Journal of clinical monitoring and computing
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J Clin Monit Comput · Oct 2019
Clinical TrialDevelopment and validation of an android-based application for anaesthesia neuromuscular monitoring.
Quantitative neuromuscular block (NMB) assessment is an internationally recognised necessity in anesthesia care whenever neuromuscular blocking agents are administered. Despite this, the incidence of residual neuromuscular block and its associated major respiratory morbidity and mortality remain unacceptably high considering its preventable nature. Recent surveys show that quantitative NMB assessment is not consistently employed by anesthesiologists. ⋯ This average inter-method difference was not significantly different than the a priori hypothesized difference cut-off of 0.001 (p = 0.78). Lin's concordance correlation coefficient and Pearson's correlation were both of 0.98. The custom developed Android application proved accurate for diagnosis of residual neuromuscular block.
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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
A retrospective evaluation of the risk of bias in perioperative temperature metrics.
The prevention and treatment of hypothermia is an important part of routine anesthesia care. Avoidance of perioperative hypothermia was introduced as a quality metric in 2010. We sought to assess the integrity of the perioperative hypothermia metric in routine care at a single large center. ⋯ Provider-entered temperatures exhibit values that are unlikely to represent a normal probability distribution around a central physiologic value. Manually-entered perioperative temperatures appear to cluster around salient anchoring values, either deliberately, or as an unintended result driven by cognitive bias. Automatically-acquired temperatures may be superior for quality metric purposes.
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J Clin Monit Comput · Oct 2019
Closed-loop vasopressor control: in-silico study of robustness against pharmacodynamic variability.
Initial feasibility of a novel closed-loop controller created by our group for closed-loop control of vasopressor infusions has been previously described. In clinical practice, vasopressor potency may be affected by a variety of factors including other pharmacologic agents, organ dysfunction, and vasoplegic states. The purpose of this study was therefore to evaluate the effectiveness of our controller in the face of large variations in drug potency, where 'effective' was defined as convergence on target pressure over time. ⋯ Wobble was below 3% and divergence remained negative (i.e. the controller tended to converge towards the target over time) in all norepinephrine response levels, but at the highest response level of 10 × the value approached zero, suggesting the controller may be approaching instability. Response levels of 0.1 × and 0.2 × exhibited significantly higher time-out-of-target in the lower ranges (p < 0.001) compared to the 1 × response level as the controller was slower to correct the initial hypotension. In this simulation study, the closed-loop vasopressor controller remained effective in simulated patients exhibiting 0.1 to 10 × the expected population drug response.