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Predicting Persistent Opioid Use, Abuse, and Toxicity Among Cancer Survivors.
- Source :
- JNCI: Journal of the National Cancer Institute; Jul2020, Vol. 122 Issue 7, p720-727, 8p, 2 Charts, 2 Graphs
- Publication Year :
- 2020
-
Abstract
- <bold>Background: </bold>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.<bold>Methods: </bold>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.<bold>Results: </bold>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).<bold>Conclusion: </bold>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. [ABSTRACT FROM AUTHOR]
- Subjects :
- CANCER survivors
CANCER pain
OPIOID abuse
OPIOIDS
SECONDARY primary cancer
VETERANS
LOGISTIC regression analysis
SUBSTANCE abuse treatment
TUMOR treatment
NARCOTICS
DATABASES
RESEARCH
SUBSTANCE abuse
ANALGESICS
RESEARCH methodology
MEDICAL cooperation
EVALUATION research
COMPARATIVE studies
RESEARCH funding
TUMORS
STATISTICAL models
LONGITUDINAL method
Subjects
Details
- Language :
- English
- ISSN :
- 00278874
- Volume :
- 122
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- JNCI: Journal of the National Cancer Institute
- Publication Type :
- Academic Journal
- Accession number :
- 144615442
- Full Text :
- https://doi.org/10.1093/jnci/djz200