Journal of clinical monitoring and computing
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J Clin Monit Comput · Oct 2019
Observational StudyThe focus of temperature monitoring with zero-heat-flux technology (3M Bair-Hugger): a clinical study with patients undergoing craniotomy.
In the noninvasive zero-heat-flux (ZHF) method, deep body temperature is brought to the skin surface when an insulated temperature probe with servo-controlled heating on the skin creates a region of ZHF from the core to the skin. The sensor of the commercial Bair-Hugger ZHF device is placed on the forehead. According to the manufacturer, the sensor reaches a depth of 1-2 cm below the skin. ⋯ In Bland-Altman analysis, the agreement of ZHF temperature with the nasopharyngeal temperature was 0.11 (95% confidence interval - 0.54 to 0.75) °C and with the bladder temperature - 0.14 (- 0.81 to 0.52) °C. As conclusions, within the reported range of the Bair-Hugger ZHF measurement depth, the anatomical focus of the sensor cannot be determined. Craniotomy did not have a detectable effect on the course of the ZHF temperatures that showed good agreement with the nasopharyngeal and bladder temperatures.
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J Clin Monit Comput · Oct 2019
Comparative StudyHead-to-head comparison of two continuous glucose monitoring systems on a cardio-surgical ICU.
In critical illness hypo-and hyperglycemia have a negative influence on patient outcome. Continuous glucose monitoring (CGM) could help in early detection of hypo-and hyperglycemia. A requirement for these new methods is an acceptable accuracy and precision in clinical practice. ⋯ The Bland Altman Plots revealed an accuracy of 2.5 mg/dl, and a precision of + 43.0 mg/dl to - 38.0 mg/dl (subcutaneous sensor) and an accuracy of - 6.0 mg/dl, and a precision of + 12.4 mg/dl to - 24.4 mg/dl (intravasal sensor). No severe hypoglycemic event, defined as BG level below 40 mg/dl, occurred during treatment. Both sensors showed good accuracy in comparison to the BGA values, however they differ regarding precision, which in case of the subcutaneous sensor is considerable high.
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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 Observational StudyObservational study of newborn infant parasympathetic evaluation as a comfort system in awake patients admitted to a pediatric intensive care unit.
To compare the newborn infant parasympathetic evaluation system (NIPE) scores with a validated clinical scale using two different nebulizers in children with bronchiolitis admitted to a PICU. Comfort was evaluated using the COMFORT-behavior scale (CBS) before (T1), during (T2) and after (T3) each nebulization. In order to compare NIPE and CBS values during the whole T1 to T3 period, the variable Dif-CBS was defined as the difference between maximal and minimal CBS scores, and Dif-NIPE as the difference between 75th and 25th percentile NIPE values. ⋯ NIPE monitoring detected no significant differences between both nebulization systems (P = 0.706). NIPE monitoring showed a variation in comfort during nebulization in the patient with bronchiolitis, though correlation with CBS was poor. Further research is required before NIPE can be suggested as a comfort monitoring system for the awake infant.