Anaesthesia
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Randomized Controlled Trial
The association between epidural labour analgesia and postpartum depression: a randomised controlled trial.
There is conflicting evidence regarding the association between epidural labour analgesia and risk of postpartum depression. Most previous studies were observational trials with limited ability to account for confounders. We aimed to determine if epidural analgesia was associated with a significant change in the incidence of postpartum depression in this randomised controlled trial. ⋯ There were no significant differences in the incidence of postpartum depression between the two groups (adjusted risk difference (95%CI) 1.6 (-3.0-6.3%), p = 0.49). Similar results were obtained with per-protocol analysis (adjusted risk difference (95%CI) -1.0 (-8.3-6.3%), p = 0.79). We found no significant difference in the risk of postpartum depression between patients who received epidural labour analgesia and those who utilised non-epidural analgesic modalities.
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The 7th National Audit Project (NAP7) of the Royal College of Anaesthetists studied peri-operative cardiac arrest including those that occurred in the independent healthcare sector, which provides around 1 in 6 NHS-funded care episodes. In total, 174 (39%) of 442 independent hospitals contacted agreed to participate. A survey examining provider preparedness for cardiac arrest had a response rate of 23 (13%), preventing useful analysis. ⋯ Independent sector outcomes were similar to those in the NHS, though due to the case mix, improved outcomes might be anticipated. Assessment of quality of care was less often favourable for independent sector reports than NHS reports, though assessments were often uncertain, reflecting poor quality reports. Overall, NAP7 is unable to determine whether peri-operative care relating to cardiac arrest is more, equally or less safe than in the NHS.
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Complications are common following major surgery and are associated with increased use of healthcare resources, disability and mortality. Continued reliance on mortality estimates risks harming patients and health systems, but existing tools for predicting complications are unwieldy and inaccurate. We aimed to systematically construct an accurate pre-operative model for predicting major postoperative complications; compare its performance against existing tools; and identify sources of inaccuracy in predictive models more generally. ⋯ This novel model showed substantially superior performance over generic and specific prediction models and scores. We have developed a novel complications model with good internal accuracy, re-prioritised predictor variables and identified hospital-level variation as an important, but overlooked, source of inaccuracy in existing tools. The complexity of the best-performing model does, however, highlight the need for a step-change in clinical risk prediction to automate the delivery of informative risk estimates in clinical systems.