AI Models Identify Hidden Cardiac Arrest Risk in Routine Patient Data
Key Takeaways:
- Researchers have built AI models that analyse electronic health records and electrocardiograms to identify people at elevated risk of sudden cardiac arrest, which kills more than 400,000 Americans each year.
- In a real-world group of nearly 40,000 patients, the combined model correctly flagged 153 of 228 high-risk people who later experienced cardiac arrest, narrowing risk prediction from 1 in 1,000 to 1 in 100.
- The models also surfaced modifiable contributors such as electrolyte disorders, substance use and medication interactions, pointing to practical opportunities for clinicians to intervene.
A new approach to an unpredictable emergency
Researchers have developed artificial intelligence (AI) models capable of analysing electronic health records (EHR) and electrocardiograms to pinpoint people in the general population who face a heightened risk of sudden cardiac arrest. The condition is responsible for more than 400,000 deaths each year in the United States and carries a survival rate of just 10%, making any tool capable of forecasting it a meaningful step forward.
The work represents a notable advance in anticipating an event that is widely considered difficult, if not impossible, to predict, and which often strikes people who have no previously known heart disease.
“Using artificial intelligence applications and health records data, the prediction of cardiac arrest in the general population is feasible,” said Dr Neal Chatterjee, the study’s lead investigator and a cardiologist at the University of Washington School of Medicine.
The paper was published on 11 May in JACC: Advances, a journal of the American College of Cardiology. Additional co-senior authors are affiliated with Massachusetts General Hospital and the Broad Institute of MIT and Harvard.
How the models were built
The investigation drew on a test population of roughly 1.7 million patients enrolled in a large healthcare system in the United States. The team built three separate AI models, each trained on a distinct dataset. The first, referred to as “EKG-only,” relied solely on electrocardiogram readings. The second, “EHR-only,” weighed 156 clinical features drawn from patients’ health records. The third combined both EKG and EHR data into a single integrated model.
The researchers developed and validated their models across three distinct patient groups.
Training cohort
The models were initially trained using data from 993 people who had experienced out-of-hospital cardiac arrest between 2013 and 2021, alongside 5,479 control patients matched for age and sex who had not. This stage allowed the AI to learn which patterns in EHR entries and EKG readings were linked to a higher risk of cardiac arrest.
Testing cohort
To confirm that the models could reliably distinguish between high- and low-risk indicators, the researchers applied them to a separate group consisting of 463 cardiac arrest cases from 2022 to 2023 and 2,979 control patients. The risk associations identified in this testing group closely mirrored those established during training.
Real-world cohort
The final stage involved 39,911 people who had received EKGs during 2021, regardless of their health status. The researchers examined the records of those within this group who went on to experience cardiac arrest over the following two years, assessing how closely their profiles aligned with the risk patterns identified by the models.
Within this real-world group, the combined EHR-EKG model accurately predicted 153 of 228 people who were classified as high-risk and who later went on to experience a cardiac arrest.
Bringing theoretical risk into focus
The shift in predictive precision is one of the study’s most striking outcomes.
“With these models, we’re able to enrich risk prediction from about 1 in 1,000 down to 1 in 100,” Chatterjee said. “If your doctor were to tell you that your risk of cardiac arrest is 1 in 100, that would catch your attention. We’re bringing a theoretical risk into focus.”
Another encouraging finding concerned the performance of the EKG-based model on its own. AI-enhanced analysis of electrocardiograms alone demonstrated strong predictive ability, only modestly behind the two models that drew on EHR data.
“The 12-lead EKG is a low-cost tool that might stratify patients’ risk for cardiac arrest in any community around the world,” Chatterjee said.
Risk factors beyond traditional cardiology
The study also surfaced risk factors that lie outside the conventional cardiovascular picture. Contributors flagged by the models included electrolyte disorders, substance use and interactions between medications, all of which are often addressable through clinical attention.
“We show some relatively low hanging fruit … modifiable risk factors,” Chatterjee noted. “A model that flags a patient as high-risk might prompt somebody taking care of a patient to review their medical history and their medications.”
Open questions for clinical practice
While the results demonstrate that predicting cardiac arrest risk at the population level is achievable, Chatterjee was careful to note that the next stage of inquiry involves working out what clinicians should actually do once a patient is flagged.
“We need to figure out which follow-on studies to pursue to understand what we do with this patient information. What screening, what surveillance, what intervention is warranted?”
