Use of Wearables to Detect Infections in Kidney Transplant Recipients
- Conditions
- Kidney Transplant Infection
- Registration Number
- NCT06364618
- Lead Sponsor
- Institute for Clinical and Experimental Medicine
- Brief Summary
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:
1. What are the biometric data pattern changes in impending infections?
2. 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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 200
- kidney transplant recipient
- age 18 years or more
- kidney allograft function (eGFR based on CKD-EPI more than 15ml/min/1.73m2)
- 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
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Accuracy of the algorithm at detecting infections at presymptomatic stage 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.
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Institute for Clinical and Experimental Medicine
🇨🇿Prague, Czechia