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

Anne Estrup Olesen, Debbie Grønlund, Mikkel Gram, Frank Skorpen, Asbjørn Mohr Drewes, Pål Klepstad*

*Corresponding author for this work
    3 Citations (Scopus)
    18 Downloads (Pure)

    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.

    Original languageEnglish
    Article number78
    JournalBMC Research Notes
    Volume11
    Number of pages5
    ISSN1756-0500
    DOIs
    Publication statusPublished - 2018

    Keywords

    • Cancer pain
    • Genetics
    • SNPs
    • Support vector machine

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