Early Identification of Alcohol-associated Liver Disease in Primary Care Using a Brief Alcohol Screening Questionnaire - SUMMARY Mortality due to liver disease is increasing in the US. Alcohol-associated liver disease (ALD) accounts for most liver-related deaths, and the amount of alcohol use is the strongest predictor of major adverse liver outcomes (MALOs): liver cirrhosis, decompensated cirrhosis, liver cancer, transplant, or death. Alcohol use also interacts with metabolic factors, such as diabetes and obesity, to increase MALOs. Reducing drinking improves liver disease outcomes, and effective interventions to reduce drinking can be delivered in primary care. However, 89% of ALD is diagnosed after complications develop when prognosis is poor. Reducing ALD mortality will require early identification. Transient elastography, an imaging test, provides a presumptive diagnosis of cirrhosis. Due to cost, transient elastography is typically offered to selected patients at increased risk. U.S. guidelines recommend Fibrosis-4 Index (FIB-4), calculated from blood tests, to identify patients at increased risk, but FIB-4 misses about half of primary care patients who go on to develop MALOs. Current guidelines for screening for liver disease do not include alcohol use. The AUDIT-C alcohol screening questionnaire is a widely used, validated, scaled measure of alcohol use with scores 0-12. Widespread screening with the AUDIT-C in primary care practices could be readily incorporated into screening for liver disease, but to date, no research has evaluated this approach. Objective. The proposed study seeks to determine how routine primary care AUDIT-C alcohol screening can be used to efficiently identify patients at-risk for ALD. Aim 1 recruits a sample of 1,300 patients to determine the prevalence of current liver disease based on transient elastography across all AUDIT-C scores. Aim 2 uses existing data to determine the incidence of MALOs over 11 years follow-up across AUDIT-C scores in over 700,000 primary care patients. Aims 1-2 will provide data to assess risks overall and heterogeneity across important patient sub-groups (e.g., age, sex, diabetes, obesity), to allow clinicians to easily identify high risk patients by AUDIT-C alone. They can then offer transient elastography and provide treatments to decrease drinking and other risk factors. Aim 3 uses machine learning to develop and validate two clinical tools that combine the AUDIT-C with other clinical factors for more refined risk stratification. We will develop a simple calculator for use by frontline clinicians that combines the AUDIT-C with FIB-4 (Aim 3a), and a more precise automated algorithm combining all AUDIT-C scores and other risk factors from electronic health record data (Aim 3b). Both tools developed in Aim 3 can be rapidly integrated into primary care by clinicians and health systems. Public Health Impact. The proposed study provides critically needed information and two practical tools easily integrated into care to identify early liver disease so clinicians can support patients in decreasing drinking and modifying other risk factors to improve outcomes. If even 10% of patients with liver disease were identified and risk factors modified, 5,000 unnecessary liver-related deaths could be avoided each year in the U.S. alone.
