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Opioid Use clinical trials at UCSD

3 in progress, 0 open to eligible people

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  • Predicting Chronic Opioid Use Following Lower Extremity Joint Arthroplasty

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    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

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    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.

  • Prospective Randomized Trial of CPAP for SDB in Patients Who Use Opioids

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    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

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