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Table 1.  Algorithm Test Characteristics Among Patients Treated in the Emergency Department
Algorithm Test Characteristics Among Patients Treated in the Emergency Department
Table 2.  Algorithm Test Characteristics Among Patients Treated in the Emergency Department With No Venous Thromboembolism Diagnosis in the Previous 5 Years
Algorithm Test Characteristics Among Patients Treated in the Emergency Department With No Venous Thromboembolism Diagnosis in the Previous 5 Years
1.
Atzema  CL, Austin  PC, Miller  E, Chong  AS, Yun  L, Dorian  P.  A population-based description of atrial fibrillation in the emergency department, 2002 to 2010.  Ann Emerg Med. 2013;62(6):570-577.e7. doi:10.1016/j.annemergmed.2013.06.005PubMedGoogle ScholarCrossref
2.
McDonald  AJ, Pelletier  AJ, Ellinor  PT, Camargo  CA  Jr.  Increasing US emergency department visit rates and subsequent hospital admissions for atrial fibrillation from 1993 to 2004.  Ann Emerg Med. 2008;51(1):58-65. doi:10.1016/j.annemergmed.2007.03.007PubMedGoogle ScholarCrossref
3.
Atzema  CL, Jackevicius  CA, Chong  A,  et al.  Prescribing of oral anticoagulants in the emergency department and subsequent long-term use by older adults with atrial fibrillation.  CMAJ. 2019;191(49):E1345-E1354. doi:10.1503/cmaj.190747PubMedGoogle ScholarCrossref
4.
Canadian Institute for Health Information. National Ambulatory Care Reporting System (NACRS) metadata. https://www.cihi.ca/en/types-of-care/hospital-care/emergency-and-ambulatory-care/nacrs-metadata. Published 2019. Accessed November 22, 2019.
5.
Skanes  AC, Healey  JS, Cairns  JA,  et al; Canadian Cardiovascular Society Atrial Fibrillation Guidelines Committee.  Focused 2012 update of the Canadian Cardiovascular Society atrial fibrillation guidelines: recommendations for stroke prevention and rate/rhythm control.  Can J Cardiol. 2012;28(2):125-136. doi:10.1016/j.cjca.2012.01.021PubMedGoogle ScholarCrossref
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    Research Letter
    Cardiology
    March 3, 2020

    Assessment of an Algorithm for Prescription of Oral Anticoagulation for Patients With Atrial Fibrillation in Emergency Departments

    Author Affiliations
    • 1ICES, Toronto, Ontario, Canada
    • 2Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
    • 3Division of Emergency Medicine, Department of Medicine
    • 4Institute for Health Policy, Management and Evaluation
    • 5Division of Cardiology, Department of Medicine
    • 6Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada.
    • 7Department of Family and Community Medicine, the University of Toronto, Toronto, Ontario, Canada
    • 8University Health Network-Toronto Western Hospital Family Health Team, Toronto, Ontario, Canada
    JAMA Netw Open. 2020;3(3):e200306. doi:10.1001/jamanetworkopen.2020.0306
    Introduction

    Atrial fibrillation is seen commonly in emergency departments (EDs).1,2 Initiation of oral anticoagulation to patients treated in the ED has been associated with higher long-term use than when prescribing is left to outpatient care following discharge.3

    Current data sets do not identify ED prescriptions. The ability to identify these prescriptions in large data sets could facilitate future studies aimed at increasing such prescriptions and could support long-term monitoring of ED oral anticoagulant prescription patterns.

    Methods

    This retrospective cohort study sought to validate an algorithm that identifies ED oral anticoagulant prescription provision for patients with atrial fibrillation using administrative health data sets. Using the National Ambulatory Care Reporting System,4 we identified patients who had visited 1 of 20 Ontario EDs with a primary diagnosis of atrial fibrillation, in the years 2009 through 2014. We excluded patients who would not benefit from5 or who had been taking oral anticoagulant agents within 90 days, were younger than 65 years (because we do not have comprehensive outpatient medication data for them), or had been admitted to the hospital. Data were abstracted from patient medical records for the first visit per patient. Using unique encoded identifiers, we linked the medical record data to databases held at ICES. The study was approved by the Research Ethics Board of Sunnybrook Health Sciences Centre. This report follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

    The reference standard was medical record documentation of provision of an oral anticoagulant prescription by any physician (emergency or consultant). Thirty-two algorithms selected a priori were tested. We varied the algorithms in the following ways: (1) including oral anticoagulant prescription fills that occurred on the same day (day 0) as the patient left the ED (because some patients are sent to the ED by another clinician who may have prescribed the agent); (2) counting the number of days following discharge that patients filled a prescription; (3) excluding prescription fills that occurred after an outpatient visit with a relevant clinician (because that clinician may have written the prescription being filled); (4) excluding patients with a history of venous thromboembolism; and (5) removing patients who had seen a relevant clinician 30 days prior to the emergency visit. We calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), and 95% (CIs). All analyses were conducted with SAS statistical software version 9.3 (SAS Institute Inc).

