Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data
| Autor: | Daniel Bean, Ricardo Oliveira, Richard Dobson, Raj K. Patel, James T. Teo, Ajay M. Shah, Honghan Wu, Paul A. Scott, Rebecca Bendayan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Male
Viral Diseases Administration Oral 030204 cardiovascular system & hematology Pathology and Laboratory Medicine Antiplatelet Therapy Vascular Medicine 0302 clinical medicine Risk Factors Atrial Fibrillation Medicine and Health Sciences Medicine 030212 general & internal medicine Computational analysis Aged 80 and over Multidisciplinary Frailty Drug Substitution Pharmaceutics Atrial fibrillation Middle Aged Patient Discharge 3. Good health Oral Antiplatelet Therapy Infectious Diseases Hemorrhagic Fever with Renal Syndrome Female Information Technology Clinical risk factor Real world data Algorithms Arrhythmia Research Article Computer and Information Sciences medicine.medical_specialty Science Cardiology Specialty MEDLINE Hemorrhage Research and Analysis Methods Drug Prescriptions 03 medical and health sciences Signs and Symptoms Text mining Drug Therapy Diagnostic Medicine Computational Techniques Humans In patient Aged Retrospective Studies Natural Language Processing business.industry Computational Pipelines Anticoagulants Retrospective cohort study medicine.disease Open source Logistic Models Geriatrics Emergency medicine business Kappa |
| Zdroj: | Bean, D M, Teo, J, Wu, H, Oliveira, R, Patel, R, Bendayan, R, Shah, A M, Dobson, R J & Scott, P A 2019, ' Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data ', PLoS ONE . https://doi.org/10.1371/journal.pone.0225625 PLOS ONE PLoS ONE, Vol 14, Iss 11, p e0225625 (2019) PLoS ONE |
| DOI: | 10.1371/journal.pone.0225625 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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