British journal of anaesthesia
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Review Meta Analysis
Preoperative inflammatory mediators and postoperative delirium: systematic review and meta-analysis.
Postoperative delirium has eluded attempts to define its complex aetiology and describe specific risk factors. The role of neuroinflammation as a risk factor, determined by measuring blood levels of preoperative 'innate' inflammatory mediator levels, has been investigated. However, results have been conflicting. We conducted a systematic review and meta-analysis of the evidence on associations between preoperative blood levels of inflammatory mediators and postoperative delirium in the older person. Influence of type of surgery was also assessed. ⋯ CRD42019159471 (PROSPERO).
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Review Meta Analysis
Preoperative inflammatory mediators and postoperative delirium: systematic review and meta-analysis.
Postoperative delirium has eluded attempts to define its complex aetiology and describe specific risk factors. The role of neuroinflammation as a risk factor, determined by measuring blood levels of preoperative 'innate' inflammatory mediator levels, has been investigated. However, results have been conflicting. We conducted a systematic review and meta-analysis of the evidence on associations between preoperative blood levels of inflammatory mediators and postoperative delirium in the older person. Influence of type of surgery was also assessed. ⋯ CRD42019159471 (PROSPERO).
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Multicentre RCTs are widely used by critical care researchers to answer important clinical questions. However, few trials evaluating mortality outcomes report statistically significant results. We hypothesised that the low proportion of trials reporting statistically significant differences for mortality outcomes is plausibly explained by lower-than-expected effect sizes combined with a low proportion of participants who could realistically benefit from studied interventions. ⋯ When designing clinical trials, researchers most likely overestimate true population effect sizes for critical care interventions. Bayesian modelling demonstrates that that it is not necessarily the case that most studied interventions lack efficacy. In fact, it is plausible that many studied interventions have clinically important effects that are missed.