Prediction of opioid dose in cancer pain patients using genetic profiling: not yet an option with support vector machine learning

Autor: Anne Estrup Olesen, Debbie Grønlund, Mikkel Gram, Frank Skorpen, Asbjørn Mohr Drewes, Pål Klepstad
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: BMC Research Notes, Vol 11, Iss 1, Pp 1-5 (2018)
Druh dokumentu: article
ISSN: 1756-0500
DOI: 10.1186/s13104-018-3194-z
Popis: Abstract 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.
Databáze: Directory of Open Access Journals
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