NCT05474274Enrolling By Invitation不适用
Development of the PAIN (Pain AI iNtervention) Platform for Patients at Home
适应症
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 发起方
- Mayo Clinic
- 入组人数
- 70
- 试验地点
- 1
- 主要终点
- Using machine Learning for Postoperative Pain Pain Prediction
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients undergoing low-risk outpatient plastic surgery procedures with expected pain intensities ranging from mild to severe.
排除标准
- •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.
结局指标
主要结局
Using machine Learning for Postoperative Pain Pain Prediction
时间窗: 8 months
The primary outcome will be the accuracy of machine learning algorithms for postoperative pain prediction using root mean square errors.
次要结局
- Physiologic variable absolute Δ defining the physiologic biomarker's change in measurements after pain medication(8 months)
- Physiologic variable %Δ defining the physiologic biomarker's change in measurements after pain medication(8 months)
研究者
Antonio J. Forte
Principal Investigator
Mayo Clinic
研究点 (1)
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