A systematic review of validated methods for identifying heart failure using administrative data

• Published in: Pharmacoepidemiology and drug safety Vol. 21; no. S1; pp. 129 - 140
• Main Authors: Saczynski, Jane S., Andrade, Susan E., Harrold, Leslie R., Tjia, Jennifer, Cutrona, Sarah L., Dodd, Katherine S., Goldberg, Robert J., Gurwitz, Jerry H.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.01.2012
• Subjects: administrative data; Algorithms; congestive heart failure; Databases, Factual - statistics & numerical data; Heart Failure - diagnosis; Heart Failure - epidemiology; Humans; Insurance Claim Review - statistics & numerical data; International Classification of Diseases; Predictive Value of Tests; Sensitivity and Specificity; validation; Validation Studies as Topic
• DOI: 10.1002/pds.2313
• ISSN: 1053-8569

ABSTRACT Purpose To identify and describe the validity of algorithms used to detect heart failure (HF) using administrative and claims data sources. Methods A systematic review of PubMed and Iowa Drug Information Service searches of the English language was performed to identify studies published between 1990 and 2010 that evaluated the validity of algorithms for the identification of patients with HF using and claims data. s and articles were reviewed by two study investigators to determine their relevance on the basis of predetermined criteria. Results The initial search strategy identified 887 s. Of these, 499 full articles were reviewed and 35 studies included data to evaluate the validity of identifying patients with HF. Positive predictive values (PPVs) were in the acceptable to high range, with most being very high (>90%). Studies that included patients with a primary hospital discharge diagnosis of International Classification of Diseases, Ninth Revision, code 428.X had the highest PPV and specificity for HF. PPVs for this algorithm ranged from 84% to 100%. This algorithm, however, may compromise sensitivity because many HF patients are managed on an outpatient basis. The most common ‘gold standard’ for the validation of HF was the Framingham Heart Study criteria. Conclusions The algorithms and definitions used to identify HF using administrative and claims data perform well, particularly when using a primary hospital discharge diagnosis. Attention should be paid to whether patients who are managed on an outpatient basis are included in the study sample. Including outpatient codes in the described algorithms would increase the sensitivity for identifying new cases of HF. Copyright © 2012 John Wiley & Sons, Ltd.