Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data
Autoři:
Daniel M. Bean aff001; James Teo aff003; Honghan Wu aff004; Ricardo Oliveira aff007; Raj Patel aff008; Rebecca Bendayan aff001; Ajay M. Shah aff010; Richard J. B. Dobson aff001; Paul A. Scott aff010
Působiště autorů:
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, England, United Kingdom
aff001; Health Data Research UK London, University College London, London, England, United Kingdom
aff002; Department of Stroke and Neurology, King’s College Hospital NHS Foundation Trust, London, England, United Kingdom
aff003; Centre for Medical Informatics, Usher Institute, University of Edinburgh, Scotland, United Kingdom
aff004; School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
aff005; Health Data Research UK Scotland, Edinburgh, Scotland, United Kingdom
aff006; Unidade de Doenças Imunomediadas Sistémicas (UDIMS), S. Medicina IV, Hospital Prof. Doutor Fernando Fonseca, Amadora, Portugal
aff007; Department of Haematology, King’s College Hospital NHS Foundation Trust, London, England, United Kingdom
aff008; NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, England, United Kingdom
aff009; British Heart Foundation Centre, King’s College London, London, England, United Kingdom
aff010; Department of Cardiology, King’s College Hospital NHS Foundation Trust, London, England, United Kingdom
aff011; Institute of Health Informatics, University College London, London, England, United Kingdom
aff012
Vyšlo v časopise:
PLoS ONE 14(11)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0225625
Souhrn
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 1st January 2011 to 1st 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 (CHA2DS2-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 CHA2DS2-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 (CHA2DS2-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 CHA2DS2-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.
Klíčová slova:
Atrial fibrillation – Cardiology – Computational pipelines – Frailty – Hemorrhagic fever with renal syndrome – Natural language processing – Oral antiplatelet therapy
Zdroje
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