PURPOSE: Automated pharmacy databases are increasingly available for assessing medication use, but research on the validity of these data is incomplete. This study aimed to measure agreement on warfarin and aspirin use between medical records and automated pharmacy data among patients with newly detected atrial fibrillation (AF). METHODS: Patients with newly detected AF (n = 1953) were previously identified in a cohort study at Group Health (GH) in Washington State. Medical records were reviewed for information on risk factors and medication use, as well as clinical care during the 6 months after AF onset. Medication data were also obtained from the GH pharmacy database. We determined the sensitivity, specificity, and positive predictive value (PPV) as measures of the validity of the GH pharmacy database as compared with medical records for warfarin and aspirin use during the first 6 and 3 months after AF onset. We also calculated the κ statistic. RESULTS: For warfarin use, in comparison with the medical record review, the sensitivity, specificity, and PPV for the GH pharmacy database were excellent, and agreement was almost perfect in the 3- and 6-month periods after AF onset (κ = 0.92 and 0.93, respectively). For aspirin use, the GH pharmacy database had low sensitivity but high specificity, and agreement was only fair for these two periods (κ = 0.28 and 0.31, respectively). CONCLUSIONS: The GH pharmacy database is a valuable source of data for pharmacoepidemiologic research on warfarin use among patients with AF. However, the database cannot be recommended for assessment of aspirin use.
PURPOSE: Automated pharmacy databases are increasingly available for assessing medication use, but research on the validity of these data is incomplete. This study aimed to measure agreement on warfarin and aspirin use between medical records and automated pharmacy data among patients with newly detected atrial fibrillation (AF). METHODS:Patients with newly detected AF (n = 1953) were previously identified in a cohort study at Group Health (GH) in Washington State. Medical records were reviewed for information on risk factors and medication use, as well as clinical care during the 6 months after AF onset. Medication data were also obtained from the GH pharmacy database. We determined the sensitivity, specificity, and positive predictive value (PPV) as measures of the validity of the GH pharmacy database as compared with medical records for warfarin and aspirin use during the first 6 and 3 months after AF onset. We also calculated the κ statistic. RESULTS: For warfarin use, in comparison with the medical record review, the sensitivity, specificity, and PPV for the GH pharmacy database were excellent, and agreement was almost perfect in the 3- and 6-month periods after AF onset (κ = 0.92 and 0.93, respectively). For aspirin use, the GH pharmacy database had low sensitivity but high specificity, and agreement was only fair for these two periods (κ = 0.28 and 0.31, respectively). CONCLUSIONS: The GH pharmacy database is a valuable source of data for pharmacoepidemiologic research on warfarin use among patients with AF. However, the database cannot be recommended for assessment of aspirin use.
Authors: Valentin Fuster; Lars E Rydén; David S Cannom; Harry J Crijns; Anne B Curtis; Kenneth A Ellenbogen; Jonathan L Halperin; Jean-Yves Le Heuzey; G Neal Kay; James E Lowe; S Bertil Olsson; Eric N Prystowsky; Juan Luis Tamargo; Samuel Wann; Sidney C Smith; Alice K Jacobs; Cynthia D Adams; Jeffery L Anderson; Elliott M Antman; Jonathan L Halperin; Sharon Ann Hunt; Rick Nishimura; Joseph P Ornato; Richard L Page; Barbara Riegel; Silvia G Priori; Jean-Jacques Blanc; Andrzej Budaj; A John Camm; Veronica Dean; Jaap W Deckers; Catherine Despres; Kenneth Dickstein; John Lekakis; Keith McGregor; Marco Metra; Joao Morais; Ady Osterspey; Juan Luis Tamargo; José Luis Zamorano Journal: Circulation Date: 2006-08-15 Impact factor: 29.690
Authors: Jeffrey R Curtis; Andrew O Westfall; Jeroan Allison; Allison Freeman; Stacey H Kovac; Kenneth G Saag Journal: Pharmacoepidemiol Drug Saf Date: 2006-10 Impact factor: 2.890
Authors: Nicole L Glazer; Sascha Dublin; Nicholas L Smith; Benjamin French; Lisa A Jackson; Jennifer B Hrachovec; David S Siscovick; Bruce M Psaty; Susan R Heckbert Journal: Arch Intern Med Date: 2007-02-12
Authors: Susan R Heckbert; Kerri L Wiggins; Nicole L Glazer; Sascha Dublin; Bruce M Psaty; Nicholas L Smith; W T Longstreth; Thomas Lumley Journal: Am J Hypertens Date: 2009-03-05 Impact factor: 2.689
Authors: J A Roth; D Boudreau; M M Fujii; F M Farin; A E Rettie; K E Thummel; D L Veenstra Journal: Clin Pharmacol Ther Date: 2014-02-06 Impact factor: 6.875
Authors: Joshua A Roth; Katharine Bradley; Kenneth E Thummel; David L Veenstra; Denise Boudreau Journal: Pharmacoepidemiol Drug Saf Date: 2015-04-08 Impact factor: 2.890
Authors: Alvaro Alonso; Richard F MacLehose; Lin Y Chen; Lindsay Gs Bengtson; Alanna M Chamberlain; Faye L Norby; Pamela L Lutsey Journal: Heart Date: 2017-01-05 Impact factor: 5.994
Authors: Pamela L Lutsey; Neil A Zakai; Richard F MacLehose; Faye L Norby; Rob F Walker; Nicholas S Roetker; Terrence J Adam; Alvaro Alonso Journal: Br J Haematol Date: 2019-03-28 Impact factor: 6.998
Authors: Nicholas S Roetker; Pamela L Lutsey; Neil A Zakai; Alvaro Alonso; Terrence J Adam; Richard F MacLehose Journal: Thromb Haemost Date: 2018-08-13 Impact factor: 5.249
Authors: J'Neka S Claxton; Pamela L Lutsey; Richard F MacLehose; Lin Y Chen; Tené T Lewis; Alvaro Alonso Journal: J Stroke Cerebrovasc Dis Date: 2018-12-21 Impact factor: 2.136
Authors: Pamela L Lutsey; Faye L Norby; Kristine E Ensrud; Richard F MacLehose; Susan J Diem; Lin Y Chen; Alvaro Alonso Journal: JAMA Intern Med Date: 2020-02-01 Impact factor: 21.873