The "Prediction of Alcohol Withdrawal Severity Scale" (PAWSS)

Although there are several tools that can be used to evaluate the severity of ongoing alcohol withdrawal syndrome (AWS), there is no available tool that can predict which patients are at risk for developing AWS at the time admission, before the patient has developed AWS. Unfortunately, there are severe symptoms of alcohol withdrawal (e.g., seizures) which may develop early in the hospitalization, and before the development of other systemic symptoms which may warn medical personnel of the possibility of impeding alcohol withdrawal (e.g., autonomic instability, delirium). The goal of this study is to evaluate the psychometric properties (e.g., predictive validity) of a new tool, the Prediction of Alcohol Withdrawal Severity Scale (PAWSS), on identifying which patients are at risk for developing complicated AWS (i.e., seizures, hallucinosis, delirium tremens) among hospitalized, medically ill patients.

The investigators plan to study the psychometric properties of a new tool, the "Prediction of Alcohol Withdrawal Severity Scale" (PAWSS) on predicting the risk for the development of complicated AWS (i.e., seizures, delirium tremens) in hospitalized medically ill patients. This tool was developed through an extensive literature review which identified evidence-based predictors for AWS. The scale consists of three portions relating to 1) an initial screening (threshold items), 2) patient's history of alcohol use and its consequences, and 3) measures of BAL and autonomic function. The investigators predict that a scale score 4 or greater will be associated with a high risk for the development of complicated AWS. Patients will undergo examination with the PAWSS within 24 hours of hospital admission. Thereafter, all patients will undergo daily examinations with the Clinical Institute Withdrawal Assessment for Alcohol, revised (CIWA) and the Alcohol Withdrawal Severity scale (AWS scale) in order to measure the primary outcomes of the study, that is, the development and severity (i.e., moderate to severe) of AWS during the first 72-hours after admission. The study is designed to study the tool's psychometric properties including its validity and inter-rater reliability. By providing clinicians with a tool (i.e., PAWSS) that allows them to correctly predict who will develop complicated AWS it will enable them to prophylax (i.e., preventively treat) patients at risk and thus decrease patients' morbidity and mortality, shorten length of hospital stay, minimize the significant burden on the nursing and medical staff, and improve overall patient care.