AIMS: To develop a prediction model for withdrawal seizures (WS) and delirium tremens (DT) during moderate to severe alcohol withdrawal syndrome (AWS) in a large cohort of inpatients treated for AWS (n = 827). METHODS: Re-analysis of a cohort study population treated between 2000 and 2009. All patients received a score-guided and symptom-triggered therapy for AWS. Multivariable binary logistic regression models with stepwise variable selection procedures were conducted providing odds ratio (OR) estimates. RESULTS: In the multivariable regression, significant predictors of WS during AWS therapy were a delayed climax of withdrawal severity since admission [OR/10 h: 1.23; 95% confidence interval (CI): 1.1-1.4; P < 0.001)], prevalence of structural brain lesions in the patient's history (OR 6.5; 95% CI: 3.0-14.1; P < 0.001) and WS as the cause of admittance (OR 2.6; 95% CI: 1.4-4.8; P = 0.002). Significant predictors at admission for the occurrence of DT were lower serum potassium (OR/1 mmol/l 0.33; 95% CI: 0.17-0.65; P = 0.001), a lower platelet count (OR/100.000 0.42; 95% CI: 0.26-0.69; P = 0.001) and prevalence of structural brain lesions (OR 5.8; 95% CI: 2.6-12.9; P < 0.001). CONCLUSION: In this large retrospective cohort, some easily determinable parameters at admission may be useful to predict a complicated course of alcohol withdrawal regarding the occurrence of WS or DT. Using the provided nomograms, clinicians can estimate the percentage likelihood of patients to develop either WS or DT during their course of withdrawal. Prevalence of structural brain lesions in the patient's history does strongly warrant a careful observation of patients.
AIMS: To develop a prediction model for withdrawal seizures (WS) and delirium tremens (DT) during moderate to severe alcohol withdrawal syndrome (AWS) in a large cohort of inpatients treated for AWS (n = 827). METHODS: Re-analysis of a cohort study population treated between 2000 and 2009. All patients received a score-guided and symptom-triggered therapy for AWS. Multivariable binary logistic regression models with stepwise variable selection procedures were conducted providing odds ratio (OR) estimates. RESULTS: In the multivariable regression, significant predictors of WS during AWS therapy were a delayed climax of withdrawal severity since admission [OR/10 h: 1.23; 95% confidence interval (CI): 1.1-1.4; P < 0.001)], prevalence of structural brain lesions in the patient's history (OR 6.5; 95% CI: 3.0-14.1; P < 0.001) and WS as the cause of admittance (OR 2.6; 95% CI: 1.4-4.8; P = 0.002). Significant predictors at admission for the occurrence of DT were lower serum potassium (OR/1 mmol/l 0.33; 95% CI: 0.17-0.65; P = 0.001), a lower platelet count (OR/100.000 0.42; 95% CI: 0.26-0.69; P = 0.001) and prevalence of structural brain lesions (OR 5.8; 95% CI: 2.6-12.9; P < 0.001). CONCLUSION: In this large retrospective cohort, some easily determinable parameters at admission may be useful to predict a complicated course of alcohol withdrawal regarding the occurrence of WS or DT. Using the provided nomograms, clinicians can estimate the percentage likelihood of patients to develop either WS or DT during their course of withdrawal. Prevalence of structural brain lesions in the patient's history does strongly warrant a careful observation of patients.
Authors: Kate Freeman; Mary M Staehle; Zeynep H Gümüş; Rajanikanth Vadigepalli; Gregory E Gonye; Carmen N Nichols; Babatunde A Ogunnaike; Jan B Hoek; James S Schwaber Journal: Alcohol Clin Exp Res Date: 2012-04-06 Impact factor: 3.455
Authors: Don-Kelena Awissi; Genevieve Lebrun; Douglas B Coursin; Richard R Riker; Yoanna Skrobik Journal: Intensive Care Med Date: 2012-11-27 Impact factor: 17.440
Authors: David J Allsop; Jan Copeland; Melissa M Norberg; Shanlin Fu; Anna Molnar; John Lewis; Alan J Budney Journal: PLoS One Date: 2012-09-26 Impact factor: 3.240