Introduction US claims data contain medical data on large heterogeneous populations and are excellent sources for medical research. Some claims data do not contain complete death records, limiting their use for mortality or mortality-related studies. A model to predict whether a patient died at the end of the follow-up time (referred to as the end of observation) is needed to enable mortality-related studies. Objective The objective of this study was to develop a patient-level model to predict whether the end of observation was due to death in US claims data. Methods We used a claims dataset with full death records, Optum© De-Identifed Clinformatics® Data-Mart-Database—Date of Death mapped to the Observational Medical Outcome Partnership common data model, to develop a model that classifes the end of observations into death or non-death. A regularized logistic regression was trained using 88,514 predictors (recorded within the prior 365 or 30 days) and externally validated by applying the model to three US claims datasets. Results Approximately 25 in 1000 end of observations in Optum are due to death. The Discriminating End of observation into Alive and Dead (DEAD) model obtained an area under the receiver operating characteristic curve of 0.986. When defning death as a predicted risk of>0.5, only 2% of the end of observations were predicted to be due to death and the model obtained a sensitivity of 62% and a positive predictive value of 74.8%. The external validation showed the model was transportable, with area under the receiver operating characteristic curves ranging between 0.951 and 0.995 across the US claims databases. Conclusions US claims data often lack complete death records. The DEAD model can be used to impute death at various sensitivity, specifcity, or positive predictive values depending on the use of the model. The DEAD model can be readily applied to any observational healthcare database mapped to the Observational Medical Outcome Partnership common data model and is available from https://github.com/OHDSI/StudyProtocolSandbox/tree/master/DeadModel.

Additional Metadata
Persistent URL dx.doi.org/10.1007/s40264-019-00827-0, hdl.handle.net/1765/122306
Journal Drug Safety
Citation
Reps, J.M., Rijnbeek, P.R, & Ryan, P.B. (2019). Identifying the DEAD: Development and Validation of a Patient-Level Model to Predict Death Status in Population-Level Claims Data. Drug Safety, 42(11), 1377–1386. doi:10.1007/s40264-019-00827-0