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Pharmacoeconomic data are widely used along drug life cycle for supporting decision-making processes on research and development, pricing and reimbursement, and market access. In this context, the incremental cost-effectiveness ratio (ICER) is the gold standard of either cost-effectiveness analyses (CEAs) or cost-utility analyses (CUAs) of pharmaceuticals and health technologies. However, the widespread use of confidentiality clauses in the agreements between pharmaceutical companies and the payers may affect the reliability of ICER value based on transparent price. The aim of this article is to evaluate a case study and simulate the impact of price confidentiality and other managed-entry agreement conditions on the ICER value. The case study was conducted selecting a CEA submitted to the Health Economic Evaluations Office of the Italian Medicines Agency by the pharmaceutical company, which specifically compared two alternative options reimbursed by the Italian NHS using confidential managed-entry agreements. So, a real model was used to collect the output of ICERs generated by the simulation model, considering price inputs of alternative options ranging from the transparent prices to the confidential net price. The simulation showed that price confidentiality may affect the estimated value of the ICER of a new medicine and, consequently, its interpretation. From a different point of view, the published ICER values may also give a completely false economic evidence if non-disclosure agreements are not taken into account. A proposal for editors of pharmacoeconomic journals to improve reliability of CEA is also discussed.

Mucopolysaccharidoses (MPS) are a group of hereditary disorders caused by lysosomal storage of glycosaminoglycans (GAGs) and characterized by a wide variability of phenotypes from severe fetal-neonatal forms to attenuated diseases diagnosed in adult individuals. The clinical picture generally worsens with age due to progressive storage involving mucosal tissue, upper airways and lungs, bones and joints, central and peripheral nervous system, heart, liver, eye, and ear. Cardiac storage of GAGs involves valves, heart muscle, and vessels (particularly the coronary arteries), and can be specific in relation to different MPS types and enzyme defects. MPS I, II, and VI are those with the most severe cardiac involvement. The cardiologist is a key figure in MPS, and their role is expanding from cardiac-specific management to early diagnosis when the mild disease phenotypes have not yet been recognized by other specialists. Familial and personal history, electrocardiography, imaging, and laboratory findings represent important steps in the clinical investigation of these patients. New treatments have led to an increased need for cardiologists to be on the lookout for MPS patients since they can significantly improve the lives of people with MPS if they suspect the diagnosis and refer them for enzyme replacement therapy or bone marrow transplantation.

Background and Objectives: To assess the potential prognostic role of the systemic immune-inflammation index (SII) in predicting oncological outcomes in a cohort of patients treated with radical cystectomy (RC). Materials and Methods: From 2016 to 2022, a retrospective monocentric study enrolled 193 patients who were divided into two groups based on their SII levels using the optimal cutoff determined by the Youden index. The SII was obtained from a preoperative blood test approximately one month before RC. Univariable and multivariable logistic regression analyses were conducted to investigate the capacity of SII to predict lymph node invasion (N), advanced pT stage (pT3/pT4), and locally advanced condition at the time of RC. Multivariable Cox regression models adjusted for preoperative and postoperative features were used to analyze the prognostic effect of SII on recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Results: The optimal cutoff value of the SII was 640.27. An elevated SII was seen in 113 (58.5%) patients. Using the multivariable preoperative logistic regression models, an elevated SII was correlated with nodal invasion (N; p = 0.03), advanced pT stage (p = 0.04), and locally advanced disease (p = 0.005), with enhancement of AUCs for predicting locally advanced disease (p = 0.04). In multivariable Cox regression models that considered preoperative clinicopathologic factors, an elevated SII was linked to poorer RFS (p = 0.005) and OS (p = 0.01). Moreover, on multivariable Cox regression postoperative models, a high SII was linked to RFS (p = 0.004) and to OS (p = 0.01). Conclusions: In this monocentric retrospective study, higher preoperative SII values predicted worse oncological outcomes in patients with bladder cancer (BCa) who underwent RC.

ABSTRACT Renal cell carcinoma (RCC) ranks among the most prevalent malignancies worldwide, with a rising incidence attributed largely to the incidental detection of small renal masses (SRMs ≤ 4 cm) through widespread abdominal imaging. Historically managed with radical nephrectomy, treatment of SRMs has evolved significantly over recent decades. Partial nephrectomy has become the standard surgical approach, while active surveillance (AS) has emerged as a viable alternative for select patients, particularly those with comorbidities or limited life expectancy. AS involves serial imaging to monitor tumor progression, reserving intervention for signs of clinical advancement. This review synthesizes oncological outcomes and current management strategies for SRMs, comparing AS with immediate intervention. A comprehensive literature search (2005–2024) was performed across PubMed, Web of Science, and Scopus, complemented by an analysis of major international guidelines (EAU, AUA, ESMO, CUA, and Latin American Renal Cancer Group). All guidelines support AS for selected patients with cT1a tumors, though criteria vary. The AUA limits AS to tumors <2 cm, while only its guidelines define clear triggers for transitioning from AS to treatment. Imaging surveillance intervals and biopsy indications also differ, with broader support for renal mass biopsy prior to ablation but more selective use during AS. This review underscores the importance of individualized decision-making in SRM management and highlights areas of consensus and divergence among contemporary guidelines.