Limitations of the study
Several constraints temper the findings. All of the data was drawn from a single healthcare system, leaving open the question of whether the models would perform similarly across populations with different demographic profiles or patterns of care. The real-world group was also restricted to people who had received an EKG, and these individuals may differ in important ways from those who had not undergone such testing. In addition, the AI-enhanced interpretations of EKGs could reflect biases tied to demographics or to the way care is delivered.
Funding and support
The research received support from the National Institutes of Health (K23HL169839, R01 HL160003, R01 HL168889, K24 HL153669, R01HL092577, R01HL157635), the American Heart Association (23CDA1050571, 961045), the European Union (MAESTRIA 965286) and the Foundation Leducq (24CVD01). Chatterjee is supported through a philanthropic donation from Kevin and Ann Harrang and through the John and Cookie Laughlin Endowed Professorship.
Source: UW Medicine
AI and Ultrasound Data May Transform Detection of Advanced Heart Failure
Key Takeaways:
- A new artificial intelligence approach can estimate a key heart failure metric using routine ultrasound images and electronic health records
- The method may help identify people with advanced heart failure who are currently missed due to limited access to specialised testing
- Early results show approximately 85% accuracy, suggesting strong potential for real-world clinical use
A new approach to a persistent diagnostic challenge
Applying artificial intelligence to cardiac ultrasound data may offer a more accessible way to identify people with advanced heart failure, according to a new study led by researchers from Weill Cornell Medicine, Cornell Tech, Cornell Ann S. Bowers College of Computing and Information Science, Columbia University Vagelos College of Physicians and Surgeons, and NewYork-Presbyterian.
Advanced heart failure is typically diagnosed using cardiopulmonary exercise testing (CPET). While effective, this method requires specialised equipment and trained personnel and is usually limited to large medical centres. As a result, many people do not receive timely or appropriate diagnosis and care.
In the United States alone, an estimated 200,000 people are living with advanced heart failure. However, only a small proportion are properly identified each year, in part due to these diagnostic limitations.
The new study, published on 3 March in npj Digital Medicine, explored whether artificial intelligence could help overcome this bottleneck by using more widely available clinical data.
Predicting peak VO2 without specialised testing
The research team developed an artificial intelligence model capable of predicting peak oxygen consumption, known as peak VO2. This measure is a central output of CPET and a key indicator of heart failure severity and patient risk.
Instead of relying on exercise testing, the model uses routinely collected data, including cardiac ultrasound images and information from electronic health records. These sources are already embedded in standard clinical care, making the approach potentially scalable across a wide range of healthcare settings.
“This opens up a promising pathway for more efficient assessment of patients with advanced heart failure using data sources that are already embedded in routine care,” said study senior author Dr. Fei Wang, associate dean for AI and data science and the Frances and John L. Loeb Professor of Medical Informatics at Weill Cornell Medicine.
A collaborative effort across disciplines
The study represents a highly collaborative effort involving experts in artificial intelligence, informatics, and clinical cardiology. Alongside Dr. Wang’s team, key contributors included Dr. Deborah Estrin, associate dean for impact at Cornell Tech, and Dr. Nir Uriel, director of advanced heart failure and cardiac transplantation at NewYork-Presbyterian.
The work forms part of the broader Cardiovascular AI Initiative, a joint effort between Cornell, Columbia, and NewYork-Presbyterian aimed at advancing the use of artificial intelligence in heart failure diagnosis and management.
“Initially we put together a group of more than 40 heart failure specialists and asked them to tell us where they thought AI could best be applied,” said Dr. Uriel.
One of the most promising opportunities identified was the use of artificial intelligence to analyse cardiac ultrasound data in order to detect advanced heart failure earlier and more accurately.
How the AI model works
The research team developed a multi-modal, multi-instance machine learning model designed to process multiple types of clinical data simultaneously. These included:
- Moving ultrasound images of the heart
- Waveform imagery showing heart valve motion and blood flow
- Structured and unstructured data from electronic health records
“The close interaction between clinicians and AI researchers on this project ended up driving the development of new AI techniques that would not have been explored otherwise,” said Dr. Estrin. “So, this was a case of medicine shaping the future of AI – not just AI shaping the future of medicine.”
Training and validation
The model was trained using deidentified data from 1,000 people with heart failure treated at NewYork-Presbyterian/Columbia University Irving Medical Center.
