Enhancing XXXX Recurrence Algorithms Through Selective Use of Medical Record Data.

@entity5 @entity1 @entity2286 @entity0
BACKGROUND: The utility of data-based algorithms in research has been questioned because of errors in identification of @entity5 recurrences. We adapted previously published @entity0 recurrence algorithms, selectively using medical record (MR) data to improve classification. METHODS: We evaluated second @entity0 event (SBCE) and recurrence-specific algorithms previously published by Chubak and colleagues in 1535 @entity1 from the Life After Cancer Epidemiology (LACE) and 225 @entity1 from the @entity1 's Health Initiative cohorts and compared classification statistics to published values. We also sought to improve classification with minimal MR examination. We selected pairs of algorithms-one with high sensitivity/high positive predictive value ( @entity2286 ) and another with high specificity/high @entity2286 -using MR information to resolve discrepancies between algorithms, properly classifying events based on review; we called this "triangulation." Finally, in LACE, we compared associations between @entity0 survival risk factors and recurrence using MR data, single Chubak algorithms, and triangulation. RESULTS: The SBCE algorithms performed well in identifying SBCE and recurrences. Recurrence-specific algorithms performed more poorly than published except for the high-specificity/high- @entity2286 algorithm, which performed well. The triangulation method (sensitivity = 81.3%, specificity = 99.7%, @entity2286 = 98.1%, NPV = 96.5%) improved recurrence classification over two single algorithms (sensitivity = 57.1%, specificity = 95.5%, @entity2286 = 71.3%, NPV = 91.9%; and sensitivity = 74.6%, specificity = 97.3%, @entity2286 = 84.7%, NPV = 95.1%), with 10.6% MR review. Triangulation performed well in survival risk factor analyses vs analyses using MR-identified recurrences. CONCLUSIONS: Use of multiple recurrence algorithms in administrative data, in combination with selective examination of MR data, may improve recurrence data quality and reduce research costs.
