Performance of seven ECG interpretation programs in identifying arrhythmia and acute cardiovascular syndrome
| Autor: | Giulia Massaro, C. Martignani, J De Bie, Igor Diemberger |
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| Přispěvatelé: | De Bie J., Martignani C., Massaro G., Diemberger I. |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Adult
Acute coronary syndrome medicine.medical_specialty Electrocardiograph interpretation program Electrocardiography Rhythm medicine Humans Sinus rhythm Myocardial infarction cardiovascular diseases Child business.industry Flagging Australia Atrial fibrillation medicine.disease Europe Atrial Flutter Electrocardiographs Emergency medicine Cardiology and Cardiovascular Medicine business |
| Popis: | Background No direct comparison of current electrocardiogram (ECG) interpretation programs exists. Objective Assess the accuracy of ECG interpretation programs in detecting abnormal rhythms and flagging for priority review records with alterations secondary to acute coronary syndrome (ACS). Methods More than 2,000 digital ECGs from hospitals and databases in Europe, USA, and Australia, were obtained from consecutive adult and pediatric patients and converted to 10 s analog samples that were replayed on seven electrocardiographs and classified by the manufacturers' interpretation programs. We assessed ability to distinguish sinus rhythm from non-sinus rhythm, identify atrial fibrillation/flutter and other abnormal rhythms, and accuracy in flagging results for priority review. If all seven programs' interpretation statements did not agree, cases were reviewed by experienced cardiologists. Results All programs could distinguish well between sinus and non-sinus rhythms and could identify atrial fibrillation/flutter or other abnormal rhythms. However, false-positive rates varied from 2.1% to 5.5% for non-sinus rhythm, from 0.7% to 4.4% for atrial fibrillation/flutter, and from 1.5% to 3.0% for other abnormal rhythms. False-negative rates varied from 12.0% to 7.5%, 9.9% to 2.7%, and 55.9% to 30.5%, respectively. Flagging of ACS varied by a factor of 2.5 between programs. Physicians flagged more ECGs for prompt review, but also showed variance of around a factor of 2. False-negative values differed between programs by a factor of 2 but was high for all (>50%). Agreement between programs and majority reviewer decisions was 46–62%. Conclusions Automatic interpretations of rhythms and ACS differ between programs. Healthcare institutions should not rely on ECG software “critical result” flags alone to decide the ACS workflow. |
| Databáze: | OpenAIRE |
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