Use of Wearables to Detect Infections in Kidney Transplant Recipients

Use of Continuous Biomonitoring for Detection of Infectious Complications in Kidney Transplant Recipients

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.

Inclusion Criteria:

• kidney transplant recipient
• age 18 years or more
• kidney allograft function (eGFR based on CKD-EPI more than 15ml/min/1.73m2)

Exclusion Criteria:

• 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