Internal and External Validation of an Alcohol Biomarker for Screening in Trauma

Abstract

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) gamma-glutamyl-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.

Figure 1

Patient flow diagram at development/internal validation site.

Figure 2

Decision curve analysis between PEth and blood alcohol concentration for screening unhealthy alcohol use. Decision curve analysis was applied to examine the net benefit of the best derived biomarker against BAC. Net benefit is a decision analytic measure that puts benefits and harms on the same scale and is useful for clinical decisions. Net benefit is measured by sensitivity × prevalence - (1 – specificity) × (1 – prevalence) × w, where w is the odds at the threshold probability. Net benefit is plotted against threshold probabilities to yield a decision curve to weigh the relative harms of false-positive and false-negative screens. The diagram shows the scenarios for all screened (grey line) and none screened (dark black line) as well. BAC indicates blood alcohol concentration;PEth, phosphatidylethanol.