Hypertrophic cardiomyopathy (HCM) is the leading cause of sudden death in adolescents and early detection is difficult. A new UC San Francisco study has found that Artificial Intelligence-Enhanced (AI) -Electrocardiograms (ECG) can help detect the condition in its early stages and monitor for significant disease-related changes over time.
The study, led by Geoffrey Tison, MD, MPH, in the UCSF Division of Cardiology, is a joint venture between UCSF, Mayo Clinic and Myokardia Inc. In their study, published in the March 7 issue of the Journal of the American Academy of Cardiology, the authors point out that AI analysis of ECG can not only assess the diagnosis of HCM, but also AI-ECG correlates with heart rate and HCM-related lab measurements.
This study suggests that AI studies may capture more data from ECGs associated with HCM-associated disease than is obtained by manual ECG translation and is the first study to show that AI analysis of ECGs may be useful used to monitor physiologic and hemodynamic disorders. balance.
The researchers used two different AI-ECG algorithms from UCSF and Mayo Clinic to pre-treatment and treatment ECGs from phase-2 PIONEER-OLE clinical trial (clinical trial for treatment with HCM treatment Mavacamten in adults with HCM block.). After showing that both algorithms detected HCM correctly in clinical trial data without further training, they then showed that AI-ECG HCM scores correlated with resistance and disease status as measured by declining over time. in left ventricular gradients and natriuretic peptide (NT-proBNP) levels in these patients.
The resistance groups of the AI-ECG HCM values are significant and may indicate changes in the ECG wave pattern identified by the AI-ECGs and are associated with higher rates of HCM infection and severity. The value of AI-ECG is expanded by the fact that ECGs can now be measured remotely by mobile phones and may allow to estimate the progression of long-term disease and its therapeutic response.
The authors suggest that future studies are needed to determine whether AI-ECGs may follow the diagnosis and be used as a guide to measure treatment to improve safety.
Geoffrey H. Tison et al, Assessing the Status of Disease and Treatment with the Practice of Pediatric Pediatric Pediatric Pediatric Pediatric Pediatric Pediatric Pediatric Pediatric Pediatric Pediatric Pediatric Pediatric Pediatric Pediatric Pediatric Pediatrics Journal of the American Academy of Cardiology (2022). DOI: 10.1016 / j.jacc.2022.01.005
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