Clinical Trial Details
— Status: Recruiting
Administrative data
NCT number |
NCT04967352 |
Other study ID # |
201610 |
Secondary ID |
|
Status |
Recruiting |
Phase |
|
First received |
|
Last updated |
|
Start date |
July 19, 2021 |
Est. completion date |
December 31, 2023 |
Study information
Verified date |
July 2023 |
Source |
University of California, San Diego |
Contact |
Rodney A Gabriel, MD, MAS |
Phone |
858-663-7747 |
Email |
ragabriel[@]health.ucsd.edu |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial Summary
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.
Description:
The primary objective of this is to develop predictive models using artificial intelligence
algorithms to predict acute and chronic pain and opioid use in patients undergoing breast
surgery. Development of these models will involve prospectively collecting data from this
surgical population, including: 1) survey results from the Brief Pain Inventory, Fibromyalgia
Survey Criteria, and PROMIS scales (depression scale, anxiety scale, physical function scale,
fatigue scale, sleep disturbance scale); 2) pharmacogenomics (single nucleotide peptides from
COMT, BDNF, SCN11a, OPRM1, ABCB1, CYPD26, and CYP34A, to name a few); 3) preoperative
comorbidities (including but not limited to diabetes mellitus, chronic pain, psychiatric
disorders, substance abuse history, obstructive sleep apnea, etc); 4) preoperative labs (i.e.
hemoglobin); 5) demographic data (i.e. socioeconomic status, religion, ethnicity; primary
language spoken, age, body mass index, sex, etc); 6) preoperative medication use; 7) primary
surgical diagnosis; 8) surgery; and 9) social support system. Intraoperative data will
include: 1) primary anesthetic type; 2) case duration; 3) total opioid use; 4) non-opioid
analgesic use; 5) heart rate hemodynamics; and 6) blood pressure hemodynamics. Postoperative
data will include: 1) total opioid use; 2) discharge medications; 3) hospital length of stay;
4) pain scores; 5) postoperative vital signs (blood pressure, heart rate); and 6)
participation with physical therapy. The primary outcome measures will be opioid use in the
acute period and chronic postoperative stage (30 and 90 days and 6 months) and development of
chronic pain (up to 6 months after surgery). The model with the best performance will be used
to develop a predictive analytic system aimed to identify high risk opioid patients in order
to allocate expert pain management resources to those patients. We hypothesize that we can
develop an accurate model for identifying high risk opioid users and patients at-risk for
chronic pain in these surgical populations and subsequently implement a predictive analytic
system that can detect these patients early-on.