Underreporting of Delirium in Statewide Claims Data: Implications for Clinical Care and Predictive Modeling.

Background: Delirium is an acute neuropsychiatric syndrome that portends poor prognosis and represents a significant burden to the health care system. Although detection allows for efficacious treatment, the diagnosis is frequently overlooked. This underdiagnosis makes delirium an appealing target for translational predictive algorithmic modeling; however, such approaches require accurate identification in clinical training datasets.
Methods: Using the Massachusetts All-Payers Claims Database, encompassing health claims for Massachusetts residents for 2012, we calculated the rate of delirium diagnosis in index hospitalizations by reported ICD-9 diagnosis code. We performed a review of published studies formally assessing delirium to establish an expected rate of delirium when formally assessed. Secondarily, we reported a sociodemographic comparison of cases and noncases.
Results: Rates of delirium reported in the literature vary widely, from 3.6-73% with a mean of 23.6%. The statewide claims data (Massachusetts All-Payers Claims Database) identified the rate of delirium among index hospitalizations to be only 2.1%. For Massachusetts All-Payers Claims Database hospitalizations, delirium was coded in 2.8% of patients >65 years old and for 1.2% of patients ≤65.
Conclusion: The lower incidence of delirium in claims data may reflect a failure to diagnose, a failure to code, or a lower rate in community hospitals. The relative absence of the phenotype from large databases may limit the utility of data-driven predictive modeling to the problem of delirium recognition.
(Copyright © 2016. Published by Elsevier Inc.)