Artificial intelligence MacHIne learning for the detection and treatment of atrial fibrillation guidelines in the emergency department setting (AIM HIGHER): Assessing a machine learning clinical decision support tool to detect and treat non‐valvular atrial fibrillation in the emergency department. Issue 4 (9th August 2021)

• Record Type: Journal Article
• Authors: Schwab, Kim; Nguyen, Dacloc; Ungab, GilAnthony; Feld, Gregory; Maisel, Alan S.; Than, Martin; Joyce, Laura; Peacock, W. Frank
• Is Part Of: JACEP open. Volume 2:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-09
• Subjects: artificial intelligence -- atrial fibrillation -- clinical decision support -- emergency department -- guidelines -- machine learning -- oral anticoagulation; Medical emergencies -- Periodicals; 616.025
• DOI: 10.1002/emp2.12534
• Languages: English
• ISSNs: 0361-1124

Abstract

Abstract: Objective: Advanced machine learning technology provides an opportunity to improve clinical electrocardiogram (ECG) interpretation, allowing non‐cardiology clinicians to initiate care for atrial fibrillation (AF). The Lucia Atrial Fibrillation Application (Lucia App) photographs the ECG to determine rhythm detection, calculates CHA2DS2‐VASc and HAS‐BLED scores, and then provides guideline‐recommended anticoagulation. Our purpose was to determine the rate of accurate AF identification and appropriate anticoagulation recommendations in emergency department (ED) patients ultimately diagnosed with AF. Methods: We performed a single‐center, observational retrospective chart review in an urban California ED, with an annual census of 70, 000 patients. A convenience sample of hospitalized patients with AF as a primary or secondary discharge diagnosis were evaluated for accurate ED AF diagnosis and ED anticoagulation rates. This was done by comparing the Lucia App against a gold standard board‐certified cardiologist diagnosis and using the American College of Emergency Physicians AF anticoagulation guidelines. Results: Two hundred and ninety seven patients were enrolled from January 2016 until December 2019. The median age was 79 years and 44.1% were female. Compared to the gold standard diagnosis, the Lucia App detected AF in 98.3% of the cases. Physicians recommended guideline‐consistent anticoagulation therapy in 78.5% versus 98.3% for the Lucia App. Of the patients with indications for anticoagulation and discharged from the ED, only 25.0% were started at discharge. Conclusion: Use of a cloud‐based ECG identification tool can allow non‐cardiologists to achieve similar rates of AF identification as board‐certified cardiologists and achieve higher rates of guideline‐recommended anticoagulation therapy in the ED.