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A19
October 13, 2007 9:00 AM - 10:30 AM Room Room 302 |
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| Modeling Hypnotic and Analgesic Dynamic Interaction – State Entropy and Auditory Evoked Potentials | ||||||||||||||||||||
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Ana Castro, B.S., Eduarda Amadeu, M.D., Fátima Martins, M.D., Pedro Amorim, M.D., Catarina S. Nunes, Ph.D. Dep Matematica Aplicada, Faculdade de Ciencias da Universidade do Porto, Porto, Portugal |
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Background: The drug effects on depth of anesthesia indices are very important when considering drug interactions. In this study we collected data from two groups: Group1 with Entropy monitoring (SE), and Group2 with AEP monitoring (AAI); and developed models for the effect of drug dynamic interactions. Methods: Data collected during urological surgeries under general anesthesia, were recorded every 5s from AEP/2, Datex S/5 and infusion pumps with RugloopII® TCI software. Schnider1 and Minto2 PK models were used for propofol and remifentanil. Anesthesia was induced with 1% propofol at 200ml/h and remifentantil effect-site concentration (Ce) target of 2.5ng/ml. After loss of consciousness (LOC) propofol Ce TCI was started, the target being Ce at LOC. Following intubation, drugs Ce were manually adjusted regarding patient needs. Hill equation3 was used to model the propofol and remifentanil effect in both groups (SE and AAI). Hill equation parameters were obtained using nonlinear least squares optimization for two different periods of the anesthesia: Model1 – using the first 15min of induction as training phase; Model2 – using the [15-30]min of anesthesia as training phase. Model1 was applied for prediction to the data from 15min until the end. Model2 was applied for prediction to the data between [0-15]min and after 30min until the end. Models performance was evaluated using the mean absolute deviation (MAD) for both training and prediction phases. T-test and paired sample t-test were used to compare the results between and within the groups. Data as Mean±SD. Results: Group1 (SE): 15 patients, 10 male, age 55±14 years, weight 66±13kg and height 164±9cm. Group2 (AAI): 15 patients, 12 male, age 56±15 years, weight 72±10kg and height 168±7cm. With no statistical difference between groups. Group1: MAD obtained for each patient using both models (Table) are statistically different, both in the training and prediction stages (MAD2<MAD1, P<0.01). Group2: MAD for Model1 is statistically different from MAD with Model2 (MAD2<MAD1. P<0.01) in the training and prediction phases (Table). Discussion: Model1 performed well in the training phase, capturing the induction trend in both groups (Fig. 1). But it had a poor performance when predicting SE and AAI through the maintenance phase, demonstrating that dynamic interaction between drugs are different throughout the anesthesia stages (different model parameters). Future works will be needed to explore the relations between model parameters during the different anesthesia stages. Acknowledgements: FCT – Portugal References: 1-Anesthesiology 1998, 88:1170-82; 2-Anesthesiology 1997, 86:24-33; 3-Anesthesiology 2006, 105: A1201.[table1][figure1] Anesthesiology 2007; 107: A19 |
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Figure 1
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