AI Model Transforms Atrial Fibrillation Treatment Choices

Published Date : 3/9/2025 

Mount Sinai researchers have developed an AI model that makes individualized treatment recommendations for atrial fibrillation patients, potentially transforming the standard treatment course and improving global health outcomes. 

Mount Sinai researchers have developed an AI model that makes individualized treatment recommendations for atrial fibrillation (AF) patients. This model helps clinicians accurately decide whether or not to treat patients with anticoagulants (blood thinners) to prevent stroke. The model presents a new approach to clinical decision-making, potentially representing a paradigm shift in the treatment of AF.

AF is the most common abnormal heart rhythm, affecting approximately 59 million people globally. During AF, the top chambers of the heart quiver, allowing blood to become stagnant and form clots. These clots can dislodge and travel to the brain, causing a stroke. Blood thinners are the standard treatment to prevent clotting and stroke, but they can also lead to major bleeding events.

The AI model uses the patient’s entire electronic health record to recommend an individualized treatment plan. It weighs the risk of stroke against the risk of major bleeding, providing a patient-level estimate of risk. This approach is more personalized compared to current practice, where clinicians use risk scores that provide average estimates for the studied population, not for individual patients.

The study could revolutionize how clinicians treat a common disease to minimize stroke and bleeding events. It also reflects a potential paradigm change in clinical decision-making. This is the first-known individualized AI model designed to make clinical decisions for AF patients using all of their actual clinical features. It computes an inclusive net-benefit recommendation to mitigate stroke and bleeding.

Researchers trained the AI model on electronic health records of 1.8 million patients over 21 million doctor visits, 82 million notes, and 1.2 billion data points. They generated a net-benefit recommendation on whether or not to treat the patient with blood thinners. To validate the model, researchers tested its performance among 38,642 patients with atrial fibrillation within the Mount Sinai Health System and externally validated it on 12,817 patients from publicly available datasets from Stanford.

The model generated treatment recommendations that aligned with mitigating stroke and bleeding. It reclassified around half of the AF patients to not receive anticoagulation, which they would have received under current treatment guidelines. This study represents a new era in caring for patients, allowing for more personalized, tailored treatment plans.

Dr. Joshua Lampert, Director of Machine Learning at Mount Sinai Fuster Heart Hospital, says, “This study represents a profound modernization of how we manage anticoagulation for patients with atrial fibrillation and may change the paradigm of how clinical decisions are made. This approach overcomes the need for clinicians to extrapolate population-level statistics to individuals while assessing the net benefit to the individual patient.”

Dr. Girish Nadkarni, Chair of the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai, adds, “This work illustrates how advanced AI models can synthesize billions of data points across the electronic health record to generate personalized treatment recommendations. By moving beyond the ‘one size fits none’ population-based risk scores, we can now provide clinicians with individual patient-specific probabilities of stroke and bleeding, enabling shared decision-making and precision anticoagulation strategies.”

Dr. Vivek Reddy, Director of Cardiac Electrophysiology at the Mount Sinai Fuster Heart Hospital, emphasizes, “Avoiding stroke is the single most important goal in the management of patients with atrial fibrillation, a heart rhythm disorder that is estimated to affect 1 in 3 adults sometime in their life. If future randomized clinical trials demonstrate that this AI Model is even only a fraction as effective in discriminating high vs low risk patients as observed in our study, the Model would have a profound effect on patient care and outcomes.”

Justin Kauffman, Data Scientist with the Windreich Department of Artificial Intelligence and Human Health, explains, “When patients get test results or a treatment recommendation, they might ask, ‘What does this mean for me specifically?’ We created a new way to answer that question. Our system looks at your complete medical history and calculates your risk for serious problems like stroke and major bleeding prior to your medical appointment. Instead of just telling you what might happen, we show you both what and how likely it is to happen to you personally. This gives both you and your doctor a clearer picture of your individual situation, not just general statistics that may miss important individual factors.”

Mount Sinai Fuster Heart Hospital at The Mount Sinai Hospital ranks No. 2 nationally for cardiology, heart, and vascular surgery, according to U.S. News & World Report®. It also ranks No. 1 in New York and No. 6 globally, reflecting its leadership in cardiology and heart surgery. 

Frequently Asked Questions (FAQS): 

Q: What is atrial fibrillation?

A: Atrial fibrillation (AF) is an irregular and often rapid heart rate that can lead to blood clots, stroke, and heart failure. It affects approximately 59 million people globally.

Q: How does the AI model help in treating atrial fibrillation?

A: The AI model uses the patient’s entire electronic health record to recommend an individualized treatment plan, weighing the risk of stroke against the risk of major bleeding to provide a patient-level estimate of risk.

Q: What are the benefits of this AI model?

A: The model provides more personalized, tailored treatment plans for atrial fibrillation patients, potentially reducing the risk of stroke and major bleeding events.

Q: How was the AI model validated?

A: The model was trained on electronic health records of 1.8 million patients and validated among 38,642 patients within the Mount Sinai Health System and 12,817 patients from publicly available datasets from Stanford.

Q: What are the potential implications of this study?

A: This study represents a new era in patient care, allowing for more personalized treatment plans and potentially changing the paradigm of clinical decision-making in treating atrial fibrillation. 

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