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: 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

1. Yiin GSC, Howard DPJ, Paul NLM, Li L, Mehta Z, Rothwell PM, et al. Recent time trends in incidence, outcome and premorbid treatment of atrial fibrillation-related stroke and other embolic vascular events: a population-based study. J Neurol Neurosurg Psychiatry. 2015/10/20. 2017;88: 12–18. doi: 10.1136/jnnp-2015-311947 26487646

2. NICE. Atrial fibrillation: management (Aug 2014 update) [Internet]. 2014.

3. Lip GYH, Nieuwlaat R, Pisters R, Lane DA, Crijns HJGM. Refining Clinical Risk Stratification for Predicting Stroke and Thromboembolism in Atrial Fibrillation Using a Novel Risk Factor-Based Approach: The Euro Heart Survey on Atrial Fibrillation. Chest. 2010;137: 263–272. doi: 10.1378/chest.09-1584 19762550

4. Pisters R, Lane DA, Nieuwlaat R, de Vos CB, Crijns HJGM, Lip GYH. A Novel User-Friendly Score (HAS-BLED) To Assess 1-Year Risk of Major Bleeding in Patients With Atrial Fibrillation: The Euro Heart Survey. Chest. 2010;138: 1093–1100. doi: 10.1378/chest.10-0134 20299623

5. Cowan C, Healicon R, Robson I, Long WR, Barrett J, Fay M, et al. The use of anticoagulants in the management of atrial fibrillation among general practices in England. Heart. 2013;99: 1166–1172. doi: 10.1136/heartjnl-2012-303472 23393083

6. Campbell Cowan J, Wu J, Hall M, Orlowski A, West RM, Gale CP. A 10 year study of hospitalized atrial fibrillation-related stroke in England and its association with uptake of oral anticoagulation. Eur Heart J. 2018; doi: 10.1093/eurheartj/ehy411 29982405

7. Lacoin L, Lumley M, Ridha E, Pereira M, McDonald L, Ramagopalan S, et al. Evolving landscape of stroke prevention in atrial fibrillation within the UK between 2012 and 2016: a cross-sectional analysis study using CPRD. BMJ Open. 2017;7: e015363. doi: 10.1136/bmjopen-2016-015363 28951401

8. Holt TA, Hunter TD, Gunnarsson C, Khan N, Cload P, Lip GYH. Risk of stroke and oral anticoagulant use in atrial fibrillation: A cross-sectional survey. Br J Gen Pract. 2012; doi: 10.3399/bjgp12X656856 23265231

9. Hemingway H, Asselbergs FW, Danesh J, Dobson R, Maniadakis N, Maggioni A, et al. Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. Eur Heart J. 2017;39: 1481–1495. doi: 10.1093/eurheartj/ehx487 29370377

10. Kharrazi H, Anzaldi LJ, Hernandez L, Davison A, Boyd CM, Leff B, et al. The Value of Unstructured Electronic Health Record Data in Geriatric Syndrome Case Identification. J Am Geriatr Soc. 2018;66: 1499–1507. doi: 10.1111/jgs.15411 29972595

11. Jackson R, Kartoglu I, Stringer C, Gorrell G, Roberts A, Song X, et al. CogStack—Experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital. BMC Med Inform Decis Mak. 2018; doi: 10.1186/s12911-018-0623-9 29941004

12. CogStack. CogStack Pipeline [Internet]. 2019. Available: https://github.com/CogStack/CogStack-Pipeline

13. Wu H, Toti G, Morley KI, Ibrahim ZM, Folarin A, Jackson R, et al. SemEHR: A general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research. J Am Med Informatics Assoc. 2018; doi: 10.1093/JAMIA/OCX160 29361077

14. Wu H. CogStack-SemEHR [Internet]. p. 2019.

15. Wang S V, Rogers JR, Jin Y, Fischer MA, Bates DW. Use of electronic healthcare records to identify complex patients with atrial fibrillation for targeted intervention. J Am Med Informatics Assoc. 2016;24: 339–344. doi: 10.1093/jamia/ocw082 27375290

