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

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.

QT interval and short-term outcome in acute heart failure

“…Certainly, the selection of a specific cut point can ignore important relationships between pathology, physiology, and prognosis However, it is particularly challenging for the clinician to accurately discern slight variations in QTc and use of it in the context of subtle clinical presentations to predict outcomes. Hence, there is an opportunity for artificial intelligence applications to outperform clinicians [34,35]. Some have demonstrated that machine earning pattern recognition may perform better than physicians using dichotomous cut points.…”

Successes and challenges of artificial intelligence in cardiology

“…Clinical decision support systems (CDSS) combine patient information and evidence-based medicine to improve healthcare delivery by enhancing medical decisions. They can be used to successfully implement clinical guidelines, such as the Lucia Atrial Fibrillation Application which combines improved electrocardiogram-based diagnosis of atrial fibrillation with the calculation of CHA 2 DS 2 -VASc (congestive heart failure, hypertension, age ≥75 (doubled), diabetes, stroke (doubled), vascular disease, age 65–74, and female sex) and HAS-BLED (hypertension, abnormal liver/renal function, stroke history, bleeding history or predisposition, labile INR, elderly, drug/alcohol usage) scores, to support the decision for guideline-recommended anticoagulation ( 25 , 26 ). CDSS can also deliver evidence-based support in differential diagnoses or clinical management.…”