AI Predicts Short-Term Risk of Atrial Fibrillation Using 24-Hour Holter Recordings

The study consisted of training Cardiologs’ (Paris, France) deep neural network to predict the near-term presence or absence of AFib by only using the first 24 hours of an extended Holter recording. Results showed that the network was able to predict whether AFib would occur in the near future with an area under the receiver operating curve, sensitivity, and specificity of 79.4%, 76%, and 69%, respectively, and outperformed ECG features previously shown to be predictive of AFib. These results showed a 10-point improvement compared to a baseline model using age and sex.

"Cardiologs' study shows that 24-hour Holter data can be used to enhance current monitoring capabilities, bringing hope to high-risk patients who would benefit from proactive treatment and AFib mitigation strategies," said Dr. Jagmeet Singh, Cardiologist at Massachusetts General Hospital (MGH) and Professor of Medicine at Harvard Medical School, who led the study. "By getting patients the care they need sooner and potentially preventing more severe outcomes, we could help save many lives."

"The extension of AI capabilities towards predictions and digital biomarkers has the potential to bring improved health outcomes leading to new diagnostic paradigms," added Cardiologs CEO and co-founder Yann Fleureau. "Predictive biomarkers may lead to early detection, optimized patient monitoring, and improved patient management in general. At Cardiologs, we are excited to be at the forefront of innovations that help set a new standard of patient care."

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