{"id":4676,"date":"2014-01-01T00:00:00","date_gmt":"1970-01-01T00:00:00","guid":{"rendered":"http:\/\/news.christianacare.org\/2014\/01\/using-the-audit-pc-to-predict-alcohol-withdrawal-in-hospitalized-patients\/"},"modified":"2014-01-01T00:00:00","modified_gmt":"1970-01-01T00:00:00","slug":"using-the-audit-pc-to-predict-alcohol-withdrawal-in-hospitalized-patients","status":"publish","type":"post","link":"https:\/\/research.christianacare.org\/publications\/2014\/01\/01\/using-the-audit-pc-to-predict-alcohol-withdrawal-in-hospitalized-patients\/","title":{"rendered":"Using the AUDIT-PC to predict alcohol withdrawal in hospitalized patients"},"content":{"rendered":"<p>Pecoraro A, Ewen E, Horton T, Mooney R, Kolm P, McGraw P, Woody G<\/p>\n<p>J Gen Intern Med 2014 Jan;29(1):34-40<\/p>\n<p>PMID: <a href=\"http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/23959745\" target=\"_blank\">23959745<\/a><\/p>\n<h2>Abstract<\/h2>\n<p><p><strong>BACKGROUND: <\/strong>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.<\/p>\n<p><strong>OBJECTIVE: <\/strong>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.<\/p>\n<p><strong>DESIGN: <\/strong>Retrospective case\u2013control 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.<\/p>\n<p><strong>PARTICIPANTS: <\/strong>The study cohort included all patients age \u226518 years admitted to any medical or surgical units in a single health care system from 6 October 2009 to 7 October 2010.<\/p>\n<p><strong>KEY RESULTS: <\/strong>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 \u22654 at admission provides 91.0 % sensitivity and 89.7 % specificity (AUC=0.95; 95 % CI, 0.94\u20130.97) 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\u20130.93). Increasing AUDIT-PC scores were associated with an increased risk of AWS (OR=1.68, 95 % CI 1.55\u20131.82, p&lt;0.001).<\/p>\n<p><strong>CONCLUSIONS: <\/strong>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.<\/p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Pecoraro A, Ewen E, Horton T, Mooney R, Kolm P, McGraw P, Woody G J Gen Intern Med 2014 Jan;29(1):34-40 PMID: 23959745 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<\/p>\n<p><a class=\"more-link\" href=\"https:\/\/research.christianacare.org\/publications\/2014\/01\/01\/using-the-audit-pc-to-predict-alcohol-withdrawal-in-hospitalized-patients\/\">Continue reading <span class=\"screen-reader-text\">Using the AUDIT-PC to predict alcohol withdrawal in hospitalized patients<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4676","post","type-post","status-publish","format-standard","hentry","category-pubs-pres"],"acf":[],"_links":{"self":[{"href":"https:\/\/research.christianacare.org\/publications\/wp-json\/wp\/v2\/posts\/4676","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/research.christianacare.org\/publications\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/research.christianacare.org\/publications\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/research.christianacare.org\/publications\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/research.christianacare.org\/publications\/wp-json\/wp\/v2\/comments?post=4676"}],"version-history":[{"count":0,"href":"https:\/\/research.christianacare.org\/publications\/wp-json\/wp\/v2\/posts\/4676\/revisions"}],"wp:attachment":[{"href":"https:\/\/research.christianacare.org\/publications\/wp-json\/wp\/v2\/media?parent=4676"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/research.christianacare.org\/publications\/wp-json\/wp\/v2\/categories?post=4676"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/research.christianacare.org\/publications\/wp-json\/wp\/v2\/tags?post=4676"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}