2024 Innovation Grant Winner: Dr. Michaël Chassé

CDTRP 2024 Research Innovation Grant Competition Results

CDTRP is thrilled to announce the results of the CDTRP 2024 Research Innovation Grant Competition, made possible through our partnership with various esteemed organizations. We are proud to showcase all of the exciting projects that have been funded in this year’s competition.

We would like to extend our warmest congratulations to Dr. Michaël Chassé and his team for being awarded the CDTRP UdeM Research Innovation Grant. We wish them all the best as they embark on their innovative project!

The Transplantation Program of the Université de Montréal would like to congratulate Dr. Caroline Lamarche on winning the CDTRP innovation grant in the 2024 competition for her project entitled “Tacrolimus-resistant regulatory T cells for adoptive immunotherapy”. The Transplantation Program of the Université de Montréal is proud to contribute to the CDTRP grant competitions and thus participate in transplantation and organ donation research in Canada.

The 2 projects funded by a CDTRP Université de Montréal grant illustrate the mission of the Transplantation Program of the Université de Montréal, which is to support innovation in research and clinical practice in order to improve the care of transplanted donors, families and patients.

-Transplantation Program of the Université de Montréal

CDTRP UdeM Research Innovation Grant : Dr. Michaël Chassé

Project Title: From routinely collected medical data to donors: Canadian dOnation Monitoring & Alert SyStem Network (COMPASS) 2.0

Main affiliation: Université de Montréal

Theme: 2 – Inform Universal Practices for Donation

Lay Abstract

Organ transplantation depends on proper organ donor identification and conversion to actual donors. Donor identification is a major challenge that relies heavily on healthcare worker training. Canadian studies have shown that we miss many potential donors, resulting in fewer transplantations, causing harm to those awaiting transplantation and depriving those who might have wished to give their organs the opportunity to do so. This project consists of a crucial stage in the COMPASS (Canadian dOnation Monitoring & Alert SyStem) Network Program, a novel initiative using artificial intelligence to improve efficiency of organ donation.

Objective: Our objectives are to investigate the performance of a machine learning model (artificial intelligence) to identify potential organ donors among adults admitted to the ICU, and to explore the barriers, enablers and strategies required to implement the model in an intensive care unit setting.

Methods: We will conduct a multi-step validation/implementation study in the intensive care unit of Centre hospitalier de l’Université de Montréal (CHUM). We will use a neural network model (artificial intelligence) to flag potential organ donors in a real-world critical care setting and explore what are the enablers and barriers to the implementation of the model in clinical care.

Expected output: This project is an essential step to demonstrate the feasibility of using artificial intelligence, to identify potential organ donors in a real clinical setting. If accurate when used in a prospective context, a future successful implementation of our model could improve identification rates, and potentially increase the total number of transplants.