• Injury · Jan 2022

    Risk of alcohol withdrawal syndrome in hospitalized trauma patients: A national data analysis.

    • Nasim Ahmed and YenHong Kuo.
    • Professor of Surgery, Hackensack Meridian School of Medicine. Chief of Trauma & Medical Director of Surgical Critical Care, Jersey Shore University Medical Center, Neptune, NJ, USA. Electronic address: nasim.ahmed@hmhn.org.
    • Injury. 2022 Jan 1; 53 (1): 44-48.

    BackgroundAlcohol withdrawal syndrome (AWS) is an uncommon occurrence in trauma victims. However, the syndrome can cause a prolonged hospital stay. Therefore, the purpose of this study is to develop and validate the risk factors of AWS so that interventions can be applied to high-risk patients.MethodsAll adult trauma patients with an injury severity score of ≥1 and greater than one hospital day were included in the study. The Trauma Quality Improvement Program (TQIP) database from 2013-2016 was accessed for the study. Patient demography, injury and comorbidities were compared between the patients who developed AWS and who did not develop AWS. The data were split into 2 datasets: training and testing. Eighty percent (80%) of the data was randomly selected for the training dataset to develop the risk factors. The remaining 20% of the data were used for validation of the risk factors using multivariable analysis. The receiving operating characteristics (ROC) curve and area under the curve (AUC) were generated for model fitness. All P values <0.01 were considered statistically significant.ResultsA total of 497,819 patients qualified for the study. Only 6,894 (1.38%) patients developed AWS during their hospitalization. The median age of the patients, who developed AWS, was 54 years. The patients were predominantly male (84% vs. 63.1%) and Caucasian (80.3% vs. 76.1%). The multivariable analysis showed an age range of 45 years to 74 years old, male gender, Caucasian race, a history of chronic alcoholic abuse, hypertension and cirrhosis increased the risk of AWS. The AUC of the model of 0.910, 95% CI; [0.901, 0.918] showed an excellent model for predicting the risk of the development of AWS.ConclusionApproximately 1.4% of the trauma victims developed AWS. Certain patient demographic and comorbidity characteristics, and head injury have a higher risk of developing of AWS.Copyright © 2021 Elsevier Ltd. All rights reserved.

      Pubmed     Full text   Copy Citation     Plaintext  

      Add institutional full text...

    Notes

     
    Knowledge, pearl, summary or comment to share?
    300 characters remaining
    help        
    You can also include formatting, links, images and footnotes in your notes
    • Simple formatting can be added to notes, such as *italics*, _underline_ or **bold**.
    • Superscript can be denoted by <sup>text</sup> and subscript <sub>text</sub>.
    • Numbered or bulleted lists can be created using either numbered lines 1. 2. 3., hyphens - or asterisks *.
    • Links can be included with: [my link to pubmed](http://pubmed.com)
    • Images can be included with: ![alt text](https://bestmedicaljournal.com/study_graph.jpg "Image Title Text")
    • For footnotes use [^1](This is a footnote.) inline.
    • Or use an inline reference [^1] to refer to a longer footnote elseweher in the document [^1]: This is a long footnote..

    hide…

What will the 'Medical Journal of You' look like?

Start your free 21 day trial now.

We guarantee your privacy. Your email address will not be shared.