Remdesivir is the first drug approved for treatment of COVID-19 but current evidence for recommending its use for the treatment of moderate-to-severe disease is still controversial among clinical guidelines. We performed a nationwide, registry-based study including all Italian hospitalized patients with COVID-19 treated with remdesivir to assess the impact of major confounders on crude 15-day and 29-day mortality. Mortality was calculated using the Kaplan–Meier estimator and the Cox proportional-hazards model was applied to analyze the risks by patient’s baseline features. In total, 16,462 patients treated with remdesivir from 29 October 2020 to 17 December 2020 were entered in the study. Crude 15-day and 29-day mortality were 7.1% (95% CI, 6.7–7.5%) and 11.7% (95% CI, 11.2–12.2%), respectively. Being treated within two days of admission reduced the risk of death by about 40% (HR 1.4, 95% CI, 1.2–1.6). Results from the largest cohort of remdesivir-treated patients suggests that mortality in SARS-CoV-2 hospitalized patients is substantially influenced by the days between SARS-CoV-2 diagnosis and drug prescription. Current recommendations and future clinical trials for remdesivir alone or in combination should carefully consider the target population and timing for best efficacy of treatment.

This review focuses on ablative techniques for small renal masses (SRMs), including radiofrequency ablation (RFA), cryoablation (CA), microwave ablation (MWA), and irreversible electroporation (IRE), and discusses recurrence management. Through an extensive literature review, we outline the procedures, outcomes, and follow-up strategies associated with each ablative method. The review provides a detailed examination of these techniques—RFA, CA, MWA, and IRE—elucidating their respective outcomes. Recurrence rates vary among them, with RFA and CA showing comparable rates, MWA demonstrating favorable short-term results, and IRE exhibiting promise in experimental stages. For managing recurrences, various strategies are considered, including active surveillance, re-ablation, or salvage surgery. Surveillance is preferred post-RFA and post-CA, due to slow SRM growth, while re-ablation, particularly with RFA and CA, is deemed feasible without additional complications. Salvage surgery emerges as a viable option for larger or resistant tumors. While ablative techniques offer short-term results comparable to surgery, further research is essential to understand their long-term effects fully. Decisions concerning recurrence management should consider individual and tumor-specific factors. Imaging, notably contrast-enhanced ultrasounds, plays a pivotal role in assessing treatment success, emphasizing the necessity of a multidisciplinary approach for optimal outcomes. The lack of randomized trials highlights the need for further research.

Traditional statistical models often fail to capture the complex dynamics influencing survival outcomes in patients with bladder cancer after radical cystectomy, a procedure where approximately 50% of patients develop metastases within 2 years. The integration of artificial intelligence (AI) offers a promising avenue for enhancing prognostic accuracy and personalizing treatment strategies. This study aimed to develop and evaluate a machine learning algorithm for predicting disease-free survival (DFS), overall survival (OS), and the cause of death in patients with bladder cancer undergoing cystectomy, using a comprehensive dataset of clinical and pathological variables. Retrospective data of 370 patients with bladder cancer who underwent radical cystectomy at Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy, were collected. The dataset comprised 20 input variables, encompassing demographics, tumor characteristics, treatment variables, and inflammatory markers. For specific analyses and models, we used patient subcohorts. The CatBoost algorithm was used for regression tasks (DFS in 346 patients, OS in 347 patients) and a binary classification task (tumor-related death in 312 patients). Model performance was assessed using mean absolute error (MAE) for regression and F1-score for classification, prioritizing a minimum recall of 75% for tumor-related deaths. Five-fold cross-validation and Shapley additive explanations (SHAP) values were used to ensure robustness and interpretability. For DFS prediction, the CatBoost model achieved an MAE of 18.68 months, with clinical tumor stage and pathological tumor classification identified as the most influential predictors. OS prediction yielded an MAE of 17.2 months, which improved to 14.6 months after feature filtering, where tumor classification and the systemic immune-inflammation index (SII) were most impactful. For tumor-related death classification, the model achieved a recall of 78.6% and an F1-score of 0.44 for the positive class (tumor-related deaths), correctly identifying 11 of 14 cases. Bladder tumor position was the most influential feature for cause-of-death prediction. The developed machine learning algorithm demonstrates promising accuracy in predicting survival and the cause of death in patients with bladder cancer after cystectomy. The key predictors include clinical and pathological tumor staging, systemic inflammation (SII), and bladder tumor position. These findings highlight the potential of AI in providing clinicians with an objective, data-driven tool to improve personalized prognostic assessment and guide clinical decision-making.

