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Your search keyword '"Siontis, Konstantinos"' showing total 26 results
Start Over Author "Siontis, Konstantinos" Journal journal of the american college of cardiology (jacc)
26 results on '"Siontis, Konstantinos"'

1. Assessment of Disease Status and Treatment Response With Artificial Intelligence-Enhanced Electrocardiography in Obstructive Hypertrophic Cardiomyopathy.

Author

Tison, Geoffrey H., Siontis, Konstantinos C., Abreau, Sean, Attia, Zachi, Agarwal, Priyanka, Balasubramanyam, Aarthi, Li, Yunfan, Sehnert, Amy J., Edelberg, Jay M., Friedman, Paul A., Olgin, Jeffrey E., Noseworthy, Peter A., and PIONEER-OLE Investigators

Subjects
*HYPERTROPHIC cardiomyopathy, *THERAPEUTICS, *ELECTROCARDIOGRAPHY, *CARDIOMYOPATHIES, *RESEARCH, *CARDIAC hypertrophy, *RESEARCH methodology, *ARTIFICIAL intelligence, *EVALUATION research, *COMPARATIVE studies
Published
2022
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2. Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram.

Author

Ko, Wei-Yin, Siontis, Konstantinos C, Attia, Zachi I, Carter, Rickey E, Kapa, Suraj, Ommen, Steve R, Demuth, Steven J, Ackerman, Michael J, Gersh, Bernard J, Arruda-Olson, Adelaide M, Geske, Jeffrey B, Asirvatham, Samuel J, Lopez-Jimenez, Francisco, Nishimura, Rick A, Friedman, Paul A, and Noseworthy, Peter A

Abstract

Background: Hypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death.Objectives: This study sought to develop an artificial intelligence approach for the detection of HCM based on 12-lead electrocardiography (ECG).Methods: A convolutional neural network (CNN) was trained and validated using digital 12-lead ECG from 2,448 patients with a verified HCM diagnosis and 51,153 non-HCM age- and sex-matched control subjects. The ability of the CNN to detect HCM was then tested on a different dataset of 612 HCM and 12,788 control subjects.Results: In the combined datasets, mean age was 54.8 ± 15.9 years for the HCM group and 57.5 ± 15.5 years for the control group. After training and validation, the area under the curve (AUC) of the CNN in the validation dataset was 0.95 (95% confidence interval [CI]: 0.94 to 0.97) at the optimal probability threshold of 11% for having HCM. When applying this probability threshold to the testing dataset, the CNN's AUC was 0.96 (95% CI: 0.95 to 0.96) with sensitivity 87% and specificity 90%. In subgroup analyses, the AUC was 0.95 (95% CI: 0.94 to 0.97) among patients with left ventricular hypertrophy by ECG criteria and 0.95 (95% CI: 0.90 to 1.00) among patients with a normal ECG. The model performed particularly well in younger patients (sensitivity 95%, specificity 92%). In patients with HCM with and without sarcomeric mutations, the model-derived median probabilities for having HCM were 97% and 96%, respectively.Conclusions: ECG-based detection of HCM by an artificial intelligence algorithm can be achieved with high diagnostic performance, particularly in younger patients. This model requires further refinement and external validation, but it may hold promise for HCM screening. [ABSTRACT FROM AUTHOR]

Published
2020
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19. ASSESSMENT OF POSITIVE CARDIAC REMODELING IN HYPERTROPHIC OBSTRUCTIVE CARDIOMYOPATHY USING AN AI-ECG PLATFORM IN PATIENTS TREATED WITH MAVACAMTEN OR SURGICAL MYECTOMY.

Author

Suppah, Mustafa, Roehl, Kaitlin, Arsanjani, Reza, Lew, Kathryn B., Lester, Steven J., Wilansky, Susan, Cannan, Charles R., Ayoub, Chadi, Ommen, Steve R., Geske, Jeffrey Benjamin, Siontis, Konstantinos, Schaff, Hartzell V., and Alsidawi, Said

Subjects
*HYPERTROPHIC cardiomyopathy, *MYOMECTOMY
Published
2024
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24. PERSISTENCE OF LEFT ATRIAL THROMBUS AND RISK OF INCIDENT THROMBOEMBOLISM IN PATIENTS WITH HYPERTROPHIC CARDIOMYOPATHY AND ATRIAL FIBRILLATION.

Author

Julakanti, Raghav, Burczak, Dan, Balla, Abdalla Kara, Scott, Christopher, Geske, Jeffrey Benjamin, Ommen, Steve R., Gersh, Bernard J., Nkomo, Vuyisile Tlhopane, Noseworthy, Peter, and Siontis, Konstantinos

Subjects
*LEFT heart atrium, *HYPERTROPHIC cardiomyopathy, *ATRIAL fibrillation, *THROMBOEMBOLISM, *THROMBOSIS
Published
2023
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