Subgroup analyses in therapeutic cardiovascular clinical trials: are most of them misleading?

Reporting characteristics of non-primary publications of results of randomized trials: a cross-sectional review

Background

For a randomized trial, the primary publication is usually the one which reports the results of the primary outcome and provides consolidated data from all study centers. Other aspects of a randomized trial’s findings (that is, non-primary results) are often reported in subsequent publications.

Methods

We carried out a cross-sectional review of the characteristics and type of information reported in non-primary reports (n = 69) of randomized trials (indexed in PubMed core clinical journals in 2009) and whether they report pre-specified or exploratory analyses. We also compared consistency of information in non-primary publications with that reported in the primary publication.

Results

The majority (n = 56; 81%) of non-primary publications were large, multicenter trials, published in specialty journals. Most reported subgroup analyses (n = 27; 39%), analyzing a specific subgroup of patients from the randomized trial, or reported on secondary outcomes (n = 29; 42%); 19% (n = 13) reported extended follow-up. Less than half reported details of trial registration (n = 30; 43%) or the trial protocol (n = 27; 39%) and in 41% (n = 28) it was unclear from reading the abstract that the report was not the primary publication for the trial. Non-primary publications often analyzed and reported multiple different outcomes (16% reported >20 outcomes) and in 10% (n = 7) it was unclear how many outcomes had actually been assessed; in 42% (n = 29) it was unclear whether the analyses reported were pre-specified or exploratory. Only 39% (n = 27) of non-primary publications described the primary outcome of the randomized trial, 6% (n = 4) reported its numerical results and 9% (n = 6) details of how participants were randomized.

Conclusion

Non-primary publications often lack important information about the randomized trial and the type of analyses conducted and whether these were pre-specified or exploratory to enable readers to accurately identify and assess the validity and reliably of the study findings. We provide recommendations for what information authors should include in non-primary reports of randomized trials.

A systematic review of individual patient data meta-analyses on surgical interventions

BACKGROUND

Compared to subgroup analyses in a single study or in a traditional meta-analysis, an individual patient data meta-analysis (IPDMA) offers important potential advantages. We studied how many IPDMAs report on surgical interventions, how many of those surgical IPDMAs perform subgroup analyses, and whether these subgroup analyses have changed decision-making in clinical practice.

METHODS

Surgical IPDMAs were identified using a comprehensive literature search. The last search was conducted on 24 April 2012. For each IPDMA included, we obtained information using a standardized data extraction form, and the quality of reporting was assessed. We also checked whether results were implemented in clinical guidelines.

RESULTS

Of all 583 identified IPDMAs, 22 (4%) reported on a surgical intervention. Eighteen (82%) of these IPDMAs presented subgroup analyses. Subgroups were mainly based on patient and disease characteristics. The median number of reported subgroup analyses was 3.5 (IQR 1.25-6.5). Statistical methods for subgroup analyses were mentioned in 11 (61%) surgical IPDMAs.Eleven (61%) of the 18 IPDMAs performing subgroup analyses reported a significant overall effect estimate, whereas six (33%) reported a non-significant one. Of the IPDMAs that reported non-significant overall results, three IPDMAs (50%) reported significant results in one or more subgroup analyses. Results remained significant in one or more subgroups in eight of the IPDMAs (73%) that reported a significant overall result.Eight (44%) of the 18 significant subgroups appeared to be implemented in clinical guidelines. The quality of reporting among surgical IPDMAs varied from low to high quality.

CONCLUSION

Many of the surgical IPDMAs performed subgroup analyses, but overall treatment effects were more often emphasized than subgroup effects. Although, most surgical IPDMAs included in the present study have only recently been published, about half of the significant subgroups were already implemented in treatment guidelines.

Confronting diversity in the production of clinical evidence goes beyond merely including under-represented groups in clinical trials

There is increasing evidence that outcomes of health care differ by patient characteristics, such as gender and ethnicity. If evidence-based medicine is to improve quality of care for all patients, it is essential to take this diversity into account when designing clinical studies. So far, this notion has mainly been translated into recommendations for including minority populations in trials. We argue that a more comprehensive view of the production of diversity-sensitive clinical evidence is needed, one that takes heterogeneity as a starting point in research. We call for a mix of methodological approaches aimed at identifying diversity issues that matter and analysing the impact of these diversities on clinical outcomes. Institutional changes are necessary to support this methodological reform.

Randomized trials published in higher vs. lower impact journals differ in design, conduct, and analysis

OBJECTIVE

To compare methodological characteristics of randomized controlled trials (RCTs) published in higher vs. lower impact Core Clinical Journals.

STUDY DESIGN AND SETTING

We searched MEDLINE for RCTs published in 2007 in Core Clinical Journals. We randomly sampled 1,140 study reports in a 1:1 ratio in higher (five general medicine journals with the highest total citations in 2007) and lower impact journals.

RESULTS

Four hundred sixty-nine RCTs proved eligible: 219 in higher and 250 in lower impact journals. RCTs in higher vs. lower impact journals had larger sample sizes (median, 285 vs. 39), were more likely to receive industry funding (53% vs. 28%), declare concealment of allocation (66% vs. 36%), declare blinding of health care providers (53% vs. 41%) and outcome adjudicators (72% vs. 54%), report a patient-important primary outcome (69% vs. 50%), report subgroup analyses (64% vs. 26%), prespecify subgroup hypotheses (42% vs. 20%), and report a test for interaction (54% vs. 27%); P < 0.05 for all differences.

CONCLUSION

RCTs published in higher impact journals were more likely to report methodological safeguards against bias and patient-important outcomes than those published in lower impact journals. However, sufficient limitations remain such that publication in a higher impact journal does not ensure low risk of bias.

Consolidated Health Economic Evaluation Reporting Standards (CHEERS)--explanation and elaboration: a report of the ISPOR Health Economic Evaluation Publication Guidelines Good Reporting Practices Task Force

BACKGROUND

Economic evaluations of health interventions pose a particular challenge for reporting because substantial information must be conveyed to allow scrutiny of study findings. Despite a growth in published reports, existing reporting guidelines are not widely adopted. There is also a need to consolidate and update existing guidelines and promote their use in a user-friendly manner. A checklist is one way to help authors, editors, and peer reviewers use guidelines to improve reporting.

