Journal of clinical monitoring and computing
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J Clin Monit Comput · Apr 2024
Randomized Controlled TrialImplementation of a Bayesian based advisory tool for target-controlled infusion of propofol using qCON as control variable.
This single blinded randomized controlled trial aims to assess whether the application of a Bayesian-adjusted CePROP (effect-site of propofol) advisory tool leads towards a more stringent control of the cerebral drug effect during anaesthesia, using qCON as control variable. 100 patients scheduled for elective surgery were included and randomized into a control or intervention group (1:1 ratio). In the intervention group the advisory screen was made available to the clinician, whereas it was blinded in the control group. The settings of the target-controlled infusion pumps could be adjusted at any time by the clinician. ⋯ Significant differences between groups were hard to establish, most likely due to a very high performance level in the control group. More extensive control efforts were found in the intervention group. We believe that this advisory tool could be a useful educational tool for novices to titrate propofol effect-site concentrations.
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J Clin Monit Comput · Apr 2024
Observational StudyGlucose measurements with accu check inform II versus hexokinase plasma method during surgery under general anesthesia, an observational cohort study.
Limited research exists on translation of in-vitro glucose measurement interfering compounds to the in-vivo situation. We investigated whether Point-of-Care glucose measurements by Accu Chek Inform II (ACI II) were accurate to monitor glucose concentrations during surgery with general anesthesia by comparing with the reference laboratory hexokinase plasma glucose test. ⋯ The agreement between glucose measurements using ACI II and the reference laboratory hexokinase test was clinically acceptable with a percentage error of 10.0% (95% CI 8.0 to 11.9). The use of TIVA may negatively affect the measurement performance of the ACI II.
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J Clin Monit Comput · Apr 2024
Artificial intelligence model predicting postoperative pain using facial expressions: a pilot study.
This study aimed to assess whether an artificial intelligence model based on facial expressions can accurately predict significant postoperative pain. ⋯ ML models using facial expressions can accurately predict the presence of significant postoperative pain and have the potential to screen patients in need of rescue analgesia.
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J Clin Monit Comput · Apr 2024
Optic nerve sheath diameter measurement for prediction of postdural puncture headache.
Intracranial hypotension due to cerebrospinal fluid leak is mainly the causal factor for the pathophysiology of postdural puncture headache (PDPH). In this study, we aimed to evaluate the effectiveness of optic nerve sheath diameter (ONSD) measurement in predicting the development of PDPH in patients undergoing spinal anesthesia. ⋯ The difference between the ONSD values measured before and after spinal anesthesia may be an important parameter for predicting the risk of PDPH development.
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J Clin Monit Comput · Apr 2024
Early prediction of mortality at sepsis diagnosis time in critically ill patients by using interpretable machine learning.
This study applied machine learning for the early prediction of 30-day mortality at sepsis diagnosis time in critically ill patients. Retrospective study using data collected from the Medical Information Mart for Intensive Care IV database. The data of the patient cohort was divided on the basis of the year of hospitalization, into training (2008-2013), validation (2014-2016), and testing (2017-2019) datasets. 24,377 patients with the sepsis diagnosis time < 24 h after intensive care unit (ICU) admission were included. ⋯ The calibration plot for the model revealed a slope of 1.03 (95% CI 0.94-1.12) and intercept of 0.14 (95% CI 0.04-0.25). The SHAP revealed the top three most significant features, namely age, increased red blood cell distribution width, and respiratory rate. Our study demonstrated the feasibility of using the interpretable machine learning model to predict mortality at sepsis diagnosis time.