Mohamad Alshurafa

Objective: To estimate the cost implications of early angiography for patients with suspected non-ST elevation acute coronary syndrome (NSTEACS) using tissue Doppler imaging (TDI).Design: A decision tree model was used to synthesise data from the pilot study and literature sources. Sensitivity analyses tested the impact of assumptions incorporated into the analysis.Setting: Emergency department (ED), Brisbane, Australia.Participants: Patients with suspected NSTEACS.Interventions: TDI as a diagnostic tool for triaging patients within 4 hours of presentation in addition to conventional risk stratification, compared with conventional risk stratification alone.Data sources: Resource used for diagnosis and management were recorded prospectively and costed for 51 adults who had echocardiography within 24 hours of admission. Costs for conventional care were based on observed data. Cost estimates for the TDI intervention assumed patients classified as high risk at TDI (E/e’>14) progressed early to angiography with an associated 1-day reduction in length of stay.Primary outcome measures: Costs until discharge from the Australian healthcare perspective in 2016–2017 prices.Results: Findings suggest that using TDI as a diagnostic tool for triaging patients with suspected NSTEACS is likely to be cost saving by $A1090 (95% credible interval: $A573 to $A1703) per patient compared with conventional care. The results are mainly driven by the assumed reduction in length of stay due to the inclusion of early TDI in clinical decision-making.Conclusions: This pilot study indicates that compared with conventional risk stratification, triaging patients presenting with suspected NSTEACS with TDI within 4 hours of ED presentation has potential cost savings. Findings assume a reduction in hospital stay is achieved for patients considered to be high risk at TDI. Larger, comparative studies with longer follow-up are needed to confirm the clinical effectiveness of TDI as a diagnostic strategy for NSTEACS, the assumed reduction in hospital stay and any cost saving.

There is considerable unexplained heterogeneity in previous meta-analyses of randomized controlled trials (RCTs) evaluating the effects of patient decision aids on the accuracy of knowledge of outcome probabilities. The purpose of this review was to explore possible effect modification by three covariates: the type of control intervention, decision aid quality and patients' baseline knowledge of probabilities. A sub-analysis of studies previously identified in the 2011 Cochrane review on decision aids for people facing treatment and screening decisions was conducted. Additional unpublished data were requested from relevant study authors to maximize the number of eligible studies. RCTs (to 2009) comparing decision aids with standardized probability information to control interventions (lacking such information) and assessing the accuracy of patient knowledge of outcome probabilities were included. The proportions of patients with accurate knowledge of outcome probabilities in each group were converted into relative effect measures. Intervention quality was assessed using the International Patient Decision Aid Standards instrument (IPDASi) probabilities domain. A main effects analysis of 17 eligible studies confirmed that decision aids significantly improve the accuracy of patient knowledge of outcome probabilities (relative risk = 1.80 [1.51, 2.16]), with considerable heterogeneity (87%). The type of control did not modify effects. Meta-regression suggested that the IPDASi probabilities domain score (reflecting decision aid quality) is a potential effect modifier (P = 0.037), accounting for a quarter of the variability (R 2 = 0.28). Meta-regression indicated the control event rate (reflecting baseline knowledge) is a significant effect modifier (P = 0.001), with over half the variability in ln(OR) explained by the linear relationship with log-odds for the control group (R 2 = 0.52); this relationship was slightly strengthened after correcting for the statistical dependence of the effect measure on the control event rate. Patients’ baseline level of knowledge of outcome probabilities is an important variable that explains the heterogeneity of effects of decision aids on improving accuracy of this knowledge. Greater relative effects are observed when the baseline proportion of patients with accurate knowledge is lower. This may indicate that decision aids are more effective in populations with lower knowledge.

Authors of randomized trial reports seem to hold a variety of views regarding the relationship between missing outcome data (MOD) and intention to treat (ITT). The objectives of this study were to systematically investigate how authors of methodology articles define ITT in the presence of MOD, how they recommend handling MOD under ITT, and to make a proposal for potential improvement in the definition and use of ITT in relation to MOD. We systematically searched MEDLINE in February 2009 for methodological articles written in English that devoted at least one paragraph to ITT and two other paragraphs to either ITT or MOD. We excluded original trial reports, observational studies, and clinical systematic reviews. Working in teams of two, we independently extracted relevant information from each eligible article. Of 1007 titles and abstracts reviewed, 66 articles met eligibility criteria. Five (8%) did not provide a definition of ITT; 25 (38%) mentioned MOD but did not discuss its relationship to ITT; and 36 (55%) discussed the relationship of MOD with ITT. These 36 articles described one or more of three statements: complete follow-up is required for ITT (58%); ITT and MOD are separate issues (17%); and ITT requires a specific strategy for handling MOD (78%); 17 (47%) endorsed more than one relationship. The most frequently mentioned strategies for handling MOD within ITT were: using the last outcome carried forward (50%); sensitivity analysis (50%); and use of available data to impute missing data (46%). We found that there is no consensus on the definition of ITT in relation to MOD. For conceptual clarity, we suggest that both reports of randomized trials and systematic reviews separately consider and describe how they deal with participants with complete data and those with MOD.

