Details

Autor(en) / Beteiligte

• Adedinsewo, Demilade
• Carter, Rickey E
• Attia, Zachi
• Johnson, Patrick
• Kashou, Anthony H
• Dugan, Jennifer L
• Albus, Michael
• Sheele, Johnathan M
• Bellolio, Fernanda
• Friedman, Paul A
• Lopez-Jimenez, Francisco
• Noseworthy, Peter A

Titel

An Artificial Intelligence-Enabled ECG Algorithm to Identify Patients with Left Ventricular Systolic Dysfunction Presenting to the Emergency Department with Dyspnea

Ist Teil von

• Circulation. Arrhythmia and electrophysiology, 2020-08, Vol.13 (8), p.e008437-e008437

Ort / Verlag

American Heart Association, Inc

Erscheinungsjahr

2020

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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 electrocardiogram (AI-ECG) to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). METHODS-We retrospectively applied a validated AI-ECG algorithm for the identification of LVSD (defined as left ventricular 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 (AUC), accuracy, sensitivity, and specificity. RESULTS-A total of 1,606 patients were included. Median time from ECG to echocardiogram was 1 day (Q11, Q32). The AI-ECG algorithm identified LVSD with an AUC of 0.89 (95% CI0.86 – 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 < 50%, the AUC, accuracy, sensitivity, and specificity were 0.85 (95% CI0.83 – 0.88), 86%, 63%, and 91%, respectively. NT-Pro BNP alone at a cut-off of >800 identified LVSD with an AUC of 0.80 (95% CI0.76 – 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 AI and outperforms NT-Pro BNP.