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.
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
Kidney transplant recipients followed at the Institute for Clinical and Experimental Medicine.
Cohort
Prospective
Not listed
Accuracy of the algorithm at detecting infections at presymptomatic stage
Accuracy, sensitivity, specificity, negative and positive predictive value of the machine learning algorithm at detecting infections in presymptomatic stage in kidney transplant recipients.
Rate of in-patient admissions
Any hospital admission for infection treatment is considered an event of this outcome
Incidence of decrease/increase of Quality of Life
Score change of WHOQOL-BREF from baseline.
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This trial is not recruiting at the moment. You can still explore other options:
