Some anti-cancer treatments (e. g., immunotherapies) determine, on the long term, a durable survival in a small percentage of treated patients; in graphical terms, long-term survivors typically give rise to a plateau in the right tail of the survival curve. In analysing these datasets, medians are unable to recognize the presence of this plateau. To account for long-term survivors, both value-frameworks of ASCO and ESMO have incorporated post-hoc corrections that upgrade the framework scores when a survival plateau is present. However, the empiric nature of these post-hoc corrections is self-evident. To capture the presence of a survival plateau by quantitative methods, two approaches have thus far been proposed: the milestone method and the area-under-the-curve (AUC) method. The first approach identifies a long-term time-point in the follow-up (“milestone”) at which survival percentages are extracted. The second approach, which is based on the measurement of AUC of survival curves, essentially is the rearrangement of previous methods determining mean lifetime survival; similarly to the milestone method, the application of AUC can be “restricted” to a pre-specified time-point of the follow-up. This Mini-Review examines the literature published on this topic. The main characteristics of these two methods are highlighted along with their advantages and disadvantages. The conclusion is that both the milestone method and the AUC method are able to capture the presence of a survival plateau.

The Life Cycle of Health Technologies. Challenges and Ways Forward

In the area of evidence-based medicine, the IPDfromKM-Shiny method is an innovative method of survival analysis, midway between artificial intelligence and advanced statistics. Its main characteristic is that an original software investigates the Kaplan-Meier graphs of trials so that individual-patient data are reconstructed. These reconstructed patients represent a new form of original clinical material. The typical objective of investigations based on this method is to analyze the available evidence, especially in oncology, to perform indirect comparisons, and determine the place in therapy of individual agents. This review examined the most recent applications of the IPDfromKM-Shiny method, in which a new web-based software—published in 2021—was used. Reported here are 14 analyses, mostly focused on oncological treatments. Indirect comparisons were based on overall survival or progression free survival. Each of these analyses provided original information to compare treatments with one another and select the most appropriate depending on patient characteristics. These analyses can also be useful to assess equivalence from a regulatory viewpoint. All investigations stressed the importance of heterogeneity to better interpret the evidence generated by IPDfromKM-Shiny investigations. In conclusion, these investigations showed that the reconstruction of individual patient data through this online tool is a promising new method for analyzing trials based on survival endpoints. This new approach deserves further investigation, particularly in the area of indirect comparisons.

A high number of adverse drug reactions (ADRs), mainly caused by drug–drug interactions (DDIs), occur in neurological wards and few data are available about incidence and prevalence of DDIs in this context. This study investigated—(1) the prevalence of drug–drug and drug–disease interactions in patients admitted to a neurological unit in Italy, (2) the risk factors for DDIs, and (3) the diseases and the drug classes mostly involved in drug–drug and drug–disease interactions. For 2 months, we performed a retrospective, observational study in the neurological unit of St Bassiano Hospital, enrolling 79 patients who received a drug prescription at discharge. About half of the patients were discharged with 5 or more prescribed drugs, and 72% of patients showed potential, clinically relevant DDIs. Linear correlations were observed between age and number of prescribed drugs (P < .01) and between age and number of interactions (P < .01). The number of prescribed drugs was associated with the number of detected DDIs (P < .01). The application of drug interaction alerts and the use of medication inappropriateness criteria (ie, Beers criteria) were not satisfactory in choosing the best therapy for each patient. Therefore, multidisciplinary discussions of each clinical case was required. The study demonstrated that the neurological patient, especially if elderly, has a high risk of DDI and ADRs and that many of these can be avoided by case discussion in multidisciplinary team meetings, in which the presence of a dedicated clinical pharmacist is crucial.

Background: In patients with advanced melanoma, immune‐checkpoint inhibitors (ICIs) represent the mainstay for first line treatment. Recently, relatlimab+nivolumab was proposed as a new combination therapy. This review was aimed at summarizing the current data of effectiveness for ICIs. Progression‐free survival (PFS) was the endpoint of our analysis.Methods: After a standard literature search, Phase II/III studies comparing different ICI regimens in previously untreated advanced melanoma patients were analyzed. Patient‐level data were reconstructed from Kaplan–Meier curves by application of the IPDfromKM method. These reconstructed datasets were used to perform indirect comparisons between treatments. Standard statistical testing was used, including hazard ratio and medians. A secondary analysis employed the restricted mean survival time.Results: Six trials were included in our analysis. Information on PFS from these trials was pooled according to the following treatments: nivolumab or pembrolizumab as monotherapy, or in combination with ipilimumab, and relatlimab + nivolumab. Pembrolizumab+ipilimumab showed significantly better PFS compared with the other treatments; nivolumab+ipilimumab ranked second; the other treatments showed a similar survival pattern.Conclusions: The picture of comparative effectiveness resulting from our analysis is complex. The IPDfromKM method is advantageous because it accounts for the length of follow‐up but loses the balance between treatment group and controls determined by randomization. Based on indirect comparisons, the combination of pembrolizumab+ipilimumab showed a particularly high efficacy, and so deserves further investigation. While the effect of between‐trial differences in inclusion criteria plays an important role, our results do not support the proposal of relatlimab+nivolumab as a new standard of care.