Developing a non-invasive algorithm for the diagnosis of steatotic liver disease in primary healthcare: a retrospective cohort 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

SUBMITTER: Spencer-Sandino M 

PROVIDER: S-EPMC12699699 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature