Developing a non-invasive algorithm for the diagnosis of steatotic liver disease in primary healthcare: a retrospective cohort study
Year: 2025
Type: article
Study design: Observational Study
Abstract: OBJECTIVE: This study aims to develop an algorithm to detect steatotic liver disease (SLD) risk in low-resource settings without requiring imaging. METHODS: This retrospective cohort study included 826 measurements from 444 participants aged 45-60 years who participated in the MAUCO+ study. Data included ultrasound, vibration-controlled transient elastography (VCTE), anthropometrics and biomarkers. Logistic multivariable regression was used to develop two predictive models for SLD risk, with and without ultrasound, using VCTE as gold standard. Missing data were minimal and retained in the analysis, as their proportion was not statistically relevant. Predictive performance (sensitivity, specificity, positive predictive value and negative predictive value) was compared with the clinically used Fatty Liver Index (FLI). RESULTS: The algorithm without ultrasound achieved a sensitivity of 81.1% (95% CI 71.7% to 88.4%) and specificity of 71.4% (95% CI 57.9% to 80.4%). The model with ultrasound demonstrated a sensitivity of 91.5% (95% CI 84.1% to 95.6%) and specificity of 70% (95% CI 59.9% to 80.7%). FLI showed an area under the curve (AUC) of 0.762, while our models achieved higher AUCs: 0.878 (with ultrasound) and 0.794 (without ultrasound). DISCUSSION: Our models offer screening tools for SLD in low-resource primary care. The model without ultrasound outperformed FLI, making it a feasible alternative where imaging is unavailable. The ultrasound-based model demonstrated higher performance, underscoring the value of ultrasound when it is accessible. Integrating these algorithms into preventive programmes could improve early diagnosis, especially in populations with a high burden of obesity and diabetes. CONCLUSIONS: We developed two predictive models for SLD screening in a Chilean cohort. Both showed strong performance and potential for implementation in primary care to support early detection and better disease management.
Source: BMJ Health & Care Informatics
Institutions: Pontificia Universidad Católica de Chile, Advanced Center for Chronic Diseases, University of Chile, University of Cambridge, MRC Biostatistics Unit, Universidad de Santiago de Chile, Georgetown University Medical Center, Division of Cancer Epidemiology and Genetics, Hospital de Urgencia Asistencia Pública, Instituto de Salud Pública de Chile, Universidad de Los Andes, Chile, Universidad Bernardo O'Higgins, Virginia Commonwealth University, Virginia Commonwealth University Medical Center, Catholic University of the Maule
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,207
Awards: EXPLORING THE INTERSECTION OF ALCOHOL AND METABOLIC FATTY LIVER DISEASE EVALUATION OF ALCOHOL INTAKE BIOMARKERS AND THE INFLUENCE OF GENETIC BACKGROUND ON LIVER DISEASE SEVERITY, DECIPHERING MUSCLE-LIVER CONNECTIONS IN MAFLD EXPERIMENTAL AND CLINICAL STUDIES, G2734939043, CARDIOVASCULAR AND NONALCOHOLIC FATTY LIVER DISEASES AMONG GALLSTONE AND CHOLECYSTECTOMY PATIENTS AND IMPACT OF A NOVEL DIET AND MUSCLE TRAINING PROGRAM, G6213609519, G652983587, MECANISMOS DE TRANSDUCCION DE SENALES DEL IGF-1REGULACION DIFERENCIAL DE FACTORES TRANSCRIPCIONALES ASOCIADOS A LOS PROCESOS DE HIPERTROFIA Y APOPTOSIS DEL CARDIOMIOCITO, CENTRO DE ESTUDIOS AVANZADOS DE ENFERMEDADES CRONICAS (ACCDIS), G8495673431, G8957797403, G907784011, G99091514
OpenAlex ID: W4417305054