OBJECTIVE

The task force's overall goal was to provide recommendations to optimize the reporting of health economic evaluations. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement is an attempt to consolidate and update previous health economic evaluation guidelines into one current, useful reporting guidance. The CHEERS Elaboration and Explanation Report of the ISPOR Health Economic Evaluation Publication Guidelines Good Reporting Practices Task Force facilitates the use of the CHEERS statement by providing examples and explanations for each recommendation. The primary audiences for the CHEERS statement are researchers reporting economic evaluations and the editors and peer reviewers assessing them for publication.

METHODS

The need for new reporting guidance was identified by a survey of medical editors. Previously published checklists or guidance documents related to reporting economic evaluations were identified from a systematic review and subsequent survey of task force members. A list of possible items from these efforts was created. A two-round, modified Delphi Panel with representatives from academia, clinical practice, industry, and government, as well as the editorial community, was used to identify a minimum set of items important for reporting from the larger list.

RESULTS

Out of 44 candidate items, 24 items and accompanying recommendations were developed, with some specific recommendations for single study-based and model-based economic evaluations. The final recommendations are subdivided into six main categories: 1) title and abstract, 2) introduction, 3) methods, 4) results, 5) discussion, and 6) other. The recommendations are contained in the CHEERS statement, a user-friendly 24-item checklist. The task force report provides explanation and elaboration, as well as an example for each recommendation. The ISPOR CHEERS statement is available online via Value in Health or the ISPOR Health Economic Evaluation Publication Guidelines Good Reporting Practices - CHEERS Task Force webpage (http://www.ispor.org/TaskForces/EconomicPubGuidelines.asp).

CONCLUSIONS

We hope that the ISPOR CHEERS statement and the accompanying task force report guidance will lead to more consistent and transparent reporting, and ultimately, better health decisions. To facilitate wider dissemination and uptake of this guidance, we are copublishing the CHEERS statement across 10 health economics and medical journals. We encourage other journals and groups to consider endorsing the CHEERS statement. The author team plans to review the checklist for an update in 5 years.

SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials

High quality protocols facilitate proper conduct, reporting, and external review of clinical trials. However, the completeness of trial protocols is often inadequate. To help improve the content and quality of protocols, an international group of stakeholders developed the SPIRIT 2013 Statement (Standard Protocol Items: Recommendations for Interventional Trials). The SPIRIT Statement provides guidance in the form of a checklist of recommended items to include in a clinical trial protocol. This SPIRIT 2013 Explanation and Elaboration paper provides important information to promote full understanding of the checklist recommendations. For each checklist item, we provide a rationale and detailed description; a model example from an actual protocol; and relevant references supporting its importance. We strongly recommend that this explanatory paper be used in conjunction with the SPIRIT Statement. A website of resources is also available (www.spirit-statement.org). The SPIRIT 2013 Explanation and Elaboration paper, together with the Statement, should help with the drafting of trial protocols. Complete documentation of key trial elements can facilitate transparency and protocol review for the benefit of all stakeholders.

An assessment of the reporting quality of randomised controlled trials relating to anti-arrhythmic agents (2002-2011)

BACKGROUND

Despite being the gold-standard for investigations, randomised controlled trials can deliver biased results if methodology is flawed. The CONSORT statements are intended to guide the reporting of trials. We assessed the reporting quality of anti-arrhythmic drug trials over the last decade.

METHODS

Medline and Embase databases were searched for anti-arrhythmic drug trials between 2002 and 2011. Results were searched by two authors and relevant papers selected. Papers were scored according to the 2001 and 2010 CONSORT statements by two reviewers and compared against surrogate markers of paper quality.

RESULTS

694 papers were retrieved. 59 papers met the inclusion criteria. The mean CONSORT 2010 score was 15.4 out of 25 (SD 3.05). The least reported items related to abstract content (0%), randomization (6.8%), and protocol referencing (8.5%). There was a significant correlation between the CONSORT 2010 score and the annual and 5-year impact factors of the publishing journal (R=0.44 and R=0.45 respectively; p<0.001 for both). No significant correlation was found between the year of publication or number of authors, and 2010 CONSORT score.

CONCLUSIONS

Although several papers gained high scores, no paper successfully met all criteria laid out in either the CONSORT 2001 or 2010 statements. Correlation between CONSORT 2010 score and impact factor lends support to this as a marker for paper quality. The lack of reporting clarity found, indicates that application of the CONSORT guidelines remains incomplete within the cardiology literature. Further work is needed collectively by trial groups, funding agencies, authors, and journals to improve reporting.

[Beautification of data: Minimal fraud, incompetence or mixture of both]

The beautification of data is the process of reporting results of a research in a way that does not correspond to reality, in order to present them in a more favorable or attractive way. The border between errors due to methodological ignorance, embellishment of reality and fraud is sometimes difficult to determine. It is the intentional nature and the repetition of these "arrangements" that are the real switch to deliberate fraud. The emergence of regulatory procedures, such as clinical trial registries, "reporting guidelines", improvement of "peer review" and data sharing, are some of the measures used to fight against and improve transparency of clinical research.

Rudiments of subgroup analyses

Subgroup analysis in a clinical trial is the evaluation of the effect of a randomly allocated intervention within only a fraction of the patients in the entire research cohort. This article provides several examples of the use of subgroup analysis, discusses some of the interpretative difficulties that occur during the assessment of the effect of therapy within subgroups, and provides a summary of recent recommendations on reporting subgroup analyses in the literature. Although subgroup analyses can provide new, provocative, and sometimes clinically relevant findings, this group of evaluations must be handled with extreme care.

Secondary analysis of clinical trials--a cautionary note

There is concern in published reports and reviews that patients are being harmed or denied effective treatment by the use of questionable results from secondary analyses of data from clinical trials. A well-reported secondary analysis must make clear to the reader the uncertainty of the result--so clear, in fact, that it should be an obvious part of the conclusions that implementation should await confirmation as the primary outcome in an adequately powered trial. Those who write, review and publish these reports have a responsibility to ensure that reports accurately describe the sources of uncertainty, explain complex methods and their weaknesses with clarity, and convince readers to require better evidence before changing their practice.

