title: Underreporting of Delirium in Statewide Claims Data: Implications for Clinical Care and Predictive Modeling.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  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.
authors.affiliation: Department of Psychiatry, Center for Human Genetic Research, Center for Experimental Drugs and Diagnostics, Massachusetts General Hospital, Boston, MA; Avery D. Weisman Psychiatry Consultation Service, Massachusetts General Hospital, Boston, MA.
authors.forename: Thomas H
authors.initials: TH
authors.lastname: McCoy
authors.affiliation: Department of Psychiatry, Center for Human Genetic Research, Center for Experimental Drugs and Diagnostics, Massachusetts General Hospital, Boston, MA.
authors.forename: Leslie
authors.initials: L
authors.lastname: Snapper
authors.affiliation: Avery D. Weisman Psychiatry Consultation Service, Massachusetts General Hospital, Boston, MA.
authors.forename: Theodore A
authors.initials: TA
authors.lastname: Stern
authors.affiliation: Department of Psychiatry, Center for Human Genetic Research, Center for Experimental Drugs and Diagnostics, Massachusetts General Hospital, Boston, MA. Electronic address: rperlis@partners.org.
authors.forename: Roy H
authors.initials: RH
authors.lastname: Perlis
mesh_terms: D000368:Aged; D000465:Algorithms; D017281:Cost of Illness; D003430:Cross-Sectional Studies; D039702:Current Procedural Terminology; D057210:Delayed Diagnosis / Q000191:economics / Q000706:statistics & numerical data; D003693:Delirium / Q000175:diagnosis* / Q000191:economics / Q000453:epidemiology* / Q000628:therapy; D003695:Delivery of Health Care / Q000191:economics / Q000706:statistics & numerical data*; D003951:Diagnostic Errors / Q000191:economics / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007345:Insurance Claim Review / Q000191:economics / Q000706:statistics & numerical data*; D008297:Male; D008404:Massachusetts; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D012307:Risk Factors
keywords: claims data; delirium; predictive modeling.; prevalence