AI could Improve Heart Attack Diagnosis to Reduce Pressure on Emergency Departments

An algorithm developed using artificial intelligence (AI) could soon be used by doctors to diagnose heart attacks with better speed and accuracy than ever before, according to new research from the University of Edinburgh, funded by the British Heart Foundation and the National Institute for Health and Care Research, and published today in Nature Medicine [1].

The effectiveness of the algorithm, named CoDE-ACS [2], was tested on 10,286 patients in six countries around the world. Researchers found that, compared to current testing methods, CoDE-ACS was able to rule out a heart attack in more than double the number of patients, with an accuracy of 99.6 per cent.

This ability to rule out a heart attack faster than ever before could greatly reduce hospital admissions. Clinical trials are now underway in Scotland with support from the Wellcome Leap, to assess whether the tool can help doctors reduce pressure on our overcrowded Emergency Departments.

As well as quickly ruling out heart attacks in patients, CoDE-ACS could also help doctors to identify those whose abnormal troponin levels were due to a heart attack rather than another condition. The AI tool performed well regardless of age, sex, or pre-existing health conditions, showing its potential for reducing misdiagnosis and inequalities across the population.

CoDE-ACS has the potential to make emergency care more efficient and effective, by rapidly identifying patients that are safe to go home, and by highlighting to doctors all those that need to stay in hospital for further tests.

The current gold standard for diagnosing a heart attack is measuring levels of the protein troponin in the blood. But the same threshold is used for every patient. This means that factors like age, sex and other health problems which affect troponin levels are not considered, affecting how accurate heart attack diagnoses are.

This can lead to inequalities in diagnosis. For example, previous BHF-funded research has shown that women are 50 per cent more likely to get a wrong initial diagnosis. People who are initially misdiagnosed have a 70 per cent higher risk of dying after 30 days [3]. The new algorithm is an opportunity to prevent this.

CoDE-ACS was developed using data from 10,038 patients in Scotland who had arrived at hospital with a suspected heart attack. It uses routinely collected patient information, such as age, sex, ECG findings and medical history, as well as troponin levels, to predict the probability that an individual has had a heart attack. The result is a probability score from 0 to 100 for each patient.

Professor Nicholas Mills, BHF Professor of Cardiology at the Centre for Cardiovascular Science, University of Edinburgh, who led the research, said: "For patients with acute chest pain due to a heart attack, early diagnosis and treatment saves lives. Unfortunately, many conditions cause these common symptoms, and the diagnosis is not always straight forward. Harnessing data and artificial intelligence to support clinical decisions has enormous potential to improve care for patients and efficiency in our busy Emergency Departments."

Professor Sir Nilesh Samani, Medical Director of the British Heart Foundation, said: "Chest pain is one of the most common reasons that people present to Emergency Departments. Every day, doctors around the world face the challenge of separating patients whose pain is due to a heart attack from those whose pain is due to something less serious.

"CoDE-ACS, developed using cutting edge data science and AI, has the potential to rule-in or rule-out a heart attack more accurately than current approaches. It could be transformational for Emergency Departments, shortening the time needed to make a diagnosis, and much better for patients."

Doudesis D, Lee KK, Boeddinghaus J, Bularga A, Ferry AV, Tuck C, Lowry MTH, Lopez-Ayala P, Nestelberger T, Koechlin L, Bernabeu MO, Neubeck L, Anand A, Schulz K, Apple FS, Parsonage W, Greenslade JH, Cullen L, Pickering JW, Than MP, Gray A, Mueller C, Mills NL; CoDE-ACS Investigators.
Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations.
Nat Med. 2023 May 11. doi: 10.1038/s41591-023-02325-4

1. Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations. Nature Medicine 2023. DOI: 10.1038/s41591-023-02325-4. URL: https://www.nature.com/articles/s41591-023-02325-4
2. CoDE-ACS stands for Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome. https://decision-support.shinyapps.io/code-acs/
3. These statistics are taken from the BHF report Bias and Biology: https://www.bhf.org.uk/-/media/files/heart-matters/bias-and-biology-summary.pdf?rev=56108ca8e3564073a4ce42c67c513bc2&hash=52C880653E113A405B241318664D7022

Most Popular Now

Transforming Drug Discovery with AI

A new AI-powered program will allow researchers to level up their drug discovery efforts. The program, called TopoFormer, was developed by an interdisciplinary team led by Guowei Wei, a Michigan...

We may Soon be Able to Detect Cancer wit…

A new paper in Biology Methods & Protocols, published by Oxford University Press, indicates that it may soon be possible for doctors to use artificial intelligence (AI) to detect and...

Maternity Tech Launched to Help NHS Meas…

Health tech provider C2-Ai has formally launched a new 'observatory' system to help hospitals gain a better understanding of risks, outcomes and safety within maternity and neonatal services. Announced at the...

Large Language Models Illuminate a Progr…

This study is led by Prof. Bin Dong (Beijing International Center for Mathematical Research, Peking University) and Prof. Lin Shen (Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational...

Health Innovation East Partners with Cog…

Health Innovation East, the innovation arm of the NHS in the East of England and Cogniss, a no-code ecosystem for digital health solutions, have announced a strategic partnership to launch...

An AI-Powered Wearable System Tracks the…

Scientists at the University of Southern California have developed an artificial intelligence (AI)-powered system to track tiny devices that monitor markers of disease in the gut. Devices using the novel...

"Self-Taught" AI Tool Helps to…

A computer program based on data from nearly a half-million tissue images and powered by artificial intelligence (AI) can accurately diagnose cases of adenocarcinoma, the most common form of lung...

New Computational Model of Real Neurons …

Nearly all the neural networks that power modern artificial intelligence (AI) tools such as ChatGPT are based on a 1960s-era computational model of a living neuron. A new model developed...

Meet CARMEN, a Robot that Helps People w…

Meet CARMEN, short for Cognitively Assistive Robot for Motivation and Neurorehabilitation - a small, tabletop robot designed to help people with mild cognitive impairment (MCI) learn skills to improve memory...

AI Matches Protein Interaction Partners

Proteins are the building blocks of life, involved in virtually every biological process. Understanding how proteins interact with each other is crucial for deciphering the complexities of cellular functions, and...

AI Model to Improve Patient Response to …

A new artificial intelligence (AI) tool that can help to select the most suitable treatment for cancer patients has been developed by researchers at The Australian National University (ANU). DeepPT, developed...

Mobile Phone Data Helps Track Pathogen S…

A new way to map the spread and evolution of pathogens, and their responses to vaccines and antibiotics, will provide key insights to help predict and prevent future outbreaks. The...