The 'Prediction of Alcohol Withdrawal Severity Scale' (PAWSS): Systematic literature review and pilot study of a new scale for the prediction of complicated alcohol withdrawal syndrome

摘要

Background: To date, no screening tools for alcohol withdrawal syndromes (AWS) have been validated in the medically ill. Although several tools quantify the severity of AWS (e.g., Clinical Institute Withdrawal Assessment for Alcohol [CIWA]), none identify subjects at risk of AWS, thus missing the opportunity for timely prophylaxis. Moreover, there are no validated tools for the prediction of complicated (i.e., moderate to severe) AWS in the medically ill. Objectives: Our goals were (1) to conduct a systematic review of the published literature on AWS to identify clinical factors associated with the development of AWS, (2) to use the identified factors to develop a tool for the prediction of alcohol withdrawal among patients at risk, and (3) to conduct a pilot study to assess the validity of the tool. Methods: For the creation of the Prediction of Alcohol Withdrawal Severity Scale (PAWSS), we conducted a systematic literature search using PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines for clinical factors associated with the development of AWS, using PubMed, PsychInfo, MEDLINE, and Cochrane Databases. Eligibility criteria included: (i) manuscripts dealing with human subjects, age 18 years or older, (ii) manuscripts directly addressing descriptions of AWS or its predisposing factors, including case reports, naturalistic case descriptions, and all types of clinical trials (e.g., randomized, single-blind, or open label studies), (iii) manuscripts describing characteristics of alcohol use disorder (AUD), and (iv) manuscripts dealing with animal data (which were considered only if they directly dealt with variables described in humans). Obtained data were used to develop the Prediction of Alcohol Withdrawal Severity Scale, in order to assist in the identification of patients at risk for complicated AWS.A pilot study was conducted to assess the new tool's psychometric qualities on patients admitted to a general inpatient medicine unit over a 2-week period, who agreed to participate in the study. Blind to PAWSS results, a separate group of researchers retrospectively examined the medical records for evidence of AWS. Results: The search produced 2802 articles describing factors potentially associated with increased risk for AWS, increased severity of withdrawal symptoms, and potential characteristics differentiating subjects with various forms of AWS. Of these, 446 articles met inclusion criteria and underwent further scrutiny, yielding a total of 233 unique articles describing factors predictive of AWS. A total of 10 items were identified as correlated with complicated AWS (i.e., withdrawal hallucinosis, withdrawal-related seizures, and delirium tremens) and used to construct the PAWSS. During the pilot study, a total of 68 subjects underwent evaluation with PAWSS. In this pilot sample the sensitivity, specificity, and positive and negative predictive values of PAWSS were 100%, using the threshold score of 4. Discussion: The results of the literature search identified 10 items which may be correlated with risk for complicated AWS. These items were assembled into a tool to assist in the identification of patients at risk: PAWSS. The results of this pilot study suggest that PAWSS may be useful in identifying risk of complicated AWS in medically ill, hospitalized individuals. PAWSS is the first validated tool for the prediction of severe AWS in the medically ill and its use may aid in the early identification of patients at risk for complicated AWS, allowing for prophylaxis against AWS before severe alcohol withdrawal syndromes develop.