Dataset Information

Using the AUDIT-PC to predict alcohol withdrawal in hospitalized patients.

ABSTRACT:

Background

Alcohol withdrawal syndrome (AWS) occurs when alcohol-dependent individuals abruptly reduce or stop drinking. Hospitalized alcohol-dependent patients are at risk. Hospitals need a validated screening tool to assess withdrawal risk, but no validated tools are currently available.

Objective

To examine the admission Alcohol Use Disorders Identification Test-(Piccinelli) Consumption (AUDIT-PC) ability to predict the subsequent development of AWS among hospitalized medical-surgical patients admitted to a non-intensive care setting.

Design

Retrospective case–control study of patients discharged from the hospital with a diagnosis of AWS. All patients with AWS were classified as presenting with AWS or developing AWS later during admission. Patients admitted to an intensive care setting and those missing AUDIT-PC scores were excluded from analysis. A hierarchical (by hospital unit) logistic regression was performed and receiver-operating characteristics were examined on those developing AWS after admission and randomly selected controls. Because those diagnosing AWS were not blinded to the AUDIT-PC scores, a sensitivity analysis was performed.

Participants

The study cohort included all patients age ≥18 years admitted to any medical or surgical units in a single health care system from 6 October 2009 to 7 October 2010.

Key results

After exclusions, 414 patients were identified with AWS. The 223 (53.9 %) who developed AWS after admission were compared to 466 randomly selected controls without AWS. An AUDIT-PC score ≥4 at admission provides 91.0 % sensitivity and 89.7 % specificity (AUC=0.95; 95 % CI, 0.94–0.97) for AWS, and maximizes the correct classification while resulting in 17 false positives for every true positive identified. Performance remained excellent on sensitivity analysis (AUC=0.92; 95 % CI, 0.90–0.93). Increasing AUDIT-PC scores were associated with an increased risk of AWS (OR=1.68, 95 % CI 1.55–1.82, p<0.001).

Conclusions

The admission AUDIT-PC score is an excellent discriminator of AWS and could be an important component of future clinical prediction rules. Calibration and further validation on a large prospectivecohort is indicated.

SUBMITTER: Pecoraro A

PROVIDER: S-EPMC3889973 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Importance:Although severe alcohol withdrawal syndrome (SAWS) is associated with substantial morbidity and mortality, most at-risk patients will not develop this syndrome. Predicting its occurrence is important because the mortality rate is high when untreated. Objective:To assess the accuracy and predictive value of symptoms and signs for identifying hospitalized patients at risk of SAWS, defined as delirium tremens, withdrawal seizure, or clinically diagnosed severe withdrawal. Data Sources:MEDLINE and EMBASE (1946-January 2018) were searched for articles investigating symptoms and signs predictive of SAWS in adults. Reference lists of retrieved articles were also searched. Study Selection:Original studies that were included compared symptoms, signs, and risk assessment tools among patients who developed SAWS and patients who did not. Data Extraction and Synthesis:Data were extracted and used to calculate likelihood ratios (LRs), sensitivity, and specificity. A meta-analysis was performed to calculate summary LR. Results:Of 530 identified studies, 14 high-quality studies that included 71?295 patients and 1355 relevant cases of SAWS (1051 cases), seizure (53 cases), or delirium tremens (251 cases) were analyzed. A history of delirium tremens (LR, 2.9 [95% CI 1.7-5.2]) and baseline systolic blood pressure 140 mm Hg or higher (LR, 1.7 [95% CI, 1.3-2.3) were associated with an increased likelihood of SAWS. No single symptom or sign was associated with exclusion of SAWS. Six high-quality studies evaluated combinations of clinical findings and were useful for identifying patients in acute care facilities at high risk of developing SAWS. Of these combinations, the Prediction of Alcohol Withdrawal Severity Scale (PAWSS) was most useful, with an LR of 174 (95% CI, 43-696; specificity, 0.93) when patients had 4 or more individual findings and an LR of 0.07 (95% CI, 0.02-0.26; sensitivity, 0.99) when there were 3 or fewer findings. Conclusions and Relevance:Assessment tools that use a combination of symptoms and signs are useful for identifying patients at risk of developing severe alcohol withdrawal syndrome. Most studies of these tools were not fully validated, limiting their generalizability.

