The researchers — led by Santa Clara Valley internist Calvin Kwong, MD, and Stanford biomedical informatics graduate student Albee Ling — analyzed EHR data from more than 9,000 stroke patients. With this information, the researchers determined the clinical precursors of patients who went on to experience atrial fibrillation, a heart condition that often leads to a second stroke.
Based on their analysis, the researchers published an algorithm for scoring a patient’s risk of experiencing atrial fibrillation in Cardiology. The algorithm focuses on a set of seven risk factors: age, obesity, congestive heart failure, hypertension, coronary artery disease, peripheral vascular disease and disease of the heart valves.
The researchers said they hope identifying stroke patients at risk for atrial fibrillation will help to inform physicians’ treatment decisions, such as whether a patient needs to be monitored after hospitalization. “The scoring system we developed is simple to use and the results could help physicians tailor treatment to individual patients,” Ms. Ling said.
More articles on health IT:
UN: US ranks No. 2 in commitment to cybersecurity
Google DeepMind to establish 1st North American lab
Petya cyberattackers make public statement, demand $256k for encryption key
At the Becker's 11th Annual IT + Revenue Cycle Conference: The Future of AI & Digital Health, taking place September 14–17 in Chicago, healthcare executives and digital leaders from across the country will come together to explore how AI, interoperability, cybersecurity, and revenue cycle innovation are transforming care delivery, strengthening financial performance, and driving the next era of digital health. Apply for complimentary registration now.
