DOI: 10.1161/circ.148.suppl_1.17087 ISSN: 0009-7322

Abstract 17087: Novel Machine Learning Models to Enhance and Automate Cardiovascular Outpatient Triage for Patients With Atrial Fibrillation

Gurukripa N Kowlgi, Gavin Schaeferle, Yu-Li Huang, Lezli Kuster, Maribeth Jensen, Malini Madhavan
  • Physiology (medical)
  • Cardiology and Cardiovascular Medicine

Introduction: Atrial fibrillation (AF) is the most prevalent arrhythmia, placing a substantial burden on the healthcare system. Patients with AF are cared for in a variety of settings including primary care and cardiology clinics, while some require cardiac electrophysiology (EP) clinics offering specialized drugs or procedures. Appropriate triage of AF referrals to these specific clinics will lead to timely and appropriate care and reduce inefficiency in the health system. Goal: To develop a machine learning (ML) model to triage AF referrals to a large Cardiology center to either the Cardiology or EP clinic based on likelihood of requiring an invasive EP procedure or initiation of antiarrhythmic drug.

Methods: We retrospectively analyzed 6,755 patients who were referred to CV with referral diagnosis of AF (mean age 70 ± 11 years, 66% male). We employed multiple input variables to predict the suitability for EP clinic defined as need for initiating an antiarrhythmic drug or an invasive EP procedure. These encompassed patient demographics, relevant comorbidities, medications, prior procedures, medical notes analyzed using natural language processing (NLP), and Holter monitoring data.

Results: For internal referrals, XG boost model performed the best with AUC at 79%, accuracy at 72%, and F1 score at 68%. This model produces true positive (TP) at 68% and true negative (TN) at 74%, which was better in comparison with triaging using NLP only (AUC = 69%, TP = 58%, TN = 61%), Holter only (AUC = 52%, TP = 51%, TN = 77%), and NLP/Holter (AUC = 67%, TP = 60%, TN = 60%). When supplementing ML model with Holter readings on average HR ≥ 100 and maximum HR ≥ 150, the results can be improved to TP at 70% and TN at 73%.

Conclusions: Our model effectively identifies patients who are more likely to require specialized EP care, enabling the automation and increased efficiency of the AF referral triage process. Prospective validation studies are paramount in determining the true value of this tool.

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