Opioid Use clinical trials at UCSD
4 in progress, 1 open to eligible people
Prospective Randomized Trial of CPAP for SDB in Patients Who Use Opioids
open to eligible people ages 18 years and up
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. The goal of this study is to determine whether patients with chronic pain who use opioids and have SDB might benefit from the use of CPAP in terms of sleep quality, pain, quality of life, and other measures. In addition, the study will examine whether these individuals are able to adhere to CPAP, which will be important for future studies. Lastly, we anticipate that CPAP won't work for everyone due to the changes that opioids can cause in breathing patterns. We will examine how often CPAP is ineffective, and whether we can predict which individuals are least likely to resolve their SDB with CPAP.
San Diego, California
Effectiveness of iPACK on Postoperative Pain From Hamstring Autograft Following ACL Repair
Sorry, not yet accepting patients
Patients undergoing ACL repair with hamstring autograft frequently develop significant post operative pain at the hamstring grafting site. This pain is within the distribution of a commonly used regional nerve block, the Interspace between the popliteal artery and capsule of the knee (iPACK). The investigators plan to randomize consenting patients to either receiving a SHAM injection of normal saline or to an interventional group of long acting local anesthetic (Ropivacaine) injected in the popliteal fossa between the popliteal artery and capsule of the knee (iPACK). Both groups of patients will receive standard of care with respect to perioperative pain management, which includes a preoperative adductor canal nerve block and preoperative acetaminophen administration. Dual primary endpoints of postoperative pain scores and mean postoperative opioid use will be retrieved and compared between groups. Additional secondary endpoints will be PACU length of stay, PACU opioid use, POD1 opioids use, and POD1 pain scores (best, worst, average).
Predicting Chronic Opioid Use Following Lower Extremity Joint Arthroplasty
Sorry, not yet accepting patients
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.
Predicting Chronic Pain Following Breast Surgery
Sorry, not yet accepting patients
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.
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