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Factors Predict Adverse Opioid-Related Outcomes in Cancer Survivors

Last Updated: December 13, 2019.

Among cancer survivors, demographic, cancer, and treatment factors can predict adverse opioid-related outcomes, according to a study published online Nov. 22 in the Journal of the National Cancer Institute.

FRIDAY, Dec. 13, 2019 (HealthDay News) -- Among cancer survivors, demographic, cancer, and treatment factors can predict adverse opioid-related outcomes, according to a study published online Nov. 22 in the Journal of the National Cancer Institute.

Lucas K. Vitzthum, M.D., from the University of California San Diego in La Jolla, and colleagues examined the rates of persistent posttreatment opioid use, diagnoses of opioid abuse or dependence, and admissions for opioid toxicity in a cohort of 106,732 Veteran cancer survivors diagnosed between 2000 and 2015.

The researchers found that the rates of persistent opioid use in cancer survivors, opioid abuse or dependence, and opioid-related admissions were 8.3, 2.9, and 2.1 percent, respectively. Several demographic, cancer, and treatment factors associated with the risk for persistent opioid use were identified. Factors associated with increased odds of persistent opioid use in multivariable analysis included younger age, white race, unemployment, lower median income, increased comorbidity, and current or prior tobacco use as well as prior diagnoses of substance abuse and depression. A high level of discrimination was seen in predictive models when identifying individuals at risk for adverse opioid-related outcomes, including persistent opioid use, future diagnoses of opioid abuse or dependence, and admission for opioid abuse or toxicity (area under the curve, 0.85, 0.87, and 0.78, respectively).

"Improved risk stratification will allow for personalized risk assessment and improve the safety of pain management in cancer survivors," the authors write.

One author disclosed financial ties to the Boston Consulting Group; a second author disclosed ties to Peptide Logic.

Abstract/Full Text (subscription or payment may be required)


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