Kamakshi Lakshminarayan1, Joseph C Larson, Beth Virnig, Candace Fuller, Norrina Bai Allen, Marian Limacher, Wolfgang C Winkelmayer, Monika M Safford, Dale R Burwen. 1. From the Divisions of Epidemiology and Community Health (K.L., C.F.) and Health Services Research and Policy (B.V.), University of Minnesota School of Public Health, Minneapolis, MN; Women's Health Initiative Clinical Coordinating Center, Fred Hutchinson Cancer Research Center, Seattle, WA (J.C.L.); Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL (N.B.A.); Division of Cardiovascular Medicine, University of Florida, Gainesville, FL (M.L.); Department of Medicine, Division of Nephrology, Stanford University School of Medicine, Palo Alto, CA (W.C.W.); Department of Medicine, Division of Preventive Medicine, University of Alabama, Birmingham, AL (M.M.S.); and National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (D.R.B.).
Abstract
BACKGROUND AND PURPOSE: Many studies use medical record review for ascertaining outcomes. One large, longitudinal study, the Women's Health Initiative (WHI), ascertains strokes using participant self-report and subsequent physician review of medical records. This is resource-intensive. Herein, we assess whether Medicare data can reliably assess stroke events in the WHI. METHODS: Subjects were WHI participants with fee-for-service Medicare. Four stroke definitions were created for Medicare data using discharge diagnoses in hospitalization claims: definition 1, stroke codes in any position; definition 2, primary position stroke codes; and definitions 3 and 4, hemorrhagic and ischemic stroke codes, respectively. WHI data were randomly split into training (50%) and test sets. A concordance matrix was used to examine the agreement between WHI and Medicare stroke diagnosis. A WHI stroke and a Medicare stroke were considered a match if they occurred within ±7 days of each other. Refined analyses excluded Medicare events when medical records were unavailable for comparison. RESULTS: Training data consisted of 24 428 randomly selected participants. There were 577 WHI strokes and 557 Medicare strokes using definition 1. Of these, 478 were a match. With regard to algorithm performance, specificity was 99.7%, negative predictive value was 99.7%, sensitivity was 82.8%, positive predictive value was 85.8%, and κ=0.84. Performance was similar for test data. Whereas specificity and negative predictive value exceeded 99%, sensitivity ranged from 75% to 88% and positive predictive value ranged from 80% to 90% across stroke definitions. CONCLUSIONS: Medicare data seem useful for population-based stroke research; however, performance characteristics depend on the definition selected.
BACKGROUND AND PURPOSE: Many studies use medical record review for ascertaining outcomes. One large, longitudinal study, the Women's Health Initiative (WHI), ascertains strokes using participant self-report and subsequent physician review of medical records. This is resource-intensive. Herein, we assess whether Medicare data can reliably assess stroke events in the WHI. METHODS: Subjects were WHI participants with fee-for-service Medicare. Four stroke definitions were created for Medicare data using discharge diagnoses in hospitalization claims: definition 1, stroke codes in any position; definition 2, primary position stroke codes; and definitions 3 and 4, hemorrhagic and ischemic stroke codes, respectively. WHI data were randomly split into training (50%) and test sets. A concordance matrix was used to examine the agreement between WHI and Medicare stroke diagnosis. A WHI stroke and a Medicare stroke were considered a match if they occurred within ±7 days of each other. Refined analyses excluded Medicare events when medical records were unavailable for comparison. RESULTS: Training data consisted of 24 428 randomly selected participants. There were 577 WHI strokes and 557 Medicare strokes using definition 1. Of these, 478 were a match. With regard to algorithm performance, specificity was 99.7%, negative predictive value was 99.7%, sensitivity was 82.8%, positive predictive value was 85.8%, and κ=0.84. Performance was similar for test data. Whereas specificity and negative predictive value exceeded 99%, sensitivity ranged from 75% to 88% and positive predictive value ranged from 80% to 90% across stroke definitions. CONCLUSIONS: Medicare data seem useful for population-based stroke research; however, performance characteristics depend on the definition selected.
