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

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.