January 11th, 2018
Former CVRC postdoc trainee, Matthew Kalscheur, MD, assistant professor (CHS), Cardiovascular Medicine, discusses article about a machine-learning algorithm that predicts CRT clinical outcomes in patients wit heart failure based on data from 595 patients enrolled in the COMPANION trial.
Cardiac resynchronization therapy (CRT) involves using a special pacemaker to regulate the rhythm of both lower chambers (ventricles) of the heart in people with heart failure whose left and right ventricles are not pumping in sync. Matthew Kalscheur, MD, assistant professor (CHS), Cardiovascular Medicine, was quoted in Cardiology Today about an article that he and eight other UW-Madison colleagues published about a machine-learning algorithm that predicts CRT clinical outcomes in patients with heart failure based on data from 595 patients enrolled in the COMPANION trial. “Machine learning is a powerful, computational method that could allow for improved description of phenotypes and development of decision support tools to predict clinical outcomes and better inform shared decision-making with patients,” wrote Dr. Kalscheur and colleagues.
Read more: Vital Signs | Cardiology Today | Circulation: Arrhythmia and Electrophysiology (research article)