This study is not yet accepting patients

An Artificial Intelligence Driven Approach to Optimize Patient Selection for a Transitional Pain Service

a study on Postsurgical Pain Management Pain Opioid Use Postoperative Pain

Summary

Eligibility
for people ages 18 years and up (full criteria)
Location
at UCSF
Dates
study started
study ends around
Principal Investigator
by Andrew Bishara, MD (ucsf)
Headshot of Andrew Bishara
Andrew Bishara

Description

Summary

The goal of this clinical trial is to learn whether a pain-management program called a Transitional Pain Service can help prevent long-term pain and reduce opioid needs after surgery in adult surgical patients who may be at higher risk for developing persistent pain. The main questions this trial aims to answer are: 1) Does the Transitional Pain Service help high-risk surgical patients manage pain better after surgery? 2) Can a computer tool (an algorithm) improve screening of patients for long-lasting pain after surgery? The investigators will compare patients who continue with their usual care to patients who receive support from the Transitional Pain Service to see if the program leads to better pain control and lower opioid use. Participants will share information about their pain levels and the pain-relief treatments they are using before and after surgery so researchers can better understand how the program affects recovery. Patients assigned to the Transitional Pain Service will also be offered additional coordinated support before and after their surgery to help optimize their pain control and overall recovery.

Keywords

Postsurgical Pain Management, Postsurgical Pain, Chronic, Opioid Use After Surgery, Pain, Transitional Pain Service, Postsurgical Pain, Postoperative Opioid Use, Postoperative Pain, Oxycodone, Hydrocodone, Acetaminophen, Morphine, Methadone, Buprenorphine, Gabapentin, Duloxetine Hydrochloride, Pregabalin, Amitriptyline, Nortriptyline, Acupuncture Therapy, Intra-Articular Injections, Radiofrequency Ablation, Physical Therapy Modalities, TPS-Select, Pharmacologic Pain Management, Interventional Pain Procedures, Psychological Management and Pain Education, Physical Therapy

Eligibility

For people ages 18 years and up

Patients may take part in this study if they:

  • Are 18 years or older
  • Are having a planned (elective) surgery at UCSF
  • Are identified as higher risk for chronic pain after surgery by a computer model
  • Agree to join the study and sign consent

Patients cannot take part if they:

  • Have too much missing information in their medical record (more than 75%)
  • Have a surgery scheduled less than 1 month away
  • Have serious memory or thinking problems that make participation difficult
  • Are in another clinical study that would interfere with this one
  • Do not live in California
  • Choose not to participate or sign consent
  • Have severe depression with suicidal thoughts that requires urgent care
  • Are pregnant or breastfeeding

A patient who has already joined may be removed from the study if they:

  • Cannot complete most of the scheduled follow-up phone calls or pre-surgery recommendations (up to 6 months after discharge)
  • Pass away between the surgery booking date and 6 months after leaving the hospital
  • Become pregnant in the 6 months after discharge

Patients may remain enrolled but their data may not be used in the main study results if they experience:

  • A long-lasting infection after surgery
  • Hardware problems related to their surgery (for example, issues with implanted devices)
  • A major injury or life event that affects their pain levels, such as a significant fall or an emergency surgery
  • Difficulty completing the planned follow-up phone calls up to 6 months after discharge

Location

  • UCSF Hospitals
    San Francisco California 94131 United States

Lead Scientist at University of California Health

  • Andrew Bishara, MD (ucsf)
    My main research aim is to better predict and prevent complications in surgical patients using advanced machine learning techniques. Real-Time Risk Assessment: Creating models to predict acute kidney injury (AKI), pain, delirium, and blood loss during surgeries in real-time. Model Validation: Ensuring these models are reliable with the eventual goal of pursuing regulatory approval.

Details

Status
not yet accepting patients
Start Date
Completion Date
(estimated)
Sponsor
University of California, San Francisco
ID
NCT07296770
Phase
Phase 3 research study
Study Type
Interventional
Participants
Expecting 126 study participants
Last Updated