Use of Continuous Biomonitoring for Detection of Infectious Complications in Kidney Transplant Recipients
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 200
- 试验地点
- 1
- 主要终点
- Accuracy of the algorithm at detecting infections at presymptomatic stage
研究概览
简要总结
The goal of this observational study is to develop a machine learning algorithm for early detection of infections in kidney transplant recipients using data recorded by wearable digital health technologies.
The main questions it aims to answer are:
- What are the biometric data pattern changes in impending infections?
- What accuracy the machine learning algorithm can achieve?
Participants will be given/use their own wearable device that will record biometric data. Any infection event will be recorded and an algorithm will be trained to recognize changes in biometric data preceding symptomatic infection.
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
- •kidney transplant recipient
- •age 18 years or more
- •kidney allograft function (eGFR based on CKD-EPI more than 15ml/min/1.73m2)
排除标准
- •recipient of another transplanted organ
- •terminal failure of another organ (heart, liver, lung)
- •diabetes mellitus type 1
- •pregnant or breastfeeding woman
- •refusal to give informed consent
结局指标
主要结局
Accuracy of the algorithm at detecting infections at presymptomatic stage
时间窗: The primary endpoint will be assessed periodically throughout the study, up to 24 months.
Accuracy, sensitivity, specificity, negative and positive predictive value of the machine learning algorithm at detecting infections in presymptomatic stage in kidney transplant recipients.
次要结局
未报告次要终点
研究者
Prof. Ondřej Viklický, M.D., Ph.D.
Head of Transplantation Center, Principal Investigator
Institute for Clinical and Experimental Medicine