Objectives This study evaluates the cost-effectiveness of tisagenlecleucel (a CAR T-cell therapy), versus blinatumomab, for the treatment of pediatric and young adult patients with relapsed/refractory acute lymphoblastic leukemia (R/R ALL) in the Irish healthcare setting. The value of conducting further research, to investigate the value of uncertainty associated with the decision problem, is assessed by means of expected value of perfect information (EVPI) and partial EVPI (EVPPI) analyses. Methods A three-state partitioned survival model was developed. A short-term decision tree partitioned patients in the tisagenlecleucel arm according to infusion status. Survival was extrapolated to 60 months; general population mortality with a standardized mortality ratio was then applied. Estimated EVPI and EVPPI were scaled up to population according to the incidence of the decision. Results At list prices, the incremental cost-effectiveness ratio was EUR 73,086 per quality-adjusted life year (QALY) (incremental costs EUR 156,928; incremental QALYs 2.15). The probability of cost-effectiveness, at the willingness-to-pay threshold of EUR 45,000 per QALY, was 16 percent. At this threshold, population EVPI was EUR 314,455; population EVPPI was below EUR 100,000 for each parameter category. Conclusions Tisagenlecleucel is not cost effective, versus blinatumomab, for the treatment of pediatric and young adult patients with R/R ALL in Ireland (at list prices). Further research to decrease decision (parameter) uncertainty, at the defined willingness-to-pay threshold, may not be of value. However, there is a high degree of uncertainty underpinning the analysis, which may not be captured by EVPI analysis.
Previous work suggested that pacemaker evoked T wave amplitude (ETWA) may be a sensitive noninvasive marker of cardiac allograft rejection. A Topaz QT sensing rate responsive pacemaker (Vitatron Medical) was implanted at transplantation using epicardial ventricular leads in 45 recipients (35 males; median age 51 years, range 20-63). The median duration of follow-up was 129 days (range 4-327). The ETWA at a paced rate of 100 beats/min was measured daily during hospitalization and at each outpatient attendance (900 readings). Endomyocardial biopsies were at routine intervals or when otherwise clinically indicated (257 biopsies with concurrent ETWA data). There were 58 episodes of rejection > or = grade 3a in 28 patients. The biopsies were classed as either no rejection (grade < 3a) or rejection requiring treatment (grade > or = 3a). The median normalized ETWA was 100.8% (range 24.6-239.7) without rejection and 89.9% (17.0-189.7) with rejection (Mann-Whitney U Test: P = 0.028). The performance of ETWA monitoring as a diagnostic test for the individual recipient was evaluated with exponentially weighted moving average quality control charts. For the diagnosis of all rejection episodes, ETWA monitoring had a sensitivity of 55%, a specificity of 62%, a positive predictive value of 30%, and negative predictive value of 83%. It is concluded that although analysis of pooled data showed a significant reduction in normalized ETWA with biopsy proven rejection, ETWA monitoring requires further refinement to improve sensitivity before it can be considered a clinically useful technique for the non-invasive diagnosis of cardiac allograft rejection in individual recipients.
IntroductionHuman screening of title and abstracts in a systematic literature review (SLR) is labor intensive and time-consuming. In many instances, thousands of citations may be retrieved; the vast majority excluded upon screening. Text-mining semi-automates and accelerates screening by identifying patterns in relevant and irrelevant citations, as labelled by the screener. One such text-mining tool, Abstrackr, uses an algorithm within an active-learning framework to predict the likelihood of citations being relevant. The objective of this study was to assesses the performance of Abstrackr for title and abstract screening in an SLR of treatments for relapsed/refractory diffuse large B-cell lymphoma.MethodsCitations identified from searches of electronic databases were imported to Abstrackr. An investigator-selected database of terms indicating relevance of title and abstract to the research question were uploaded. These terms were partly informed by the SLR inclusion/exclusion criteria. Citations deemed most relevant by Abstrackr were screened first (screening prioritization). Screening was carried out until a maximum prediction score of 0.4 or less, based on previous experience in the literature, was reached. Remaining citations were deemed unlikely to be relevant and did not undergo screening (screening truncation). Separately, a single-human screener screened all citations using Covidence.ResultsA total of 7,723 citations and 154 initial terms were uploaded to Abstrackr. Of these citations, 2,572 (33 percent) were screened before a prediction score of 0.39 was reached. Compared to single-human screening (conducted on all citations), the workload saving associated with Abstrackr was 5 days. A total of 451 (6 percent) citations proceeded to full-text screening; ten (0.1 percent) were included in the final evidence base. No citations predicted to be irrelevant by Abstrackr were included in the final evidence base.ConclusionsText-mining tools such as Abstrackr have the potential to reduce workload associated with title and abstract screening, without missing relevant citations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.