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Use of Electronic Health Record Data to Estimate the Probability of Alcohol Withdrawal Syndrome in a National Cohort of Hospitalized Veterans.
Steel TL, Malte CA, Bradley KA, Hawkins EJ. Use of Electronic Health Record Data to Estimate the Probability of Alcohol Withdrawal Syndrome in a National Cohort of Hospitalized Veterans. Journal of addiction medicine. 2021 Sep 1; 15(5):376-382.
Inpatient alcohol withdrawal syndrome (AWS) is common and early treatment improves outcomes, but no prior study has used electronic health record (EHR) data, available at admission, to predict the probability of inpatient AWS. This study estimated the probability of inpatient AWS using prior-year EHR data, hypothesizing that documented alcohol use disorder (AUD) and AWS would be strongly associated with inpatient AWS while exploring associations with other patient characteristics.
The study investigated patients hospitalized = 24?hours on medical services in the Veterans Health Administration during 2013 using EHR data extracted from the Veterans Health Administration Corporate Data Warehouse. ICD-9-CM diagnosis code, demographic, and healthcare utilization data documented in the year before admission defined prior-year AUD, AWS, and other factors associated with inpatient AWS. The primary outcome, inpatient AWS, was defined by inpatient ICD-9-CM codes.
The unadjusted probability of AWS was 5.0% (95% CI 4.5%-5.4%) among 209,151 medical inpatients overall, 26.4% (95% CI 24.4%-28.4%) among those with prior-year AUD, and 62.5% (95% CI 35.2%-39.7%) among those with prior-year AWS. Of those with AWS, 86% had documented prior-year AUD and/or AWS. Other patient characteristics associated with increased probability of inpatient AWS (P? < 0.001) were: male sex, single relationship status, homelessness, seizure, and cirrhosis.
Although inpatient providers often use history to predict AWS, this is the first study in hospitalized patients to inform and validate this practice, showing that prior-year diagnosis of AUD and/or AWS in particular, can identify the majority of inpatients who should be monitored for AWS.