Prediction of opioid dose in cancer pain patients using genetic profiling: not yet an option with support vector machine learning
Estrup Olesen, Anne; Grønlund, Debbie; Gram, Mikkel; Skorpen, Frank; Drewes, Asbjørn Mohr; Klepstad, Pål
Journal article, Peer reviewed
Published version
Permanent lenke
http://hdl.handle.net/11250/2560842Utgivelsesdato
2018Metadata
Vis full innførselSamlinger
Sammendrag
Objective
Use of opioids for pain management has increased over the past decade; however, inadequate analgesic response is common. Genetic variability may be related to opioid efficacy, but due to the many possible combinations and variables, statistical computations may be difficult. This study investigated whether data processing with support vector machine learning could predict required opioid dose in cancer pain patients, using genetic profiling. Eighteen single nucleotide polymorphisms (SNPs) within the µ and δ opioid receptor genes and the catechol-O-methyltransferase gene were selected for analysis.
Results
Data from 1237 cancer pain patients were included in the analysis. Support vector machine learning did not find any associations between the assessed SNPs and opioid dose in cancer pain patients, and hence, did not provide additional information regarding prediction of required opioid dose using genetic profiling.