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Sökning: id:"swepub:oai:lup.lub.lu.se:96965bea-d8bf-4c25-8015-1255164ee1b3" > Novel methodology f...

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

Reynolds, Matthew R. (författare)
Baim Institute for Clinical Research,Lahey Hospital & Medical Center
Bunch, Thomas Jared (författare)
University of Utah
Steinberg, Benjamin A. (författare)
University of Utah
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Ronk, Christopher J. (författare)
Sanofi US Services Inc.
Kim, Hankyul (författare)
Evidera, USA
Wieloch, Mattias (författare)
Lund University,Lunds universitet,Klinisk koagulationsmedicin, Malmö,Forskargrupper vid Lunds universitet,Clinical Coagulation, Malmö,Lund University Research Groups,Sanofi S.A., France
Lip, Gregory Y. H. (författare)
Liverpool Heart and Chest Hospital,Aalborg University,University of Liverpool
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 (creator_code:org_t)
2023-01-06
2023
Engelska.
Ingår i: Journal of Cardiovascular Electrophysiology. - : Wiley. - 1045-3873 .- 1540-8167. ; 34:4, s. 790-799
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Kardiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)

Nyckelord

atrial fibrillation
dronedarone
electronic medical records
natural language processing
sotalol

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art (ämneskategori)
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