Impact of diabetes mellitus on the safety and effectiveness of bivalirudin in patients with acute myocardial infarction undergoing primary angioplasty: analysis from the HORIZONS-AMI (Harmonizing Outcomes with RevasculariZatiON and Stents in Acute Myocardial Infarction) trial

OBJECTIVES

We sought to evaluate the safety and efficacy of bivalirudin compared with glycoprotein IIb/IIIa inhibitors (GPI) in diabetic patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PCI).

BACKGROUND

Prior studies have demonstrated that GPI are especially beneficial in patients with diabetes with acute coronary syndromes and/or those undergoing PCI.

METHODS

In the multicenter, prospective HORIZONS-AMI (Harmonizing Outcomes with RevasculariZatiON and Stents in Acute Myocardial Infarction) trial, 3,602 patients with STEMI were randomized to bivalirudin or unfractionated heparin plus a GPI. Clinical outcomes were analyzed at 30 days and 1 year in patients with diabetes.

RESULTS

Diabetes mellitus was present in 593 patients (16.5%). The rates of cardiac death were significantly lower in diabetic patients treated with bivalirudin compared with heparin plus GPI (30 days: 2.1% vs. 5.5%, p = 0.04; 1 year: 2.5% vs. 7.1%, p = 0.01), and bivalirudin resulted in lower 30-day rates of stroke (0% vs. 2%, p = 0.02). There were no significant differences among diabetic patients randomized to bivalirudin versus heparin plus GPI in the 1-year rates of major adverse cardiac events (14.2% vs. 16.2%, p = 0.44), major bleeding (8.7% vs. 10.7%, p = 0.42), or stent thrombosis (4.2% vs. 3.8%, p = 0.85). By interaction testing, the relative effects of bivalirudin compared with heparin plus GPI were not significantly different in patients with and without diabetes.

CONCLUSIONS

In patients with diabetes mellitus presenting with STEMI undergoing primary PCI, anticoagulant therapy with bivalirudin compared with heparin plus GPI is safe and effective and might reduce cardiac mortality at 30 days and 1 year. (Harmonizing Outcomes With Revascularization and Stents in Acute Myocardial Infarction; NCT00433966).

Identifying patient subgroups who benefit most from a treatment: using administrative claims data to uncover treatment heterogeneity

OBJECTIVES

To illustrate how claims data can be used to (1) develop outcome scores that predict response to a traditional treatment and (2) estimate the economic impact of individualized assignment to a newer treatment based on the outcome score. An example application is based on two treatments for attention deficit hyperactivity disorder (ADHD): osmotic-release oral system methylphenidate (OROS-MPH) and lisdexamfetamine dimesylate (LDX).

METHODS

Adolescents with ADHD initiating OROS-MPH (n=6320) or LDX (n=6394) were selected from the MarketScan claims database. A model was developed for predicting risk of switching/augmentation with OROS-MPH using multiple baseline characteristics. The model was applied to an independent sample to stratify patients by their predicted risk and, within each stratum, risk of switching/augmentation and ADHD-related total costs were compared between OROS-MPH and LDX patients using inverse probability of treatment weighting.

RESULTS

The prediction model resulted in substantial stratification, showing risk of switching/augmentation with OROS-MPH ranging from 11.3-42.1%. In the two strata where OROS-MPH had highest risk of switching/augmentation, LDX had significantly lower risk of switching/augmentation than OROS-MPH (by 7.0-8.2%) and lower ADHD-related annual total costs (by $264-$625 per patient).

LIMITATIONS

The current study has used the risk of switching/augmentation as a proxy measure for treatment efficacy to establish the prediction model. Future research using a clinical measure for ADHD symptoms is warranted to verify the findings.

CONCLUSIONS

Combining multiple patient characteristics into a predicted score for treatment outcomes with a traditional treatment can help identify subgroups of patients who benefit most from a new treatment. In this analysis, ADHD patients with a high predicted score for switching/augmentation with OROS-MPH had a lower rate of switching/augmentation with LDX. Assigning OROS-MPH and LDX treatments based on the predicted scores that are heterogeneous in a patient population may help improve clinical outcomes and the cost-effectiveness of care.

Baseline matters: the importance of covariation for baseline severity in the analysis of clinical trials

BACKGROUND

Clinical trials testing the effectiveness of interventions for addictions, HIV transmission risk, and other behavioral health problems are important to advancing evidence-based treatment. Such trials are expensive and time-consuming to conduct, but the underlying effect sizes tend to be modest, and often findings are disappointing, failing to show evidence of treatment effects.

OBJECTIVES

To demonstrate how appropriate covariation for baseline severity can enhance detection of treatment effects.

METHODS

Explication and case example.

RESULTS

Baseline severity (the score of the outcome measure at baseline, prior to randomization) is often strongly associated with outcome in such studies. Covariation for baseline score may enhance detection of treatment effects, because the variance explained by the baseline score is removed from the error variance in the estimate of the difference in outcome between treatments. Alternatively, the effect of treatment may manifest in the form of a baseline-by-treatment interaction. Common interaction patterns include that treatment may be more effective among patients with higher levels of baseline severity, or treatment may be more effective among patients with low severity at baseline ('relapse prevention' effect). Such effects may be important to developing treatment guidelines and offer clues toward understanding the mechanisms of action of treatments and of the disorders.

CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE

This article illustrates principles of covariation for baseline and the baseline-by-treatment interaction in nontechnical graphical terms, and discusses examples from clinical trials. Implications for the design and analysis of clinical trials are discussed, and it is argued that covariation for baseline severity of the outcome measure and testing of the baseline-by-treatment interaction should be considered for inclusion in the primary outcome analyses of treatment effectiveness trials of substantial size.

Secondary use of randomized controlled trials to evaluate drug safety: a review of methodological considerations

Background Randomized clinical trials (RCTs) are often positioned at the top of evidence hierarchies. Meta-analyses of RCTs aim to integrate the state of knowledge on a given scientific question, particularly for rare drug-related outcomes. However, although RCTs are valuable tools in our armamentarium, they are rarely designed to evaluate drug safety and are thus susceptible to limitations that may hamper the ability of both RCTs and meta-analyses to fully characterize the safety profiles of drugs. Their potential limitations might be exacerbated in the study of rare outcomes, often encountered in drug safety assessment, when even minor deviations from the intended randomization could impact the stability of the risk estimates.

