Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
1981-11-22
pubmed:abstractText
Most previously suggested methods for predicting phenytoin dosage from steady-state drug levels (Cpss) measured in the clinical setting fail to fully exploit all relevant (population) information. A bayesian prediction method, applicable to any drug, is available. It appropriately combines all types of information. In this paper, we compare the Bayesian method as applied to phenytoin to two other prediction methods (and a baseline, nonfeedback one). Actual doses are compared to predictions in 49 patients. Each method is optimized, as far as possible, for the test data. The comparison favors the Bayesian method. Since each of the other prediction methods for phenytoin can be shown to be a theoretically suboptimal special case of the Bayesian one, the superiority of the latter may be a general phenomenon. Because the pharmacokinetic model linking steady-state phenytoin levels and dosage is so simple, a good approximation of the general Bayesian method can be implemented as a graphical device, or as a program for a programmable calculator. We present and describe both of these approximations.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0090-466X
pubmed:author
pubmed:issnType
Print
pubmed:volume
9
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
131-46
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
1981
pubmed:articleTitle
Predicting individual phenytoin dosage.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't