Once trained, it was tested on a separate group of 127 people with heart failure from three additional NewYork-Presbyterian campuses. The goal was to assess how accurately the model could predict peak VO2 and identify individuals at high risk.
Strong early performance
The results demonstrated a level of accuracy that exceeds previous artificial intelligence approaches for predicting peak VO2.
Using a standard performance metric for risk prediction, the model achieved an overall accuracy of approximately 85%. This suggests that the tool could effectively distinguish between people at higher and lower risk of advanced heart failure in clinical settings.
Implications for clinical practice
If validated in further studies, this approach could significantly expand access to advanced heart failure assessment. By removing the need for specialised exercise testing in some cases, clinicians may be able to identify high-risk individuals earlier and initiate appropriate treatment sooner.
“If we can use this approach to identify many advanced heart failure patients who would not be identified otherwise, then this will change our clinical practice and significantly improve patient outcomes and quality of life,” Dr. Uriel said.
Next steps towards clinical adoption
The research team is now planning prospective clinical studies to further evaluate the model. These studies will be essential for regulatory approval, including review by the U.S. Food and Drug Administration, and for eventual integration into routine clinical workflows.
The work was partially supported by funding from NewYork-Presbyterian as part of the Cardiovascular AI Initiative. As with other research at Weill Cornell Medicine, relationships with external organisations are disclosed publicly to ensure transparency.
Source: Weill Cornell Medicine
Read More
Heart Disease Now Affects Nearly Half of US Adults, as Obesity and Diabetes Continue to Rise
Key Takeaways:
- Nearly half of adults in the United States are now living with cardiovascular disease, with prevalence projected to rise further as obesity, diabetes, and hypertension increase.
- New data highlight worsening cardiometabolic risk factors, including declining blood pressure and glycaemic control, alongside rising concerns around sleep health, physical inactivity, and nicotine exposure.
- The report underscores the urgent need for prevention-focused, equitable approaches to cardiovascular, kidney, and metabolic health across the life course.
A comprehensive annual snapshot of cardiovascular health
The 2026 Heart Disease and Stroke Statistics Report from the American Heart Association, published in the journal Circulation, provides an updated and wide-ranging overview of heart disease, stroke, and cardiovascular risk factors. Updated annually, the report integrates the most recent data, adds new thematic chapters, and removes outdated material to reflect the evolving cardiovascular health landscape.
The latest edition draws on a year-long collaborative effort involving volunteers, scientists, clinicians, government representatives, and AHA staff. It includes an expanded chapter on nicotine and tobacco use and exposure, alongside a new chapter focused on cardiovascular, kidney, and metabolic (CKM) syndrome. Together, these additions reflect growing recognition of the interconnected nature of cardiometabolic risk factors and their cumulative impact on population health.
Cardiovascular health trajectories and nicotine exposure
According to the report, several major cardiometabolic conditions are projected to rise substantially by 2050 among adults in the United States. Hypertension prevalence is expected to reach 61 percent, diabetes 26.8 percent, and obesity 60.6 percent. In contrast, hypercholesterolaemia is the only major risk factor projected to decline, falling from 45 percent to 24 percent.
Most core health behaviours are projected to worsen over time. An important exception is sleep, where inadequate sleep duration is expected to increase. Evidence from a 2010 to 2022 meta-analysis showed that people with ideal cardiovascular health experienced a 74 percent lower risk of cardiovascular disease events compared with those with poor cardiovascular health.
Nicotine exposure remains a major concern. People who smoke have a mortality risk three times higher than those who have never smoked. While smoking prevalence among adults in the United States has declined, the use of e-cigarettes has increased sharply. National Health Interview Survey data from 2017 to 2023 indicate that e-cigarette use has quadrupled over this period.
Physical activity and sleep health
Levels of physical activity remain suboptimal across age groups and regions. Only one in five children and adolescents aged 6 to 17 years achieved at least 60 minutes of daily physical activity. Globally, around one-third of adults across 163 countries did not meet recommended activity levels.
Sleep health has emerged as a significant cardiovascular risk factor. Data from the National Health and Nutrition Examination Survey covering 2017 to 2020 showed that 30 percent of adults experienced at least one hour of sleep debt, defined as the difference between sleep duration on workdays and free days. Observational analyses linked poor sleep with higher odds of type 2 diabetes, hypercholesterolaemia, and hypertension.
