Exposure to Secondhand Smoke and Arrhythmogenic Cardiac Alternans in a Mouse Model.

BackgroundEpidemiological evidence suggests that a majority of deaths attributed to secondhand smoke (SHS) exposure are cardiovascular related. However, to our knowledge, the impact of SHS on cardiac electrophysiology, [Formula: see text] handling, and arrhythmia risk has not been studied.ObjectivesThe purpose of this study was to investigate the impact of an environmentally relevant concentration of SHS on cardiac electrophysiology and indicators of arrhythmia.MethodsMale C57BL/6 mice were exposed to SHS [total suspended particles (THS): [Formula: see text], nicotine: [Formula: see text], carbon monoxide: [Formula: see text], or filtered air (FA) for 4, 8, or 12 wk ([Formula: see text]]. Hearts were excised and Langendorff perfused for dual optical mapping with voltage- and [Formula: see text]-sensitive dyes.ResultsAt slow pacing rates, SHS exposure did not alter baseline electrophysiological parameters. With increasing pacing frequency, action potential duration (APD), and intracellular [Formula: see text] alternans magnitude progressively increased in all groups. At 4 and 8 wk, there were no statistical differences in APD or [Formula: see text] alternans magnitude between SHS and FA groups. At 12 wk, both APD and [Formula: see text] alternans magnitude were significantly increased in the SHS compared to FA group ([Formula: see text]). SHS exposure did not impact the time constant of [Formula: see text] transient decay ([Formula: see text]) at any exposure time point. At 12 wk exposure, the recovery of [Formula: see text] transient amplitude with premature stimuli was slightly (but nonsignificantly) delayed in SHS compared to FA hearts, suggesting that [Formula: see text] release via ryanodine receptors may be impaired.ConclusionsIn male mice, chronic exposure to SHS at levels relevant to social situations in humans increased their susceptibility to cardiac alternans, a known precursor to ventricular arrhythmia. https://doi.org/10.1289/EHP3664

Against pragmatism: on efficacy, effectiveness and the real world

Explanatory and pragmatic trials represent ends of a continuum of attitudes about clinical trial design. Recent literature argues that pragmatic trials are more informative about clinical care in the real world. Although there is place for more pragmatic studies to inform clinical practice and health policy decision-making, we are concerned that it is generally under-appreciated that extrapolating the results of broadly inclusive pragmatic trials to the care of real patients may often be as problematic as extrapolating the results of narrowly focused explanatory or efficacy trials. Simplistic interpretation of pragmatic trials runs the risk of driving harmful policies

Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal

Mounting evidence suggests that there is frequently considerable variation in the risk of the outcome of interest in clinical trial populations. These differences in risk will often cause clinically important heterogeneity in treatment effects (HTE) across the trial population, such that the balance between treatment risks and benefits may differ substantially between large identifiable patient subgroups; the "average" benefit observed in the summary result may even be non-representative of the treatment effect for a typical patient in the trial. Conventional subgroup analyses, which examine whether specific patient characteristics modify the effects of treatment, are usually unable to detect even large variations in treatment benefit (and harm) across risk groups because they do not account for the fact that patients have multiple characteristics simultaneously that affect the likelihood of treatment benefit. Based upon recent evidence on optimal statistical approaches to assessing HTE, we propose a framework that prioritizes the analysis and reporting of multivariate risk-based HTE and suggests that other subgroup analyses should be explicitly labeled either as primary subgroup analyses (well-motivated by prior evidence and intended to produce clinically actionable results) or secondary (exploratory) subgroup analyses (performed to inform future research). A standardized and transparent approach to HTE assessment and reporting could substantially improve clinical trial utility and interpretability

Competing risk and heterogeneity of treatment effect in clinical trials

It has been demonstrated that patients enrolled in clinical trials frequently have a large degree of variation in their baseline risk for the outcome of interest. Thus, some have suggested that clinical trial results should routinely be stratified by outcome risk using risk models, since the summary results may otherwise be misleading. However, variation in competing risk is another dimension of risk heterogeneity that may also underlie treatment effect heterogeneity. Understanding the effects of competing risk heterogeneity may be especially important for pragmatic comparative effectiveness trials, which seek to include traditionally excluded patients, such as the elderly or complex patients with multiple comorbidities. Indeed, the observed effect of an intervention is dependent on the ratio of outcome risk to competing risk, and these risks – which may or may not be correlated – may vary considerably in patients enrolled in a trial. Further, the effects of competing risk on treatment effect heterogeneity can be amplified by even a small degree of treatment related harm. Stratification of trial results along both the competing and the outcome risk dimensions may be necessary if pragmatic comparative effectiveness trials are to provide the clinically useful information their advocates intend

The influence of gravimetric moisture content on studded shoe–surface interactions in soccer

