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Estimation of within-trial interactions in meta-analysis is crucial for reliable assessment of how treatment effects vary across participant subgroups. However, current methods have various limitations. Patients, clinicians and policy-makers need reliable estimates of treatment effects within specific covariate subgroups, on relative and absolute scales, in order to target treatments appropriately--which estimation of an interaction effect does not in itself provide. Also, the focus has been on covariates with only two subgroups, and may exclude relevant data if only a single subgroup is reported. Therefore, in this article we further develop the "within-trial" framework by providing practical methods to (1) estimate within-trial interactions across two or more subgroups; (2) estimate subgroup-specific ("floating") treatment effects that are compatible with the within-trial interactions and make maximum use of available data; and (3) clearly present this data using novel implementation of forest plots. We described the steps involved and apply the methods to two examples taken from previously published meta-analyses, and demonstrate a straightforward implementation in Stata based upon existing code for multivariate meta-analysis. We discuss how the within-trial framework and plots can be utilised with aggregate (or "published") source data, as well as with individual participant data, to effectively demonstrate how treatment effects differ across participant subgroups.

Hospitalized patients with COVID-19 are at high risk for thrombosis and systemic inflammation. Anticoagulation can improve outcomes, but trials have varied in the doses used. This systematic review and prospective meta-analysis includes and analyzes randomized trials registered by September 2024. The analysis compares therapeutic-, prophylactic-, and intermediate-dose anticoagulation with heparins on 28-day mortality, major bleeding, and other clinical outcomes. Background: Reported results of clinical trials assessing higher-dose anticoagulation in patients hospitalized for COVID-19 have been inconsistent. Purpose: To estimate the association of higher- versus lower-dose anticoagulation with clinical outcomes. Data Sources: Randomized trials were identified from the World Health Organization's International Clinical Trials Registry Platform and ClinicalTrials.gov with no restriction by trial status or language. Study Selection: Eligible randomized trials assigned patients hospitalized for COVID-19 to higher- versus lower-dose anticoagulation. Data Extraction: 20 eligible trials provided data in a prospectively agreed format. Two further studies were included based on published data. The primary outcome was all-cause mortality 28 days after randomization. Secondary outcomes were progression to invasive mechanical ventilation or death, thromboembolic events, and major bleeding. Data Synthesis: Therapeutic- compared with prophylactic-dose anticoagulation with heparins reduced 28-day mortality (OR, 0.77 [95% CI, 0.64 to 0.93]; I 2  = 29%; 11 trials, 6297 patients, of whom 5456 required low or no oxygen at randomization). The ORs for 28-day mortality were 1.21 (CI, 0.93 to 1.58; I 2  = 0%) for therapeutic-dose compared with intermediate-dose anticoagulation (6 trials, 1803 patients, 843 receiving noninvasive ventilation at randomization) and 0.95 (CI, 0.76 to 1.19; I 2  = 0%; 10 trials, 3897 patients, 2935 receiving no or low oxygen at randomization) for intermediate- versus prophylactic-dose anticoagulation. Treatment effects appeared broadly consistent across predefined patient subgroups, although some analyses were limited in power. Higher- compared with lower-dose anticoagulation was associated with fewer thromboembolic events, but a greater risk for major bleeding. Conclusion: Therapeutic-dose compared with prophylactic-dose anticoagulation reduced 28-day mortality. Mortality was similar for intermediate-dose compared with prophylactic-dose anticoagulation and higher for therapeutic-dose compared with intermediate-dose anticoagulation, although this comparison was not estimated precisely. Primary Funding Source: No direct funding. (PROSPERO: CRD42020213461) [ABSTRACT FROM AUTHOR]

Synthetic chemists have spent considerable effort optimizing the synthesis of nitrogen and oxygen containing compounds through a number of methods; however, direct introduction of N- and O-functionality remains challenging. Presented herein is a general method to allow for the simultaneous installation of N- and O-functionality to construct unexplored N-O heterocyclic and amino-alcohol scaffolds. This transformation uses earth abundant copper salts to facilitate the formation of a carbon-centered radical and subsequent carbon-nitrogen bond formation. The intermediate aminoxyl radical is terminated by an intramolecularly appended carbon-centered radical. We have exploited this methodology to also access amino-alcohols with a range of aliphatic and aromatic linkers.

The synthesis of sterically hindered amines has been a significant challenge in organic chemistry. Herein, we report a modular, three-component coupling that constructs two carbon-nitrogen bonds including a sterically hindered Csp3-N bond using commercially available materials. This process uses an earth-abundant copper catalyst and mild reaction conditions, allowing access to a variety of complex aromatic amines.

