BACKGROUND: Population-based administrative registers could be used for identifying heart failure (HF) cases. However, the validity of the classification obtained from administrative registers is not known. DESIGN: The validity of HF diagnoses obtained by record linkage of administrative databases in Finland was assessed against classification by three independent physicians. METHODS: Data from the nationwide registers in Finland - the Hospital Discharge Register, Causes of Death Register, Drug Reimbursement Register, and pharmacy prescription data - were linked with the FINRISK 1997 survey data. Cases with hospitalizations before the survey date with HF as one of the discharge diagnoses, cases with special reimbursement for HF drugs before the survey date and cases with the use of furosemide before the survey date were classified as HF in the registers. All these cases, cases with baseline brain natriuretic peptide > 100 pg/ml, and cases with use of digoxin were independently assessed by two physicians as HF/no HF. Discrepant cases were solved by a third physician. This classification was considered as the gold standard, against which the registers were assessed. RESULTS: The specificity of the registers was 99.7% (95% CI 99.5-99.8%), positive predictive value 85.9% (95% CI 79.7-90.5%), negative predictive value 97.9% (95% CI 97.6-98.2%), and sensitivity 48.5% (95% CI 42.9-54.2%). CONCLUSIONS: Classification obtained from administrative registers has high specificity and can be used in follow-up studies with HF as an end point. Sensitivity is modest and administrative data should be used with caution for surveillance.
BACKGROUND: Population-based administrative registers could be used for identifying heart failure (HF) cases. However, the validity of the classification obtained from administrative registers is not known. DESIGN: The validity of HF diagnoses obtained by record linkage of administrative databases in Finland was assessed against classification by three independent physicians. METHODS: Data from the nationwide registers in Finland - the Hospital Discharge Register, Causes of Death Register, Drug Reimbursement Register, and pharmacy prescription data - were linked with the FINRISK 1997 survey data. Cases with hospitalizations before the survey date with HF as one of the discharge diagnoses, cases with special reimbursement for HF drugs before the survey date and cases with the use of furosemide before the survey date were classified as HF in the registers. All these cases, cases with baseline brain natriuretic peptide > 100 pg/ml, and cases with use of digoxin were independently assessed by two physicians as HF/no HF. Discrepant cases were solved by a third physician. This classification was considered as the gold standard, against which the registers were assessed. RESULTS: The specificity of the registers was 99.7% (95% CI 99.5-99.8%), positive predictive value 85.9% (95% CI 79.7-90.5%), negative predictive value 97.9% (95% CI 97.6-98.2%), and sensitivity 48.5% (95% CI 42.9-54.2%). CONCLUSIONS: Classification obtained from administrative registers has high specificity and can be used in follow-up studies with HF as an end point. Sensitivity is modest and administrative data should be used with caution for surveillance.
Authors: Suzette J Bielinski; Jyotishman Pathak; David S Carrell; Paul Y Takahashi; Janet E Olson; Nicholas B Larson; Hongfang Liu; Sunghwan Sohn; Quinn S Wells; Joshua C Denny; Laura J Rasmussen-Torvik; Jennifer Allen Pacheco; Kathryn L Jackson; Timothy G Lesnick; Rachel E Gullerud; Paul A Decker; Naveen L Pereira; Euijung Ryu; Richard A Dart; Peggy Peissig; James G Linneman; Gail P Jarvik; Eric B Larson; Jonathan A Bock; Gerard C Tromp; Mariza de Andrade; Véronique L Roger Journal: J Cardiovasc Transl Res Date: 2015-07-21 Impact factor: 4.132
Authors: Rubina Tabassum; Joel T Rämö; Pietari Ripatti; Jukka T Koskela; Mitja Kurki; Juha Karjalainen; Priit Palta; Shabbeer Hassan; Javier Nunez-Fontarnau; Tuomo T J Kiiskinen; Sanni Söderlund; Niina Matikainen; Mathias J Gerl; Michal A Surma; Christian Klose; Nathan O Stitziel; Hannele Laivuori; Aki S Havulinna; Susan K Service; Veikko Salomaa; Matti Pirinen; Matti Jauhiainen; Mark J Daly; Nelson B Freimer; Aarno Palotie; Marja-Riitta Taskinen; Kai Simons; Samuli Ripatti Journal: Nat Commun Date: 2019-09-24 Impact factor: 14.919
Authors: Johannes Tobias Neumann; Aki S Havulinna; Tanja Zeller; Sebastian Appelbaum; Tarja Kunnas; Seppo Nikkari; Pekka Jousilahti; Stefan Blankenberg; Karsten Sydow; Veikko Salomaa Journal: PLoS One Date: 2014-03-04 Impact factor: 3.240
Authors: Reetta Kivioja; Arto Pietilä; Nicolas Martinez-Majander; Daniel Gordin; Aki S Havulinna; Veikko Salomaa; Karoliina Aarnio; Sami Curtze; Jaana Leiviskä; Jorge Rodríguez-Pardo; Ida Surakka; Markku Kaste; Turgut Tatlisumak; Jukka Putaala Journal: J Am Heart Assoc Date: 2018-11-06 Impact factor: 5.501