British journal of anaesthesia
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Editorial Comment Review
Case duration prediction and estimating time remaining in ongoing cases.
In this issue of the British Journal of Anaesthesia, Jiao and colleagues applied a neural network model for surgical case durations to predict the operating room times remaining for ongoing anaesthetics. We review estimation of case durations before each case starts, showing why their scientific focus is useful. ⋯ Most cases have few or no historical data for the scheduled procedures. Generalizability of observational results such as theirs, and automatic computer assisted clinical and managerial decision-making, are both facilitated by using structured vocabularies when analysing surgical procedures.
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Multicenter Study
Multicentre analysis of practice patterns regarding benzodiazepine use in cardiac surgery.
There is controversy regarding optimal use of benzodiazepines during cardiac surgery, and it is unknown whether and to what extent there is variation in practice. We sought to describe benzodiazepine use and sources of variation during cardiac surgeries across patients, clinicians, and institutions. ⋯ Two-thirds of the variation in benzodiazepine administration during cardiac surgery are associated with institutions and attending anaesthesiology clinicians (consultants). These data, showing wide variations in administration, suggest that rigorous research is needed to guide evidence-based and patient-centred benzodiazepine administration.
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New onset atrial fibrillation (NOAF) is the most common arrhythmia affecting critically unwell patients. NOAF can lead to worsening haemodynamic compromise, heart failure, thromboembolic events, and increased mortality. The aim of this systematic review and narrative synthesis is to evaluate the non-pharmacological and pharmacological management strategies for NOAF in critically unwell patients. ⋯ PROSPERO CRD42019121739.
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Continuous real-time prediction of surgical case duration using a modular artificial neural network.
Real-time prediction of surgical duration can inform perioperative decisions and reduce surgical costs. We developed a machine learning approach that continuously incorporates preoperative and intraoperative information for forecasting surgical duration. ⋯ A real-time neural network model using preoperative and intraoperative data had significantly better performance than a Bayesian approach or scheduled duration, offering opportunities to avoid overtime labour costs and reduce the cost of surgery by providing superior real-time information for perioperative decision support.