Pain clinical trials at UC Cancer

Chronic pain (CP) is a major health problem for military Veterans, and CP is often associated with comorbid mental health problems, including posttraumatic stress disorder (PTSD) and depression. CP with psychological comorbidity is associated with increased healthcare costs, medication use, risk of suicide and rates of disability and reduced quality of life. Current empirically supported treatments do not always lead to substantial improvements (up to 50% of patients drop out or are do not respond to treatment). This project was designed to evaluate the efficacy of a novel intervention for addressing these challenges. Compassion meditation (CM), a meditative practice that focuses on the wish to remove suffering, is a contemplative practice that has promise for the amelioration of physical and mental health problems as well as promoting positive affect and improving quality of life. This study will evaluate the efficacy of Cognitively-Based Compassion Training for Chronic Pain with Psychological Comorbidity (CBCT-CP+) compared to Health Education while Living with Pain (H.E.L.P.) control condition, in a sample of among Veterans with CP conditions and psychological comorbidity.

Breast surgery, which includes mastectomy, breast reconstructive surgery, or lumpectomies with sentinel node biopsies, may lead to the development of chronic pain and long-term opioid use. In the era of an opioid crisis, it is important to risk-stratify this surgical population for risk of these outcomes in an effort to personalize pain management. The opioid epidemic in the United States resulted in more than 40,000 deaths in 2016, 40% of which involved prescription opioids. Furthermore, it is estimated that 2 million patients become opioid-dependent after elective, outpatient surgery each year. After major breast surgery, chronic pain has been reported to develop anywhere between 35% - 62% of patients, while about 10% use long-term opioids. Precision medicine is a concept at which medical management is tailored to an individual patient based on a specific patient's characteristics, including social, demographic, medical, genetic, and molecular/cellular data. With a plethora of data specific to millions of patients, the use of artificial intelligence (AI) modalities to analyze big data in order to implement precision medicine is crucial. We propose to prospectively collect rich data from patients undergoing various breast surgeries in order to develop predictive models using AI modalities to predict patients at-risk for chronic pain and opioid use.