Uptake of systematic reviews and meta-analyses based on individual participant data in clinical practice guidelines: descriptive study.

To establish the extent to which systematic reviews and meta-analyses of individual participant data (IPD) are being used to inform the recommendations included in published clinical guidelines

Large droplet impact on water layers

The impact of large droplets onto an otherwise undisturbed layer of water is considered. The work, which is motivated primarily with regard to aircraft icing, is to try and help understand the role of splashing on the formation of ice on a wing, in particular for large droplets where splash appears, to have a significant effect. Analytical and numerical approaches are used to investigate a single droplet impact onto a water layer. The flow for small times after impact is determined analytically, for both direct and oblique impacts. The impact is also examined numerically using the volume of fluid (VOF) method. At small times there are promising comparisons between the numerical results, the analytical solution and experimental work capturing the ejector sheet. At larger times there is qualitative agreement with experiments and related simulations. Various cases are considered, varying the droplet size to layer depth ratio, including surface roughness, droplet distortion and air effects. The amount of fluid splashed by such an impact is examined and is found to increase with droplet size and to be significantly influenced by surface roughness. The makeup of the splash is also considered, tracking the incoming fluid, and the splash is found to consist mostly of fluid originating in the layer

Meta-analysis of continuous outcomes: using pseudo IPD created from aggregate data to adjust for baseline imbalance and assess treatment-by-baseline modification.

Meta-analysis of individual participant data (IPD) is considered the "gold-standard" for synthesizing clinical study evidence. However, gaining access to IPD can be a laborious task (if possible at all) and in practice only summary (aggregate) data are commonly available. In this work we focus on meta-analytic approaches of comparative studies where aggregate data are available for continuous outcomes measured at baseline (pre-treatment) and follow-up (post-treatment). We propose a method for constructing pseudo individual baselines and outcomes based on the aggregate data. These pseudo IPD can be subsequently analysed using standard analysis of covariance (ANCOVA) methods. Pseudo IPD for continuous outcomes reported at two timepoints can be generated using the sufficient statistics of an ANCOVA model i.e., the mean and standard deviation at baseline and follow-up per group, together with the correlation of the baseline and follow-up measurements. Applying the ANCOVA approach, which crucially adjusts for baseline imbalances and accounts for the correlation between baseline and change scores, to the pseudo IPD results in identical estimates to the ones obtained by an ANCOVA on the true IPD. In addition, an interaction term between baseline and treatment effect can be added. There are several modelling options available under this approach, which makes it very flexible. Methods are exemplified using reported data of a previously published IPD metaanalysis of 10 trials investigating the effect of antihypertensive treatments on systolic blood pressure, leading to identical results compared with the true IPD analysis and of a meta-analysis of fewer trials, where baseline imbalance occurred. This article is protected by copyright. All rights reserved

Impact of Aldosterone Antagonists on Sudden Cardiac Death Prevention in Heart Failure and Post-Myocardial Infarction Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND AND OBJECTIVES: Sudden cardiac death (SCD) is a severe burden of modern medicine. Aldosterone antagonist is publicized as effective in reducing mortality in patients with heart failure (HF) or post myocardial infarction (MI). Our study aimed to assess the efficacy of AAs on mortality including SCD, hospitalization admission and several common adverse effects. METHODS: We searched Embase, PubMed, Web of Science, Cochrane library and clinicaltrial.gov for randomized controlled trials (RCTs) assigning AAs in patients with HF or post MI through May 2015. The comparator included standard medication or placebo, or both. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Event rates were compared using a random effects model. Prospective RCTs of AAs with durations of at least 8 weeks were selected if they included at least one of the following outcomes: SCD, all-cause/cardiovascular mortality, all-cause/cardiovascular hospitalization and common side effects (hyperkalemia, renal function degradation and gynecomastia). RESULTS: Data from 19,333 patients enrolled in 25 trials were included. In patients with HF, this treatment significantly reduced the risk of SCD by 19% (RR 0.81; 95% CI, 0.67-0.98; p = 0.03); all-cause mortality by 19% (RR 0.81; 95% CI, 0.74-0.88, p<0.00001) and cardiovascular death by 21% (RR 0.79; 95% CI, 0.70-0.89, p<0.00001). In patients with post-MI, the matching reduced risks were 20% (RR 0.80; 95% CI, 0.66-0.98; p = 0.03), 15% (RR 0.85; 95% CI, 0.76-0.95, p = 0.003) and 17% (RR 0.83; 95% CI, 0.74-0.94, p = 0.003), respectively. Concerning both subgroups, the relative risks respectively decreased by 19% (RR 0.81; 95% CI, 0.71-0.92; p = 0.002) for SCD, 18% (RR 0.82; 95% CI, 0.77-0.88, p < 0.0001) for all-cause mortality and 20% (RR 0.80; 95% CI, 0.74-0.87, p < 0.0001) for cardiovascular mortality in patients treated with AAs. As well, hospitalizations were significantly reduced, while common adverse effects were significantly increased. CONCLUSION: Aldosterone antagonists appear to be effective in reducing SCD and other mortality events, compared with placebo or standard medication in patients with HF and/or after a MI