Purpose This article considers the methodological caveats of both RCTs and meta-analyses of RCTs pertinent to the study of drug-related harms. It is intended to stimulate discussion about the impact of these caveats on interpreting findings of RCTs and meta-analyses for drug safety, which would foster more robust, critical evaluations, and thus enhance clinical and regulatory decision-making.

Methods Pertinent issues that can influence the interpretation of drug-related harms discussed in this article were based on authors’ expertise and review of the literature.

Results Investigators and clinicians should be cognizant of the potential limitations of the secondary use of RCTs and meta-analyses in the assessment of drug-related harms and, when applicable, should consider potential remedies to overcome these limitations.

Limitations Only few practical examples are included in the article due to the fact that many of the discussed caveats are not examined and/or reported in many publications. In addition, the confidential nature of data reviewed at a regulatory agency forestalls an in depth discussion of examples pertaining to specific drugs. Furthermore, our ability to quantify the extent of encountering, or the actual impact of, the caveats addressed in this review on the RCTs findings is limited. It is worth noting that the mere encounter of a given caveat does not mean that it will obviate the utility of drug safety information from a given trial. The extent of its impact is expected to vary based on the specifics of the trial, the drugs studied, the indications, and the nature of the adverse events.

Conclusions Although some of the limitations described are inherent in RCTs, some of the sources of bias highlighted in this article could be minimized by careful RCT design, planned follow-up, and improved collection of information on adverse events. As future research sheds more light on pertinent knowledge gaps and issues, the ability to maximize the use of RCTs and meta-analyses of RCTs to address drug safety questions of interest will be greatly enhanced.

A comparison of subgroup analyses in grant applications and publications

In this paper, the authors compare subgroup analyses as outlined in grant applications and their related publications. Grants awarded by the Netherlands Organization for Health Research and Development (ZonMw) from 2001 onward that were finalized before March 1, 2010, were studied. Of the 79 grant proposals, 50 (63%) were intervention studies, 18 (23%) were diagnostic studies, and 6 (8%) were prognostic studies. Subgroups were mentioned in 49 (62%) grant applications and in 53 (67%) publications. In 20 of the 79 projects (25%), the publications were completely in agreement with the grant proposal; that is, subgroups that were prespecified in the grant proposal were reported and no new subgroup analyses were introduced in the publications. Of the 149 prespecified subgroups, 46 (31%) were reported in the final report or scientific publications, and 143 of the 189 (76%) reported subgroups were based on post-hoc findings. For 77% of the subgroup analyses in the publications, there was no mention of whether these were prespecified or post hoc. Justification for subgroup analysis and methods to study subgroups were rarely reported. The authors conclude that there is a large discrepancy between grant applications and final publications regarding subgroup analyses. Both nonreporting prespecified subgroup analyses and reporting post-hoc subgroup analyses are common. More guidance is clearly needed.

Examining the evidence: a systematic review of the inclusion and analysis of older adults in randomized controlled trials

BACKGROUND

Due to a shortage of studies focusing on older adults, clinicians and policy makers frequently rely on clinical trials of the general population to provide supportive evidence for treating complex, older patients.

OBJECTIVES

To examine the inclusion and analysis of complex, older adults in randomized controlled trials.

REVIEW METHODS

A PubMed search identified phase III or IV randomized controlled trials published in 2007 in JAMA, NEJM, Lancet, Circulation, and BMJ. Therapeutic interventions that assessed major morbidity or mortality in adults were included. For each study, age eligibility, average age of study population, primary and secondary outcomes, exclusion criteria, and the frequency, characteristics, and methodology of age-specific subgroup analyses were reviewed.

RESULTS

Of the 109 clinical trials reviewed in full, 22 (20.2%) excluded patients above a specified age. Almost half (45.6%) of the remaining trials excluded individuals using criteria that could disproportionately impact older adults. Only one in four trials (26.6%) examined outcomes that are considered highly relevant to older adults, such as health status or quality of life. Of the 42 (38.5%) trials that performed an age-specific subgroup analysis, fewer than half examined potential confounders of differential treatment effects by age, such as comorbidities or risk of primary outcome. Trials with age-specific subgroup analyses were more likely than those without to be multicenter trials (97.6% vs. 79.1%, p < 0.01) and funded by industry (83.3% vs. 62.7%, p < 0.05). Differential benefit by age was found in seven trials (16.7%).

CONCLUSION

Clinical trial evidence guiding treatment of complex, older adults could be improved by eliminating upper age limits for study inclusion, by reducing the use of eligibility criteria that disproportionately affect multimorbid older patients, by evaluating outcomes that are highly relevant to older individuals, and by encouraging adherence to recommended analytic methods for evaluating differential treatment effects by age.

High prevalence of potential biases threatens the interpretation of trials in patients with chronic disease

BACKGROUND

The complexity of chronic diseases is a challenge for investigators conducting randomized trials. The causes for this include the often difficult control for confounding, the selection of outcomes from many potentially important outcomes, the risk of missing data with long follow-up and the detection of heterogeneity of treatment effects. Our aim was to assess such aspects of trial design and analysis for four prevalent chronic diseases.

METHODS

We included 161 randomized trials on drug and non-drug treatments for chronic obstructive pulmonary disease, type 2 diabetes mellitus, stroke and heart failure, which were included in current Cochrane reviews. We assessed whether these trials defined a single outcome or several primary outcomes, statistically compared baseline characteristics to assess comparability of treatment groups, reported on between-group comparisons, and we also assessed how they handled missing data and whether appropriate methods for subgroups effects were used.

