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Tackling in-hospital cardiac arrest with advanced AI

Hospital staff holding tablet

Canadas health-care system faces a serious, but often ignored concern: in-hospital cardiac arrest (IHCA). Despite medical advances and significant in-hospital supports, a Statistics Canada report on the leading causes of death nationally indicates that heart-related disease is one of the most common.  

To explore this issue, Telfer professor and Scientist at the University of Ottawa Heart Institute, Christopher Sun has received funding from the Canadian Institutes of Health Research Project Grant Program for his project titled “Artificial Intelligence Supported In-Hospital Cardiac Arrest Prediction, Prevention, and Management. Sun will go beyond current methods to develop a complex and reliable IHCA prediction model.  

For Sun, “This project is a unique, unified effort that combines advanced analytical and clinical expertise to create a powerful tool for improving patient care and enabling timely interventions that can save lives.” 

Interestingly, even when patients are in the hospital, cardiac arrests are a significant health-care issue, with a survival rate of only 1825%. More alarmingly, many of these IHCAs are preventable, if the patients are properly identified as at-risk.  

While current methods offer some ability to predict when patients are at risk, they lack the complexity or reliability necessary to ensure patient resilience. Advanced artificial intelligence (AI) programs could be a way to evaluate IHCA risk quickly and efficiently and present life-saving interventions. 

An innovative prediction and intervention program 

Sun wants to pioneer an IHCA predication model that won’t only identify IHCA Image of Christopher Sunrisk factors but also identify specific interventions and actionable advice that can be applied to patients. Drawing on an expert consensus, Sun aims to create a deep-learning IHCA model that will identify both long and short term IHCA risk for adult patients. 

His project will be based on an analysis of patient datasets collected from the Ottawa Heart Institute, Ottawa Hospital and Montefiore Medical Center (New York). 

A novel IHCA prediction model could be implemented in hospitals worldwide, not only optimizing use of scarce hospital resources but also improving the outcomes of IHCA at-risk patients, and ultimately, saving countless lives globally.  


About the Author

Phoenix a rejoint l'équipe de Direction de la recherche de Telfer en 2021 grâce au régime travail-études. Elle est diplômée du baccalauréat spécialisé en enseignement des langues secondes et est présentement inscrite au programme de formation à l’enseignement (B.Éd.). Ses responsabilités comprennent la gestion et l'analyse de données, la rédaction d'articles, en plus d'autres tâches administratives.<br/><br/>Phoenix joined the Telfer Research Office in 2021 through the work-study program. She holds an Honours Bachelor of Arts in Second Language Teaching and is working toward a Bachelor of Education. Her responsibilities include data management and analytics, story writing, and other administrative tasks.

Profile Photo of Phoenix Hudson