Novel methodology for the evaluation of symptoms reported by patients with newly diagnosed atrial fibrillation: Application of natural language processing to electronic medical records data.

Autor: Reynolds MR; Division of Cardiology, Lahey Hospital and Medical Center, Burlington, Massachusetts, USA.; Economics and Quality of Life Research, Baim Institute for Clinical Research, Boston, Massachusetts, USA., Bunch TJ; Department of Medicine, University of Utah, Salt Lake City, Utah, USA., Steinberg BA; Department of Medicine, University of Utah, Salt Lake City, Utah, USA., Ronk CJ; Digital Function, Sanofi, Bridgewater, New Jersey, USA., Kim H; Real-World Evidence Team, Evidera, Boston, Massachusetts, USA., Wieloch M; General Medicines Global Medical, Sanofi, Paris, France.; Department of Clinical Sciences Malmö, Center for Thrombosis and Haemostasis, Lund University, Malmö, Sweden., Lip GYH; Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK.; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark.
Jazyk: angličtina
Zdroj: Journal of cardiovascular electrophysiology [J Cardiovasc Electrophysiol] 2023 Apr; Vol. 34 (4), pp. 790-799. Date of Electronic Publication: 2023 Jan 06.
DOI: 10.1111/jce.15784
Abstrakt: Introduction: Understanding symptom patterns in atrial fibrillation (AF) can help in disease management. We report on the application of natural language processing (NLP) to electronic medical records (EMRs) to capture symptom reports in patients with newly diagnosed (incident) AF.
Methods and Results: This observational retrospective study included adult patients with an index diagnosis of incident AF during January 1, 2016 through June 30, 2018, in the Optum datasets. The baseline and follow-up periods were 1 year before/after the index date, respectively. The primary objective was identification of the following predefined symptom reports: dyspnea or shortness of breath; syncope, presyncope, lightheadedness, or dizziness; chest pain; fatigue; and palpitations. In an exploratory analysis, the incidence rates of symptom reports and cardiovascular hospitalization were assessed in propensity-matched patient cohorts with incident AF receiving first-line dronedarone or sotalol. Among 30 447 patients with an index AF diagnosis, the NLP algorithm identified at least 1 predefined symptom in 9734 (31.9%) patients. The incidence rate of symptom reports was highest at 0-3 months post-diagnosis and lower at >3-6 and >6-12 months (pre-defined timepoints). Across all time periods, the most common symptoms were dyspnea or shortness of breath, followed by syncope, presyncope, lightheadedness, or dizziness. Similar temporal patterns of symptom reports were observed among patients with prescriptions for dronedarone or sotalol as first-line treatment.
Conclusion: This study illustrates that NLP can be applied to EMR data to characterize symptom reports in patients with incident AF, and the potential for these methods to inform comparative effectiveness.
(© 2022 Wiley Periodicals LLC.)
Databáze: MEDLINE
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