Campus Bio Medico University Hospital

To compare two sodium hyaluronate formulations (HYMOVIS® ONE and MONOVISC®) for single intra-articular (IA) injection in patients with knee osteoarthritis pain. This randomized, controlled study included 347 subjects allocated to two treatment arms: HYMOVIS® ONE (n = 175) and MONOVISC® (n = 172). The primary endpoint assessed non-inferiority of HYMOVIS® ONE versus MONOVISC® based on change from baseline in the WOMAC LK3.1 A1 Pain subscale. Secondary outcomes evaluated patient response using OMERACT-OARSI criteria, changes in WOMAC stiffness and function scores, health-related quality of life (SF-12), global assessments by patients and clinicians, and rescue medication use. The mean WOMAC LK3.1 A1 walking pain subscale score decreased significantly from baseline at week 12 in both groups, with reductions of 65 % for HYMOVIS® ONE and 66 % for MONOVISC®. At week 26, reductions were 73 % for HYMOVIS® ONE and 69 % for MONOVISC®. The non-inferiority test yielded a p-value of 0.0003. Clinically significant change from baseline was observed for all secondary endpoints. For the WOMAC LK3.1 Function subscale, the comparison between groups at week 26 showed a statistically significant difference (p = 0.0367) favoring HYMOVIS® ONE. Both treatments were well tolerated. A single injection of HYMOVIS® ONE was well tolerated and non-inferior to MONOVISC®, with improvements across all study endpoints maintained through 26 weeks, indicating sustained symptomatic relief of knee OA. NCT06528600 (www.clinicaltrials.gov).

Functional mitral regurgitation (FMR) is common in patients with heart failure (HF) and is associated with an adverse prognosis. Therefore, both ventricular and atrial phenotypes of FMR represent relevant therapeutic targets in clinical practice. Although randomized clinical trials have primarily focused on the treatment of severe FMR, the prognostic implications and management of moderate FMR have emerged as a topic of increasing interest. This review aims to provide an overview of epidemiology, prognosis, and management of moderate FMR, with a focus on the role of pharmacological treatments and emerging percutaneous strategies.

Elevated low-density lipoprotein cholesterol (LDL-C) represents a key causal factor in atherosclerotic cardiovascular disease. Robust evidence demonstrates that early, marked, and sustained reduction of LDL-C is associated with a significant decrease in the risk of major adverse cardiovascular events. However, a substantial proportion of high-risk patients fail to achieve guideline-recommended LDL-C targets with statin therapy alone, thus requiring additional lipid-lowering treatments. Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, particularly the monoclonal antibodies evolocumab and alirocumab, have been shown to reduce LDL-C levels by ∼60% and to provide clear cardiovascular outcome benefits in secondary prevention. More recently, the VESALIUS-CV trial extended these findings to the setting of primary prevention in high cardiovascular risk patients without prior major cardiovascular events. In this trial, evolocumab achieved a significant and sustained reduction in LDL-C levels and significantly reduced the risk of a first cardiovascular event, including myocardial infarction and ischaemia-driven revascularization, while maintaining a safety profile comparable to placebo. Overall, the available evidence supports the role of evolocumab as an effective and safe strategy for cardiovascular risk reduction across the entire risk continuum, from secondary to primary prevention, underscoring the importance of a personalized therapeutic approach based on the patient's global cardiovascular risk profile.

Airway management remains a critical aspect of anesthesia, intensive care and emergency medicine, particularly when facing difficult airways. Traditional methods, such as endotracheal intubation via direct laryngoscopy, have evolved with the introduction of advanced tools designed to enhance oxygenation and facilitate airway control. This narrative review presents 10 essential tips for safe up-to-date airway management, emphasizing the importance of a patient-centered, oxygen-focused and strategic approach rather than a purely device-oriented one. By incorporating strategies for assessment, prediction and timely interventions and transition, healthcare professionals can improve patient outcomes and minimize complications during airway management.

Sepsis is a leading cause of morbidity and mortality worldwide, and its outcomes depend on early recognition and timely intervention. Conventional clinical scores and biomarkers provide prognostic value but often lack accuracy for individualized prediction. Machine learning (ML) offers the ability to integrate multidimensional data to improve risk stratification. We analyzed 477 patients admitted to our hospital, including 251 with sepsis, 100 with septic shock, and 126 controls. Demographic, clinical, and laboratory data were collected. Univariate correlation analyses explored associations with sepsis severity and mortality (in-hospital, 30-day, and 90-day). Several ML models were tested, with performance assessed by area under the receiver operating characteristic curve (AUC-ROC) and Matthews's correlation coefficient (MCC). Model interpretability was evaluated using SHAP (SHapley Additive exPlanations). Sepsis severity and mortality correlated with biomarkers (procalcitonin, mid-regional pro-adrenomedullin, lactate) and clinical scores (SOFA, qSOFA). In-hospital mortality was associated with ADM, catecholamine use, and SOFA, while 90-day mortality involved smoking and Gram-negative or polymicrobial infections. Different machine learning models were evaluated, and the model achieving the highest performance on the validation set was selected. The selected model either outperformed or demonstrated comparable performance to logistic regression, depending on the specific prediction task (AUC 0.99 for sepsis, 0.96 for septic shock, 0.70 for ICU admission; 0.90, 0.72, and 0.87 for in-hospital, 30-day, and 90-day mortality). SHAP confirmed the clinical relevance of these predictors. ML models integrating clinical and biochemical data outperform conventional methods in predicting sepsis progression and mortality, while maintaining interpretability. These findings support the use of ML-based tools for early diagnosis and personalized risk stratification in sepsis, though external validation is required before clinical application.