Validation of an oncology-specific opioid risk calculator in cancer survivors

Cancer. 2021 May 1;127(9):1529-1535. doi: 10.1002/cncr.33410. Epub 2020 Dec 30.

Abstract

Background: Clinical guidelines recommend that providers risk-stratify patients with cancer before prescribing opioids. Prior research has demonstrated that a simple cancer opioid risk score might help identify to patients with cancer at the time of diagnosis with a high likelihood of long-term posttreatment opioid use. This current project validates this cancer opioid risk score in a generalizable, population-based cohort of elderly cancer survivors.

Methods: This study identified 44,932 Medicare beneficiaries with cancer who had received local therapy. Longitudinal opioid use was ascertained from Medicare Part D data. A risk score was calculated for each patient, and patients were categorized into low-, moderate-, and high-risk groups on the basis of the predicted probability of persistent opioid use. Model discrimination was assessed with receiver operating characteristic curves.

Results: In the study cohort, 5.2% of the patients were chronic opioid users 1 to 2 years after the initiation of cancer treatment. The majority of the patients (64%) were at low risk and had a 1.2% probability of long-term opioid use. Moderate-risk patients (33% of the cohort) had a 5.6% probability of long-term opioid use. High-risk patients (3.5% of the cohort) had a 75% probability of long-term opioid use. The opioid risk score had an area under the receiver operating characteristic curve of 0.869.

Conclusions: This study found that a cancer opioid risk score could accurately identify individuals with a high likelihood of long-term opioid use in a large, generalizable cohort of cancer survivors. Future research should focus on the implementation of these scores into clinical practice and how this could affect prescriber behavior and patient outcomes.

Lay summary: A novel 5-question clinical decision tool allows physicians treating patients with cancer to accurately predict which patients will persistently be using opioid medications after completing therapy.

Keywords: analgesics; cancer pain; cancer survivors; clinical decision rules; decision support techniques; opioid; opioid-related disorders.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Aged
  • Analgesics, Opioid / therapeutic use*
  • Cancer Pain / drug therapy*
  • Cancer Survivors / statistics & numerical data*
  • Cohort Studies
  • Female
  • Humans
  • Male
  • Medicare Part D / statistics & numerical data
  • Prescription Drug Misuse / statistics & numerical data*
  • Probability
  • ROC Curve
  • Risk Assessment / methods
  • SEER Program
  • Time Factors
  • United States

Substances

  • Analgesics, Opioid