ArticleBMC anesthesiology2017
Development of an algorithm using clinical tests to avoid post-operative residual neuromuscular block.
Article in BMC anesthesiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 6 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Development of an Algorithm Using Clinical Tests to Avoid Post-operative Residual Neuromuscular Block
Evaluation of the NeuroMuscular Junction Using the Single Fiber Electromyography and Reliability of Train-Of-Four in Critically Ill Patients.
Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Intraoperative Monitoring of Neuromuscular Blockade.Life (Basel, Switzerland) · 2023Review
- Association of neuromuscular reversal by sugammadex and neostigmine with 90-day mortality after non-cardiac surgery.BMC anesthesiology · 2020Article
- [Algorithm-based preventive strategies for avoidance of residual neuromuscular blocks].Der Anaesthesist · 2019Review
- Neuromuscular Block and Blocking Agents in 2018.Turkish journal of anaesthesiology and reanimation · 2018Article
- Neuromuscular monitoring: an update.Romanian journal of anaesthesia and intensive care · 2018Review
- [Deep neuromuscular blockade : Benefits and risks].Der Anaesthesist · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundQuantitative 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.
methodsAfter 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.
resultsAfter 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 (100% vs. 42.9%) and <0.9 (89.7% vs. 34.5%) compared to the algorithm. However, due to the high sensitivity of the algorithm (100% vs. 94.4%), the risk to overlook an awake patient with a train-of-four ratio < 0.7 was minimal. Tactile judgement of TOF fading showed poorest sensitivity and specifity at train of four ratio < 0.9 (33.7%, 0%) and <0.7 (18.8%, 16.7%).
conclusionsResidual neuromuscular blockade can be detected by uncalibrated acceleromyography and if not available by a pathway of four clinical muscle function tests in awake patients. The algorithm has a discriminative power comparable to uncalibrated AMG within TOF-values >0.7 and <0.3.
trial registrationClinical Trials.gov (principal investigator's name: CU, and identifier: NCT03219138) on July 8, 2017.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.