Feasibility and accuracy of linking a heart failure registry to the national claims database using indirect identifiers.

Background: Heart failure (HF) registries include rich data on patient inclusion characteristics, but follow-up information is often incomplete. Medicoadministrative databases may provide less clinical information than registries, e.g. on left ventricular ejection fraction (LVEF), but long-term data are exhaustive and reliable. The combination of the two types of database is therefore appealing, but the feasibility and accuracy of such linking are largely unexplored.
Aims: To assess the feasibility and accuracy of linking an HF registry (FRESH; FREnch Survey on Heart Failure) with the French National Healthcare System database (SNDS).
Methods: A probabilistic algorithm was developed to link and match patient data included in the FRESH HF registry with anonymized records from the SNDS, which include: hospitalizations and diagnostic codes; all care-related reimbursements by national health system; and deaths. Consistency was assessed between deaths recorded in the registry and in the SNDS. A comparison between the two databases was carried out on several identifiable clinical characteristics (history of HF hospitalization, diabetes, atrial fibrillation, chronic bronchopneumopathy, severe renal failure and stroke) and on events during 1-year follow-up after inclusion.
Results: Of 2719 patients included in the FRESH registry (1049 during decompensation; 1670 during outpatient follow-up), 1885 could be matched with a high accuracy of 94.3% for deaths. Mortality curves were superimposable, including curves according to type of HF and LVEF. The rates of missing data in the FRESH registry were 2.3-8.4% for clinical characteristics and 17.5% for hospitalizations during follow-up. The discrepancy rate for clinical characteristics was 3-13%. Hospitalization rates were significantly higher in the SNDS than in the registry cohort.
Conclusions: The anonymous matching of an HF research cohort with a national health database is feasible, with a significant proportion of patients being accurately matched, and facilitates combination of clinical data and a reduced rate of losses to follow-up.
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