期刊
PLOS ONE
卷 14, 期 11, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0225625
关键词
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资金
- UKRI Innovation Fellowship as part of Health Data Research UK [MR/S00310X/1]
- UKRI Rutherford Fellowship as part of Health Data Research UK [MR/S004149/1]
- UK Medical Research Council (MRC) [MR/R016372/1]
- National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust [IS-BRC-1215-20018]
- King's College London
- British Heart Foundation
- NIHR Biomedical Research Centre funding
- KCL
- Health Data Research UK - UK Medical Research Council
- Engineering and Physical Sciences Research Council
- Economic and Social Research Council
- Department of Health and Social Care (England)
- Chief Scientist Office of the Scottish Government Health and Social Care Directorates
- Health and Social Care Research and Development Division (Welsh Government)
- Public Health Agency (Northern Ireland)
- Wellcome Trust
- Innovative Medicines Initiative-2 Joint Undertaking [116074]
- European Union
- EFPIA
- National Institute for Health Research University College London Hospitals Biomedical Research Centre
- National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust
- MRC [MR/S00310X/1, MR/S004149/1] Funding Source: UKRI
Atrial fibrillation (AF) is the most common arrhythmia and significantly increases stroke risk. This risk is effectively managed by oral anticoagulation. Recent studies using national registry data indicate increased use of anticoagulation resulting from changes in guidelines and the availability of newer drugs. The aim of this study is to develop and validate an open source risk scoring pipeline for free-text electronic health record data using natural language processing. AF patients discharged from 1(st) January 2011 to 1(st) October 2017 were identified from discharge summaries (N = 10,030, 64.6% male, average age 75.3 +/- 12.3 years). A natural language processing pipeline was developed to identify risk factors in clinical text and calculate risk for ischaemic stroke (CHA(2)DS(2)-VASc) and bleeding (HAS-BLED). Scores were validated vs two independent experts for 40 patients. Automatic risk scores were in strong agreement with the two independent experts for CHA(2)DS(2)-VASc (average kappa 0.78 vs experts, compared to 0.85 between experts). Agreement was lower for HAS-BLED (average kappa 0.54 vs experts, compared to 0.74 between experts). In high-risk patients (CHA(2)DS(2)-VASc >= 2) OAC use has increased significantly over the last 7 years, driven by the availability of DOACs and the transitioning of patients from AP medication alone to OAC. Factors independently associated with OAC use included components of the CHA(2)DS(2)-VASc and HAS-BLED scores as well as discharging specialty and frailty. OAC use was highest in patients discharged under cardiology (69%). Electronic health record text can be used for automatic calculation of clinical risk scores at scale. Open source tools are available today for this task but require further validation. Analysis of routinely collected EHR data can replicate findings from large-scale curated registries.
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