title: Artificial neural network modeling to predict the plasma concentration of aminoglycosides in burn patients.
abstract: 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.
authors.affiliation: School of Pharmaceutical Sciences, Toho University, Miyama 2-2-1, Funabashi, Chiba 274-8510, Japan. yamamura@phar.toho-u.ac.jp
authors.forename: Shigeo
authors.initials: S
authors.lastname: Yamamura
authors.forename: Keiko
authors.initials: K
authors.lastname: Kawada
authors.forename: Rieko
authors.initials: R
authors.lastname: Takehira
authors.forename: Kenji
authors.initials: K
authors.lastname: Nishizawa
authors.forename: Shirou
authors.initials: S
authors.lastname: Katayama
authors.forename: Masaaki
authors.initials: M
authors.lastname: Hirano
authors.forename: Yasunori
authors.initials: Y
authors.lastname: Momose
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000617:Aminoglycosides / Q000097:blood* / Q000493:pharmacokinetics; D000900:Anti-Bacterial Agents / Q000097:blood* / Q000493:pharmacokinetics; D002056:Burns / Q000097:blood / Q000188:drug therapy*; D003982:Dibekacin / Q000031:analogs & derivatives* / Q000097:blood* / Q000493:pharmacokinetics; D005260:Female; D016231:Fluorescence Polarization Immunoassay; D006801:Humans; D007262:Infusions, Intravenous; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer
chemical_list: D000617:Aminoglycosides; D000900:Anti-Bacterial Agents; D003982:Dibekacin; C031986:arbekacin