The AWOL tool: Derivation and validation of a delirium prediction rule

Abstract

BACKGROUND Risk factors for delirium are well‐described, yet there is no widely used tool to predict the development of delirium upon admission in hospitalized medical patients. OBJECTIVE To develop and validate a tool to predict the likelihood of developing delirium during hospitalization. DESIGN Prospective cohort study with derivation (May 2010–November 2010) and validation (October 2011–March 2012) cohorts. SETTING Two academic medical centers and 1 Veterans Affairs medical center. PATIENTS Consecutive medical inpatients (209 in the derivation and 165 in the validation cohort) over age 50 years without delirium at the time of admission. MEASUREMENTS Delirium assessed daily for up to 6 days using the Confusion Assessment Method. RESULTS The AWOL prediction rule was derived by assigning 1 point to each of 4 items assessed upon enrollment that were independently associated with the development of delirium (Age ≥ 80 years, failure to spell “World” backward, disOrientation to place, and higher nurse‐rated iLlness severity). Higher scores were associated with higher rates of delirium in the derivation and validation cohorts (P for trend < 0.001 and 0.025, respectively). Rates of delirium according to score in the combined population were: 0(1/50, 2%), 1(5/141, 4%), 2(15/107, 14%), 3(10/50, 20%), and 4(7/11, 64%) (P for trend < 0.001). Area under the receiver operating characteristic curve for the derivation and validation cohorts was 0.81 (0.73–0.90) and 0.69 (0.54–0.83) respectively. CONCLUSIONS The AWOL prediction rule characterizes medical patients' risk for delirium at the time of hospital admission and could be used for clinical stratification and in trials of delirium prevention. Journal of Hospital Medicine 2013;8:493–499. © 2013 Society of Hospital Medicine

Predicting delirium: a review of risk-stratification models

“…Carrasco [31] asserted that, in addition to requiring special assessments, earlier models relied on "cognitive assessments [that], if not done properly, may misdiagnose delirium or previous cognitive impairment." Finally, Douglas [28] also suggested that "existing prediction rules…have been limited by their complexity. "…”

“…Given the well-established importance of this particular risk factor, it is somewhat surprising that it was not included in all models. Three other studies (27,28,31) included either advanced age or dependence in activities of daily living but not cognitive impairment. In addition, Douglas et al [28] found that brief screening for cognitive impairment on admission, that is, inability to spell WORLD backwards or answer orientation questions accurately, predicted delirium risk.…”

“…Martinez et al [27] included all adults, and age N 85 emerged as a model element; Kobayashi et al [19] included all adults, and their model included age stratifications at 50 and 75; Douglas et al [28] included adults as young as 50, and age N80 emerged as a significant factor.…”

Risk Factors for Delirium in Older Medical Inpatients in Tanzania

“…Yet, there is significant heterogeneity in measurement, with some studies preferring a physician or nurse's subjective opinion over measurements of physiological parameters [27,34]. Where physiological parameters have been used, the most commonly employed tool is the Acute Physiology and Chronic Health Evaluation II (APACHE II) [4,5], originally devised and developed for use in critical care settings [35].…”

Transitional Care Strategies From Hospital to Home

“…Specific interventions, such as secondary stroke prevention (antithrombotic, antihypertensive, and lipid-lowering agents), screening for dysphagia, and reducing urinary catheter use in the hospital have been shown in published studies to reduce readmissions and postdischarge adverse events such as cerebrovascular accidents, pneumonia, and catheter-associated urinary tract infections. [53][54][55][56][57][58][59] Neurohospitalists are in a position to have a direct impact on patient care beyond the hospitalization period by implementing interventions that can improve quality of care, reduce cost, and reduce the risks associated with preventable readmissions and adverse events.…”