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Researchers at the Mayo Clinic and AliveCor Inc. are using artificial intelligence (AI) to develop a mobile device that can identify certain patients at risk for sudden cardiac death. This research has enabled a breakthrough in determining the health of the electrical charging system in a patient’s heart.
Researchers have determined that a smartphone-enabled mobile EKG machine can quickly and accurately determine a patient’s QTc, thereby identifying patients at risk for sudden cardiac death due to congenital long QT syndrome (LQTS) or prolongation. drug-induced QT interval.
The heart beats by a complex system of electrical signals triggering regular and necessary contractions. Clinicians assess the heart rate corrected QT interval, or QTc, as a barometer of the vital health of the heart’s electrical charging system. A potentially dangerous prolonged QTc, equal to or greater than 50 milliseconds, can be caused by:
- Over 100 drugs approved by the Food and Drug Administration (FDA).
- Genetics, including congenital long QT syndrome.
- Many systemic diseases, including even SARS-CoV-2-mediated COVID-19.
Such a prolonged QTc can predispose people to dangerously fast and chaotic heartbeats, or even sudden cardiac death. For over 100 years, the assessment and monitoring of the QTc interval has relied heavily on the 12-lead electrocardiogram (ECG). But that could be about to change, according to this research.
Under the direction of Michael Ackerman, MD, Ph.D., a genetic cardiologist at the Mayo Clinic, researchers trained and validated an AI-based deep neural network to detect QTc prolongation using the device. KardiaMobile 6L EKG from AliveCor. The results, which were published in Circulation, compared the ability of an AI-activated mobile EKG to a traditional 12-lead EKG to detect prolongation of the QT interval.
“This collaborative effort with researchers in academia and industry has resulted in what I call a ‘pivotal’ discovery,” says Dr. Ackerman, director of the Mayo Windland Smith Rice comprehensive sudden cardiac death program. Clinic. “In this way, we will move from the old method of obtaining QTc to this new method. Since the first major article on Einthoven’s ECG in 1903, 2021 will mark the new beginning of the QT interval.”
The team used over 1.6 million 12-lead EKGs from more than half a million patients to train and validate an AI-based deep neural network to accurately recognize and measure the QTc. Next, this new AI-based QTc assessment? The “QT meter”? has been prospectively tested on nearly 700 patients evaluated by Dr. Ackerman at the Windland Smith Rice Genetic Heart Rhythm Clinic at the Mayo Clinic. Half of these patients had congenital long QT syndrome.
The aim was to compare the QTc values of a 12-lead EKG to those of the prototype portable EKG machine used with a smartphone. The two sets of EKGs were administered during the same clinical visit, usually within five minutes of each other.
The ability of the AI algorithm to recognize a clinically significant prolongation of the QTc interval on a mobile EKG machine was similar to ECG evaluations performed by a trained QT expert and a commercial laboratory specializing in blood pressure measurements. QTc interval for drug studies. The mobile device has indeed detected a QTc value greater than or equal to 500 milliseconds, operating with:
- 80% sensitivity This means that fewer cases of QTc prolongation have been missed.
- 94.4% specificity
This means he was very accurate in predicting who did not have a prolonged QTc.
“The ability to equip mobile EKG machines with precise AI-powered approaches capable of accurately calculating QTc represents a potential paradigm shift regarding how and where the QT interval can be assessed.” , says John Giudicessi, MD, Ph.D., a Mayo Researcher in Clinical Cardiology and first author of the study.
“Currently, AliveCor’s KardiaMobile 6L EKG device is FDA approved for the detection of atrial fibrillation, bradycardia and tachycardia. Once FDA clearance is received for this AI-based QTc assessment, we will have a true QT counter that can activate this emerging vital sign. obtainable easily and accurately, ”says Dr. Ackerman. “Similar to a diabetic blood glucose meter, for example, this QT meter will provide an early warning system, helping to identify patients with congenital or acquired LQTS and make potentially life-saving adjustments to their medications and electrolytes. . “
This point-of-service artificial intelligence application is massively scalable, since it is connected to a smartphone. It can save lives by telling a person that a specific medicine may be harmful before they take the first pill. This helps detect a potentially life-threatening condition before symptoms develop. “
Paul Friedman, MD, chair, Department of Cardiovascular Medicine, Mayo Clinic, Rochester
“Regular monitoring of LQTS using KardiaMobile 6L enables accurate, real-time data collection outside the walls of a hospital,” says David Albert, MD, Founder and Medical Director of AliveCor Inc. “Because LQTS can be intermittent and elusive, the ability to detect this rhythm abnormality without a 12-lead EKG – which requires the patient to be hospitalized – can improve patient outcomes and save on hospital resources, while providing the reliable and timely data physicians and their patients need. “
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