Acad Emerg Med. 2021 Jun 16. doi: 10.1111/acem.14312. Online ahead of print.
ABSTRACT
OBJECTIVES: To conduct an umbrella review of therapeutic studies relevant to emergency medicine, analyzing patterns in effect size, power and signals of potential bias across an entire field of clinical research.
METHODS: We combined topic and journal-driven searches of PUBMED and Google Scholar for published articles of systematic reviews and meta-analyses relevant to emergency medicine (last search in November 2020). Data were screened and extracted by 6 investigators. Redundant meta-analyses were removed. Whenever possible for each comparison we extracted one meta-analysis on mortality with the most events, and one meta-analysis on a non-mortality outcome with the most studies. From each meta-analysis we extracted all individual study effects; outcomes were converted to odds ratios and placed on a common scale where an odds ratio <1.0 represents a reduction in a harmful outcome with an experimental treatment versus control. Outcomes were analyzed at the level of individual studies and at the level of summary effects across meta-analyses.
RESULTS: 332 articles contained 431 eligible meta-analyses with a total of 3129 individual study outcomes; of these, 2593 (83%) were from randomized controlled trials. The median odds ratio across all studies was 0.70. Within each meta-analysis, the earliest study effect on average demonstrated larger benefit compared to the overall summary effect. Only 57 of 431 meta-analyses (13%) both favored the experimental intervention and did not show any signal of small study effects or excess significance, and of those only 12 had at least one study with 80% or higher power to detect an odds ratio of 0.70. Of these, no interventions significantly decreased mortality in well-powered trials. Although the power of studies increased somewhat over time, the majority of studies were underpowered.
CONCLUSIONS: Few interventions studied within systematic reviews and meta-analyses relevant to emergency medicine seem to have strong and unbiased evidence for improving outcomes. The field would benefit from more optimally powered trials.
PMID:34133813 | DOI:10.1111/acem.14312
