Background: Computer-assisted target controlled infusions (TCI) result in prediction errors that are influenced by pharmacokinetic variability among and within patients. It is uncertain whether the selection of a propofol pharmacokinetic parameter set significantly influences drug concentrations and clinical acceptability.
Methods: Thirty patients received similar propofol TCI regimens after being randomly allocated to one of three parameter sets. Arterial and venous concentrations were measured and prediction errors calculated from pooled and intrasubject data.
Results: Arterial propofol concentrations in the Dyck group revealed greater bias (mean 43%) than did those in the Marsh (-1%) and Tackley (-3%) groups. The Dyck group also showed greater inaccuracy (mean:47%) than the Marsh (29%) and Tackley (24%) groups. There was little tendency for measured concentrations to vary from targeted values over time (divergence). Variability about an observed mean in individual patients (wobble) was low. Venous propofol concentrations were initially much less than arterial concentrations, but this difference decreased over time.
Conclusions: Although it may be preferable to administer propofol TCI by using a locally derived parameter set, it is acceptable to use a model from elsewhere. The Marsh and Tackley models produced equally good performance and are appropriate for propofol TCI within the range of 3-6 micrograms/ml. The Dyck model was less accurate at maintaining anesthetic concentrations, possibly because it was derived from low concentrations. Concentrations in blood, the most sensitive indicators of performance, demonstrated differences among the parameter sets. Clinically, TCI worked well, and by clinical criteria, the choice of pharmacokinetic model did not appear to make a difference.
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http://dx.doi.org/10.1097/00000542-199506000-00003 | DOI Listing |
Quant Imaging Med Surg
January 2025
Department of Imaging Medicine and Nuclear Medicine, Shandong Second Medical University, Weifang, China.
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View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
Department of Nuclear Medicine, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
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Methods: This retrospective study involved consecutive patients diagnosed with PCa who underwent both preoperative mpMRI and PSMA PET/CT scans between April 2019 and June 2022.
Sci Rep
January 2025
College of Safety Science and Engineering, Anhui University of Science and Technology, Huainan, 232001, People's Republic of China.
The construction of a predictive model that accurately reflects the spontaneous combustion temperature of coal in goaf is fundamental to monitoring and early warning systems for thermodynamic disasters, including coal spontaneous combustion and gas explosions. In this paper, on the basis of programming temperature experiment and industrial analysis, 381 data sets of 9 coal types are established, and feature selection was executed through the utilization of the Pearson correlation coefficient, ultimately identifying O, CO, CO, CH, CH, CH/CH, CH/CH, CH/CH, CO/CO, and CO/O as input indicators for the prediction model. The chosen indicator data were divided into training and testing sets in a 4:1 ratio, the Particle Swarm Optimization (PSO) methodology was applied to optimize the parameters of the XGBoost regressor, and a universal PSO-XGBoost prediction model is proposed.
View Article and Find Full Text PDFFront Robot AI
January 2025
Institute of Automatic Control, Leibniz University Hannover, Hannover, Germany.
In this paper, we present a global reactive motion planning framework designed for robotic manipulators navigating in complex dynamic environments. Utilizing local minima-free circular fields, our methodology generates reactive control commands while also leveraging global environmental information from arbitrary configuration space motion planners to identify promising trajectories around obstacles. Furthermore, we extend the virtual agents framework introduced in Becker et al.
View Article and Find Full Text PDFSoft Matter
January 2025
James Franck Institute and Department of Physics, The University of Chicago, Chicago, Illinois 60637, USA.
We measure the response of open-cell polyurethane foams filled with a dense suspension of fumed silica particles in polyethylene glycol at compression speeds spanning several orders of magnitude. The gradual compressive stress increase of the composite material indicates the existence of shear rate gradients in the interstitial suspension caused by wide distributions in pore sizes in the disordered foam network. The energy dissipated during compression scales with an effective internal shear rate, allowing for the collapse of three data sets for different pore-size foams.
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