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酒精戒断入院患者升级至重症监护病房护理水平的预测因素。

Predictors of Escalation to Intensive Care Unit Level of Care Among Admissions for Alcohol Withdrawal.

作者信息

Mohan Gaurav, Bhide Poorva, Abu-Shanab Amer, Ghose Medha, Rajamohan Adhithya, Muhammad Tayyeb, Khan Anosh A, Khan Mahrukh, Khalid Farhan, Padappayil Rana P, Du Doantrang

机构信息

Department of Internal Medicine, Rutgers-Monmouth Medical Center, Long Branch, NJ, USA.

Office of Scientific Affairs and Research, King Hussein Cancer Center, Amman, Jordan.

出版信息

J Community Hosp Intern Med Perspect. 2023 Sep 2;13(5):8-14. doi: 10.55729/2000-9666.1241. eCollection 2023.

Abstract

According to the 2019 National Survey on Drug Use and Health, 14.5 million people ages 12 and older had alcohol abuse disorder. Alcohol withdrawal syndrome (AWS) can be defined as a collection of physical symptoms experienced due to abrupt cessation of alcohol after long-term dependence. In instances where regular inpatient management fails to control AWS symptoms, patients are shifted to intensive care units (ICUs) for closer monitoring and prevention of life-threatening complications like withdrawal seizures and delirium tremens (DTs), labeled as severe alcohol withdrawal syndrome (SAWS). Although this represents a significant healthcare burden, minimal studies have been conducted to determine objective predictors. In this study, we aim to determine the effect of patient demographics, socio-economic status, biochemical parameters, and clinical factors on the need for escalation to ICU level of care among admissions for AWS. Our study showed that factors such as a history of DTs or alcohol-related seizures, the initial protocol of management, degree of reported alcohol usage, activation of rapid response teams, mean corpuscular value, alcohol level on admission, highest Clinical Institute Withdrawal Assessment Alcohol Revised (CIWA-Ar) scored during the hospital stay, and the total amount of sedatives used were significantly associated with escalation to ICU level of care. Clinicians must use these objective parameters to identify high-risk patients and intervene early. We encourage further studies to establish a scoring algorithm incorporating biochemical parameters to tailor management algorithms that might better suit high-risk patients.

摘要

根据2019年全国药物使用和健康调查,1450万12岁及以上的人患有酒精滥用障碍。酒精戒断综合征(AWS)可定义为长期依赖酒精后突然戒酒所经历的一系列身体症状。在常规住院治疗无法控制AWS症状的情况下,患者会被转移到重症监护病房(ICU)进行密切监测,并预防诸如戒断性癫痫发作和震颤谵妄(DTs)等危及生命的并发症,这被称为严重酒精戒断综合征(SAWS)。尽管这带来了巨大的医疗负担,但针对确定客观预测因素的研究却很少。在本研究中,我们旨在确定患者人口统计学、社会经济状况、生化参数和临床因素对AWS入院患者升级到ICU护理水平需求的影响。我们的研究表明,诸如DTs或酒精相关癫痫发作史、初始管理方案、报告的酒精使用程度、快速反应团队的启动、平均红细胞值、入院时的酒精水平、住院期间最高的修订版临床研究所酒精戒断评估(CIWA-Ar)评分以及使用的镇静剂总量等因素与升级到ICU护理水平显著相关。临床医生必须使用这些客观参数来识别高危患者并尽早进行干预。我们鼓励进一步开展研究,以建立一种纳入生化参数的评分算法,从而定制可能更适合高危患者的管理算法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bce/10589036/01dafbdf5d25/jchim-13-05-008f1.jpg

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