JMIR Publications

ABSTRACT

Background:

The number of solid organ transplants (SOT) in Canada has increased 33% over the past decade. Hospital readmissions are common within the first year after transplant and are linked to increased morbidity and mortality. Nearly half of these admissions to hospital appear to be preventable. Mobile health (mHealth) technologies hold promise to reduce admission to hospital and improve patient outcomes as they allow real-time monitoring and timely clinical intervention.

Objective:

To determine whether an innovative mHealth intervention can reduce hospital readmission and unscheduled visits to the emergency department (ED) or transplant clinic. Our second objective is to assess the use clinical and continuous ambulatory physiologic data to develop machine learning algorithms to predict risk of infection, organ rejection, and early mortality in adult heart, kidney, and liver transplant recipients.

Methods:

REmote moBile Outpatient mOnitoring in Transplant (Reboot) 2.0 is a two-phased single-center study to be conducted at the University Health Network (UHN) in Toronto, Canada. Phase 1 will consist of a 1-year concealed randomized control trial of 400 adult heart, kidney, and liver transplant recipients. Participants will be randomized to receive either personalized communication using a mHealth application in addition to standard of care phone communication (intervention group), or standard of care communication only (control group). In phase two, the prior collected dataset will be utilized to develop machine learning (ML) algorithms to identify early markers of rejection, infection, and graft dysfunction post-transplantation. The primary outcome will be a composite of any unscheduled hospital admission, visits to the ED or transplant clinic following discharge from the index admission. Secondary outcomes will include: 1) patient-reported outcomes using validated self-administered questionnaires; 2) 1-year graft survival rate; 3) 1-year patient survival rate; and 4) number of standard of care phone voice messages.

Results:

At the time of this manuscript’s completion, no results are available.

Conclusions:

Building from previous work, this project will aim to leverage an innovative mHealth application to improve outcomes and reduce hospital readmission in adult SOT recipients. Additionally, the development of ML algorithms to better predict adverse health outcomes will allow for personalized medicine to tailor clinician-patient interactions, and mitigate the healthcare burden of a growing patient population.