Cancer Opioid Risk Score

Cancer opioid risk score

Summary

Riviere P, Vitzthum LK, Nalawade V, Deka R, Furnish T, Mell LK, Rose BS, Wallace M, Murphy JD. Validation of an oncology-specific opioid risk calculator in cancer survivors. Cancer. 2020 Dec 30. doi: 10.1002/cncr.33410. Epub ahead of print. PMID: 33378556.

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.

Selections

Opioid Use (select the option that best describes the patient):

Opioid-naïve (The patient had no prescriptions filled in the 1 to 12 months before their first day of treatment.)

Chronic Opioid Use (The patient had received a ≥120-day supply of opioids in the 1 to 12 months before treatment or ≥3 opioid prescriptions in the 3 to 6 months before treatment)

Intermittent prior opioid user (The patient had opioid use in the 1 to 12 months before the start of local treatment that did not meet the criteria for opioid-naive patients or chronic opioid users)

Prior diagnosis of depression?

Patient has received chemotherapy?

Reference

Vitzthum LK, Riviere P, Sheridan P, et al. Predicting persistent opioid use, abuse and toxicity among cancer survivors. J Natl Cancer Inst. 2020;112:720-727.

Background: Although opioids play a critical role in the management of cancer pain, the ongoing opioid epidemic has raised
concerns regarding their persistent use and abuse. We lack data-driven tools in oncology to understand the risk of adverse
opioid-related outcomes. This project seeks to identify clinical risk factors and create a risk score to help identify patients at
risk of persistent opioid use and abuse.

Methods: Within a cohort of 106 732 military veteran cancer survivors diagnosed between 2000 and 2015, we determined
rates of persistent posttreatment opioid use, diagnoses of opioid abuse or dependence, and admissions for opioid toxicity. A
multivariable logistic regression model was used to identify patient, cancer, and treatment risk factors associated with
adverse opioid-related outcomes. Predictive risk models were developed and validated using a least absolute shrinkage and
selection operator regression technique.

Results: The rate of persistent opioid use in cancer survivors was 8.3% (95% CI-8.1% to 8.4%); the rate of opioid abuse or
dependence was 2.9% (95% CI-2.8% to 3.0%); and the rate of opioid-related admissions was 2.1% (95% CI-2.0% to 2.2%). On
multivariable analysis, several patient, demographic, and cancer and treatment factors were associated with risk of persistent
opioid use. Predictive models showed a high level of discrimination when identifying individuals at risk of adverse
opioid-related outcomes including persistent opioid use (area under the curve [AUC] - 0.85), future diagnoses of opioid abuse
or dependence (AUC-0.87), and admission for opioid abuse or toxicity (AUC-0.78).

Conclusion: This study demonstrates the potential to predict adverse opioid-related outcomes among cancer survivors. With
further validation, personalized risk-stratification approaches could guide management when prescribing opioids in cancer
patients

References

Riviere P, Vitzthum LK, Nalawade V, Deka R, Furnish T, Mell LK, Rose BS, Wallace M, Murphy JD. Validation of an oncology-specific opioid risk calculator in cancer survivors. Cancer. 2020 Dec 30. doi: 10.1002/cncr.33410. Epub ahead of print. PMID: 33378556.

Vitzthum LK, Riviere P, Sheridan P, et al. Predicting persistent opioid use, abuse and toxicity among cancer survivors. J Natl Cancer Inst. 2020;112:720-727.