Program

In-person AND virtual! – We are pioneering a new conference format that will connect in-person and virtual audiences to create a unique experience. Matching the innovation that comes through our members’ work, ISPOR is pushing the boundaries of innovation to design an event that works in today’s quickly changing environment. 

In-person registration included the full virtual experience, and virtual-only attendees will be able to tune into live in-person sessions and/or watch captured in-person sessions on-demand in addition to having a variety of virtual-only sessions to attend.

Leading Predictors and Their Associations with Combination Pain Therapy in Older Adults with Cancer: Application of Machine Learning Approaches

Speaker(s)

Xavier C1, Manning S2, Madhavan S2, Rasu R2, Sambamoorthi U2
1University of North Texas Health Science Center, ARLINGTON, TX, USA, 2University of North Texas Health Science Center, Fort Worth, TX, USA

Presentation Documents

OBJECTIVES: Opioid combination therapy is frequently prescribed in older adult cancer survivors despite negative outcomes. The objective of this study was to identify the leading predictors and their associations with opioid combination therapy prescribing after cancer diagnosis using interpretable machine learning approaches.

METHODS: This is a retrospective longitudinal cohort of older (> 66 years old) cancer survivors (N = 2,673) diagnosed with primary and incident cancer in 2014 using the Surveillance, Epidemiology, and End Results (SEER) cancer registry linked with Medicare claims. Recursive feature elimination with random forest was used to extract the optimal number of predictors out of 119 likely ones for predictive modeling. eXtreme Gradient Boosting (XGBoost), SHapley Additive exPlanations (SHAP), and global feature importance were used to identify the leading predictors and their associations with opioid combination therapy. SAS 9.4 was used for data management and Python 3.9.7 was used for machine learning model calibration and tuning.

RESULTS: Specificity (0.858), sensitivity (0.843), and area under the curve (AUC, 0.85) of our predictive model were high. Thirty-four features were included in the final predictive model. Baseline use of NSAIDs, opioids, benzodiazepines, and gabapentinoids, and chemotherapy, surgery, Complex relationships were observed between zip code percent of Hispanic and Native American residents living below poverty, care fragmentation (FCI), age at diagnosis, and opioid combination therapy.

CONCLUSIONS: 1 in 3 older cancer survivors were prescribed opioid combination therapy. Patient-level baseline medication use, biological factors, cancer treatment, and zip code level social determinants were leading predictors of opioid combination therapy. Although observed relationships were complex, further analysis of predictors may help compute individual risk of patients on combination therapy, which in turn may help clinicians and policy makers utilize targeted interventions at the outset and prevent long-term effects of combination pain therapy such as prolonged and inappropriate use.

Code

EPH114

Topic

Epidemiology & Public Health, Methodological & Statistical Research, Real World Data & Information Systems, Study Approaches

Topic Subcategory

Artificial Intelligence, Machine Learning, Predictive Analytics, Health & Insurance Records Systems, Registries, Safety & Pharmacoepidemiology

Disease

Geriatrics