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Predicting Chronic Pain Following Breast Surgery

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ClinicalTrials.gov Identifier: NCT04967352
Recruitment Status : Recruiting
First Posted : July 19, 2021
Last Update Posted : March 24, 2022
Sponsor:
Information provided by (Responsible Party):
Rodney Gabriel, University of California, San Diego

Brief 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.

Condition or disease
Chronic Pain Opioid Use Breast Pain Breast Cancer

Detailed 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.

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Study Type : Observational
Estimated Enrollment : 500 participants
Observational Model: Case-Control
Time Perspective: Prospective
Official Title: Development of Predictive Models Using Artificial Intelligence for Postoperative Chronic Pain and Opioid Use Following Breast Surgery: A Prospectively-Designed Study
Actual Study Start Date : July 19, 2021
Estimated Primary Completion Date : July 31, 2023
Estimated Study Completion Date : December 31, 2023

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Chronic Pain

Group/Cohort
Developed Persistent Opioid Use after 3 months following surgery
Did not develop persistent opioid use after 3 months following surgery



Primary Outcome Measures :
  1. Persistent opioid use after 90 days [ Time Frame: 90 days ]
    continual use of opioids after 90 days following surgery

  2. Persistent pain after 90 days [ Time Frame: 90 days ]
    persistent surgical pain after 90 days following surgery


Secondary Outcome Measures :
  1. Persistent opioid use after 30 days [ Time Frame: 30 days ]
    continual use of opioids 30 days after surgery

  2. Persistent pain after 30 days [ Time Frame: 30 days ]
    persistent surgical pain after 30 days following surgery

  3. Persistent opioid use after 6 months [ Time Frame: 6 months ]
    continual use of opioids 6 months after surgery

  4. Persistent pain after 6 months [ Time Frame: 6 months ]
    persistent surgical pain after 6 months following surgery

  5. Acute opioid use [ Time Frame: 3 days ]
    total opioid use during the first 3 days following surgery

  6. Acute pain [ Time Frame: 3 days ]
    median pain scores (numeric rating scale) during the first 3 days following surgery


Biospecimen Retention:   Samples With DNA
We will collect buccal swab specimens from patients for pharmacogenomic screening


Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Surgical patients undergoing major breast surgery
Criteria

Inclusion Criteria:

  • Patient undergoing major breast surgery (except for simple lumpectomy)

Exclusion Criteria:

  • refusal to consent
  • lack of independent decision-making capacity
  • inability to communicate effectively with research personnel

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04967352


Contacts
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Contact: Rodney A Gabriel, MD, MAS 858-663-7747 ragabriel@health.ucsd.edu

Locations
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United States, California
University of California San Diego Recruiting
La Jolla, California, United States, 92037
Contact: Rodney A Gabriel, MD    858-663-7747    ragabriel@health.ucsd.edu   
Sponsors and Collaborators
University of California, San Diego
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Responsible Party: Rodney Gabriel, Associate Professor, University of California, San Diego
ClinicalTrials.gov Identifier: NCT04967352    
Other Study ID Numbers: 201610
First Posted: July 19, 2021    Key Record Dates
Last Update Posted: March 24, 2022
Last Verified: March 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Chronic Pain
Mastodynia
Pain
Neurologic Manifestations