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Late-stage failure of the left side of the heart is an often-fatal condition affecting hundreds of thousands of people in the U.S. alone. A mechanical heart pump can be a lifesaving intervention for such patients, but the surgery to implant the pump can be risky. One of the most serious risks is right heart failure, in which the right side of the heart becomes unable to pump enough blood to the lungs. Identifying patients who have a high risk of right heart failure can help doctors better prepare patients for heart pump placement. But predicting who is most at risk has proven difficult.
A nationwide team led by researchers at 麻豆学生精品版 has now developed a way to predict a patient鈥檚 individualized risk of right heart failure following surgery to place the pump. The team is now using this risk calculator to tailor care to each patient before and during heart pump placement.
Finding the Needle in the Data Haystack
For people who undergo surgery to implant a left heart pump, the risk of subsequent right heart failure is high: 15 to 30 percent. But the large number of factors that contribute to an individual鈥檚 risk of right heart failure make personalized risk prediction 鈥渆xceptionally difficult,鈥 says currently a cardiology fellow at University of California, San Diego and first author on the study. Taleb helped develop the risk calculator during his clinical research fellowship at U of U Health.
鈥淓ach patient is unique with different health conditions and heart characteristics,鈥 Taleb says. 鈥淗eart pumps also have specific traits, and the combination of these factors makes predictions tough.鈥
Stavros Drakos, M.D., Ph.D., professor of cardiology at U of U Health and senior author on the publication describing the study, says that 鈥渢here have been efforts in the past to predict which patients will get a heart pump [also called a left ventricular assist device, or LVAD] and will not do well, but they didn鈥檛 perform well in the real world.鈥 Even models that seemed to predict outcomes in one hospital often failed to give accurate predictions at another.
Aiming to develop a more accurate and broadly usable risk calculator, the researchers used patient data from 1,125 people across six health centers, including U of U Health. Taking into account variables ranging from pre-existing health conditions to medications and demographic information, they used machine learning to generate and test many models of risk and find the one that best described patients鈥 health outcomes.
Their model identified several variables that are especially useful when predicting whether a patient will develop right heart failure (RVF), such as whether patients needed additional forms of heart support before their initial surgery in order to better prepare them and lead to better outcomes. The researchers used these factors to develop that determines a patient鈥檚 percent risk of right heart failure after surgery.
The new risk calculator, called STOP-RVF, describes individual risk more accurately than earlier models. Importantly, it also works well in a variety of situations. After creating the risk calculator, the researchers 鈥渃hecked their work鈥 by using it to calculate risks retrospectively for patients in another hospital system. The scientists then compared the calculator鈥檚 predictions to the patients鈥 real-world outcomes, finding that their tool was still able to accurately model patients鈥 risk of subsequently developing right heart failure.
Predicting Outcomes Nationwide
Building the model on data from a large and diverse population was essential to accurately describe risk for patients nationwide. 鈥淚t鈥檚 important because we live in a very diverse country," Drakos says. 鈥淏y basing this analysis in multiple sites all over the country鈥攖he Washington, DC, area, the Detroit area, California, Utah, and the broader Mountain West area鈥攊t鈥檚 representative of a large part of our country. It strengthens the generalizability of the work.鈥
The cardiologists, surgeons, and nurse coordinators of the heart failure and LVAD team at U of U Health have already started using the calculator in their own clinical practice to personalize care. 鈥淚t helps tailor the risk assessment for each patient, allowing for better preparation before surgery,鈥 Taleb explains. For patients who have a high risk of right heart failure, doctors can delay the surgery, use different medications to improve patients鈥 odds of recovery, or consider alternative treatments.
Since the calculator has only been in use in the clinic for a short time, it鈥檚 too early to say if it will improve patient outcomes. But Drakos expects that it will be more useful than previous models because it was developed using patient populations from multiple hospitals. 鈥淲e validated it in other hospitals, and it performed very well,鈥 he says. 鈥淏ut of course, time will tell how significant its impact on patient outcomes will be.鈥