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Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea
- Source :
- Circulation: Arrhythmia and Electrophysiology. 13
- Publication Year :
- 2020
- Publisher :
- Ovid Technologies (Wolters Kluwer Health), 2020.
-
Abstract
- Background: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). Methods: We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction ≤35%) to a cohort of patients aged ≥18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86–0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, respectively. To identify an ejection fraction 800 identified LVSD with an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.76–0.84). Conclusions: The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.
- Subjects :
- Male
Emergency Medical Services
medicine.medical_specialty
Systole
030204 cardiovascular system & hematology
Ventricular Function, Left
Electrocardiography
Ventricular Dysfunction, Left
03 medical and health sciences
0302 clinical medicine
Artificial Intelligence
Predictive Value of Tests
Physiology (medical)
Humans
Medicine
Diagnosis, Computer-Assisted
Aged
Retrospective Studies
business.industry
Reproducibility of Results
Signal Processing, Computer-Assisted
Stroke Volume
030208 emergency & critical care medicine
Emergency department
Middle Aged
medicine.disease
Dyspnea
Heart failure
Emergency medicine
Female
Cardiology Service, Hospital
Cardiology and Cardiovascular Medicine
business
Acute dyspnea
Heart Failure, Systolic
Subjects
Details
- ISSN :
- 19413084 and 19413149
- Volume :
- 13
- Database :
- OpenAIRE
- Journal :
- Circulation: Arrhythmia and Electrophysiology
- Accession number :
- edsair.doi.dedup.....43dad4d07c015b59e97f75027ca7cfd8
- Full Text :
- https://doi.org/10.1161/circep.120.008437