Dr. Douglas Lee, Ted Rogers Chair in Heart Function Outcomes and cardiologist at the PMCC, will lead the first funded project, creating a new machine learning model to predict prognoses of patients with heart failure, preventing unnecessary admissions to hospital.
The project marks a significant step forward in advancing solutions for a disease affecting more than a million Canadians, and costing the healthcare system $3 billion each year.
Heart failure is a leading cause of hospital readmissions that bear a substantial burden on the healthcare systems of most countries. In general, the disease is treated reactively: a patient experiences symptoms, heads to the emergency department and is admitted to hospital.
“We have hit a wall in predicting readmissions using traditional clinical methods,” says Dr. Lee. “As it stands, without the right tools in place, low-risk patients may be unnecessarily admitted while high-risk patients could be inadvertently discharged home.”
To better predict outcomes and improve care, the team will develop a new algorithm based on an array of information that includes biomarkers, physiologic data, blood samples and a patient’s own reported symptoms. Combined with evolving technology such as remote patient monitoring and machine learning, the aim is to develop a complete, integrated model to predict heart failure readmissions.