Karen Tu1, Robby Nieuwlaat2, Stephanie Y Cheng3, Laura Wing3, Noah Ivers4, Clare L Atzema5, Jeff S Healey6, Paul Dorian7. 1. Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada; Department of Family and Community Medicine, Department of Medicine, University of Toronto, Toronto, Ontario, Canada; University Health Network-Toronto Western Hospital Family Health Team, Toronto, Ontario, Canada. Electronic address: karen.tu@ices.on.ca. 2. Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada. 3. Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada. 4. Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada; Department of Family and Community Medicine, Department of Medicine, University of Toronto, Toronto, Ontario, Canada. 5. Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada; Division of Emergency Medicine, University of Toronto, Toronto, Ontario, Canada. 6. Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada; Population Health Research Institute, Hamilton, Ontario, Canada. 7. Division of Cardiology, University of Toronto, Toronto, Ontario, Canada.
Abstract
BACKGROUND: Identifying patients with atrial fibrillation (AF) using administrative data is important for epidemiologic and outcomes research. Although administrative data cover large populations, it is necessary to assess their validity in identifying AF patients. METHODS: We used Ontario family physician electronic medical records from the Electronic Medical Record Administrative data Linked Database (EMRALD) as a reference standard to assess the accuracy of administrative data algorithms in identifying patients with AF. From a random sample of 7500 adult patients, patients with AF as recorded in family physician records were identified. RESULTS: The optimal algorithm consisted of any of: hospitalization or an emergency room code for AF or prescription for an AF-specific antiarrhythmic agent or billing code for cardioversion, or prescription for an anticoagulant that was accompanied by a physician billing code. for arrhythmia. The algorithm sensitivity was 80.7% (95% confidence interval [CI], 75.1-86.3), specificity 99.1% (95% CI, 98.9-99.3), positive predictive value 71.1% (95% CI, 65.1-77.1), and negative predictive value 99.5% (95% CI, 99.3-99.7). This algorithm, applied to the Ontario population, resulted in a calculated increase in AF prevalence from 1.68% to 2.36% over the years 2000-2014. Anticoagulation rates for AF patients increased from 53% in 2011 to 60% in 2014. Among AF patients receiving anticoagulants, novel oral anticoagulant utilization increased from < 5% in 2011 to > 50% in 2014. CONCLUSIONS: Identifying patients with AF can be done using administrative data, and the algorithm can be used to assess trends in disease burden over time and patterns of care in large populations. Copyright Â
BACKGROUND: Identifying patients with atrial fibrillation (AF) using administrative data is important for epidemiologic and outcomes research. Although administrative data cover large populations, it is necessary to assess their validity in identifying AFpatients. METHODS: We used Ontario family physician electronic medical records from the Electronic Medical Record Administrative data Linked Database (EMRALD) as a reference standard to assess the accuracy of administrative data algorithms in identifying patients with AF. From a random sample of 7500 adult patients, patients with AF as recorded in family physician records were identified. RESULTS: The optimal algorithm consisted of any of: hospitalization or an emergency room code for AF or prescription for an AF-specific antiarrhythmic agent or billing code for cardioversion, or prescription for an anticoagulant that was accompanied by a physician billing code. for arrhythmia. The algorithm sensitivity was 80.7% (95% confidence interval [CI], 75.1-86.3), specificity 99.1% (95% CI, 98.9-99.3), positive predictive value 71.1% (95% CI, 65.1-77.1), and negative predictive value 99.5% (95% CI, 99.3-99.7). This algorithm, applied to the Ontario population, resulted in a calculated increase in AF prevalence from 1.68% to 2.36% over the years 2000-2014. Anticoagulation rates for AFpatients increased from 53% in 2011 to 60% in 2014. Among AFpatients receiving anticoagulants, novel oral anticoagulant utilization increased from < 5% in 2011 to > 50% in 2014. CONCLUSIONS: Identifying patients with AF can be done using administrative data, and the algorithm can be used to assess trends in disease burden over time and patterns of care in large populations. Copyright Â
Authors: Reilly P Musselman; Tara Gomes; Deanna M Rothwell; Rebecca C Auer; Husein Moloo; Robin P Boushey; Carl van Walraven Journal: J Gastrointest Surg Date: 2018-12-03 Impact factor: 3.452
Authors: Clare L Atzema; Cynthia A Jackevicius; Alice Chong; Paul Dorian; Noah M Ivers; Ratika Parkash; Peter C Austin Journal: CMAJ Date: 2019-12-09 Impact factor: 8.262
Authors: Amy Y X Yu; Eric E Smith; Murray Krahn; Peter C Austin; Mohammed Rashid; Jiming Fang; Joan Porter; Manav V Vyas; Susan E Bronskill; Richard H Swartz; Moira K Kapral Journal: Neurology Date: 2021-08-18 Impact factor: 11.800
Authors: Mohammed Shurrab; Cynthia A Jackevicius; Peter C Austin; Karen Tu; Feng Qiu; Joseph Caswell; Faith Michael; Jason G Andrade; Dennis T Ko Journal: J Interv Card Electrophysiol Date: 2022-09-23 Impact factor: 1.759
Authors: Eric Y Ding; Daniella Albuquerque; Michael Winter; Sophia Binici; Jaclyn Piche; Syed Khairul Bashar; Ki Chon; Allan J Walkey; David D McManus Journal: J Intensive Care Med Date: 2019-07-28 Impact factor: 3.510
Authors: Mohammed Shurrab; Maria Koh; Cynthia A Jackevicius; Feng Qiu; Michael Conlon; Joseph Caswell; Karen Tu; Peter C Austin; Dennis T Ko Journal: Int J Cardiol Heart Vasc Date: 2021-04-29
Authors: Husam Abdel-Qadir; Madison Gunn; Iliana C Lega; Andrea Pang; Peter C Austin; Sheldon M Singh; Cynthia A Jackevicius; Karen Tu; Paul Dorian; Douglas S Lee; Dennis T Ko Journal: J Am Heart Assoc Date: 2022-02-08 Impact factor: 6.106
Authors: Stephen B Wilton; Padma Kaul; Sunjidatul Islam; Clare L Atzema; Jennifer Cruz; Kendra MacFarlane; Robert McKelvie; Stephanie Poon; Laurie Lambert; Kathy Rush; Marc Deyell; D George Wyse; Jafna L Cox; Allan Skanes; Roopinder K Sandhu Journal: CJC Open Date: 2021-01-13