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