Developing Predictive Models to Determine Patients in End-of-Life Care in Administrative Datasets

In observational studies with mortality endpoints, one needs to consider how to account for subjects whose interventions appear to be part of ‘end-of-life’ care. The objective of this study was to develop a diagnostic predictive model to identify those in end-of-life care at the time of a drug exposure. We used data from four administrative claims datasets from 2000 to 2017. The index date was the date of the first prescription for the last new drug subjects received during their observation period. The outcome of end-of-life care was determined by the presence of one or more codes indicating terminal or hospice care. Models were developed using regularized logistic regression. Internal validation was through examination of the area under the receiver operating characteristic curve (AUC) and through model calibration in a 25% subset of the data held back from model training. External validation was through examination of the AUC after applying the model learned on one dataset to the three other datasets. The models showed excellent performance characteristics. Internal validation resulted in AUCs ranging from 0.918 (95% confidence interval [CI] 0.905–0.930) to 0.983 (95% CI 0.978–0.987) for the four different datasets. Calibration results were also very good, with slopes near unity. External validation also produced very good to excellent performance metrics, with AUCs ranging from 0.840 (95% CI 0.834–0.846) to 0.956 (95% CI 0.952–0.960). These results show that developing diagnostic predictive models for determining subjects in end-of-life care at the time of a drug treatment is possible and may improve the validity of the risk profile for those treatments.