PAIN (Pain AI iNtervention) Platform for Patients at Home

Pain Clinical Trial

Official title:

Development of the PAIN (Pain AI iNtervention) Platform for Patients at Home

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

• NCT number: NCT05474274
• Other study ID #: 21-013443
• Status: Enrolling by invitation
• Start date: November 23, 2022
• Est. completion date: November 2025

Study information

• Verified date: November 2023
• Source: Mayo Clinic
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

The purpose of this research is to identify physiological markers to determine pain intensity and build an Artificial Intelligence (AI) enabled system to objectively measure pain intensity. Researchers hope to personalize pain medication regimens to help prevent medication over-use.

Recruitment information / eligibility

• Status: Enrolling by invitation
• Enrollment: 70
• Est. completion date: November 2025
• Est. primary completion date: November 2024
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Patients undergoing low-risk outpatient plastic surgery procedures with expected pain intensities ranging from mild to severe.

Exclusion Criteria:

• Patients with treated or untreated cardiopulmonary syndromes.

• Patients with treated or untreated ophthalmologic pathologies.

• Patients with skin pathologies that prevent us from using the TENS device.

• Patients with pathologies or conditions preventing them from appropriately using their voice.

• Patients with barriers to effective communication.

• Patients with poor digital literacy.

• Patients incapable of taking oral medication.

• Patients who are currently taking medical therapy for chronic pain.

• Patients with a previous diagnosis of severe anxiety disorders.

• Patients who are immobile at baseline.

Related Conditions & MeSH terms

• Pain

Intervention

Other:
• Machine learning algorithms
Machine learning techniques to rank order physiologic variables obtained via the wearable and handheld devices as well as remove low-importance and redundant variables to accurately determine postoperative pain intensity in outpatients

Locations

Country	Name	City	State
United States	Mayo Clinic Florida	Jacksonville	Florida

Sponsors (1)

Lead Sponsor	Collaborator
Mayo Clinic

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Using machine Learning for Postoperative Pain Pain Prediction	The primary outcome will be the accuracy of machine learning algorithms for postoperative pain prediction using root mean square errors.	8 months
Secondary	Physiologic variable %? defining the physiologic biomarker's change in measurements after pain medication	The secondary outcome will be the physiologic variable's use to define the physiologic biomarker's change in measurements after pain medication (%? in signal's respective units).	8 months
Secondary	Physiologic variable absolute ? defining the physiologic biomarker's change in measurements after pain medication	The secondary outcome will be the physiologic variable's use to define the physiologic biomarker's change in measurements after pain medication (absolute ? in signal's respective units).	8 months

External Links

•   Mayo Clinic Clinical Trials