    Results

    Of 2015 qualifying patients, 65% had a CHADS2 (congestive heart failure, hypertension, age >75 years, diabetes, and stroke) score of 2 or higher. Emergency department physicians prescribed warfarin for a median of 7 days and oral anticoagulant agents for a median of 30 days. Inclusion of day 0 substantially improved sensitivity, eg, 34% of patients analyzed in algorithm 3, prescriptions filled between days 1 and 3, to 83% of patients analyzed in algorithm 2, prescriptions filled between days 0 and 3 (Table 1). Using a longer follow-up period for prescription fills lowered specificity (91% in algorithm 2 to 74% in algorithm 10, prescriptions filled between days 0 and 30, and only slightly improved sensitivity (83% to 88%).

    After restricting to patients who filled a prescription before they saw another clinician, the number of false-positive results (ie, prescriptions filled that were not written in the ED) decreased. Thus, specificity improved from 74% in algorithm 10 to 97% in algorithm 13; however, sensitivity fell from 88% to 68%. Removal of patients with an outpatient visit prior to the ED visit (eg, algorithm 15) further increased specificity (99%) but decreased sensitivity (24%). Excluding patients with a prior venous thromboembolism resulted in minimal change (Table 2).

    Algorithm 2 maximized specificity (91%; 95% CI, 90%-92%) while maintaining reasonable sensitivity (83%; 95% CI, 79%-87%) (PPV, 70%; 95% CI, 66%-74%, NPV, 96%; 95% CI, 95%-97%). Algorithm 13, prescription filled prior to seeing another relevant clinician, provided high specificity (97%; 95% CI, 96%-98%).

    Limitations include using medical record documentation of ED prescription provision as reference standard, and our results may not apply to patients younger than 65 years.

    We derived several algorithms that identify ED oral anticoagulant prescriptions in a large health data set. Depending on project goals, an algorithm can be selected to optimize specificity, sensitivity, PPV, or NPV.

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    Article Information

    Accepted for Publication: January 9, 2019.

    Published: March 3, 2020. doi:10.1001/jamanetworkopen.2020.0306

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Atzema CL et al. JAMA Network Open.

    Corresponding Author: Clare L. Atzema, MD, MSc, Sunnybrook Health Sciences Centre and ICES, 2075 Bayview Ave, G146, Toronto, ON M4N 3M5 (clare.atzema@ices.on.ca).

    Author Contributions: Dr Atzema had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Atzema.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Atzema, Chong.

    Critical revision of the manuscript for important intellectual content: Fang, Cox, Tu, Austin.

    Statistical analysis: Atzema, Fang, Chong.

    Obtained funding: Atzema.

    Conflict of Interest Disclosures: Dr Atzema reported receiving grants from Canadian Institutes for Health Research (CIHR); being supported by a Mid Investigator Award from the Heart and Stroke Foundation of Ontario (HSFO), the Practice Plan of the Department of Emergency Services at Sunnybrook Health Sciences Centre, ICES, and the Sunnybrook Research Institute. Dr Cox reported receiving support from the Heart and Stroke Foundation of Nova Scotia Endowed Chair in Cardiovascular Outcomes Research. Dr Tu reported support from a Research Scholar Award from the Department of Family and Community Medicine at the University of Toronto. Dr Austin reported receiving support from a Mid-Career Investigator Award from the HSFO. No other disclosures were reported.

    Funding/Support: This project was supported by a grant from the Canadian Stroke Prevention Intervention Network (C-SPIN), which is funded by a CIHR Institute for Circulatory and Respiratory Health (ICRH) Emerging Network, and by ICES, which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC).

    Role of the Funder/Sponsor: The Heart and Stroke Foundation of Ontario had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Disclaimer: The opinions, results, and conclusions reported herein are those of the authors and are independent from the funding sources. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred.

    References
    1.
    Atzema  CL, Austin  PC, Miller  E, Chong  AS, Yun  L, Dorian  P.  A population-based description of atrial fibrillation in the emergency department, 2002 to 2010.  Ann Emerg Med. 2013;62(6):570-577.e7. doi:10.1016/j.annemergmed.2013.06.005PubMedGoogle ScholarCrossref
    2.
    McDonald  AJ, Pelletier  AJ, Ellinor  PT, Camargo  CA  Jr.  Increasing US emergency department visit rates and subsequent hospital admissions for atrial fibrillation from 1993 to 2004.  Ann Emerg Med. 2008;51(1):58-65. doi:10.1016/j.annemergmed.2007.03.007PubMedGoogle ScholarCrossref
    3.
    Atzema  CL, Jackevicius  CA, Chong  A,  et al.  Prescribing of oral anticoagulants in the emergency department and subsequent long-term use by older adults with atrial fibrillation.  CMAJ. 2019;191(49):E1345-E1354. doi:10.1503/cmaj.190747PubMedGoogle ScholarCrossref
    4.
    Canadian Institute for Health Information. National Ambulatory Care Reporting System (NACRS) metadata. https://www.cihi.ca/en/types-of-care/hospital-care/emergency-and-ambulatory-care/nacrs-metadata. Published 2019. Accessed November 22, 2019.
    5.
    Skanes  AC, Healey  JS, Cairns  JA,  et al; Canadian Cardiovascular Society Atrial Fibrillation Guidelines Committee.  Focused 2012 update of the Canadian Cardiovascular Society atrial fibrillation guidelines: recommendations for stroke prevention and rate/rhythm control.  Can J Cardiol. 2012;28(2):125-136. doi:10.1016/j.cjca.2012.01.021PubMedGoogle ScholarCrossref
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