| Autor: |
Masato Shimizu, Makoto Suzuki, Hiroyuki Fujii, Shigeki Kimura, Mitsuhiro Nishizaki, Tetsuo Sasano |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Cardiovascular Digital Health Journal. 3:179-188 |
| ISSN: |
2666-6936 |
| DOI: |
10.1016/j.cvdhj.2022.07.001 |
| Popis: |
Qualitative differences in 12-lead electrocardiograms (ECG) at onset have been reported in patients with takotsubo syndrome (TTS) and acute anterior myocardial infarction (Ant-AMI). We aimed to distinguish these diseases by machine learning (ML) approach of microvolt-level quantitative measurements.We enrolled 56 consecutive patients with sinus rhythm TTS (median age, 77 years; 16 men), and 1-to-1 random matching was performed based on age and sex of the patients. The ECG in the emergency room was evaluated using an automated system (ECAPs12c; Nihon-Koden). Statistical and ML predictive models for TTS were constructed using clinical features and ECG parameters.Statistically significant differences were observed in 25 parameters; the VML applied to automated microvolt-level ECG measurements showed the possibility of distinguishing between TTS and Ant-AMI, which may be a clinically useful ECG-based discriminator. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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