PAIN (Pain AI iNtervention) Platform for Patients at Home

Study Overview
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Brief Summary
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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.

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

Conditions
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Pain

Intervention / Treatment
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* Other: Machine learning algorithms


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Post-Surgery data collection system<br>Subjects undergoing standard of care low-risk plastic surgery be provided with wearable sensors to take home and start recording heart rate, body temperature and body movements | Other: Machine learning algorithms<br>* 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<br>* Other names: Artificial Intelligence (AI) enabled system;|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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). | 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). | 8 months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Physiological markers for pain intensity, AI to objectively measure pain intensity, Medication over-use