Development of an Algorithm Using Clinical Tests to Avoid Post-operative Residual Neuromuscular Block
Development of an Algorithm Using Clinical Tests to Avoid Post-operative Residual Neuromuscular Block
Objective neuromuscular monitoring is the gold standard to detect postoperative residual curarization (PORC). Many anesthesiologist just use qualitative neuromuscular monitoring or unreliable, clinical tests. Goal of this study is to develop an algorithm of muscle function tests to identify PORC
Background: Quantitative neuromuscular monitoring is the gold standard to detect postoperative residual curarization (PORC). Many anesthesiologists, however, use insensitive, qualitative neuromuscular monitoring or unreliable, clinical tests. Goal of this multicentre, prospective, double-blinded, assessor controlled study is to develop an algorithm of muscle function tests to identify PORC.
Methods: After extubation a blinded anesthetist performs eight clinical tests in 165 patients. Test results are correlated to calibrated electromyography train-of-four (TOF) ratio and to a postoperatively applied uncalibrated acceleromyography. A classification and regression tree (CART) is calculated developing the algorithm to identify PORC. This is validated against uncalibrated acceleromyography and tactile judgement of TOF fading in separate 100 patients.
Inclusion Criteria: The patients were scheduled for elective low risk surgical procedures:
Exclusion Criteria:
Kiel, 24105, Germany
Mainz, 55131, Germany
Münster, 48149, Germany
Reutlingen, 72764, Germany