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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 3 of 3 Research Studies DisplayedAlkhouri N, Almomani A, Le P
The prevalence of alcoholic and nonalcoholic fatty liver disease in adolescents and young adults in the United States: analysis of the NHANES database.
The purpose of this study was to evaluate the prevalence of non-alcoholic fatty liver disease (NAFLD) and alcohol-associated fatty liver disease (ALD) in a cohort of adolescents and young adults (AYAs) using transient elastography to directly measure hepatic steatosis and suspected fibrosis. AYA’s aged 15-39 years without viral hepatitis, pregnancy, or ALT/ AST greater than 500 U/L were included in the study. The researchers compared subjects with excessive alcohol consumption to those without and found that those with excessive alcohol consumption suspected ALD was present in 56.59% and suspected significant fibrosis was present in 12.3% and suspected advanced fibrosis was present in 6.31%. In subjects without excessive alcohol consumption, suspected NAFLD was present in 40.04%. In those with suspected NAFLD, suspected significant fibrosis was present in 31.07% and suspected advanced fibrosis was present in 20.15%. The study concluded that a significant percentage of AYAs are at risk for ALD and NAFLD and a subset of these subjects is at risk for significant fibrosis.
AHRQ-funded; HS026937.
Citation: Alkhouri N, Almomani A, Le P .
The prevalence of alcoholic and nonalcoholic fatty liver disease in adolescents and young adults in the United States: analysis of the NHANES database.
BMC Gastroenterol 2022 Jul 30;22(1):366. doi: 10.1186/s12876-022-02430-7..
Keywords: Children/Adolescents, Young Adults, Alcohol Use, Chronic Conditions
Singh AN, Sanchez V, Kenzie ES
Improving screening, treatment, and intervention for unhealthy alcohol use in primary care through clinic, practice-based research network, and health plan partnerships: protocol of the ANTECEDENT study.
This study evaluates tailored implementation support to increase screening, brief intervention, referral to treatment (SBIRT) and medication-assisted treatment for alcohol use disorder (MAUD) in primary care. It will explore how primary care clinics implement SBIRT and MAUD in routine practice and how practice facilitators vary implementation support across diverse clinic settings. It is anticipated that findings will inform how effectively to align implementation support to context, advance understanding of practice facilitator skill development over time, and ultimately improve detection and treatment of unhealthy alcohol use across diverse primary care clinics.
AHRQ-funded; HS027080.
Citation: Singh AN, Sanchez V, Kenzie ES .
Improving screening, treatment, and intervention for unhealthy alcohol use in primary care through clinic, practice-based research network, and health plan partnerships: protocol of the ANTECEDENT study.
PLoS One 2022 Jun 28;17(6):e0269635. doi: 10.1371/journal.pone.0269635..
Keywords: Alcohol Use, Substance Abuse, Behavioral Health, Primary Care, Care Management
Lin Y, Sharma B, Thompson HM
External validation of a machine learning classifier to identify unhealthy alcohol use in hospitalized patients.
This study’s objective was to validate a machine learning approach to alcohol screening using a natural language processing (NLP) classifier developed at an independent medical center. This retrospective cohort study took place at a midwestern US tertiary-care, urban medical center that has an inpatient structured universal screening model for unhealthy substance use and an active addiction consult service. The cohort included 57,605 unplanned admissions of adult patients between October 23, 2017 and December 31, 2019 with electronic health record (EHR) documentation of manual alcohol screening. The authors examined error in manual screening and reviewed discordance between the NLP classifier and AUDIT-derived reference. The classifier demonstrated adequate sensitivity and specificity for routine clinical use as an automated screening tool for identifying at-risk patients.
AHRQ-funded; HS026385.
Citation: Lin Y, Sharma B, Thompson HM .
External validation of a machine learning classifier to identify unhealthy alcohol use in hospitalized patients.
Addiction 2022 Apr;117(4):925-33. doi: 10.1111/add.15730..
Keywords: Alcohol Use, Behavioral Health, Screening, Electronic Health Records (EHRs), Health Information Technology (HIT)