Predictors of severe alcohol withdrawal state in hospitalised patients
Keywords:alcohol withdrawal syndrome, risk factors, predictors, intensive care, alcohol abuse
The objective of this study was to develop a prediction model for severe alcohol withdrawal syndrome (AWS) in hospitalized AWS patients.
Methods: 336 patients were admitted to this observational prospective study. Multivariable binary regression models with stepwise selection procedures were conducted providing odds ratio (OR) estimates.
Results: In the multivariable regression, significant predictors of severe AWS were: history of AWS [OR: 29; 95 % confidence interval (CI): 14–68; p=0,001]; history of AWS seizures [OR 4; 95 % CI: 1,3–16; p=0,008]; history of other psychoactive drugs (e.g. benzodiazepines, opioids) used [OR 3; 95 % CI: 1,3–10; p=0,04]; tachycardia [OR 5; 95 % CI: 3–11; p=0,0001]; trombocitopenia [OR 3; 95 % CI: 1,5–7]; liver cirrhosis class B [OR 8; 95 % CI: 1,1–344; p=0,04] or C by Child-Pugh [OR 4; 95 % CI: 1,1–21; p=0,03].
Conclusions: In this large prospective study we determine the predictors of severe AWS course in hospitalized patients, they are: history of AWS or AWS seizures, history of other psychoactive drugs (e.g. benzodiazepines, opioids) used, trombocitopenia, liver cirrhosis class B or C by Child-Pugh
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