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Meta-analyses of randomized controlled trials (RCTs) used to be considered as the optimum evidence to guide clinical practices. Generally, a high-quality meta-analysis with conclusive information should meet the minimum requirements of a well-conducted RCT, which includes prospective protocol development, limitation of bias, and adequate sample size [1]. Conversely, meta-analyses based on limited RCTs may trigger the potential overestimation of the authentic intervention effect owing to weak statistical power [1]. More interestingly, increasing studies indicated that pooled results with false positive were frequently existed in published meta-analyses including many Cochrane ones [2, 3]. Trial sequential analysis (TSA) was introduced to monitor potential random error, false positive, and false negative in meta-analyses of RCTs [3]. Moreover, it was recommended that TSA should be performed to assess the “imprecision” of outcomes of interest in the Grading of Recommendations Assessment, Development and Evaluation (GRADE) [4]. A recent meta-analysis indicated that carbonic anhydrase inhibitor (CAI) may have a positive effect on respiratory failure and metabolic alkalosis [5]. Considering that limited trials with small information size included in the study, we assumed that the effect of CAI for respiratory failure and metabolic alkalosis may be overestimated. Subsequently, we performed TSA for one of outcomes (i.e. PaCO2) with the most included RCTs to estimate whether the evidence is enough reliable and credible. TSA in Fig. 1 showed that the cumulative Z-curve did not cross the trial sequential monitoring boundary for benefit and the required information size boundary, which suggested that the current evidence (the positive effect of CAI on PaCO2) was inconclusive.

Trial sequential analysis suggested the potential overestimated effect of carbonic anhydrase inhibitor for respiratory failure and metabolic alkalosis