Objective To assess the reporting, extent, and handling of loss to follow-up and its potential impact on the estimates of the effect of treatment in randomised controlled trials. Design Systematic review. We calculated the percentage of trials for which the relative risk would no longer be significant under a number of assumptions about the outcomes of participants lost to follow-up. Data sources Medline search of five top general medical journals, 2005-07. Eligibility criteria Randomised controlled trials that reported a significant binary primary patient important outcome. Results Of the 235 eligible reports identified, 31 (13%) did not report whether or not loss to follow-up occurred. In reports that did give the relevant information, the median percentage of participants lost to follow-up was 6% (interquartile range 2-14%). The method by which loss to follow-up was handled was unclear in 37 studies (19%); the most commonly used method was survival analysis (66, 35%). When we varied assumptions about loss to follow-up, results of 19% of trials were no longer significant if we assumed no participants lost to follow-up had the event of interest, 17% if we assumed that all participants lost to follow-up had the event, and 58% if we assumed a worst case scenario (all participants lost to follow-up in the treatment group and none of those in the control group had the event). Under more plausible assumptions, in which the incidence of events in those lost to follow-up relative to those followed-up is higher in the intervention than control group, results of 0% to 33% trials were no longer significant. Conclusion Plausible assumptions regarding outcomes of patients lost to follow-up could change the interpretation of results of randomised controlled trials published in top medical journals.

Background: Subgroup analyses in randomized trials examine whether effects of interventions differ between subgroups of study populations according to characteristics of patients or interventions. However, findings from subgroup analyses may be misleading, potentially resulting in suboptimal clinical and health decision making. Few studies have investigated the reporting and conduct of subgroup analyses and a number of important questions remain unanswered. The objectives of this study are: 1) to describe the reporting of subgroup analyses and claims of subgroup effects in randomized controlled trials, 2) to assess study characteristics associated with reporting of subgroup analyses and with claims of subgroup effects, and 3) to examine the analysis, and interpretation of subgroup effects for each study's primary outcome. Methods: We will conduct a systematic review of 464 randomized controlled human trials published in 2007 in the 118 Core Clinical Journals defined by the National Library of Medicine. We will randomly select journal articles, stratified in a 1:1 ratio by higher impact versus lower impact journals. According to 2007 ISI total citations, we consider the New England Journal of Medicine, JAMA, Lancet, Annals of Internal Medicine, and BMJ as higher impact journals. Teams of two reviewers will independently screen full texts of reports for eligibility, and abstract data, using standardized, pilot-tested extraction forms. We will conduct univariable and multivariable logistic regression analyses to examine the association of pre-specified study characteristics with reporting of subgroup analyses and with claims of subgroup effects for the primary and any other outcomes. Discussion: A clear understanding of subgroup analyses, as currently conducted and reported in published randomized controlled trials, will reveal both strengths and weaknesses of this practice. Our findings will contribute to a set of recommendations to optimize the conduct and reporting of subgroup analyses, and claim and interpretation of subgroup effects in randomized trials.

Background: Incomplete ascertainment of outcomes in randomized controlled trials (RCTs) is likely to bias final study results if reasons for unavailability of patient data are associated with the outcome of interest. The primary objective of this study is to assess the potential impact of loss to follow-up on the estimates of treatment effect. The secondary objectives are to describe, for published RCTs, (1) the reporting of loss to follow-up information, (2) the analytic methods used for handling loss to follow-up information, and (3) the extent of reported loss to follow-up.Methods: We will conduct a systematic review of reports of RCTs recently published in five top general medical journals. Eligible RCTs will demonstrate statistically significant effect estimates with respect to primary outcomes that are patient-important and expressed as binary data. Teams of 2 reviewers will independently determine eligibility and extract relevant information from each eligible trial using standardized, pre-piloted forms. To assess the potential impact of loss to follow-up on the estimates of treatment effect we will, for varying assumptions about the outcomes of participants lost to follow-up (LTFU), calculate (1) the percentage of RCTs that lose statistical significance and (2) the mean change in effect estimate across RCTs. The different assumptions we will test are the following: (1) none of the LTFU participants had the event; (2) all LTFU participants had the event; (3) all LTFU participants in the treatment group had the event; none of those in the control group had it (worst case scenario); (4) the event incidence among LTFU participants (relative to observed participants) increased, with a higher relative increase in the intervention group; and (5) the event incidence among LTFU participants (relative to observed participants) increased in the intervention group and decreased in the control group.Discussion: We aim to make our objectives and methods transparent. The results of this study may have important implications for both clinical trialists and users of the medical literature.