Glasziou, Paul

Abstract Objective Risk scores and accelerated diagnostic protocols can identify chest pain patients with low risk of major adverse cardiac event who could be discharged early from the ED , saving time and costs. We aimed to derive and validate a chest pain score and accelerated diagnostic protocol ( ADP ) that could safely increase the proportion of patients suitable for early discharge. Methods Logistic regression identified statistical predictors for major adverse cardiac events in a derivation cohort. Statistical coefficients were converted to whole numbers to create a score. Clinician feedback was used to improve the clinical plausibility and the usability of the final score (Emergency Department Assessment of Chest pain Score [ EDACS ]). EDACS was combined with electrocardiogram results and troponin results at 0 and 2 h to develop an ADP ( EDACS‐ADP ). The score and EDACS‐ADP were validated and tested for reproducibility in separate cohorts of patients. Results In the derivation ( n = 1974) and validation ( n = 608) cohorts, the EDACS‐ADP classified 42.2% (sensitivity 99.0%, specificity 49.9%) and 51.3% (sensitivity 100.0%, specificity 59.0%) as low risk of major adverse cardiac events, respectively. The intra‐class correlation coefficient for categorisation of patients as low risk was 0.87. Conclusion The EDACS‐ADP identified approximately half of the patients presenting to the ED with possible cardiac chest pain as having low risk of short‐term major adverse cardiac events, with high sensitivity. This is a significant improvement on similar, previously reported protocols. The EDACS‐ADP is reproducible and has the potential to make considerable cost reductions to health systems.

We review controversies associated with randomized controlled trials (RCTs) stopped early for apparent benefit (truncated RCTs or tRCTs) and present our groups’ perspective. Long-established theory, simulations and recent empirical evidence demonstrate that tRCTs will on average overestimate treatment effects, and this overestimation may be large, particularly when tRCTs have small number of events. Theoretical considerations and simulations demonstrate that on average, meta-analyses of RCTs with appropriate stopping rules will lead to only trivial overestimation of treatment effects. However, tRCTs will disproportionally contribute to meta-analytic estimates when tRCTs occur early in the sequence of trials with few subsequent studies, publication of nontruncated RCTs is delayed, there is publication bias, or tRCTs result in a ‘freezing’ effect in which ‘correcting’ trials are never undertaken. To avoid applying overestimates of effect to clinical decision-making, clinicians should view the results of individual tRCTs with small sample sizes and small number of events with skepticism. Pooled effects from meta-analyses including tRCTs are likely to overestimate effect when there is a substantial difference in effect estimates between the tRCTs and the nontruncated RCTs, and in which the tRCTs have a substantial weight in the meta-analysis despite themselves having a relatively small number of events. Such circumstances call for sensitivity analyses omitting tRCTs.