RESULTS

We found that only 21% of all chronic disease trials had a single primary outcome, whereas 33% reported one or more primary outcomes. Two of the fifty-one trials that tested for statistical significance of baseline characteristics adjusted the comparison for a characteristic that was significantly different. Of the 161 trials, 10% reported a within-group comparison only; 17% (n = 28) of trials reported how missing data were handled (50% (n = 14) carried forward last values, 27% (n = 8) performed a complete case analysis, 13% (n = 4) used a fixed value imputation and 10% (n = 3) used more advanced methods); and 27% of trials performed a subgroup analysis but only 23% of them (n = 10) reported an interaction test. Drug trials, trials published after wide adoption of the CONSORT (CONsolidated Standards of Reporting Trials) statement (2001 or later) and trials in journals with higher impact factors were more likely to report on some of these aspects of trial design and analysis.

CONCLUSION

Our survey showed that an alarmingly large proportion of chronic disease trials do not define a primary outcome, do not use appropriate methods for subgroup analyses, or use naïve methods to handle missing data, if at all. As a consequence, biases are likely to be introduced in many trials on widely prescribed treatments for patients with chronic disease.

Random treatment assignment using mathematical equipoise for comparative effectiveness trials

In controlled clinical trials, random assignment of treatment is appropriate only when there is equipoise, that is, no clear preference among treatment options. However, even when equipoise appears absent because prior trials show, on average, one treatment yields superior outcomes, random assignment still may be appropriate for some patients and circumstances. In such cases, enrollment into trials may be assisted by real-time patient-specific predictions of treatment outcomes, to determine whether there is equipoise to justify randomization. The percutaneous coronary intervention thrombolytic predictive instrument (PCI-TPI) computes probabilities of 30-day mortality for patients having ST elevation myocardial infarction (STEMI), if treated with thrombolytic therapy (TT), and if treated with PCI. We estimated uncertainty around differences in their respective predicted benefits using the estimated uncertainty of the model coefficients. Using the 2,781-patient PCI-TPI development dataset, we evaluated the distribution of predicted benefits for each patient. For three typical clinical situations, randomization was potentially warranted for 70%, 93%, and 80% of patients. Predictive models may allow real-time patient-specific determination of whether there is equipoise that justifies trial enrollment for a given patient. This approach may have utility for comparative effectiveness trials and for application of trial results to clinical practice.

Randomized comparison of everolimus- and paclitaxel-eluting stents. 2-year follow-up from the SPIRIT (Clinical Evaluation of the XIENCE V Everolimus Eluting Coronary Stent System) IV trial

OBJECTIVES

We sought to determine whether the differences in outcomes present between everolimus-eluting stents (EES) and paclitaxel-eluting stents (PES) in the SPIRIT (Clinical Evaluation of the XIENCE V Everolimus Eluting Coronary Stent System) IV trial at 1 year were sustained with longer-term follow-up.

BACKGROUND

In the SPIRIT IV trial, patients undergoing percutaneous coronary intervention who were randomized to EES compared with PES experienced lower 1-year rates of target lesion failure (cardiac death, target vessel myocardial infarction [MI], or ischemia-driven target lesion revascularization [TLR]), with significant reductions in the individual rates of MI, TLR, and stent thrombosis.

METHODS

We prospectively randomized 3,687 patients with up to 3 noncomplex previously untreated native coronary artery lesions to EES versus PES at 66 U.S. sites. Follow-up through 2 years is complete in 3,578 patents (97.0%).

RESULTS

Treatment with EES compared with PES reduced the 2-year rates of TLF (6.9% vs. 9.9%, p = 0.003), all MI (2.5% vs. 3.9%, p = 0.02), Q-wave MI (0.1% vs. 0.8%, p = 0.002), stent thrombosis (0.4% vs. 1.2%, p = 0.008), and ischemia-driven TLR (4.5% vs. 6.9%, p = 0.004), with nonsignificantly different rates of all-cause and cardiac mortality. Between 1 year and 2 years, there were no significant differences in adverse event rates between the 2 stent types.

CONCLUSIONS

In the large-scale, prospective, multicenter, randomized SPIRIT IV trial, the benefits of EES compared with those of PES present at 1 year were sustained at 2 years.

The influence of study characteristics on reporting of subgroup analyses in randomised controlled trials: systematic review

OBJECTIVE

To investigate the impact of industry funding on reporting of subgroup analyses in randomised controlled trials.

DESIGN

Systematic review.

DATA SOURCES

Medline.

STUDY SELECTION

Randomised controlled trials published in 118 core clinical journals (defined by the National Library of Medicine) in 2007. 1140 study reports in a 1:1 ratio by high (five general medicine journals with largest number of total citations in 2007) versus lower impact journals, were randomly sampled. Two reviewers, independently and in duplicate, used standardised, piloted forms to screen study reports for eligibility and to extract data. They also used explicit criteria to determine whether a randomised controlled trial reported subgroup analyses. Logistic regression was used to examine the association of prespecified study characteristics with reporting versus not reporting of subgroup analyses.

RESULTS

469 randomised controlled trials were included, of which 207 (44%) reported subgroup analyses. High impact journals (adjusted odds ratio 2.64, 95% confidence interval 1.62 to 4.33), non-surgical (versus surgical) trials (2.10, 1.26 to 3.50), and larger sample size (3.38, 1.64 to 6.99) were associated with more frequent reporting of subgroup analyses. The strength of association between trial funding and reporting of subgroups differed in trials with and without statistically significant primary outcomes (interaction P=0.02). In trials without statistically significant results for the primary outcome, industry funded trials were more likely to report subgroup analyses (2.29, 1.30 to 4.72) than non-industry funded trials. This was not true for trials with a statistically significant primary outcome (0.79, 0.46 to 1.36). Industry funded trials were associated with less frequent prespecification of subgroup hypotheses (31.3% v 38.0%, adjusted odds ratio 0.49, 0.26 to 0.94), and less use of the interaction test for analyses of subgroup effects (41.4% v 49.1%, 0.52, 0.28 to 0.97) than non-industry funded trials.

CONCLUSION

Industry funded randomised controlled trials, in the absence of statistically significant primary outcomes, are more likely to report subgroup analyses than non-industry funded trials. Industry funded trials less frequently prespecify subgroup hypotheses and less frequently test for interaction than non-industry funded trials. Subgroup analyses from industry funded trials with negative results for the primary outcome should be viewed with caution.

