Chronic PainNCT04967352
Predicting Chronic Pain Following Breast Surgery
An observational study of Chronic Pain and Opioid Use, run by University of California, San Diego at 1 site in United States.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Chronic Pain, Opioid Use, Breast Pain
- How many people
- 500 participants
- Where
- 1 siteUnited States
- Study dates
- 19 Jul 2021to 31 Dec 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Surgical patients undergoing major breast surgery
Who may be able to join
- Patient undergoing major breast surgery (except for simple lumpectomy)
Who cannot join
- refusal to consent
- lack of independent decision-making capacity
- inability to communicate effectively with research personnel
Trial locations and contacts
1 site in 1 country.
United States1 site, 1 recruiting
- Recruiting
University of California San Diego
La Jolla, California, 92037
Rodney A Gabriel, MD, 858-663-7747, ragabriel@health.ucsd.edu
About this study
Official title: Development of Predictive Models Using Artificial Intelligence for Postoperative Chronic Pain and Opioid Use Following Breast Surgery: A Prospectively-Designed Study
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.
Read the 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.
- Conditions
- Chronic Pain, Opioid Use, Breast Pain, Breast Cancer
- Medical subject headings
- Chronic Pain, Mastodynia, Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Developed Persistent Opioid Use after 3 months following surgery | Not described | |
| Did not develop persistent opioid use after 3 months following surgery | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
Persistent opioid use after 90 days
continual use of opioids after 90 days following surgery
Measured: 90 days
Persistent pain after 90 days
persistent surgical pain after 90 days following surgery
Measured: 90 days
Secondary outcomes (6)
Persistent opioid use after 30 days
continual use of opioids 30 days after surgery
Measured: 30 days
Persistent pain after 30 days
persistent surgical pain after 30 days following surgery
Measured: 30 days
Persistent opioid use after 6 months
continual use of opioids 6 months after surgery
Measured: 6 months
Persistent pain after 6 months
persistent surgical pain after 6 months following surgery
Measured: 6 months
Acute opioid use
total opioid use during the first 3 days following surgery
Measured: 3 days
Acute pain
median pain scores (numeric rating scale) during the first 3 days following surgery
Measured: 3 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Jul 2021
- Study start19 Jul 2021
- Primary completion31 Jul 2023 (estimated)
- Study completion31 Dec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Chronic PainStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of California, San Diego
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Rodney Gabriel, University of California, San Diego
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04967352
- Sponsor's protocol ID
- 201610
- Status last verified
- Jul 2023
- First submitted
- 13 Jul 2021
- Last updated
- 27 Jul 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 27 Jul 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of Predictive Models Using Artificial Intelligence for Postoperative Chronic Pain and Opioid Use Following Breast Surgery: A Prospectively-Designed Study
- ClinicalTrials.gov ID
- NCT04967352
- Acronym / sponsor ID
- 201610
- Conditions
- Chronic Pain, Opioid Use, Breast Pain, Breast Cancer
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- University of California, San DiegoUniversity, hospital or other
- Study start
- 19 Jul 2021 (actual)
- Primary completion
- 31 Jul 2023 (estimated)
- Study completion
- 31 Dec 2023 (estimated)
- First posted
- 19 Jul 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Developed Persistent Opioid Use after 3 months following surgery | – | – |
| Did not develop persistent opioid use after 3 months following surgery | – | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Surgical patients undergoing major breast surgery
Key inclusion criteria (1 listed)
- Patient undergoing major breast surgery (except for simple lumpectomy)
Key exclusion criteria (3 listed)
- refusal to consent
- lack of independent decision-making capacity
- inability to communicate effectively with research personnel
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Persistent opioid use after 90 days
Time frame: 90 days
continual use of opioids after 90 days following surgery
Persistent pain after 90 days
Time frame: 90 days
persistent surgical pain after 90 days following surgery
6 secondary outcomes
Persistent opioid use after 30 days
Time frame: 30 days
Persistent pain after 30 days
Time frame: 30 days
Persistent opioid use after 6 months
Time frame: 6 months
Persistent pain after 6 months
Time frame: 6 months
Acute opioid use
Time frame: 3 days
Acute pain
Time frame: 3 days
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Jul 2023.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Chronic Pain and Opioid Use, run by University of California, San Diego at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 27 Jul 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Rodney A Gabriel, MD, MAS (858-663-7747, ragabriel@health.ucsd.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Persistent opioid use after 90 days (time frame: 90 days). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Jul 2023.