dibekacin and Burns

The goal was to use an artificial neural network model to predict the plasma concentration of aminoglycosides in burn patients and identify patients whose plasma antibiotic concentration would be sub-therapeutic based on the patients' physiological data and taking into account burn severity. Physiological data and some indicators of burn severity were collected from 30 burn patients who received arbekacin. A three-layer artificial neural network with five neurons in the hidden layer was used to predict the plasma concentration of arbekacin. Linear modeling for prediction of plasma concentration and logistic regression modeling for the classification of patients were also used and the predictive performance was compared to results from the artificial neural network model. Dose, body mass index, serum creatinine concentration and amount of parenteral fluid were selected as covariates for the plasma concentration of arbekacin. Area of burn after skin graft was a good covariate for indicating burn severity. Predictive performance of the artificial neural network model including burn severity was much better than linear modeling and logistic regression analysis. An artificial neural network model should be helpful for the prediction of plasma concentration using patients' physiological data, and burn severity should be included for improved prediction in burn patients. Because the relationship between burn severity and plasma concentration of aminoglycosides is thought to be nonlinear, it is not surprising that the artificial neural network model showed better predictive performance compared to the linear or logistic regression models.

Topics: Adult; Aged; Aged, 80 and over; Aminoglycosides; Anti-Bacterial Agents; Burns; Dibekacin; Female; Fluorescence Polarization Immunoassay; Humans; Infusions, Intravenous; Linear Models; Logistic Models; Male; Middle Aged; Neural Networks, Computer

To predict the response of aminoglycoside antibiotics (arbekacin: ABK) against methicillin-resistant Staphylococcus aureus (MRSA) infection in burn patients after considering the severity of the burn injury by using artificial neural network (ANN). Predictive performance was compared with logistic regression modeling.. The physiologic data and some indicators of the severity of the burn injury were collected from 25 burn patients who received ABK against MRSA infection. A three-layered ANN architecture with six neurons in the hidden layer was used to predict the ABK response. The response was monitored using three clinical criteria: number of bacteria, white blood cell count, and C-reactive protein level. Robustness of models was investigated by the leave-one-out cross-validation.. The peak plasma level, serum creatinine level, duration of ABK administration, and serum blood sugar level were selected as the linear input parameters to predict the ABK response. The area of the burn after skin grafting was the best parameter for assessing the severity of the burn injury in patients to predict the ABK response in the ANN model. The ANN model with the severity of the burn injury was superior to the logistic regression model in terms of predicting the performance of the ABK response.. Based on the patients' physiologic data, ANN modeling would be useful for the prediction of the ABK response in burn patients with MRSA infection. Severity of the burn injury was a parameter that was necessary for better prediction.

Topics: Adult; Aged; Aged, 80 and over; Aminoglycosides; Anti-Bacterial Agents; Blood Glucose; Burns; Creatinine; Dibekacin; Female; Forecasting; Humans; Logistic Models; Male; Methicillin Resistance; Middle Aged; Neural Networks, Computer; Retrospective Studies; Severity of Illness Index; Skin Transplantation; Staphylococcal Infections; Staphylococcus aureus; Treatment Outcome

The aim of this study was to estimate the pharmacokinetics (PK) of arbekacin in burn patients using a population-PK approach. Therapeutic drug monitoring data consisting of 126 plasma concentrations (including 17 values that were below the quantitation limit) from 47 burn patients were retrospectively analyzed using a mixed effect method (NONMEM, ver. 6.0). Covariates, such as burn index, age, sex, among others, were tested on the basic one-compartment model. In the basic model, positive correlations of body weight (WT) and creatinine clearance (CLcr) on total clearance (CL) and volume of distributions (V) were assumed. In the final model, V increased with burn index (BI). The final model was: CL(L/h) = 3.18x WT/ 70 + 4.49x CLcr/120; V(L) = 27.5x WT/70 + 0.28x (BI-23.5). Between-subject variability in terms of CL and V were 35 and 39%, respectively. The CL of our burn patients was significantly greater than that reported in unburned patients, and V increased proportionally with increasing BI.

Topics: Adolescent; Adult; Aged; Aged, 80 and over; Anti-Infective Agents; Burns; Dibekacin; Female; Humans; Male; Metabolic Clearance Rate; Middle Aged; Models, Biological; Retrospective Studies