Authors: J David Curb; Anne McTiernan; Susan R Heckbert; Charles Kooperberg; Janet Stanford; Michael Nevitt; Karen C Johnson; Lori Proulx-Burns; Lisa Pastore; Michael Criqui; Sandra Daugherty Journal: Ann Epidemiol Date: 2003-10 Impact factor: 3.797
Authors: Susan E Andrade; Leslie R Harrold; Jennifer Tjia; Sarah L Cutrona; Jane S Saczynski; Katherine S Dodd; Robert J Goldberg; Jerry H Gurwitz Journal: Pharmacoepidemiol Drug Saf Date: 2012-01 Impact factor: 2.890
Authors: Peter M Wahl; Keith Rodgers; Sebastian Schneeweiss; Brian F Gage; Javed Butler; Charles Wilmer; Marshall Nash; Gregory Esper; Norman Gitlin; Neal Osborn; Louise J Short; Rhonda L Bohn Journal: Pharmacoepidemiol Drug Saf Date: 2010-06 Impact factor: 2.890
Authors: Kamakshi Lakshminarayan; David C Anderson; David R Jacobs; Cheryl A Barber; Russell V Luepker Journal: Am J Epidemiol Date: 2009-03-24 Impact factor: 4.897
Authors: Jacqueline B Shreibati; JoAnn E Manson; Karen L Margolis; Rowan T Chlebowski; Marcia L Stefanick; Mark A Hlatky Journal: Am Heart J Date: 2017-12-27 Impact factor: 4.749
Authors: Justin Blackburn; Karen C Albright; William E Haley; Virginia J Howard; David L Roth; Monika M Safford; Meredith L Kilgore Journal: J Am Geriatr Soc Date: 2017-10-26 Impact factor: 5.562
Authors: Patricia O Guimarães; Arun Krishnamoorthy; Lisa A Kaltenbach; Kevin J Anstrom; Mark B Effron; Daniel B Mark; Patrick L McCollam; Linda Davidson-Ray; Eric D Peterson; Tracy Y Wang Journal: JAMA Cardiol Date: 2017-07-01 Impact factor: 14.676
Authors: J Bradley Layton; Christoph R Meier; Julie L Sharpless; Til Stürmer; Susan S Jick; M Alan Brookhart Journal: JAMA Intern Med Date: 2015-07 Impact factor: 21.873
Authors: J Bradley Layton; Dongmei Li; Christoph R Meier; Julie L Sharpless; Til Stürmer; M Alan Brookhart Journal: Clin Endocrinol (Oxf) Date: 2018-03-06 Impact factor: 3.478
Authors: Samia Mora; Nanette K Wenger; Nancy R Cook; Jingmin Liu; Barbara V Howard; Marian C Limacher; Simin Liu; Karen L Margolis; Lisa W Martin; Nina P Paynter; Paul M Ridker; Jennifer G Robinson; Jacques E Rossouw; Monika M Safford; JoAnn E Manson Journal: JAMA Intern Med Date: 2018-09-01 Impact factor: 21.873
Authors: Cristina M Arce; Jinnie J Rhee; Katharine L Cheung; Haley Hedlin; Kristopher Kapphahn; Nora Franceschini; Roberto S Kalil; Lisa W Martin; Lihong Qi; Nawar M Shara; Manisha Desai; Marcia L Stefanick; Wolfgang C Winkelmayer Journal: Am J Kidney Dis Date: 2015-09-01 Impact factor: 8.860
Authors: Arjun Seth; Yasmin Mossavar-Rahmani; Victor Kamensky; Brian Silver; Kamakshi Lakshminarayan; Ross Prentice; Linda Van Horn; Sylvia Wassertheil-Smoller Journal: Stroke Date: 2014-09-04 Impact factor: 7.914