No abstract

In patients hospitalized for AH, well-known prognostic models can be used to predict 90-day mortality, particularly to identify patients with a low risk for death.

BackgroundEpidemiologic studies of alcoholic hepatitis (AH) have been hindered by the lack of a validated International Classification of Disease (ICD) coding algorithm for use with administrative data. Our objective was to validate coding algorithms for AH using a hospitalization database.MethodsThe Hospital Discharge Abstract Database (DAD) was used to identify consecutive adults (≥18 years) hospitalized in the Calgary region with a diagnosis code for AH (ICD-10, K70.1) between 01/2008 and 08/2012. Medical records were reviewed to confirm the diagnosis of AH, defined as a history of heavy alcohol consumption, elevated AST and/or ALT (<300 U/L), serum bilirubin >34 μmol/L, and elevated INR. Subgroup analyses were performed according to the diagnosis field in which the code was recorded (primary vs. secondary) and AH severity. Algorithms that incorporated ICD-10 codes for cirrhosis and its complications were also examined.ResultsOf 228 potential AH cases, 122 patients had confirmed AH, corresponding to a positive predictive value (PPV) of 54 % (95 % CI 47–60 %). PPV improved when AH was the primary versus a secondary diagnosis (67 % vs. 21 %; P < 0.001). Algorithms that included diagnosis codes for ascites (PPV 75 %; 95 % CI 63–86 %), cirrhosis (PPV 60 %; 47–73 %), and gastrointestinal hemorrhage (PPV 62 %; 51–73 %) had improved performance, however, the prevalence of these diagnoses in confirmed AH cases was low (29–39 %).ConclusionsIn conclusion the low PPV of the diagnosis code for AH suggests that caution is necessary if this hospitalization database is used in large-scale epidemiologic studies of this condition.

Objective: We aimed to examine biomarkers for screening unhealthy alcohol use in the trauma setting. Summary and Background Data: Self-report tools are the practice standard for screening unhealthy alcohol use; however, their collection suffers from recall bias and incomplete collection by staff. Methods: We performed a multi-center prospective clinical study of 251 adult patients who arrived within 24 hours of injury with external validation in another 60 patients. The Alcohol Use Disorders Identification Test served as the reference standard. The following biomarkers were measured: (1) PEth; (2) ethyl glucuronide; (3) ethyl sulfate; (4) gammaglutamyl-transpeptidase; (5) carbohydrate deficient transferrin; and (6) blood alcohol concentration (BAC). Candidate single biomarkers and multivariable models were compared by considering discrimination (AUROC). The optimal cutpoint for the final model was identified using a criterion for setting the minimum value for specificity at 80% and maximizing sensitivity. Decision curve analysis was applied to compare to existing screening with BAC. Results: PEth alone had an AUROC of 0.93 [95% confidence interval (CI): 0.92-0.93] in internal validation with an optimal cutpoint of 25 ng/ mL. A 4-variable biomarker model and the addition of any single biomarker to PEth did not improve AUROC over PEth alone (P > 0.05). Decision curve analysis showed better performance of PEth over BAC across most predicted probability thresholds. In external validation, sensitivity and specificity were 76.0% (95% CI: 53.0%-92.0%) and 73.0% (95% CI: 56.0%-86.0%), respectively. Conclusion and Relevance: PEth alone proved to be the single best biomarker for screening of unhealthy alcohol use and performed better than existing screening systems with BAC. PEth may overcome existing screening barriers.