Validating administrative data in XXXX research.

@entity1 @entity64 @entity510 @entity1779 @entity385
BACKGROUND AND PURPOSE: Research based on administrative data has advantages, including large numbers, consistent data, and low cost. This study was designed to compare different methods of @entity64 classification using administrative data. METHODS: Administrative hospital discharge data and medical record review of 206 @entity1 were used to evaluate 3 algorithms for classifying @entity64 @entity1 . These algorithms were based on all (algorithm 1), the first 2 (algorithm 2), or the primary (algorithm 3) administrative discharge diagnosis code(s). The diagnoses after review of medical record data were considered the gold standard. Then, using a large administrative data set, we compared @entity1 with a primary discharge diagnosis of @entity64 with @entity1 with their @entity64 discharge diagnosis code in a nonprimary position. RESULTS: Compared with the gold standard, algorithm 1 had the highest kappa for classifying @entity385 , with a sensitivity of 86%, specificity of 95%, positive predictive value of 90%, and kappa=0.82. Algorithm 3 had the highest kappa values for @entity1779 and @entity510 . For @entity1779 , the sensitivity was 85%, specificity was 96%, positive predictive value was 89%, and kappa=0.82. For @entity510 , those values were 90%, 97%, 94%, and 0.88, respectively. Nonprimary position @entity385 @entity1 had significantly greater comorbidity and 30-day mortality (odds ratio, 3.2) than primary position @entity385 @entity1 . CONCLUSIONS: @entity64 classification in these administrative data were optimal using all discharge diagnoses for @entity385 and primary discharge diagnosis only for intracerebral and @entity510 . Selecting @entity385 @entity1 on the basis of primary discharge diagnosis may bias administrative samples toward more benign, unrepresentative outcomes and should be avoided.
