Identification of recurrent atrial fibrillation using natural language processing applied to electronic health records

• Published in: European heart journal. Quality of care & clinical outcomes Vol. 10; no. 1; pp. 77 - 88
• Main Authors: Zheng, Chengyi, Lee, Ming-sum, Bansal, Nisha, Go, Alan S, Chen, Cheng, Harrison, Teresa N, Fan, Dongjie, Allen, Amanda, Garcia, Elisha, Lidgard, Ben, Singer, Daniel, An, Jaejin
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 12.01.2024
• Subjects: Ablation; Adult; Algorithms; Antiarrhythmics; Atrial fibrillation; Atrial Fibrillation - epidemiology; Atrial Fibrillation - therapy; Automation; Cardiac arrhythmia; Cardioversion; Computational linguistics; Diagnosis; Diseases; Electronic Health Records; Health aspects; Humans; Language processing; Medical records; Natural language interfaces; Natural Language Processing; Relapse; Technology application; Treatment Outcome; United States; Recurrence; Natural language processing; Electronic health record; Artificial intelligence; Atrial fibrillation
• ISSN: 2058-5225; 2058-1742; 2058-1742
• DOI: 10.1093/ehjqcco/qcad021

Aims This study aimed to develop and apply natural language processing (NLP) algorithms to identify recurrent atrial fibrillation (AF) episodes following rhythm control therapy initiation using electronic health records (EHRs). Methods and results We included adults with new-onset AF who initiated rhythm control therapies (ablation, cardioversion, or antiarrhythmic medication) within two US integrated healthcare delivery systems. A code-based algorithm identified potential AF recurrence using diagnosis and procedure codes. An automated NLP algorithm was developed and validated to capture AF recurrence from electrocardiograms, cardiac monitor reports, and clinical notes. Compared with the reference standard cases confirmed by physicians’ adjudication, the F-scores, sensitivity, and specificity were all above 0.90 for the NLP algorithms at both sites. We applied the NLP and code-based algorithms to patients with incident AF (n = 22 970) during the 12 months after initiating rhythm control therapy. Applying the NLP algorithms, the percentages of patients with AF recurrence for sites 1 and 2 were 60.7% and 69.9% (ablation), 64.5% and 73.7% (cardioversion), and 49.6% and 55.5% (antiarrhythmic medication), respectively. In comparison, the percentages of patients with code-identified AF recurrence for sites 1 and 2 were 20.2% and 23.7% for ablation, 25.6% and 28.4% for cardioversion, and 20.0% and 27.5% for antiarrhythmic medication, respectively. Conclusion When compared with a code-based approach alone, this study's high-performing automated NLP method identified significantly more patients with recurrent AF. The NLP algorithms could enable efficient evaluation of treatment effectiveness of AF therapies in large populations and help develop tailored interventions. Graphical Abstract Graphical Abstract Development and application of NLP algorithms to identify AF recurrences in EHRs.