geriatric rehabilitation unit. Functional status was assessed at admission and discharge using the Modified Barthel Index (autonomy), Hendrich II Fall Risk Model (fall risk), and Tinetti Scale (balance and gait). Patients were stratified into age tertiles and compared using the Kruskal-Wallis and Mann-Whitney U tests. Multivariable linear regression models evaluated the independent associations of age, sex, and frailty index with functional outcomes. Comparisons of changes in functional outcomes between the youngest (T1) and oldest (T3) age tertiles were performed using the Mann-Whitney U test. Older age was associated with worse baseline functional status, reflected by lower Barthel and Tinetti scores and higher Hendrich II scores. However, in multivariable models, frailty index-but not chronological age-was the strongest independent predictor of all functional outcomes, including Barthel (β =  -89.9, p < 0.001), Tinetti (β =  -39.9, p < 0.001), and Hendrich II (β = 6.8, p < 0.001). Both younger and older patients achieved significant functional gains, particularly in mobility, whereas improvements in autonomy and fall risk were more limited among the oldest individuals. In conclusion, chronological age was associated with poorer baseline performance but did not impair motor recovery. Frailty, rather than age itself, emerged as the key determinant of rehabilitation outcomes. These findings underscore the importance of integrating standardized frailty assessments to guide rehabilitation strategies and promote equitable access to multidisciplinary rehabilitation for older adults.

The application of Artificial Intelligence (AI) in medical imaging has demonstrated substantial potential in enhancing diagnostic accuracy, reducing inter-operator variability, and improving workflow efficiency. This review examines the integration of AI into three ultrasound pillars: echocardiography, carotid ultrasound, and transcranial Doppler (TCD). These techniques have long relied on expert interpretation, but AI-driven advancements now promise to revolutionize clinical practice by providing automated image analysis, improving diagnostic capabilities, and enhancing prognostic prediction. While echocardiography remains a cornerstone in the diagnosis of heart disease, carotid ultrasound and TCD are critical for assessing brain atherosclerosis and stroke risk and management. This narrative review explores AI applications in these domains, highlighting the current state of technology, clinical implications, challenges, and future prospects.