Remote Mobile Outpatient Monitoring in Transplant (Reboot) 2.0: Protocol for a Randomized Controlled Trial.

Area of Science:

• Transplant medicine
• Digital health
• Machine learning in healthcare

Background:

• Solid organ transplants in Canada have increased by 33% in the last decade.
• Hospital readmissions post-transplant are common, increasing morbidity and mortality, with nearly half deemed preventable.
• Mobile health (mHealth) technologies offer potential for real-time monitoring and timely interventions to reduce readmissions.

Purpose of the Study:

• To determine if an mHealth intervention reduces hospital readmissions and unscheduled visits in transplant recipients.
• To develop machine learning algorithms using clinical and physiological data to predict infection, organ rejection, and mortality risk.
• To enable personalized medicine approaches for transplant patient care.

Main Methods:

• A two-phased, single-center study (Reboot 2.0) at University Health Network, Toronto.
• Phase one: A 1-year concealed randomized controlled trial with 400 adult heart, kidney, and liver transplant recipients comparing mHealth app intervention to standard care.
• Phase two: Development of machine learning algorithms using collected data to identify early markers of adverse post-transplant events.

Main Results:

• No results are available at the time of this paper's completion.

Conclusions:

• This project aims to use an innovative mHealth app to improve outcomes and reduce hospital readmissions in adult solid organ transplant recipients.
• Machine learning algorithms will be developed to predict adverse health outcomes, facilitating personalized medicine.
• The study seeks to mitigate the healthcare burden associated with a growing transplant patient population.