British journal of anaesthesia
-
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.
-
The pathophysiological mechanisms by which venous congestion and hypotension lead to acute adverse kidney events after cardiac surgery with cardiopulmonary bypass have not been elucidated. We tested the hypothesis that intraoperative hypotension and venous congestion are associated with acute kidney injury and acute kidney disease. ⋯ Although both venous congestion and intraoperative hypotension are associated with acute kidney injury, only venous congestion correlates with acute kidney disease among patients undergoing cardiac surgery requiring cardiopulmonary bypass. The reported associations are suggestive of a pathophysiological role of venous congestion in acute kidney disease.