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AI-enabled EKGs find difference between numerical age and biological age significantly affects health -- ScienceDaily
he AI model accurately predicted the age of most subjects, with a mean age gap of 0.88 years between EKG age and actual age. However, a number of subjects had a gap that was much larger, either seemingly much older or much younger by EKG age.
The likelihood to die during follow-up was much higher among those seemingly older by EKG age, compared to those whose EKG age was the same as their chronologic or actual age. The association was even stronger when predicting death caused by heart disease. Conversely, those who had a lesser age gap ? considered younger by EKG -- had decreased risk.
"Our results validate and expand on our prior observations that EKG age using AI may detect accelerated aging by proving that those with older-than-expected age by EKG die sooner, particularly from heart disease. We know that mortality rate is one of the best ways to measure biological age, and our model proved that," says Francisco Lopez-Jimenez, M.D., chair of the Division of Preventive Cardiology at Mayo Clinic. Dr. Lopez-Jimenez is senior author of the study.
Next for Apple Watch: a clinical trial with J&J to track heart health - STAT
Typically, clinical trials use their own research infrastructure to track how patients do, but the HEARTLINE trial will plug into insurance claims databases to track patients. This approach, called a pragmatic clinical trial, could be cheaper and more efficient than the way studies are conducted now, without sacrificing the clarity and certainty that comes from having a control group in a study. But this is also risky: Using insurance claims data this way is new and untested.
“It’s certainly a vast and gross departure form the bricks and mortar model,” said Gibson. “This is one of the most exciting things, the idea that you’re going to find participants through the media, the news, potentially through insurers and even health care providers.”
A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation - ScienceDirect
Cardiologs® vs. Veritas® accuracy for finding a major abnormality was 92.2% vs. 87.2% (p < 0.0001), with comparable sensitivity (88.7% vs. 92.0%, p = 0.086), improved specificity (94.0% vs. 84.7%, p < 0.0001) and improved positive predictive value (PPV 88.2% vs. 75.4%, p < 0.0001). Cardiologs® had accurate ECG interpretation for 72.0% (95% CI: 69.6–74.2) of ECGs vs. 59.8% (57.3–62.3) for Veritas® (P < 0.0001). Sensitivity for any abnormal group for Cardiologs® and Veritas®, respectively, was 69.6% (95CI 66.7–72.3) vs. 68.3% (95CI 65.3–71.1) (NS). Positive Predictive Value was 74.0% (71.1–76.7) for Cardiologs® vs. 56.5% (53.7–59.3) for Veritas® (P < 0.0001).