It is desirable for the studs of a soccer shoe to penetrate the sport surface and provide the player with sufficient traction when accelerating. Mechanical tests are often used to measure the traction of shoe–surface combinations. Mechanical testing offers a repeatable measure of shoe–surface traction, eliminating the inherent uncertainties that exist when human participant testing is employed, and are hence used to directly compare the performance of shoe–surface combinations. However, the influence specific surface characteristics has on traction is often overlooked. Examining the influence of surface characteristics on mechanical test results improves the understanding of the traction mechanisms at the shoe–surface interface. This allows footwear developers to make informed decisions on the design of studded outsoles. The aim of this paper is to understand the effect gravimetric moisture content has on the tribological mechanisms at play during stud–surface interaction. This study investigates the relationships between: the gravimetric moisture content of a natural sand-based soccer surface; surface stiffness measured via a bespoke impact test device; and surface traction measured via a bespoke mechanical test device. Regression analysis revealed that surface stiffness decreases linearly with increased gravimetric moisture content (p = 0.04). Traction was found to initially increase and then decrease with gravimetric moisture content. It was observed that: a surface of low moisture content provides low stud penetration and therefore reduced traction; a surface of high moisture content provides high stud penetration but also reduced traction due to a lubricating effect; and surfaces with moisture content in between the two extremes provide increased traction. In this study a standard commercially available stud was used and other studs may provide slightly different results. The results provide insight into the traction mechanisms at the stud–surface interface which are described in the paper. The variation between traction measurements shows the influence gravimetric moisture content will have on player performance. This highlights the requirement to understand surface conditions prior to making comparative shoe–surface traction studies and the importance of using a studded outsole that is appropriate to the surface condition during play

Research methods for subgrouping low back pain

<p>Abstract</p> <p>Background</p> <p>There is considerable clinician and researcher interest in whether the outcomes for patients with low back pain, and the efficiency of the health systems that treat them, can be improved by 'subgrouping research'. Subgrouping research seeks to identify subgroups of people who have clinically important distinctions in their treatment needs or prognoses. Due to a proliferation of research methods and variability in how subgrouping results are interpreted, it is timely to open discussion regarding a conceptual framework for the research designs and statistical methods available for subgrouping studies (a method framework). The aims of this debate article are: (1) to present a method framework to inform the design and evaluation of subgrouping research in low back pain, (2) to describe method options when investigating prognostic effects or subgroup treatment effects, and (3) to discuss the strengths and limitations of research methods suitable for the hypothesis-setting phase of subgroup studies.</p> <p>Discussion</p> <p>The proposed method framework proposes six phases for studies of subgroups: studies of assessment methods, hypothesis-setting studies, hypothesis-testing studies, narrow validation studies, broad validation studies, and impact analysis studies. This framework extends and relabels a classification system previously proposed by McGinn et al (2000) as suitable for studies of clinical prediction rules. This extended classification, and its descriptive terms, explicitly anchor research findings to the type of evidence each provides. The inclusive nature of the framework invites appropriate consideration of the results of diverse research designs. Method pathways are described for studies designed to test and quantify prognostic effects or subgroup treatment effects, and examples are discussed. The proposed method framework is presented as a roadmap for conversation amongst researchers and clinicians who plan, stage and perform subgrouping research.</p> <p>Summary</p> <p>This article proposes a research method framework for studies of subgroups in low back pain. Research designs and statistical methods appropriate for sequential phases in this research are discussed, with an emphasis on those suitable for hypothesis-setting studies of subgroups of people seeking care.</p

Multivariable risk prediction can greatly enhance the statistical power of clinical trial subgroup analysis

BACKGROUND: When subgroup analyses of a positive clinical trial are unrevealing, such findings are commonly used to argue that the treatment's benefits apply to the entire study population; however, such analyses are often limited by poor statistical power. Multivariable risk-stratified analysis has been proposed as an important advance in investigating heterogeneity in treatment benefits, yet no one has conducted a systematic statistical examination of circumstances influencing the relative merits of this approach vs. conventional subgroup analysis. METHODS: Using simulated clinical trials in which the probability of outcomes in individual patients was stochastically determined by the presence of risk factors and the effects of treatment, we examined the relative merits of a conventional vs. a "risk-stratified" subgroup analysis under a variety of circumstances in which there is a small amount of uniformly distributed treatment-related harm. The statistical power to detect treatment-effect heterogeneity was calculated for risk-stratified and conventional subgroup analysis while varying: 1) the number, prevalence and odds ratios of individual risk factors for risk in the absence of treatment, 2) the predictiveness of the multivariable risk model (including the accuracy of its weights), 3) the degree of treatment-related harm, and 5) the average untreated risk of the study population. RESULTS: Conventional subgroup analysis (in which single patient attributes are evaluated "one-at-a-time") had at best moderate statistical power (30% to 45%) to detect variation in a treatment's net relative risk reduction resulting from treatment-related harm, even under optimal circumstances (overall statistical power of the study was good and treatment-effect heterogeneity was evaluated across a major risk factor [OR = 3]). In some instances a multi-variable risk-stratified approach also had low to moderate statistical power (especially when the multivariable risk prediction tool had low discrimination). However, a multivariable risk-stratified approach can have excellent statistical power to detect heterogeneity in net treatment benefit under a wide variety of circumstances, instances under which conventional subgroup analysis has poor statistical power. CONCLUSION: These results suggest that under many likely scenarios, a multivariable risk-stratified approach will have substantially greater statistical power than conventional subgroup analysis for detecting heterogeneity in treatment benefits and safety related to previously unidentified treatment-related harm. Subgroup analyses must always be well-justified and interpreted with care, and conventional subgroup analyses can be useful under some circumstances; however, clinical trial reporting should include a multivariable risk-stratified analysis when an adequate externally-developed risk prediction tool is available