Journal of clinical monitoring and computing
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J Clin Monit Comput · Oct 2019
Multicenter Study Comparative Study Clinical Trial Observational StudyMindray 3-directional NMT Module (a new generation "Tri-axial" neuromuscular monitor) versus the Relaxometer mechanomyograph and versus the TOF-Watch SX acceleromyograph.
Recently introduced Mindray "3-directional" neuromuscular transmission transducer (NMT, Shenzhen, China) acceleromyograph) claim to monitor thumb movement in 3 different directions. We compared NMT with the gold standard Relaxometer® mechanomyograph (MMG, Groningen University, Netherlands) in Study-1 and with TOF-Watch SX™ (WTCH) acceleromyograph from which it was developed in Study-2. We used first twitch (T1%) and train-of-four (TOF) ratio rocuronium 0.6 mg kg-1 neuromuscular block to evaluate NMT diagnostic accuracy in indicating 3 clinically relevant time points namely; MMG T1 5% (95% twitch depression) for tracheal intubation, MMG T1 25% for repeat neuromuscular blocking agents (NMBAs) administration, and MMG 0.9 TOF ratio full neuromuscular block recovery. ⋯ NMT could not efficaciously detect MMG time for tracheal intubation; NMBAs repeat dose administration or full neuromuscular block recovery. Data from NMT cannot be used interchangeably with MMG. Our study revealed that NMT Tri-axial acceleromyography seems to offer no advantage over the MMG gold standard or the classic Mono-axial TOF-Watch SX monitor.
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J Clin Monit Comput · Oct 2019
ReviewApplying machine learning to continuously monitored physiological data.
The use of machine learning (ML) in healthcare has enormous potential for improving disease detection, clinical decision support, and workflow efficiencies. In this commentary, we review published and potential applications for the use of ML for monitoring within the hospital environment. We present use cases as well as several questions regarding the application of ML to the analysis of the vast amount of complex data that clinicians must interpret in the realm of continuous physiological monitoring. ⋯ Finally, innovations in monitoring, including those supported by ML, will pose regulatory and medico-legal challenges, as well as questions regarding precisely how to incorporate these features into clinical care and medical education. Rigorous evaluation of ML techniques compared to traditional methods or other AI methods will be required to validate the algorithms developed with consideration of database limitations and potential learning errors. Demonstration of value on processes and outcomes will be necessary to support the use of ML as a feature in monitoring system development: Future research is needed to evaluate all AI based programs before clinical implementation in non-research settings.
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J Clin Monit Comput · Oct 2019
Randomized Controlled Trial Comparative StudyCricoid-mental distance-based versus weight-based criteria for size selection of classic laryngeal mask airway in adults: a randomized controlled study.
The optimal size selection of laryngeal mask airway (LMA) based on body weight is not always applicable. This study was prospectively conducted to evaluate the efficacy of cricoid-mental distance-based method versus weight-based method in optimal size selection of LMA in adults. Seventy-four patients (aged from 18 to 65) undergoing ophthalmic surgery were randomly assigned into cricoid-mental (CM) distance-based group or weight-based group to select appropriate size of LMA. ⋯ The overall success rate of LMA insertion in CM distance-based group was slightly increased in comparison with the weight-based group (100% vs. 91.9%, P = 0.240). There were no significant differences in score of fiber-optic view and postoperative pharyngolaryngeal morbidity between both groups (all P > 0.05). CM distance-based criteria is an alternative choice for optimizing size selection of classic LMA in adults.