Predicting Chronic Pain Following Breast Surgery

Breast Cancer Clinical Trial

Official title:

Development of Predictive Models Using Artificial Intelligence for Postoperative Chronic Pain and Opioid Use Following Breast Surgery: A Prospectively-Designed Study

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04967352
• Other study ID #: 201610
• Status: Recruiting
• 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
• 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 500
• Est. completion date: December 31, 2023
• Est. primary completion date: July 31, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Breast Cancer
• Breast Pain
• Chronic Pain
• Mastodynia
• Opioid Use

Locations

Country	Name	City	State
United States	University of California San Diego	La Jolla	California

Sponsors (1)

Lead Sponsor	Collaborator
University of California, San Diego

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Persistent opioid use after 90 days	continual use of opioids after 90 days following surgery	90 days
Primary	Persistent pain after 90 days	persistent surgical pain after 90 days following surgery	90 days
Secondary	Persistent opioid use after 30 days	continual use of opioids 30 days after surgery	30 days
Secondary	Persistent pain after 30 days	persistent surgical pain after 30 days following surgery	30 days
Secondary	Persistent opioid use after 6 months	continual use of opioids 6 months after surgery	6 months
Secondary	Persistent pain after 6 months	persistent surgical pain after 6 months following surgery	6 months
Secondary	Acute opioid use	total opioid use during the first 3 days following surgery	3 days
Secondary	Acute pain	median pain scores (numeric rating scale) during the first 3 days following surgery	3 days