Researchers have developed artificial intelligence (AI) models that can scrutinize electronic health records (EHR) and electrocardiograms to identify those at elevated risk for sudden cardiac arrest\u2014a condition that causes more than 400,000 U.S. deaths annually and has a survival rate of only 10%.\u00a0<\/p>\n
The finding represents a significant advancement in predicting a largely unpredictable medical emergency that often strikes people with no known heart disease. \u201cUsing artificial intelligence applications and health records data, the prediction of cardiac arrest in the general population is feasible,\u201d said Dr. Neal Chatterjee, the study\u2019s lead investigator and a cardiologist at the University of Washington School of Medicine.\u00a0 \u00a0<\/p>\n
JACC: Advances, a journal of the American College of Cardiology, published the paper this past spring. Other co-senior authors are from Massachusetts General Hospital and the Broad Institute of MIT and Harvard. \u00a0<\/p>\n
The researchers\u2019 test population comprised approximately 1.7 million patients in a large U.S. healthcare system. Three AI models were developed with discrete datasets: \u201cEKG-only,\u201d \u201cEHR-only\u201d (weighing 156 clinical features of patients\u2019 records), and a combined model that integrated EKG and EHR data sets. \u00a0<\/p>\n
The investigators developed and validated the AI model with three distinct patient groups: \u00a0<\/p>\n
With the real-world cohort, the combined EHR-EKG model correctly predicted 153 of 228 people who were high-risk and who went on to experience a cardiac arrest. \u00a0<\/p>\n
\u201cWith these models, we\u2019re able to enrich risk prediction from about 1 in 1,000 down to 1 in 100,\u201d Dr. Chatterjee said. \u201cIf your doctor were to tell you that your risk of cardiac arrest is 1 in 100, that would catch your attention. \u201cWe\u2019re bringing a theoretical risk into focus.\u201d\u00a0<\/p>\n
Another promising finding was that AI-enhanced EKG analysis alone showed strong predictive ability, only modestly lower than the two models that incorporated EHR data.\u00a0<\/p>\n
\u201cThe 12-lead EKG is a low-cost tool that might stratify patients\u2019 risk for cardiac arrest in any community around the world,\u201d Dr. Chatterjee stated.\u00a0<\/p>\n
The study also identified cardiac-arrest risk features outside of those typically associated with cardiovascular disease. Among these contributors were electrolyte disorders, substance use and medication interactions.\u00a0<\/p>\n
\u201cWe show some relatively low hanging fruit\u2026modifiable risk factors,\u201d Dr. Chatterjee noted. \u201cA model that flags a patient as high-risk might prompt somebody taking care of a patient to review their medical history and their medications.\u201d\u00a0<\/p>\n
While the findings demonstrate feasibility of risk prediction, Dr. Chatterjee emphasized that more study is needed to determine best clinical responses when a model flags a patient\u2019s elevated cardiac-arrest risk.\u00a0\u201cWe 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?\u201d he remarked.\u00a0<\/p>\n
The study has important limitations. All data came from a single healthcare system, and generalizability to other populations with different demographics and care patterns is unknown. The real-world cohort was limited to individuals who received an EKG, who may differ from those not undergoing EKG evaluation. The AI-enhanced EKG representations could potentially reflect biases linked to demographics and healthcare patterns.\u00a0<\/p>\n
The research was supported with funding from the National Institutes of Health, the American Heart Association, the European Union, and from the Foundation Leducq. Dr. Chatterjee is supported by philanthropic donation of Kevin and Ann Harrang and the John and Cookie Laughlin Endowed Professorship.\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"Researchers have developed artificial intelligence (AI) models that can scrutinize electronic health records (EHR) and electrocardiograms to identify…\n","protected":false},"author":2,"featured_media":613754,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_share_on_mastodon":"0"},"categories":[78],"tags":[10389,259000,19493,18,73494,259001,135,1695,19,17,259002,11322],"class_list":["post-613753","post","type-post","status-publish","format-standard","has-post-thumbnail","category-health","tag-cardiologist","tag-dr-chatterjee","tag-ehr","tag-eire","tag-ekg","tag-electrocardiograms","tag-health","tag-heart-disease","tag-ie","tag-ireland","tag-neal-chatterjee","tag-sudden-cardiac-arrest"],"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@ie\/117015608592017344","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/613753","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/comments?post=613753"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/613753\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media\/613754"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media?parent=613753"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/categories?post=613753"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/tags?post=613753"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}