Critical Care Centre, King's College London, London, United Kingdom; School of Immunology & Microbial Sciences, King's College London, London, United Kingdom.
Service de Médecine Intensive Réanimation, Faculté de Médecine Sorbonne Université, Hôpital Pitié Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France.
Chest. 2022 Nov;162(5):1048-1062. doi: 10.1016/j.chest.2022.05.018. Epub 2022 May 26.
Indeterminate randomized controlled trials (RCTs) in ARDS may arise from sample size misspecification, leading to abandonment of efficacious therapies.
If evidence exists for sample size misspecification in ARDS RCTs, has this led to rejection of potentially beneficial therapies? Does evidence exist for prognostic enrichment in RCTs using mortality as a primary outcome?
We identified 150 ARDS RCTs commencing recruitment after the 1994 American European Consensus Conference ARDS definition and published before October 31, 2020. We examined predicted-observed sample size, predicted-observed control event rate (CER), predicted-observed average treatment effect (ATE), and the relationship between observed CER and observed ATE for RCTs with mortality and nonmortality primary outcome measures. To quantify the strength of evidence, we used Bayesian-averaged meta-analysis, trial sequential analysis, and Bayes factors.
Only 84 of 150 RCTs (56.0%) reported sample size estimations. In RCTs with mortality as the primary outcome, CER was overestimated in 16 of 28 RCTs (57.1%). To achieve predicted ATE, interventions needed to prevent 40.8% of all deaths, compared with the original prediction of 29.3%. Absolute reduction in mortality ≥ 10% was observed in 5 of 28 RCTs (17.9%) but was predicted in 21 of 28 RCTs (75.0%). For RCTs with mortality as the primary outcome, no association was found between observed CER and observed ATE (pooled OR: β = -0.04; 95% credible interval, -0.18 to 0.09). We identified three interventions that are not currently standard of care with a Bayesian-averaged effect size of > 0.20 and moderate strength of existing evidence: corticosteroids, airway pressure release ventilation, and noninvasive ventilation.
Reporting of sample size estimations was inconsistent in ARDS RCTs, and misspecification of CER and ATE was common. Prognostic enrichment strategies in ARDS RCTs based on all-cause mortality are unlikely to be successful. Bayesian methods can be used to prioritize interventions for future effectiveness RCTs.
ARDS 中的不确定随机对照试验（RCT）可能是由于样本量指定不当导致的，这可能会导致有效的治疗方法被放弃。
如果 ARDS RCT 中存在样本量指定不当的证据，这是否导致了潜在有益疗法的被拒绝？使用死亡率作为主要结局的 RCT 中是否存在预后富集的证据？
我们确定了 150 项自 1994 年美国欧洲共识会议 ARDS 定义以来开始招募并于 2020 年 10 月 31 日之前发表的 ARDS RCT。我们检查了预测观察到的样本量、预测观察到的对照组事件率（CER）、预测观察到的平均治疗效果（ATE）以及具有死亡率和非死亡率主要结局的 RCT 中观察到的 CER 与观察到的 ATE 之间的关系。为了量化证据的强度，我们使用了贝叶斯平均荟萃分析、试验序贯分析和贝叶斯因子。
只有 150 项 RCT 中的 84 项（56.0%）报告了样本量估计值。在以死亡率为主要结局的 RCT 中，28 项 RCT 中有 16 项（57.1%）的 CER 被高估。为了达到预测的 ATE，干预措施需要预防所有死亡的 40.8%，而最初的预测值为 29.3%。5 项 RCT（17.9%）观察到死亡率绝对降低≥10%，但 21 项 RCT（75.0%）预测到了。对于以死亡率为主要结局的 RCT，观察到的 CER 与观察到的 ATE 之间没有关联（汇总 OR：β= -0.04；95%可信区间，-0.18 至 0.09）。我们发现三种目前不是标准治疗方法的干预措施具有大于 0.20 的贝叶斯平均效应大小和现有的中等强度证据：皮质类固醇、气道压力释放通气和无创通气。
ARDS RCT 中报告样本量估计值不一致，且 CER 和 ATE 指定不当的情况很常见。基于全因死亡率的 ARDS RCT 中的预后富集策略不太可能成功。贝叶斯方法可用于为未来的有效性 RCT 确定干预措施的优先级。
