Detecting Lung and XXXX Recurrence Using Structured Clinical/Administrative Data to Enable Outcomes Research and Population Health Management.

@entity5 @entity1 @entity14
INTRODUCTION: Recurrent @entity5 is common, costly, and lethal, yet we know little about it in community-based populations. Electronic health records and @entity5 registries contain vast amounts of data regarding community-based @entity1 , but usually lack recurrence status. Existing algorithms that use structured data to detect recurrence have limitations. METHODS: We developed algorithms to detect the presence and timing of recurrence after definitive therapy for stages I-III lung and @entity14 using 2 data sources that contain a widely available type of structured data (claims or electronic health record encounters) linked to gold-standard recurrence status: Medicare claims linked to the @entity5 Care Outcomes Research and Surveillance study, and the @entity5 Research Network Virtual Data Warehouse linked to registry data. Twelve potential indicators of recurrence were used to develop separate models for each @entity5 in each data source. Detection models maximized area under the ROC curve (AUC); timing models minimized average absolute error. Algorithms were compared by @entity5 type/data source, and contrasted with an existing binary detection rule. RESULTS: Detection model AUCs (>0.92) exceeded existing prediction rules. Timing models yielded absolute prediction errors that were small relative to follow-up time (<15%). Similar covariates were included in all detection and timing algorithms, though differences by @entity5 type and dataset challenged efforts to create 1 common algorithm for all scenarios. CONCLUSIONS: Valid and reliable detection of recurrence using big data is feasible. These tools will enable extensive, novel research on quality, effectiveness, and outcomes for lung and @entity14 @entity1 and those who develop recurrence.