Obesity, lipids, blood pressure, and diabetes
Obesity prevalence continues to rise among both children and adults in the United States. Estimates from the Global Burden of Diseases, Injuries, and Risk Factors study indicated that in 2021 more than 15 million children aged 5 to 14 years, 21 million young people aged 15 to 24 years, and 172 million adults aged 25 years or older were living with overweight or obesity.
While the prevalence of high total cholesterol has decreased, low-density lipoprotein cholesterol remains a major driver of cardiovascular mortality. Global data from 2021 attributed a cardiovascular disease mortality rate of 43.7 per 100,000 people to elevated low-density lipoprotein cholesterol.
Hypertension prevalence remained broadly stable between 2013 and 2023. However, blood pressure control worsened, declining from 54.1 percent in 2013 to 2014 to 48.3 percent in 2017 to 2020. Some improvement was observed among non-Hispanic Black adults between 2017 to 2020 and 2021 to 2023.
Diabetes prevalence also remains high. Between 2021 and 2023, an estimated 29.5 million adults had diagnosed diabetes, 96 million had prediabetes, and 9.6 million were living with undiagnosed diabetes. Among people with diagnosed diabetes, glycated haemoglobin levels increased significantly from 2017 to 2020 and again from 2021 to 2023, while overall glycaemic control rates declined.
Kidney disease, CKM syndrome, and pregnancy outcomes
The burden of kidney disease has risen markedly over the past two decades. The prevalence of end-stage kidney disease nearly doubled between 2002 and 2019, before stabilising in subsequent years. Across 114 cohort studies, both albuminuria and reduced kidney function were consistently associated with increased risk of kidney failure and mortality.
Data from NHANES between 2011 and 2020 suggest that approximately 90 percent of adults in the United States were in stage 1 or higher of CKM syndrome. People from underrepresented ethnic and racial groups experienced a disproportionately higher burden of advanced CKM stages. More advanced stages were strongly associated with increased cardiovascular disease mortality.
The report also highlights links between cardiometabolic health and pregnancy outcomes. In Japan, pregnant individuals with higher healthy lifestyle scores before pregnancy had around a one-third lower risk of adverse pregnancy outcomes compared with those with the lowest scores. Although maternal mortality rates declined across all ethnic and racial groups between 2021 and 2022, persistent disparities remain.
Cardiovascular disease, stroke, dementia, and congenital conditions
Overall cardiovascular disease prevalence reached nearly 49 percent among adults aged 20 years or older, based on NHANES data from 2021 to 2023. Prevalence increased with age in both women and men. At the population level, stronger adherence to healthy dietary patterns was associated with lower cardiovascular disease risk.
Stroke incidence declined between 1993 and 2015 among both Black and White adults, although rates remained consistently higher in Black populations. Dementia prevalence among older adults decreased between 2011 and 2021, though findings varied depending on study design and population. Evidence from selected intervention studies suggested that high-intensity training may help slow cognitive decline.
Congenital cardiovascular defects were estimated to affect around 1 in 80 babies in high-income regions of North America. Globally, survival into adulthood among people born with congenital heart disease improved substantially between 1990 and 2019. Population-based analyses linked limited prenatal care, neighbourhood deprivation, and air pollution to increased risk of heart defects, poorer outcomes, and delayed diagnosis.
Heart rhythm disorders, cardiac arrest, and heart failure
Heart rhythm disorders and heart failure continue to contribute significantly to cardiovascular morbidity. Atrial fibrillation affected an estimated 10.55 million adults in the United States, representing 4.48 percent of the adult population.
Patterns of cardiac arrest have also shifted. Opioid-related out-of-hospital cardiac arrests accounted for less than 1 percent of cases in 2000 but rose to between 7 percent and 14 percent by 2023. Coronary heart disease prevalence was estimated at 5.2 percent among adults aged 20 years or older between 2021 and 2023. Over the same period, heart failure prevalence increased from 6.7 million people in 2017 to 2020 to 7.7 million in 2021 to 2023.
A growing burden with global implications
Taken together, the 2026 Heart Disease and Stroke Statistics Report paints a picture of a growing cardiovascular disease burden affecting around half of the adult population. Despite major advances in diagnostics, prevention strategies, and treatment options, ageing populations, widening health inequalities, and rising cardiometabolic risk factors continue to place increasing pressure on healthcare systems.
The report emphasises the need for coordinated, prevention-led approaches that prioritise early intervention and equitable access to care. Without sustained action across policy, healthcare, and community settings, current trends are likely to continue, with profound long-term health and economic consequences.
Read More