Identifying Risk Factors For Persistent Opioid Use Following Major Cancer Surgery
- Augmented & Artificial Intelligence,
- Clinical Transformation
This project will develop predictive models of transition to opioid use in patients with cancer using big data approaches. Understanding the factors that predict who is most likely to transition to persistent opioid use is essential for risk stratification and prescribing decisions among patients with cancer.
Opioid treatment is a standard of care for acute post-surgical pain. Most research on risk and mitigation factors for persistent opioid usage after surgery has focused on non-cancer patients. Few studies have directly compared rates of transition to persistent use following surgery in matched samples of cancer and non-cancer patients. This project will develop predictive models of transition to opioid use in cancer patients using big data approaches.
Understanding the factors that predict who is most likely to transition to persistent opioid use is essential for risk stratification and prescribing decisions among patients with cancer.
National Cancer Institute (NCI)
USC Norris Comprehensive Cancer Center; Abramson Cancer Center; Perelman School of Medicine Department of Psychiatry, Biostatistics, Epidemiology, and Informatics; Perelman School of Medicine Department of Obstetrics and Gynecology; Perelman School of Medicine Department of Surgery
Project Leads
-
Caryn Lerman
PhD
Distinguished Professor of Psychiatry and the Behavioral Sciences and Psychology, USC Norris Comprehensive Cancer Center
-
Mary Falcone
PhD
Research Scientist, Keck School of Medicine of the University of Southern California
Project Team
-
Justin Bekelman
-
David Birtwell
-
Yong Chen
-
Peter Gabriel
-
Lifang He
-
Emily Ko
-
Chongliang Luo
-
Danielle Mowery
-
E. Carter Paulson
-
Robert Schnoll
-
Emily Schriver