The problem of subgroup analyses: an example from a trial on ruptured intracranial aneurysms

The randomized ISAT demonstrated the superiority of endovascular treatment in patients with ruptured intracranial aneurysms considered suitable for either clipping or coiling. A later publication proposed a second look at the results, demonstrating that older patients with ruptured MCA aneurysms appeared to benefit from clipping, in disagreement with the general findings of the trial. Subgroup analyses in randomized trials and observational studies examine whether effects of interventions differ between subgroups according to the characteristics of patients. However, many apparent subgroup effects have been shown to be spurious. Misleading subgroup effects can result in withholding efficacious treatment from patients who would benefit or can encourage ineffective or potentially harmful treatments for patients who would fare better without. Some guidelines for the prudent interpretation of subgroup findings are reviewed.

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.

The REFLECT statement: reporting guidelines for randomized controlled trials in livestock and food safety: explanation and elaboration

Concerns about the completeness and accuracy of reporting of randomized clinical trials (RCTs) and the impact of poor reporting on decision-making have been documented in the medical field over the past several decades. Experience from RCTs in human medicine would suggest that failure to report critical trial features can be associated with biased estimated effect measures, and there is evidence to suggest similar biases occur in RCTs conducted in livestock populations. In response to these concerns, standardized guidelines for reporting RCTs were developed and implemented in human medicine. The Consolidated Standards of Reporting Trials (CONSORT) statement was first published in 1996 with a revised edition published in 2001. The CONSORT statement consists of a 22-item checklist for reporting a RCT and a flow diagram to follow the number of participants at each stage of a trial. An explanation and elaboration document not only defines and discusses the importance of each of the items, but also provides examples of how this information could be supplied in a publication. Differences between human and livestock populations necessitate modifications to the CONSORT statement to maximize its usefulness for RCTs involving livestock. These have been addressed in an extension of the CONSORT statement titled the REFLECT statement: Methods and processes of creating reporting guidelines for randomized control trials for livestock and food safety. The modifications made for livestock trials specifically addressed the common use of group housing and group allocation to intervention in livestock studies, the use of a deliberate challenge model in some trials, and common use of non-clinical outcomes, such as contamination with a foodborne pathogen. In addition, the REFLECT statement for RCTs in livestock populations proposed specific terms or further clarified terms as they pertained to livestock studies.

Subgroup Analysis of Trials Is Rarely Easy (SATIRE): a study protocol for a systematic review to characterize the analysis, reporting, and claim of subgroup effects in randomized trials

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.

Impact of chronic kidney disease on early (30-day) and late (1-year) outcomes of patients with acute coronary syndromes treated with alternative antithrombotic treatment strategies: an ACUITY (Acute Catheterization and Urgent Intervention Triage strategY) substudy

OBJECTIVES

In this substudy of the ACUITY (Acute Catheterization and Urgent Intervention Triage strategY) trial, we investigated the relationship between chronic kidney disease (CKD) and clinical outcomes, and compared the safety and efficacy of bivalirudin monotherapy versus heparin plus a glycoprotein IIb/IIIa inhibitor (GPI).

BACKGROUND

CKD is an important predictor of prognosis in the general population. The outcomes of patients with CKD and acute coronary syndromes (ACS) have not been well studied.

METHODS

In the ACUITY study, 13,819 patients with moderate- and high-risk ACS undergoing an early, invasive strategy were randomly assigned to 1 of 3 antithrombin regimens: a heparin plus a GPI, bivalirudin plus a GPI, or bivalirudin monotherapy. CKD (creatinine clearance <60 ml/min) was present in 2,469 (19.1%) of 12,939 randomized patients with baseline creatinine clearance data.

RESULTS

Patients with CKD had worse 30-day and 1-year clinical outcomes than those with normal renal function. There were no significant differences between bivalirudin monotherapy and heparin plus a GPI in rates of 30-day composite ischemia (11.1% vs. 9.4%, p = 0.27) and net clinical adverse outcomes (16.1% vs. 16.9%, p = 0.65). There was remarkably less major bleeding (6.2% vs. 9.8%, p = 0.008) at 30 days, but no significant difference in 1-year composite ischemia (22.0% vs. 18.9%, p = 0.10) or mortality (7.1% vs. 7.3%, p = 0.96).

CONCLUSIONS

In patients with ACS, CKD is associated with higher 30-day and 1-year adverse event rates. Compared with heparin plus a GPI, the use of bivalirudin monotherapy in patients with CKD results in nonstatistically different ischemic outcomes, but significantly less 30-day major bleeding.

Dealing with heterogeneity of treatment effects: is the literature up to the challenge?

Background

Some patients will experience more or less benefit from treatment than the averages reported from clinical trials; such variation in therapeutic outcome is termed heterogeneity of treatment effects (HTE). Identifying HTE is necessary to individualize treatment. The degree to which heterogeneity is sought and analyzed correctly in the general medical literature is unknown. We undertook this literature sample to track the use of HTE analyses over time, examine the appropriateness of the statistical methods used, and explore the predictors of such analyses.

Methods

Articles were selected through a probability sample of randomized controlled trials (RCTs) published in Annals of Internal Medicine, BMJ, JAMA, The Lancet, and NEJM during odd numbered months of 1994, 1999, and 2004. RCTs were independently reviewed and coded by two abstractors, with adjudication by a third. Studies were classified as reporting: (1) HTE analysis, utilizing a formal test for heterogeneity or treatment-by-covariate interaction, (2) subgroup analysis only, involving no formal test for heterogeneity or interaction; or (3) neither. Chi-square tests and multiple logistic regression were used to identify variables associated with HTE reporting.

Results

319 studies were included. Ninety-two (29%) reported HTE analysis; another 88 (28%) reported subgroup analysis only, without examining HTE formally. Major covariates examined included individual risk factors associated with prognosis, responsiveness to treatment, or vulnerability to adverse effects of treatment (56%); gender (30%); age (29%); study site or center (29%); and race/ethnicity (7%). Journal of publication and sample size were significant independent predictors of HTE analysis (p < 0.05 and p < 0.001, respectively).

Conclusion

HTE is frequently ignored or incorrectly analyzed. An iterative process of exploratory analysis followed by confirmatory HTE analysis will generate the data needed to facilitate an individualized approach to evidence-based medicine.