The frequency of patients who switch to a second‐line therapy from a frontline second‐generation (2gen) tyrosine kinase inhibitor (TKI) such as dasatinib and nilotinib, is still substantially unknown. We retrospectively investigated a large series of chronic phase chronic myeloid leukemia (CP‐CML) patients initially treated with 2gen TKIs monitored through the Italian Medicines Agency (AIFA Agenzia Italiana del farmaco) registries. Overall, 2420 patients were analyzed over a period of 6 years. One hundred and fifty‐seven patients (16.3%) treated with dasatinib and 164 treated with nilotinib (11.3%) have switched to another drug, with an overall frequency of 13.2%. In the dasatinib cohort, 39.4% of patients changed treatment for failure and 36.3% for intolerance as compared to 45.7% and 27.4% respectively in the nilotinib cohort. Overall, the median time to switch due to resistance was 293 days, whereas it was 317 days in case of intolerance. Resistance was observed mainly in younger male patients with high‐risk features, while intolerance was not related to any baseline parameter. After resistance/intolerance to nilotinib, the majority of patients switched to dasatinib (53.8%) whereas in case of frontline dasatinib to ponatinib (43.2%). To the best of our knowledge these data provide the first report on the frequency of discontinuation of frontline 2gen TKIs and on the main causes and pattern of choice to a second‐line therapy in the real‐life setting. Overall, 2420 patients were analyzed over a period of 6 years. One hundred and fifty‐seven patients (16.3%) treated with dasatinib and 164 treated with nilotinib (11.3%) have switched to another drug, with an overall frequency of 13.2%. In the dasatinib cohort, 39.4% of patients changed treatment for failure and 36.3% for intolerance as compared to 45.7% and 27.4% respectively in the nilotinib cohort. Resistance was observed mainly in younger male patients with high‐risk features, while intolerance was not related to any baseline parameter. To the best of our knowledge these data provide the first report on the frequency of discontinuation of frontline 2gen TKIs and on the main causes and pattern of choice to a second‐line therapy in the real‐life setting.

In this analysis we describe the effectiveness of first-line ibrutinib in 747 patients with chronic lymphocytic leukemia (CLL) and TP53 aberrations in a nationwide study with a 100% capture of patients who received the study drug. Median age was 71 years (range 32–95). An estimated treatment persistence rate of 63.4% (95% CI 60.0%-67.0%) and survival rate of 82.6% (95% CI 79.9–85.4%) were recorded at 24 months. Disease progression or death were the reasons for discontinuation in 182/397 patients (45.8%). A higher risk of treatment discontinuation was found to be associated with age, ECOG-PS and pre-existing heart disease, whereas ECOG ≥ 1, age ≥ 70 years and male sex were associated with an increased risk of death. Median post-progression overall survival (OS) was 12.2 months (95% CI 9.2–22.0). Post-discontinuation median OS in patients who discontinued ibrutinib for other reasons was not reached (95% CI 42.3 months – NA). Ibrutinib was an effective first-line treatment for CLL and TP53 aberrations in patients treated at large academic centers and community practice hospitals. Clinical characteristics at baseline may influence the effectiveness of ibrutinib, whereas the experience of prescribing centers and multi-hit or single-hit TP53 aberrations had no impact on outcome in this high-risk population.

BackgroundThe increasing demand for orphan drugs and the financial challenges associated with their reimbursement highlights the need to understand the dynamics between expected and actual pharmaceutical expenditures, particularly in the context of pricing and reimbursement negotiations.ObjectiveThe study aims to identify the potential determinants of the difference in expected pharmaceutical spending after negotiation and the actual expenditure incurred (ΔS), as well as the difference in expected pharmaceutical spending before and after the price and reimbursement negotiation process (ΔSn) for rare disease drugs in Italy.MethodsThe analysis focused on orphan drugs authorised by European Medicines Agency, with reimbursement applications to the Italian Medicines Agency from January 2013 to January 2019. Expected post-negotiation spending was estimated by applying negotiated discounts and financial-based market entry agreements to the expected pharmaceutical expenditure. The actual expenditure was approximated through company turnovers. Beta regression models were applied to identify potential determinants of ΔS and ΔSn.ResultsThe study analysed 52 reimbursed orphan drugs, with 41 (78.8%) with a single indication, 25 (48.1%) antineoplastics and immunomodulators and 18 (34.6%) conditionally/fully innovative. The median expenditure in the first 3 years post-commercialisation was 7.6% lower than expected post-negotiation. The reduction was less pronounced for innovative drugs (p = 0.011), drugs with a prices and reimbursement procedure in the subsequent 3 years (p = 0.007) and those with multiple indications (p = 0.021). Payment-by-result was the only significant variable associated with the expected spending ratio before and after negotiation (p = 0.002).ConclusionThe actual expenditure for orphan drugs aligns with the expected post-negotiation. Yet, innovative orphan drugs exhibit a higher actual expenditure than estimated, suggesting the market values their added therapeutic value or the actual therapeutic need they meet, and configuring innovativeness status as the main determinant of the orphan drugs financial impact in the multiple regression analysis.