PAIN (Pain AI iNtervention) Platform for Patients at Home

N/AEnrolling By InvitationNCT05474274

Mayo Clinic70 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

Conditions

Pain

Interventions

Machine learning algorithmsOTHER

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

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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.

Locations (1)

Mayo Clinic Florida

Jacksonville, Florida, United States

Outcomes

Primary Outcomes

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.

Time frame: 8 months

Secondary Outcomes

Physiologic variable %Δ 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 (%Δ in signal's respective units).

Time frame: 8 months

Physiologic variable absolute Δ 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 Δ in signal's respective units).

Time frame: 8 months

Data from ClinicalTrials.gov

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