Predicting Persistent Opioid Use, Abuse and Toxicity Among Cancer Survivors
Menée aux Etats-Unis à partir de données portant sur 106 732 patients ayant survécu à un cancer diagnostiqué entre 2000 et 2015, cette étude identifie les facteurs associés à une utilisation persistante et abusive d'opioïdes et analyse les événements indésirables associés
Background : While 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 Veteran cancer survivors diagnosed between 2000 and 2015, we determined rates of persistent post-treatment 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 (LASSO) regression technique. Results : The rate of persistent opioid use in cancer survivors was 8.3% (95% CI = 8.1 - 8.4%), the rate of opioid abuse or dependence was 2.9% (95%CI=2.8-3.0%), and the rate of opioid-related admissions was 2.1% (95%CI=2.0-2.2%). On multivariable analysis, several patient, demographic, 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 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.