The synthesis of sterically hindered anilines has been a significant challenge in organic chemistry. Here we report a Cu-catalyzed radical addition with in situ-generated nitroso compounds to prepare sterically hindered amines directly from readily available materials. The transformation is conducted at room temperature, uses abundant copper salts, and is tolerant of a range of functional groups.

Abstract Background The primary goal of this study was to investigate the relationship between increasing output power levels and clot fragmentation during high-intensity focused ultrasound (HIFU)-induced thrombolysis. Methods A HIFU headsystem, designed for brain applications in humans, was used for this project. A human calvarium was mounted inside the water-filled hemispheric transducer. Artificial thrombi were placed inside the skull and located at the natural focus point of the transducer. Clots were exposed to a range of acoustic output power levels from 0 to 400 W. The other HIFU operating parameters remained constant. To assess clot fragmentation, three filters of different mesh pore sizes were used. To assess sonothrombolysis efficacy, the clot weight loss was measured. Results No evidence of increasing clot fragmentation was found with increasing acoustic intensities in the majority of the study groups of less than 400 W. Increasing clot lysis could be observed with increasing acoustic output powers. Conclusion Transcranial sonothrombolysis could be achieved in vitro within seconds in the absence of tPA and without producing relevant clot fragmentation, using acoustic output powers of

Abstract The goal was to test the effects of various combinations of pulse widths (PW) and duty cycles (DC) on high-intensity focused ultrasound (HIFU)-induced sonothrombolysis efficacy using an in vitro flow model. An ExAblate™ 4000 HIFU headsystem (InSightec, Inc., Israel) was used. Artificial blood clots were placed into test tubes inside a human calvarium and exposed to pulsatile flow. Four different duty cycles were tested against four different pulse widths. For all study groups, an increase in thrombolysis efficacy could be seen in association with increasing DC and/or PW (p

Background The vast majority of systematic reviews are planned retrospectively, once most eligible trials have completed and reported, and are based on aggregate data that can be extracted from publications. Prior knowledge of trial results can introduce bias into both review and meta-analysis methods, and the omission of unpublished data can lead to reporting biases. We present a collaborative framework for prospective, adaptive meta-analysis (FAME) of aggregate data to provide results that are less prone to bias. Also, with FAME, we monitor how evidence from trials is accumulating, to anticipate the earliest opportunity for a potentially definitive meta-analysis. Methodology We developed and piloted FAME alongside 4 systematic reviews in prostate cancer, which allowed us to refine the key principles. These are to: (1) start the systematic review process early, while trials are ongoing or yet to report; (2) liaise with trial investigators to develop a detailed picture of all eligible trials; (3) prospectively assess the earliest possible timing for reliable meta-analysis based on the accumulating aggregate data; (4) develop and register (or publish) the systematic review protocol before trials produce results and seek appropriate aggregate data; (5) interpret meta-analysis results taking account of both available and unavailable data; and (6) assess the value of updating the systematic review and meta-analysis. These principles are illustrated via a hypothetical review and their application to 3 published systematic reviews. Conclusions FAME can reduce the potential for bias, and produce more timely, thorough and reliable systematic reviews of aggregate data., Author(s): Jayne F. Tierney *, David J. Fisher, Claire L. Vale, Sarah Burdett, Larysa H. Rydzewska, Ewelina Rogozinska, Peter J. Godolphin, Ian R. White, Mahesh K. B. Parmar Background The [...]

Individual participant data (IPD) meta‐analyses of randomised trials are considered a reliable way to assess participant‐level treatment effect modifiers but may not make the best use of the available data. Traditionally, effect modifiers are explored one covariate at a time, which gives rise to the possibility that evidence of treatment‐covariate interaction may be due to confounding from a different, related covariate. We aimed to evaluate current practice when estimating treatment‐covariate interactions in IPD meta‐analysis, specifically focusing on involvement of additional covariates in the models. We reviewed 100 IPD meta‐analyses of randomised trials, published between 2015 and 2020, that assessed at least one treatment‐covariate interaction. We identified four approaches to handling additional covariates: (1) Single interaction model (unadjusted): No additional covariates included (57/100 IPD meta‐analyses); (2) Single interaction model (adjusted): Adjustment for the main effect of at least one additional covariate (35/100); (3) Multiple interactions model: Adjustment for at least one two‐way interaction between treatment and an additional covariate (3/100); and (4) Three‐way interaction model: Three‐way interaction formed between treatment, the additional covariate and the potential effect modifier (5/100). IPD is not being utilised to its fullest extent. In an exemplar dataset, we demonstrate how these approaches lead to different conclusions. Researchers should adjust for additional covariates when estimating interactions in IPD meta‐analysis providing they adjust their main effects, which is already widely recommended. Further, they should consider whether more complex approaches could provide better information on who might benefit most from treatments, improving patient choice and treatment policy and practice. [ABSTRACT FROM AUTHOR]