Research

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

Margaret A Pisani¹ , Katy LB Araujo² , Peter H Van Ness² , Ying Zhang² , E Wesley Ely³ and Sharon K Inouye⁴

¹  Department of Internal Medicine, Pulmonary & Critical Care Section, and the Program on Aging, Yale University School of Medicine, Cedar Street, New Haven, Connecticut 06520-8057, USA

²  Department of Internal Medicine, Geriatrics Section, and the Program on Aging, Yale University School of Medicine, Cedar Street, New Haven, Connecticut 06520-8057, USA

³  Department of Medicine and Center for Health Services Research, Veterans Affairs Geriatric Research and Clinical Education Center (GRECC) and the Vanderbilt University School of Medicine, 6109 Medical Center East, Nashville, Tennessee 37232, USA

⁴  Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School and the Aging Brain Center, Hebrew Rehabilitation Center for Aged, 1200 Centre Street, Boston, Massachusetts 02131, USA

author email corresponding author email

Critical Care 2006, 10:R121doi:10.1186/cc5027

Published:	18 August 2006

Abstract

Introduction

Delirium is a serious and prevalent problem in intensive care units (ICUs). 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

A prospective cohort study was conducted in 178 patients aged 60 years and older who were admitted to the medical ICU. The Confusion Assessment Method for the ICU (CAM-ICU) and a validated chart review method for detecting 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 and 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 found to be 80% of patients (143 out of 178) or 64% of patient-days (929 out of 1,457). Of these patient-days, 292 were classified as delirium by the CAM-ICU. The remainder (637 patient-days) were classified as delirium by the validated chart review method when CAM-ICU was missing because the assessment was conducted for weekends or holidays (404 patient-days), when CAM-ICU was not performed because of 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%.

Conclusion

Eight out 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 detecting delirium in a research setting, taking into account the fluctuation that occurs with delirium, which is a key component of accurate determination of delirium status. Improving detection of delirium is of paramount importance both to advance delirium research and to enhance clinical care and patient safety.