Mount Sinai has undertaken a study that could transform standard treatment course and one which has ramifications for global health. Researchers developed an AI model to make individualized treatment recommendations for atrial fibrillation (AF) patients.
Individualized treatment recommendations are decision rules that map individual patient characteristics to specific treatment options to achieve the best clinical benefit for that patient. This framework, central to precision medicine, uses patient-specific information to move beyond one-size-fits-all approaches. Machine learning and statistical methods are used to develop these rules by analysing data and using advanced processing to assess the outcomes.
The new breakthrough should help clinicians to accurately decide whether or not to treat them with anticoagulants (blood thinner medications) to prevent stroke, which is currently the standard treatment course in this patient population. This model presents a completely new approach for how clinical decisions are made for AF patients and could represent a potential paradigm shift in this area.
In the recent study, the AI model recommended against anticoagulant treatment for up to half of the AF patients who otherwise would have received it based on standard-of-care tools. This could have profound ramifications for global health.
AF is the most common abnormal heart rhythm, impacting roughly 59 million people globally. During AF, the top chambers of the heart quiver, which allows blood to become stagnant and form clots. These clots can then dislodge and go to the brain, causing a stroke. Blood thinners are the standard treatment for this patient population to prevent clotting and stroke; however, in some cases this medication can lead to major bleeding events.
Electronic health records analysed
This AI model uses the patient’s whole electronic health record to recommend an individualized treatment recommendation. It weighs the risk of having a stroke against the risk of major bleeding (whether this would occur organically or as a result of treatment with the blood thinner).
This approach to clinical decision-making is truly individualized compared to current practice, where clinicians use risk scores/tools that provide estimates of risk on average over the studied patient population, not for individual patients. Thus, this model provides a patient-level estimate of risk, which it then uses to make an individualized recommendation taking into account the benefits and risks of treatment for that person.
The study could alter the approach clinicians take to treat a very common disease to minimize stroke and bleeding events.
First individualised clinical model
This is the first-known individualized AI model designed to make clinical decisions for AF patients using underlying risk estimates for the specific patient based on 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 the model’s performance among 38,642 patients with atrial fibrillation within the Mount Sinai Health System. They also externally validated the model 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. These patients would have received anticoagulants under current treatment guidelines.
