Fenglong Xie1,2, Lisandro D Colantonio1, Jeffrey R Curtis1,2, Meredith L Kilgore3, Emily B Levitan1, Keri L Monda4, Monika M Safford5, Ben Taylor4, Mark Woodward6,7,8, Paul Muntner1. 1. Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA. 2. Department of Medicine, Division of Clinical Immunology and Rheumatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA. 3. Department of Health Care Organization and Policy, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA. 4. The Center for Observational Research, Amgen Inc., California, USA. 5. Department of Medicine, Division of General Internal Medicine, Weill Cornell Medicine, New York, NY, USA. 6. The George Institute for Global Health, University of Oxford, Oxford, UK. 7. The George Institute for Global Health, University of New South Wales, Sydney, Australia. 8. Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA.
Abstract
BACKGROUND: Cause of death is often not available in administrative claims data. OBJECTIVE: To develop claims-based algorithms to identify deaths due to fatal cardiovascular disease (CVD; ie, fatal coronary heart disease [CHD] or stroke), CHD, and stroke. METHODS: Reasons for Geographic and Racial Differences in Stroke (REGARDS) study data were linked with Medicare claims to develop the algorithms. Events adjudicated by REGARDS study investigators were used as the gold standard. Stepwise selection was used to choose predictors from Medicare data for inclusion in the algorithms. C-index, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to assess algorithm performance. Net reclassification index (NRI) was used to compare the algorithms with an approach of classifying all deaths within 28 days following hospitalization for myocardial infarction and stroke to be fatal CVD. RESULTS: Data from 2,685 REGARDS participants with linkage to Medicare, who died between 2003 and 2013, were analyzed. The C-index for discriminating fatal CVD from other causes of death was 0.87. Using a cut-point that provided the closest observed-to-predicted number of fatal CVD events, the sensitivity was 0.64, specificity 0.90, PPV 0.65, and NPV 0.90. The algorithms resulted in positive NRIs compared with using deaths within 28 days following hospitalization for myocardial infarction and stroke. Claims-based algorithms for discriminating fatal CHD and fatal stroke performed similarly to fatal CVD. CONCLUSION: The claims-based algorithms developed to discriminate fatal CVD events from other causes of death performed better than the method of using hospital discharge diagnosis codes.
BACKGROUND: Cause of death is often not available in administrative claims data. OBJECTIVE: To develop claims-based algorithms to identify deaths due to fatal cardiovascular disease (CVD; ie, fatal coronary heart disease [CHD] or stroke), CHD, and stroke. METHODS: Reasons for Geographic and Racial Differences in Stroke (REGARDS) study data were linked with Medicare claims to develop the algorithms. Events adjudicated by REGARDS study investigators were used as the gold standard. Stepwise selection was used to choose predictors from Medicare data for inclusion in the algorithms. C-index, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to assess algorithm performance. Net reclassification index (NRI) was used to compare the algorithms with an approach of classifying all deaths within 28 days following hospitalization for myocardial infarction and stroke to be fatal CVD. RESULTS: Data from 2,685 REGARDS participants with linkage to Medicare, who died between 2003 and 2013, were analyzed. The C-index for discriminating fatal CVD from other causes of death was 0.87. Using a cut-point that provided the closest observed-to-predicted number of fatal CVD events, the sensitivity was 0.64, specificity 0.90, PPV 0.65, and NPV 0.90. The algorithms resulted in positive NRIs compared with using deaths within 28 days following hospitalization for myocardial infarction and stroke. Claims-based algorithms for discriminating fatal CHD and fatal stroke performed similarly to fatal CVD. CONCLUSION: The claims-based algorithms developed to discriminate fatal CVD events from other causes of death performed better than the method of using hospital discharge diagnosis codes.
Authors: Russell V Luepker; Fred S Apple; Robert H Christenson; Richard S Crow; Stephen P Fortmann; David Goff; Robert J Goldberg; Mary M Hand; Allan S Jaffe; Desmond G Julian; Daniel Levy; Teri Manolio; Shanthi Mendis; George Mensah; Andrzej Pajak; Ronald J Prineas; K Srinath Reddy; Veronique L Roger; Wayne D Rosamond; Eyal Shahar; A Richey Sharrett; Paul Sorlie; Hugh Tunstall-Pedoe Journal: Circulation Date: 2003-11-10 Impact factor: 29.690
Authors: Jewell H Halanych; Faisal Shuaib; Gaurav Parmar; Rajasekhar Tanikella; Virginia J Howard; David L Roth; Ronald J Prineas; Monika M Safford Journal: Am J Epidemiol Date: 2011-05-03 Impact factor: 4.897
Authors: Maarten J G Leening; Moniek M Vedder; Jacqueline C M Witteman; Michael J Pencina; Ewout W Steyerberg Journal: Ann Intern Med Date: 2014-01-21 Impact factor: 25.391
Authors: David L Roth; Kimberly A Skarupski; Deidra C Crews; Virginia J Howard; Julie L Locher Journal: Soc Sci Med Date: 2016-03-16 Impact factor: 4.634
Authors: David J Graham; Rita Ouellet-Hellstrom; Thomas E MaCurdy; Farzana Ali; Christopher Sholley; Christopher Worrall; Jeffrey A Kelman Journal: JAMA Date: 2010-06-28 Impact factor: 56.272
Authors: Elsayed Z Soliman; Ronald J Prineas; L Douglas Case; Gregory Russell; Wayne Rosamond; Thomas Rea; Nona Sotoodehnia; Wendy S Post; David Siscovick; Bruce M Psaty; Gregory L Burke Journal: Heart Date: 2011-07-20 Impact factor: 5.994
Authors: Jie Zhang; Fenglong Xie; Huifeng Yun; Lang Chen; Paul Muntner; Emily B Levitan; Monika M Safford; Shia T Kent; Mark T Osterman; James D Lewis; Kenneth Saag; Jasvinder A Singh; Jeffrey R Curtis Journal: Ann Rheum Dis Date: 2016-01-20 Impact factor: 19.103
Authors: Jeffrey R Curtis; Fenglong Xie; Cynthia S Crowson; Eric H Sasso; Elena Hitraya; Cheryl L Chin; Richard D Bamford; Rotem Ben-Shachar; Alexander Gutin; Darl D Flake; Brent Mabey; Jerry S Lanchbury Journal: Arthritis Res Ther Date: 2020-12-04 Impact factor: 5.156