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

Journal: BMC research notes
PMID: 29374492

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.

Authors

  • Anne Estrup Olesen
    Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Aalborg, Denmark.
  • Debbie Grønlund
    Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Aalborg, Denmark.
  • Mikkel Gram
    Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Aalborg, Denmark.
  • Frank Skorpen
    Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.
  • Asbjørn Mohr Drewes
    Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Aalborg, Denmark.
  • Pål Klepstad
    Department of Cancer Research and Molecular Medicine, European Palliative Care Research Centre, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. Pal.klepstad@ntnu.no.

Keywords

External Resources

Popular Topics
Recent Journals