Background And Aims: Entropy monitoring entails measurement of the effect of anesthetic on its target organ rather than merely the concentration of anesthetic in the brain (indicated by alveolar concentration based on which minimum alveolar concentration [MAC] is displayed). We proposed this prospective randomised study to evaluate the effect of entropy monitoring on isoflurane consumption and anesthesia recovery period.
Material And Methods: Sixty patients undergoing total abdominal hysterectomy under general anesthesia using an endotracheal tube were enrolled in either clinical practice (CP) or entropy (E) group. In group CP, isoflurane was titrated as per clinical parameters and MAC values, while in Group E, it was titrated to entropy values between 40 and 60. Data including demographics, vital parameters, alveolar isoflurane concentration, MAC values, entropy values, and recovery profile were recorded in both groups.
Results: Demographic data and duration of surgery were comparable. Time to eye opening on command and time to extubation (mean ± standard deviation) were significantly shorter, in Group E (6.6 ± 3.66 and 7.27 ± 4.059 min) as compared to Group CP (9.77 ± 5.88 and 11.63 ± 6.90 min), respectively. Mean isoflurane consumption (ml/h) was 10.81 ± 2.08 in Group E and 11.45 ± 2.24 in Group CP and was not significantly different between the groups. Time to readiness to recovery room discharge and postanesthesia recovery scores were also same in both groups.
Conclusion: Use of entropy monitoring does not change the amount of isoflurane consumed during maintenance of anesthesia or result in clinically significant faster recovery.
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http://dx.doi.org/10.4103/0970-9185.222523 | DOI Listing |
Sci Rep
December 2024
Harman International, HarmanX Neurosense, 30001 Cabot Dr, Novi, MI, 48377, USA.
Cognitive load (CL) is one of the leading factors moderating states and performance among drivers. Heavily increased CL may contribute to the development of mental stress. Averaged heart rate (HR) and heart rate variability (HRV) indices are shown to reflect CL levels in different tasks.
View Article and Find Full Text PDFCrit Care
December 2024
Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Background: Entropy quantifies the level of disorder within a system. Low entropy reflects increased rigidity of homeostatic feedback systems possibly reflecting failure of protective physiological mechanisms like cerebral autoregulation. In traumatic brain injury (TBI), low entropy of heart rate and intracranial pressure (ICP) predict unfavorable outcome.
View Article and Find Full Text PDFACS Appl Mater Interfaces
December 2024
Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, Jiangnan University, 214122 Jiangsu, China.
Nanometric solid solution alloys are utilized in a broad range of fields, including catalysis, energy storage, medical application, and sensor technology. Unfortunately, the synthesis of these alloys becomes increasingly challenging as the disparity between the metal elements grows, due to differences in atomic sizes, melting points, and chemical affinities. This study utilized a data-driven approach incorporating sample balancing enhancement techniques and multilayer perceptron (MLP) algorithms to improve the model's ability to handle imbalanced data, significantly boosting the efficiency of experimental parameter optimization.
View Article and Find Full Text PDFJ Clin Monit Comput
December 2024
Department of Anesthesiology and Intensive Care, School of Medicine and Health, Technical University of Munich, Ismaninger Str 22, 81675, Munich, Germany.
EEG monitoring during anesthesia or for diagnosing sleep disorders is a common standard. Different approaches for measuring the important information of this biosignal are used. The most often and efficient one for entropic parameters is permutation entropy as it can distinguish the vigilance states in the different settings.
View Article and Find Full Text PDFEnviron Monit Assess
December 2024
School of Big Data and Statistics, Anhui University, Hefei, 230601, Anhui, China.
The monitoring of air pollution through the air quality index (AQI) is a fundamental tool in ensuring public health protection. Accurate prediction of air quality is necessary for the timely implementation of measures to control and manage air pollution, thereby mitigating its detrimental impact on human health. A novel hybrid prediction model is proposed, which is EMD-KMC-EC-SSA-VMD-LSTM.
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