Outcome based subgroup analysis: a neglected concern

BACKGROUND

A subgroup of clinical trial subjects identified by baseline characteristics is a proper subgroup while a subgroup determined by post randomization events or measures is an improper subgroup. Both types of subgroups are often analyzed in clinical trial papers. Yet, the extensive scrutiny of subgroup analyses has almost exclusively attended to the former. The analysis of improper subgroups thereby not only flourishes in numerous disguised ways but also does so without a corresponding awareness of its pitfalls. Comparisons of the grade of angina in a heart disease trial, for example, usually include only the survivors. This paper highlights some of the distinct ways in which outcome based subgroup analysis occurs, describes the hazards associated with it, and proposes a simple alternative approach to counter its analytic bias.

RESULTS

Data from six published trials show that outcome based subgroup analysis, like proper subgroup analysis, may be performed in a post-hoc fashion, overdone, selectively reported, and over interpreted. Six hypothetical trial scenarios illustrate the forms of hidden bias related to it. That bias can, however, be addressed by assigning clinically appropriate scores to the usually excluded subjects and performing an analysis that includes all the randomized subjects.

CONCLUSION

A greater level of awareness about the practice and pitfalls of outcome based subgroup analysis is needed. When required, such an analysis should maintain the integrity of randomization. This issue needs greater practical and methodologic attention than has been accorded to it thus far.

Evaluation of heterogeneity in pharmacotherapy trials for drug dependence: a Bayesian approach

AIMS

Difficulty identifying effective pharmacotherapies for cocaine dependence has led to suggestions that subgroup differences may account for some of the heterogeneity in treatment response. Well-attested methodological difficulties associated with these analyses recommend the use of Bayesian statistical reasoning for evaluation of salient interaction effects.

METHODS

A secondary data analysis of a previously published, double-blind, randomized controlled trial examines the interaction of decision-making, as measured by the Iowa Gambling Task, and citalopram in increasing longest sustained abstinence from cocaine use.

RESULTS

Bayesian analysis indicated that there was a 99% chance that improved decision-making enhances response to citalopram. Given the strong positive nature of this finding, a formal, quantitative Bayesian approach to evaluate the result from the perspective of a skeptic was applied.

CONCLUSIONS

Bayesian statistical reasoning provides a formal means of weighing evidence for the presence of an interaction in scenarios where conventional, Frequentist analyses may be less informative. [Supplementary materials are available for this article. Go to the publisher's online edition of The American Journal of Drug and Alcohol Abuse for the following free supplemental resource: Appendix 1].

Primer: the fallacy of subgroup analysis

The identification of subgroups of patients from randomized clinical trials that are of specific interest for guiding clinical decisions can be an attractive idea; however, since such trials are designed for the comparison of groups of patients, performing subgroup analyses can result in misinterpretation of the data. Such analyses must, therefore, be performed and evaluated with caution: these should be pre-planned and included in the design of a suitably powered trial. Data obtained should be analyzed using formal statistical tests of interaction on proper subgroups rather than improper subgroups of patients, the results obtained should be delineated carefully, and details of how these analyses were performed, and how the data should be interpreted, should be reported in the trial paper. The caveats associated with this approach, such as the occurrence of false positive or false negative effects, chance differences in observed effects, lack of power to perform the analysis, floor or ceiling effects, issues relating to multiple statistical testing, and over-reporting and under-reporting are discussed in this review. Subgroup analyses can, however, provide valuable, albeit predominantly exploratory, information on which to base clinical decisions if they are performed in accordance with recommendations and guidelines, and do, therefore, have a legitimate place in rheumatology clinical trials.

Bivalirudin in patients with acute coronary syndromes undergoing percutaneous coronary intervention: a subgroup analysis from the Acute Catheterization and Urgent Intervention Triage strategy (ACUITY) trial

BACKGROUND

The aim of this study was to assess anticoagulation with the direct thrombin inhibitor bivalirudin during percutaneous coronary intervention in individuals with moderate and high-risk acute coronary syndromes.

METHODS

13,819 individuals in the Acute Catheterization and Urgent Intervention Triage strategy (ACUITY) trial were prospectively randomly assigned to receive heparin (unfractionated or enoxaparin) plus glycoprotein IIb/IIIa inhibitors, bivalirudin plus glycoprotein IIb/IIIa inhibitors, or bivalirudin alone. Of these individuals, 7789 underwent percutaneous coronary intervention after angiography. The effect of the three regimens on the primary 30-day endpoints of composite ischaemia (death, myocardial infarction, or unplanned revascularisation for ischaemia), major bleeding, and net clinical outcomes (composite ischaemia or major bleeding) was assessed in this subgroup. Analyses were done by intention to treat. This trial is registered with ClinicalTrials.gov, with the number NCT00093158.

FINDINGS

Of the individuals who underwent percutaneous coronary intervention, 2561 received heparin plus glycoprotein IIb/IIIa inhibitors, 2609 received bivalirudin plus glycoprotein IIb/IIIa inhibitors, and 2619 received bivalirudin alone. 26 (0.3%) individuals dropped out or were lost to follow-up. There was no significant difference in the proportion of individuals with composite ischaemia, major bleeding, or net clinical outcomes at 30 days between those who received bivalirudin plus glycoprotein IIb/IIIa inhibitors and those who received heparin plus glycoprotein IIb/IIIa inhibitors (composite ischaemia: 243 [9%] patients vs 210 [8%] patients, p=0.16; major bleeding: 196 [8%] patients vs 174 [7%] patients, p=0.32; net clinical outcomes: 389 [15%] patients vs 341 [13%] patients, p=0.1). Rates of composite ischaemia were much the same in those who received bivalirudin alone and those who received heparin plus glycoprotein IIb/IIIa inhibitors (230 [9%] patients vs 210 [8%] patients, p=0.45); however, there were significantly fewer individuals who experienced major bleeding among those who received bivalirudin alone than among those who received heparin plus glycoprotein IIb/IIIa inhibitors (92 [4%] patients vs 174 [7%] patients, p<0.0001, relative risk 0.52, 95% CI 0.40-0.66), resulting in a trend towards better 30-day net clinical outcomes (303 [12%] patients vs 341 [13%] patients, p=0.057; 0.87, 0.75-1.00).