Investigation of one-stage meta-analysis methods for joint longitudinal and time-to-event data through simulation and real data application

Background: Joint modeling of longitudinal and time‐to‐event data is often advantageous over separate longitudinal or time‐to‐event analyses as it can account for study dropout, error in longitudinally measured covariates, and correlation between longitudinal and time‐to‐event outcomes. The current literature on joint modeling focuses mainly on the analysis of single studies with a lack of methods available for the meta‐analysis of joint data from multiple studies. Methods: We investigate a variety of one‐stage methods for the meta‐analysis of joint longitudinal and time‐to‐event outcome data. These methods are applied to the INDANA dataset to investigate longitudinally measured systolic blood pressure, with each of time to death, time to myocardial infarction, and time to stroke. Results are compared to separate longitudinal or time‐to‐event meta‐analyses. A simulation study is conducted to contrast separate versus joint analyses over a range of scenarios. Results: The performance of the examined one‐stage joint meta‐analytic models varied. Models that accounted for between study heterogeneity performed better than models that ignored it. Of the examined methods to account for between study heterogeneity, under the examined association structure, fixed effect approaches appeared preferable, whereas methods involving a baseline hazard stratified by study were least time intensive. Conclusions: One‐stage joint meta‐analytic models that accounted for between study heterogeneity using a mix of fixed effects or a stratified baseline hazard were reliable; however, models examined that included study level random effects in the association structure were less reliable

Individual participant data meta-analysis to examine interactions between treatment effect and participant-level covariates: statistical recommendations for conduct and planning

Precision medicine research often searches for treatment‐covariate interactions, which refers to when a treatment effect (eg, measured as a mean difference, odds ratio, hazard ratio) changes across values of a participant‐level covariate (eg, age, gender, biomarker). Single trials do not usually have sufficient power to detect genuine treatment‐covariate interactions, which motivate the sharing of individual participant data (IPD) from multiple trials for meta‐analysis. Here, we provide statistical recommendations for conducting and planning an IPD meta‐analysis of randomized trials to examine treatment‐covariate interactions. For conduct, two‐stage and one‐stage statistical models are described, and we recommend: (i) interactions should be estimated directly, and not by calculating differences in meta‐analysis results for subgroups; (ii) interaction estimates should be based solely on within‐study information; (iii) continuous covariates and outcomes should be analyzed on their continuous scale; (iv) nonlinear relationships should be examined for continuous covariates, using a multivariate meta‐analysis of the trend (eg, using restricted cubic spline functions); and (v) translation of interactions into clinical practice is nontrivial, requiring individualized treatment effect prediction. For planning, we describe first why the decision to initiate an IPD meta‐analysis project should not be based on between‐study heterogeneity in the overall treatment effect; and second, how to calculate the power of a potential IPD meta‐analysis project in advance of IPD collection, conditional on characteristics (eg, number of participants, standard deviation of covariates) of the trials (potentially) promising their IPD. Real IPD meta‐analysis projects are used for illustration throughout

Do drugs interact together in cardiovascular prevention? A meta-analysis of powerful or factorial randomized controlled trials.

