Pain clinical trials at University of California Health

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.

The purpose of this study is to investigate the safety and efficacy of the current hard gelatin capsule formulation of NRD135S.E1 80 mg once daily in the treatment of PDPN when administered for 13 weeks.

This is a Platform Protocol to perform Phase II clinical trials in The Early Phase Pain Investigation Clinical Network (EPPIC-Net), under The Helping to End Addiction Long-termSM Initiative, or NIH HEAL InitiativeSM, related to the treatment of Painful Diabetic Peripheral Neuropathy (PDPN) in a platform setting to test multiple assets under a single protocol.

This study aims to look at the long term efficacy of using repetitive transcranial magnetic stimulation (rTMS) in relieving Gulf War Illness related headaches and pain.

Patients with chronic pain who use opioids appear to be at increased risk for breathing issues during sleep, termed sleep disordered breathing (SDB). Treatment of SDB often consists of use of a device during sleep that provides continuous positive airway pressure (CPAP) via a mask interface. However, this device is not effective or tolerated in all individuals. The goal of this study is to examine whether a medication called acetazolamide can improve SDB, as an alternative to CPAP treatment. The investigators will measure the improvement in SDB, as well as any change in symptoms, during a 1 week treatment with acetazolamide compared with 1 week of placebo (sugar pill). This study will help to provide data for longer term studies of treatment for SDB in patients who use opioids.

The primary aim of this implementation-effectiveness trial is to examine the effectiveness of Mindfulness-Oriented Recovery Enhancement (MORE) and patient-centered chronic pain management visits in primary care as interventions to reduce chronic pain, improve quality of life, and reduce opioid-related harms among chronic pain patients on long-term opioid therapy.

The Prehospital Analgesia INtervention trial (PAIN) is a proposed 4 year (3-year enrollment) multicenter, prehospital, randomized, double-blind, clinical trial that will enroll approximately 994 patients at select LITES Network sites. The objective is to perform a prospective, interventional, randomized trial among prehospital trauma patients with compensated shock (SI≥0.9) and an indication for pain management, comparing patient centered outcomes following prehospital administration of ketamine hydrochloride versus fentanyl citrate.

Personalized medicine is a concept in which medical care is individualized to a patient based on their unique characteristics, including comorbidities, demographics, genetics, and microbiome. After major surgery, some patients are at increased risk of opioid dependence. By identifying unique genetic and microbiome markers, clinicians may potentially identify individual risk factors for opioid dependence. By identifying these high risk patients early-on, personalized interventions may be applied to these patients in order to reduce the incidence of opioid-dependence.

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.