16. Grouin C, Deléger L, Rosier A, Temal L, Dameron O, Van Hille P, et al. Automatic computation of CHA2DS2-VASc score: information extraction from clinical texts for thromboembolism risk assessment. AMIA. Annu Symp proceedings AMIA Symp. 2011;

17. Rosier A, Mabo P, Temal L, Van Hille P, Dameron O, Deléger L, et al. Personalized and automated remote monitoring of atrial fibrillation. Europace. 2016; doi: 10.1093/europace/euv234 26487670

18. U.S. National Library of Medicine. Unified Medical Language System (UMLS) [Internet]. Available: https://www.nlm.nih.gov/research/umls/

19. Köhler S, Carmody L, Vasilevsky N, Jacobsen JOB, Danis D, Gourdine JP, et al. Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Res. 2019; doi: 10.1093/nar/gky1105 30476213

20. Gilbert T, Neuburger J, Kraindler J, Keeble E, Smith P, Ariti C, et al. Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study. Lancet. 2018; doi: 10.1016/S0140-6736(18)30668-8 29706364

21. Whetzel PL, Noy NF, Shah NH, Alexander PR, Nyulas C, Tudorache T, et al. BioPortal: Enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications. Nucleic Acids Res. 2011; doi: 10.1093/nar/gkr469 21672956

22. Kreimeyer K, Foster M, Pandey A, Arya N, Halford G, Jones SF, et al. Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review. Journal of Biomedical Informatics. 2017. doi: 10.1016/j.jbi.2017.07.012 28729030

23. Piazza G, Hurwitz S, Galvin CE, Harrigan L, Baklla S, Hohlfelder B, et al. Alert-based computerized decision support for high-risk hospitalized patients with atrial fibrillation not prescribed anticoagulation: a randomized, controlled trial (AF-ALERT). Eur Heart J. 2019; doi: 10.1093/eurheartj/ehz385 31228189

24. Bahri O, Roca F, Lechani T, Druesne L, Jouanny P, Serot J-M, et al. Underuse of Oral Anticoagulation for Individuals with Atrial Fibrillation in a Nursing Home Setting in France: Comparisons of Resident Characteristics and Physician Attitude. J Am Geriatr Soc. 2015;63: 71–76. doi: 10.1111/jgs.13200 25597559

25. Lefebvre M-CD, St-Onge M, Glazer-Cavanagh M, Bell L, Kha Nguyen JN, Viet-Quoc Nguyen P, et al. The Effect of Bleeding Risk and Frailty Status on Anticoagulation Patterns in Octogenarians With Atrial Fibrillation: The FRAIL-AF Study. Can J Cardiol. 2016;32: 169–176. doi: 10.1016/j.cjca.2015.05.012 26277091

26. Pilotto A, Gallina P, Copetti M, Pilotto A, Marcato F, Mello AM, et al. Warfarin Treatment and All-Cause Mortality in Community-Dwelling Older Adults with Atrial Fibrillation: A Retrospective Observational Study. J Am Geriatr Soc. 2016/06/13. 2016;64: 1416–1424. doi: 10.1111/jgs.14221 27295351

27. Faxon DP, Burgess A. Cardiovascular Registries: Too Much of Good Thing? Circulation. Cardiovascular interventions. United States; 2016. p. e003866. doi: 10.1161/CIRCINTERVENTIONS.116.003866 27083199

28. Marzec LN, Wang J, Shah ND, Chan PS, Ting HH, Gosch KL, et al. Influence of Direct Oral Anticoagulants on Rates of Oral Anticoagulation for Atrial Fibrillation. J Am Coll Cardiol. 2017;69: 2475–2484. doi: 10.1016/j.jacc.2017.03.540 28521884

29. Fosbol EL, Holmes DN, Piccini JP, Thomas L, Reiffel JA, Mills RM, et al. Provider specialty and atrial fibrillation treatment strategies in United States community practice: findings from the ORBIT-AF registry. J Am Heart Assoc. 2013;2: e000110–e000110. doi: 10.1161/JAHA.113.000110 23868192

30. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25: 44–56. doi: 10.1038/s41591-018-0300-7 30617339


Článek vyšel v časopise

PLOS One


2019 Číslo 11