New AI Models Predict Heart Failure Risk Decades Earlier Than Before

New models for detecting heart failure risk have been developed with the help of Estonian data. These tools aim to identify high-risk individuals much earlier than before. Researchers hope they will allow doctors to intervene sooner and reduce cases of this growing health problem. Public health researcher Laura Lõo from the University of Tartu contributed to creating two new risk assessment models. The first, SCORE2-HF, predicts heart failure risk over up to 30 years for the general public. The second, SMART2-HF, focuses on people with existing cardiovascular disease and evaluates their risk within the next decade.

Both models rely on large pan-European datasets, with Estonian data playing a key role in their development and validation. Heart failure remains a major public health challenge, particularly as populations age and diagnoses rise. Estonia falls into the high-risk category, alongside many other Eastern European nations. The models are expected to be included in European Society of Cardiology guidelines. Their purpose is to help doctors pinpoint individuals at greater risk sooner. Early detection could lead to interventions that delay or even prevent heart failure.

These new risk prediction tools offer a clearer way to assess heart failure likelihood in different groups. By identifying at-risk individuals earlier, healthcare providers can take steps to manage or reduce their chances of developing the condition. The models may soon become a standard part of cardiovascular risk assessment across Europe.