Comparative effectiveness of neutralising monoclonal antibodies in high risk COVID-19 patients: a Bayesian network meta-analysis.

The purpose of this work was to review and synthesise the evidence on the comparative effectiveness of neutralising monoclonal antibody (nMAB) therapies in individuals exposed to or infected with SARS-CoV-2 and at high risk of developing severe COVID-19. Outcomes of interest were mortality, healthcare utilisation, and safety. A rapid systematic review was undertaken to identify and synthesise relevant RCT evidence using a Bayesian Network Meta-Analysis. Relative treatment effects for individual nMABs (compared with placebo and one another) were estimated. Pooled effects for the nMAB class compared with placebo were estimated. Relative effects were combined with baseline natural history models to predict the expected risk reductions per 1000 patients treated. Eight articles investigating four nMABs (bamlanivimab, bamlanivimab/etesevimab, casirivimab/imdevimab, sotrovimab) were identified. All four therapies were associated with a statistically significant reduction in hospitalisation (70-80% reduction in relative risk; absolute reduction of 35-40 hospitalisations per 1000 patients). For mortality, ICU admission, and invasive ventilation, the risk was lower for all nMABs compared with placebo with moderate to high uncertainty due to small event numbers. Rates of serious AEs and infusion reactions were comparable between nMABs and placebo. Pairwise comparisons between nMABs were typically uncertain, with broadly comparable efficacy. In conclusion, nMABs are effective at reducing hospitalisation among infected individuals at high-risk of severe COVID-19, and are likely to reduce mortality, ICU admission, and invasive ventilation rates; the effect on these latter outcomes is more uncertain. Widespread vaccination and the emergence of nMAB-resistant variants make the generalisability of these results to current patient populations difficult.

A text-mining tool generated title-abstract screening workload savings: performance evaluation versus single-human screening.

BACKGROUND AND OBJECTIVES: Text-mining tool, Abstrackr, may potentially reduce the workload burden of title and abstract screening (Stage 1), using screening prioritization and truncation. This study aimed to evaluate the performance of Abstrackr's text-mining functions ('Abstrackr-assisted screening'; screening undertaken by a single-human screener and Abstrackr) vs. Single-human screening. METHODS: A systematic review of treatments for relapsed/refractory diffuse large B cell lymphoma (n = 7,723) was used. Citations, uploaded to Abstrackr, were screened by a human screener until a pre-specified maximum prediction score of 0.39540 was reached. Abstrackr's predictions were compared with the judgments of a second, human screener (who screened all citations in Covidence). The performance metrics were sensitivity, specificity, precision, false negative rate, proportion of relevant citations missed, workload savings, and time savings. RESULTS: Abstrackr reduced Stage 1 workload by 67% (5.4 days), when compared with Single-human screening. Sensitivity was high (91%). The false negative rate at Stage 1 was 9%; however, none of those citations were included following full-text screening. The high proportion of false positives (n = 2,001) resulted in low specificity (72%) and precision (15.5%). CONCLUSION: Abstrackr-assisted screening provided Stage 1 workload savings that did not come at the expense of omitting relevant citations. However, Abstrackr overestimated citation relevance, which may have negative workload implications at full-text screening.

The Use Of A Text-Mining Screening Tool For Systematic Review Of Treatments For Relapsed/Refractory Diffuse Large B-Cell Lymphoma

IntroductionHuman screening of title and s 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 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 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 screening, without missing relevant citations.