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Comparative Study
. 2006;10(4):R121.
doi: 10.1186/cc5027.

A research algorithm to improve detection of delirium in the intensive care unit

Margaret A Pisani  1 , Katy L B Araujo, Peter H Van Ness, Ying Zhang, E W Ely, Sharon K Inouye
Affiliations
Comparative Study

A research algorithm to improve detection of delirium in the intensive care unit

Margaret A Pisani et al. Crit Care. 2006.

Abstract

Introduction: 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.

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