Development of an algorithm using clinical tests to avoid post-operative residual neuromuscular block

Abstract

Abstract 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 was to develop an algorithm of muscle function tests to identify PORC. Methods After extubation a blinded anesthetist performed eight clinical tests in 165 patients. Test results were correlated to calibrated electromyography train-of-four (TOF) ratio and to a postoperatively applied uncalibrated acceleromyography. A classification and regression tree (CART) was calculated developing the algorithm to identify PORC. This was validated against uncalibrated acceleromyography and tactile judgement of TOF fading in separate 100 patients. Results After eliminating three tests with poor correlation, a model with four tests (r = 0.844) and uncalibrated acceleromyography (r = 0.873) were correlated to electromyographical TOF-values without losing quality of prediction. CART analysis showed that three consecutively performed tests (arm lift, head lift and swallowing or eye opening) can predict electromyographical TOF. Prediction coefficients reveal an advantage of the uncalibrated acceleromyography in terms of specificity to identify the EMG measured train-of-four ratio 0.7 and <0.3. Trial registration Clinical Trials.gov (principal investigator’s name: CU, and identifier: NCT03219138) on July 8, 2017.

Keywords

• Postoperative residual curarization
• electromyography
• acceleromyography
• algorithm
• clinical muscle function tests