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出境医 / 临床实验 / Predicting Chronic Pain Following Breast Surgery
Predicting Chronic Pain Following Breast Surgery
Study Description
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.
Study Design
| 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 |
| Estimated Study Start Date : | July 19, 2021 |
| Estimated Primary Completion Date : | July 31, 2023 |
| Estimated Study Completion Date : | December 31, 2023 |
Arms and Interventions
| Group/Cohort |
|---|
| Developed Persistent Opioid Use after 3 months following surgery |
| Did not develop persistent opioid use after 3 months following surgery |
Outcome Measures
Primary Outcome Measures :
- 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
Secondary Outcome Measures :
- Persistent opioid use after 30 days [ Time Frame: 30 days ]continual use of opioids 30 days after surgery
- Persistent pain after 30 days [ Time Frame: 30 days ]persistent surgical pain after 30 days following surgery
- Persistent opioid use after 6 months [ Time Frame: 6 months ]continual use of opioids 6 months after surgery
- Persistent pain after 6 months [ Time Frame: 6 months ]persistent surgical pain after 6 months following surgery
- Acute opioid use [ Time Frame: 3 days ]total opioid use during the first 3 days following surgery
- 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
Eligibility Criteria
| 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
Contacts and Locations
Contacts
| Contact: Rodney A Gabriel, MD, MAS | 858-663-7747 | ragabriel@health.ucsd.edu |
Sponsors and Collaborators
University of California, San Diego
| Tracking Information | |||||
|---|---|---|---|---|---|
| First Submitted Date | July 13, 2021 | ||||
| First Posted Date | July 19, 2021 | ||||
| Last Update Posted Date | July 19, 2021 | ||||
| Estimated Study Start Date | July 19, 2021 | ||||
| Estimated Primary Completion Date | July 31, 2023 (Final data collection date for primary outcome measure) | ||||
| Current Primary Outcome Measures |
|
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| Original Primary Outcome Measures | Same as current | ||||
| Change History | No Changes Posted | ||||
| Current Secondary Outcome Measures |
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| Original Secondary Outcome Measures | Same as current | ||||
| Current Other Pre-specified Outcome Measures | Not Provided | ||||
| Original Other Pre-specified Outcome Measures | Not Provided | ||||
| Descriptive Information | |||||
| Brief Title | Predicting Chronic Pain Following Breast Surgery | ||||
| Official Title | Development of Predictive Models Using Artificial Intelligence for Postoperative Chronic Pain and Opioid Use Following Breast Surgery: A Prospectively-Designed Study | ||||
| 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. | ||||
| 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 Type | Observational | ||||
| Study Design | Observational Model: Case-Control Time Perspective: Prospective |
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| Target Follow-Up Duration | Not Provided | ||||
| Biospecimen | Retention: Samples With DNA Description:
We will collect buccal swab specimens from patients for pharmacogenomic screening
|
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| Sampling Method | Non-Probability Sample | ||||
| Study Population | Surgical patients undergoing major breast surgery | ||||
| Condition |
|
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| Intervention | Not Provided | ||||
| Study Groups/Cohorts |
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| Publications * | Not Provided | ||||
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* Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline. |
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| Recruitment Information | |||||
| Recruitment Status | Not yet recruiting | ||||
| Estimated Enrollment |
500 | ||||
| Original Estimated Enrollment | Same as current | ||||
| Estimated Study Completion Date | December 31, 2023 | ||||
| Estimated Primary Completion Date | July 31, 2023 (Final data collection date for primary outcome measure) | ||||
| Eligibility Criteria |
Inclusion Criteria:
Exclusion Criteria:
|
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| Sex/Gender |
|
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| Ages | 18 Years and older (Adult, Older Adult) | ||||
| Accepts Healthy Volunteers | No | ||||
| Contacts |
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| Listed Location Countries | Not Provided | ||||
| Removed Location Countries | |||||
| Administrative Information | |||||
| NCT Number | NCT04967352 | ||||
| Other Study ID Numbers | 201610 | ||||
| Has Data Monitoring Committee | No | ||||
| U.S. FDA-regulated Product |
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| IPD Sharing Statement |
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| Responsible Party | Rodney Gabriel, University of California, San Diego | ||||
| Study Sponsor | University of California, San Diego | ||||
| Collaborators | Not Provided | ||||
| Investigators | Not Provided | ||||
| PRS Account | University of California, San Diego | ||||
| Verification Date | July 2021 | ||||