Use of Wearables to Detect Infections in Kidney Transplant Recipients

Condition(s) studied

Study 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.

Eligibility

Primary outcome measure(s)

• 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.

Trial sites (1)

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