Mechanisms of action of therapeutic exercise for knee and hip OA remain a black box phenomenon: an individual patient data mediation study with the OA Trial Bank

Objectives To evaluate mediating factors for the effect of therapeutic exercise on pain and physical function in people with knee/hip osteoarthritis (OA). Methods For Subgrouping and TargetEd Exercise pRogrammes for knee and hip OsteoArthritis (STEER OA), individual participant data (IPD) were sought from all published randomised controlled trials (RCTs) comparing therapeutic exercise to non-exercise controls in people with knee/hip OA. Using the Counterfactual framework, the effect of the exercise intervention and the percentage mediated through each potential mediator (muscle strength, proprioception and range of motion (ROM)) for knee OA and muscle strength for hip OA were determined. Results Data from 12 of 31 RCTs of STEER OA (1407 participants) were available. Within the IPD data sets, there were generally statistically significant effects from therapeutic exercise for pain and physical function in comparison to non-exercise controls. Of all potential mediators, only the change in knee extension strength was statistically and significantly associated with the change in pain in knee OA (β -0.03 (95% CI -0.05 to -0.01), 2.3% mediated) and with physical function in knee OA (β -0.02 (95% CI -0.04 to -0.00), 2.0% mediated) and hip OA (β -0.03 (95% CI -0.07 to -0.00), no mediation). Conclusions This first IPD mediation analysis of this scale revealed that in people with knee OA, knee extension strength only mediated ±2% of the effect of therapeutic exercise on pain and physical function. ROM and proprioception did not mediate changes in outcomes, nor did knee extension strength in people with hip OA. As 98% of the effectiveness of therapeutic exercise compared with non-exercise controls remains unexplained, more needs to be done to understand the underlying mechanisms of actions

The association between antihypertensive treatment and adverse events: a systematic review and meta-analysis of 58 randomised controlled trials including 280,638 patients

Objectives: There are many meta-analyses of randomised controlled trials (RCTs) which examine the efficacy of antihypertensive treatment, but few have studied the potential harms. The aim of this study was to examine the association between antihypertensive treatment and specific adverse events. Design: Systematic review and meta-analysis of RCTs. Eligibility criteria: Articles were eligible if they examined adults taking antihypertensive treatment compared to placebo/no treatment, more treatment vs. less treatment, or higher blood pressure targets vs. lower targets. To avoid small early phase trials, studies were required to have at least 650 patient-years of follow-up. Information sources: Searches were conducted in Embase, MEDLINE, Cochrane CENTRAL and the Science Citation Index databases from inception until 14/04/2020. Main outcome measures: The primary outcome was falls at any time point during trial follow-up. Secondary outcomes were acute kidney injury (AKI), fractures, gout, hyperkalaemia, hypokalaemia, hypotension and syncope. Additional outcomes related to death and major cardiovascular events were extracted. Risk of bias was assessed using the Cochrane risk of bias tool, and random-effects meta-analysis was used to pool rate ratios, odds ratios and hazard ratios across studies allowing for between-study heterogeneity (tau2). Results: A total of 15,023 articles were screened for inclusion and 58 RCTs were identified, including 280,638 participants, followed-up for a median of 3 years (IQR 2-4). The majority of trials (69%) had a low risk of bias. Across seven trials, there was no evidence of an association between antihypertensive treatment and falls (summary risk ratio [RR] 1.05, 95%CI 0.89-1.24, tau2=0.009). Antihypertensives were associated with an increased risk of AKI (RR 1.18, 95%CI 1.01-1.39, tau2=0.037, n=15), hyperkalaemia (RR 1.89, 95%CI 1.56-2.30, tau2=0.122, n=26), hypotension (RR 1.97, 95%CI 1.67-2.32, tau2=0.132, n=35) and syncope (RR 1.28, 95%CI 1.03-1.59, tau2=0.050, n=16). The heterogeneity between studies assessing AKI and hyperkalaemia events was reduced when focussing on medications affecting the renin angiotensin-aldosterone system. Results were robust to sensitivity analyses focusing on adverse events leading to withdrawal from each trial. Antihypertensive therapy was associated with a reduced risk of all-cause mortality, cardiovascular death and stroke, but not myocardial infarction. Conclusions: This meta-analysis does not suggest that antihypertensive treatment is associated with falls, but provides evidence of an association with other mild/severe adverse events, some of which were drug class specific. These data may be used to inform shared decision-making between physicians and patients regarding antihypertensive initiation and continuation, especially in patients at high risk of harm.</p

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