INTERPRETATION

Substitution of unfractionated heparin or enoxaparin with bivalirudin results in comparable clinical outcomes in patients with moderate and high-risk acute coronary syndromes treated with glycoprotein IIb/IIIa inhibitors in whom percutaneous coronary intervention is done. Anticoagulation with bivalirudin alone suppresses adverse ischaemic events to a similar extent as does heparin plus glycoprotein IIb/IIIa inhibitors, while significantly lowering the risk of major haemorrhagic complications.

Analyse d'un essai thérapeutique

Subgroup analyses examine the effect of a treatment regimen on different subgroups in the overall population. If the subgroups are not comparable, differences between them cannot be attributed to the treatment alone, and analyses can only be considered as exploratory. When, however, strata are defined before randomization and subjects are randomly allocated to treatment groups within each stratum, analysis can be conducted within these subgroups and differences between them can be shown to be caused by the treatment. Intermediate analyses, by multiplying the number of statistical tests, increase the risk of rejecting the null hypothesis when it is in fact true. Accordingly, specific statistical methods should be used to compensate for this risk. Composite outcome criteria combine several different elements, all of which must be clinically relevant. Unequal treatments effects on the various criteria may complicate interpretation of the results.

A New and Rapid Scoring System to Assess the Scientific Evidence from Clinical Trials

Guidelines are based on a scientific analysis from existing data of randomized controlled trials (RCTs), registry trials, simple registries, case reports, and the personal experience of the task force members. Furthermore, meta-analyses and subgroup analyses are used to derive the strengths of recommendations. Fortunately, the major cardiac societies, i.e., the American College of Cardiology (ACC), the American Heart Association (AHA), and the European Society of Cardiology (ESC), are essentially using the same definitions for the levels of recommendations. In the expanding field of cardiology, however, the overwhelming and increasing number of clinical studies reveals the limitations of the traditional ranking of these studies: Applying the standard definitions of the ACC/AHA/ESC criteria for the levels of recommendation, almost every PCI procedure would easily reach the level of evidence A, even with two small, underpowered studies and a surrogate endpoint. Although meta-analyses are important tools for creating an overview of major diagnostic or treatment modalities, they are bound to severe limitations. The compilation of several underpowered, small trials can generate a statistically artificial "significant" result. This is especially important because only two meta-analyses containing almost identical studies could easily yield an evidence level A. RCTs are usually designed and conducted according to a power calculation, for which a primary clinical or surrogate endpoint can be chosen. Surrogate endpoints, however, do not necessarily correlate with the clinical outcome. The history of medicine is full of errors introduced by underpowered studies with surrogate endpoints. Many investigators and companies attempt to tease out treatment effects in specific subpopulations of patients. These subgroup analyses are usually underpowered. Another major limitation of the ACC/AHA/ESC scoring system is that neither the power of a study nor the choice of a primary clinical endpoint is included in their definitions. Yet another limitation of the ACC/AHA/ESC grading system is that two "simple" registries may already lead to a level of evidence B. A new scoring system is presented addressing most of these limitations: a primary clinical endpoint receives three points, whereas all of the following receive one point: double-blind design, evaluation interval of primary endpoint > or = 6 months, multicenter (at least three centers), independent data and safety monitoring, power of > or = 80% for primary endpoint achieved, and follow-up > or = 80% for a surrogate primary endpoint or follow-up of > or = 95% for a clinical primary endpoint. Thus, the maximum achievable points is 10. This scoring system can also be applied for high-quality registry controlled trials using a predefined control group and power calculation. For simple registry studies and subgroup analyses, a modified scoring system has been developed (maximum achievable points is 5). The advantage of the suggested new scoring system is its transparency, reproducibility, and ease of use by quickly answering the key quality questions for clinical trials. The new scoring system suggested here should help make decisions regarding which treatment to use and stimulate discussions.

Bivalirudin for patients with acute coronary syndromes

BACKGROUND

Current guidelines for patients with moderate- or high-risk acute coronary syndromes recommend an early invasive approach with concomitant antithrombotic therapy, including aspirin, clopidogrel, unfractionated or low-molecular-weight heparin, and glycoprotein IIb/IIIa inhibitors. We evaluated the role of thrombin-specific anticoagulation with bivalirudin in such patients.

METHODS

We assigned 13,819 patients with acute coronary syndromes to one of three antithrombotic regimens: unfractionated heparin or enoxaparin plus a glycoprotein IIb/IIIa inhibitor, bivalirudin plus a glycoprotein IIb/IIIa inhibitor, or bivalirudin alone. The primary end points were a composite ischemia end point (death, myocardial infarction, or unplanned revascularization for ischemia), major bleeding, and the net clinical outcome, defined as the combination of composite ischemia or major bleeding.

RESULTS

Bivalirudin plus a glycoprotein IIb/IIIa inhibitor, as compared with heparin plus a glycoprotein IIb/IIIa inhibitor, was associated with noninferior 30-day rates of the composite ischemia end point (7.7% and 7.3%, respectively), major bleeding (5.3% and 5.7%), and the net clinical outcome end point (11.8% and 11.7%). Bivalirudin alone, as compared with heparin plus a glycoprotein IIb/IIIa inhibitor, was associated with a noninferior rate of the composite ischemia end point (7.8% and 7.3%, respectively; P=0.32; relative risk, 1.08; 95% confidence interval [CI], 0.93 to 1.24) and significantly reduced rates of major bleeding (3.0% vs. 5.7%; P<0.001; relative risk, 0.53; 95% CI, 0.43 to 0.65) and the net clinical outcome end point (10.1% vs. 11.7%; P=0.02; relative risk, 0.86; 95% CI, 0.77 to 0.97).

CONCLUSIONS

In patients with moderate- or high-risk acute coronary syndromes who were undergoing invasive treatment with glycoprotein IIb/IIIa inhibitors, bivalirudin was associated with rates of ischemia and bleeding that were similar to those with heparin. Bivalirudin alone was associated with similar rates of ischemia and significantly lower rates of bleeding. (ClinicalTrials.gov number, NCT00093158 [ClinicalTrials.gov].).