Project description:BackgroundBaclofen shows potential for rapidly reducing symptoms of severe alcohol withdrawal syndrome (AWS) in people with alcoholism. Treatment with baclofen is easy to manage and rarely produces euphoria or other pleasant effects, or craving for the drug. This is an updated version of the original Cochrane Review published in 2015, Issue 4.ObjectivesTo assess the efficacy and safety of baclofen for people with AWS.Search methodsWe updated our searches of the following databases to March 2017: the Cochrane Drugs and Alcohol Group Specialised Register, CENTRAL, PubMed, Embase, and CINAHL. We also searched registers of ongoing trials. We handsearched the references quoted in the identified trials, and sought information from researchers, pharmaceutical companies, and relevant trial authors about unpublished or uncompleted trials. We placed no restrictions on language.Selection criteriaWe included all randomised controlled clinical trials (RCTs) evaluating baclofen versus placebo or any other treatment for people with AWS. We excluded uncontrolled, non-randomised, or quasi-randomised trials. We included both parallel group and cross-over studies.Data collection and analysisWe used standard methodological procedures expected by Cochrane.Main resultsWe included three RCTs with 141 randomised participants. We did not perform meta-analyses due to the different control interventions. For the comparison of baclofen and placebo (1 study, 31 participants), there was no significant difference in Clinical Institute Withdrawal Assessment of Alcohol Scale, Revised (CIWA-Ar) scores (very low quality evidence). For the comparison of baclofen and diazepam (1 study, 37 participants), there was no significant difference in CIWA-Ar scores (very low quality evidence), adverse events (risk difference (RD) 0.00, 95% confidence interval (CI) -0.10 to 0.10; very low quality evidence), dropouts (RD 0.00, 95% CI -0.10 to 0.10; very low quality evidence), and dropouts due to adverse events (RD 0.00, 95% CI -0.10 to 0.10; very low quality evidence). For the comparison of baclofen and chlordiazepoxide (1 study, 60 participants), there was no significant difference in CIWA-Ar scores (mean difference (MD) 1.00, 95% CI 0.70 to 1.30; very low quality evidence), global improvement (MD 0.10, 95% CI -0.03 to 0.23; very low quality evidence), adverse events (RD 2.50, 95% CI 0.88 to 7.10; very low quality of evidence), dropouts (RD 0.00, 95% CI -0.06 to 0.06; very low quality evidence), and dropouts due to adverse events (RD 0.00, 95% CI -0.06 to 0.06; very low quality evidence).Authors' conclusionsNo conclusions can be drawn about the efficacy and safety of baclofen for the management of alcohol withdrawal because we found insufficient and very low quality evidence.

Project description:Alcohol withdrawal syndrome (AWS) is a distressing and life-threatening condition that usually affects people who are alcohol dependent when they discontinue or decrease their alcohol consumption. Baclofen shows potential for rapidly reducing symptoms of severe AWS in people with alcoholism. Treatment with baclofen is easy to manage and rarely produces euphoria or other pleasant effects, or craving for the drug. This is an updated version of the original Cochrane Review first published in 2011 and last updated in 2017. To assess the efficacy and safety of baclofen for people with AWS. We updated our searches of the following databases to June 2019: the Cochrane Drugs and Alcohol Group Specialised Register, CENTRAL, PubMed, Embase, and CINAHL. We also searched registers of ongoing trials. We handsearched the references quoted in the identified trials, and sought information from researchers, pharmaceutical companies, and relevant trial authors about unpublished or uncompleted trials. We placed no restrictions on language. We included all randomised controlled clinical trials (RCTs) evaluating baclofen versus placebo or any other treatment for people with AWS. We excluded uncontrolled, non-randomised, or quasi-randomised trials. We included both parallel group and cross-over studies. We used standard methodological procedures expected by Cochrane. We included four RCTs with 189 randomised participants (one RCT new for this update). None of the included studies reported the primary outcomes of alcohol withdrawal seizures, alcohol withdrawal delirium, or craving. For the comparison of baclofen and placebo (1 study, 31 participants), there was no evidence of a difference in Clinical Institute Withdrawal Assessment of Alcohol Scale, Revised (CIWA-Ar) scores in eight-hour periods from days one to five (very low-quality evidence). For the comparison of baclofen and diazepam (2 studies, 85 participants), there was no evidence of a difference in change from baseline to days 10 to 15 on CIWA-Ar scores (very low-quality evidence, meta-analysis was not performed due to insufficient data). In one study (37 participants), there was no evidence of a difference in participants with at least one adverse event (risk difference (RD) 0.00, 95% confidence interval (CI) -0.10 to 0.10; very low-quality evidence), dropouts (RD 0.00, 95% CI -0.10 to 0.10; very low-quality evidence), and dropouts due to adverse events (RD 0.00, 95% CI -0.10 to 0.10; very low-quality evidence). For the comparison of baclofen and chlordiazepoxide (1 study, 60 participants), there was no evidence of a difference in difference from baseline to nine-day decremental fixed-dose intervention: CIWA-Ar scores (mean difference (MD) 1.00, 95% CI 0.70 to 1.30; very low-quality evidence), global improvement (MD 0.10, 95% CI -0.03 to 0.23; very low-quality evidence), 14/60 participants with adverse events (RD 2.50, 95% CI 0.88 to 7.10; very low-quality of evidence), dropouts (RD 0.00, 95% CI -0.06 to 0.06; very low-quality evidence), and dropouts due to adverse events (RD 0.00, 95% CI -0.06 to 0.06; very low-quality evidence). None of the RCTs provided information on random sequence generation or allocation concealment, therefore, we assessed them at unclear risk of bias. Two RCTs were not of double-blind design and had a high risk of bias in blinding (Addolorato 2006; Girish 2016). One RCT had more than 5% dropouts with high risk of attrition bias (Lyon 2011). We could not assess reporting bias as none of the prepublished protocols were available. No conclusions can be drawn about the efficacy and safety of baclofen for the management of alcohol withdrawal because we found insufficient and very low-quality evidence.