title: A research algorithm to improve detection of delirium in the intensive care unit.
abstract: BACKGROUND
  Delirium is a serious and prevalent problem in intensive care units (ICU). The purpose of this study was to develop a research algorithm to enhance detection of delirium in critically ill ICU patients using chart review to complement a validated clinical delirium instrument.
  
  
  METHODS
  Prospective cohort study of 178 patients 60 years and older admitted to the Medical ICU. The Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) and a validated chart review method for delirium were performed daily. We assessed the diagnostic accuracy of the chart-based delirium method using the CAM-ICU as the gold standard. We then used an algorithm to detect delirium first using the CAM-ICU ratings, then chart review when the CAM-ICU was unavailable.
  
  
  RESULTS
  When using both the CAM-ICU and the chart-based review the prevalence of delirium was 143/178 (80%) patients or 929/1457 (64%) of patient-days. Of these, 292 patient-days were classified as delirium by the CAM-ICU, and the remainder (n=637 patient-days) were classified as delirium by the validated chart review method when the CAM-ICU was missing due to weekends or holidays (404 patient-days), when CAM-ICU was not performed due to stupor or coma (205 patient-days), and when the CAM-ICU was negative (28 patient-days). Sensitivity of the chart-based method was 64% and specificity was 85%. Overall agreement between chart and the CAM-ICU was 72%.
  
  
  CONCLUSIONS
  Eight of 10 patients in this cohort study developed delirium in the ICU. Although use of a validated delirium instrument with frequent direct observations is recommended for clinical care, this approach may not always be feasible, especially in a research setting. The algorithm proposed here comprises a more comprehensive method for delirium detection in a research setting taking into account the fluctuation that occurs with delirium, a key component to accurately determining delirium status. Improving delirium detection is of paramount importance first to advance delirium research and, subsequently to enhance clinical care and patient safety.
authors.affiliation: Department of Internal Medicine, Pulmonary & Critical Care Section, and the Program on Aging, Yale University School of Medicine, New Haven, CT 06520-8057, USA. Margaret.Pisani@yale.edu
authors.forename: Margaret A
authors.initials: MA
authors.lastname: Pisani
authors.forename: Katy L B
authors.initials: KL
authors.lastname: Araujo
authors.forename: Peter H
authors.initials: PH
authors.lastname: Van Ness
authors.forename: Ying
authors.initials: Y
authors.lastname: Zhang
authors.forename: E W
authors.initials: EW
authors.lastname: Ely
authors.forename: Sharon K
authors.initials: SK
authors.lastname: Inouye
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015331:Cohort Studies; D003693:Delirium / Q000175:diagnosis* / Q000453:epidemiology / Q000523:psychology; D005260:Female; D006801:Humans; D007362:Intensive Care Units; D008297:Male; D011446:Prospective Studies; D011569:Psychiatric Status Rating Scales; D012107:Research Design