A systematic review of validated methods for identifying heart failure using administrative data
Meyers Primary Care Institute; Department of Medicine, Division of Rheumatology; Department of Medicine, Division of Geriatric Medicine; Department of Medicine, Division of Cardiovascular Medicine; Department of Quantitative Health Sciences
Medical Subject Headings
Heart Failure; Diagnostic Techniques, Cardiovascular
Cardiovascular Diseases | Health Services Research | Primary Care
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. Abstracts 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 abstracts. 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 (c) 2012 John Wiley and Sons, Ltd.
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Citation: Pharmacoepidemiol Drug Saf. 2012 Jan;21 Suppl 1:129-40. doi: 10.1002/pds.2313. Link to article on publisher's site