Use of Wearables to Detect Infections in Kidney Transplant Recipients

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

Status

Not yet recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06364618

Acronym

RENALERT

Enrollment

200

Registered

2024-04-15

Start date

2024-09-01

Completion date

2027-12-31

Last updated

2024-04-15

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Kidney Transplant Infection

Keywords

biometric data, continuous monitoring, machine learning, early alert system, infection, kidney transplantation

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

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

Design outcomes

Primary

Measure	Time frame	Description
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.

Other

Measure	Time frame	Description
Rate of in-patient admissions	Periodically throughout the study, up to 24 months.	Any hospital admission for infection treatment is considered an event of this outcome
Incidence of decrease/increase of Quality of Life	Periodically throughout the study, up to 24 months.	Score change of WHOQOL-BREF from baseline.

Countries

Czechia

Contacts

Primary ContactVojtech Petr, MD

petv@ikem.cz720-639-0909

Outcome results