To explore whether preventive cardiovascular drugs (antihypertensive, antiplatelet, lipid lowering and hypoglycemic agents) interact together in cardiovascular prevention. We searched PubMed®, Web of science™, Embase and Cochrane library for powerful randomized placebo-controlled trials (>1000 patients). We explored whether drug effect on major vascular events changed according to cross-exposure to other drug classes or to cardiovascular risk factors (hypertension or type 2 diabetes), through a meta-analysis of relative odds ratio computed by trial subgroups. A significant interaction was suggested from confidence intervals of the ratio of odds ratios, when they excluded neutral value of 1. In total, 14 trials with 178,398 patients were included. No significant interaction was observed between co-prescribed drugs or between these medications and type 2 diabetes/hypertension status. Our meta-analysis is the first one to evaluate drug-drug and drug-hypertension/type 2 diabetes status interactions in terms of cardiovascular risks: we did not observe any significant interaction. This indirectly reinforces the rationale of using several contrasted mechanisms to address cardiovascular prevention; and allows the combination effect prediction by a simple multiplication of their odds ratios. The limited availability of data reported or obtained from authors is a strong argument in favor of data sharing

Differences in need for antihypertensive drugs among those aware and unaware of their hypertensive status: a cross sectional survey

BACKGROUND: Lack of antihypertensive use among hypertensive individuals is a major public health problem. It remains unclear as to how much of this lack of treatment is because of failure to diagnose hypertension or failure to initiate drug treatment for those with a diagnosis of hypertension. The primary aim of this study was to determine the proportion of those untreated individuals who would be recommended to start drug therapy for control of blood pressure among those aware or unaware of their diagnosis of hypertension. METHODS: The Canadian Heart Health Surveys (1986 – 1992), a national, cross-sectional descriptive survey (n = 23 129), was used to determine the proportion of individuals who were untreated, yet satisfied the 2004 Canadian hypertension guidelines for initiating drug therapy. Patients were divided into subgroups of those aware and unaware of having a diagnosis of hypertension according to self reported awareness from the survey. RESULTS: Of those with untreated hypertension (= 140/90 mmHg), only 37% were aware of their diagnosis. 74% of untreated individuals aware of their diagnosis of hypertension would require drug therapy, compared to 57% of those who were unaware. Of those >65 years of age, 52% of aware individuals needed drug therapy whereas only 34% of unaware elderly would need drug treatment. CONCLUSION: In both unaware and aware subgroups, the majority of patients with untreated hypertension would benefit from antihypertensive drug therapy according to the 2004 Canadian Hypertension recommendations. The proportion of untreated patients that still need drug therapy was higher among those who were aware compared to those who were unaware. This finding suggests that the major gap in hypertension control may be in initiating drug therapy rather than in diagnosing hypertension. Further studies are needed to confirm these results to ultimately help strategize public health efforts in controlling hypertension

Generalized Navier Boundary Condition and Geometric Conservation Law for surface tension

We consider two-fluid flow problems in an Arbitrary Lagrangian Eulerian (ALE) framework. The purpose of this work is twofold. First, we address the problem of the moving contact line, namely the line common to the two fluids and the wall. Second, we perform a stability analysis in the energy norm for various numerical schemes, taking into account the gravity and surface tension effects. The problem of the moving contact line is treated with the so-called Generalized Navier Boundary Conditions. Owing to these boundary conditions, it is possible to circumvent the incompatibility between the classical no-slip boundary condition and the fact that the contact line of the interface on the wall is actually moving. The energy stability analysis is based in particular on an extension of the Geometry Conservation Law (GCL) concept to the case of moving surfaces. This extension is useful to study the contribution of the surface tension. The theoretical and computational results presented in this paper allow us to propose a strategy which offers a good compromise between efficiency, stability and artificial diffusion