ArticleBMJ health & care informatics2025
Developing a non-invasive algorithm for the diagnosis of steatotic liver disease in primary healthcare: a retrospective cohort study.
Article in BMJ health & care informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveThis study aims to develop an algorithm to detect steatotic liver disease (SLD) risk in low-resource settings without requiring imaging.
methodsThis 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).
resultsThe 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.
conclusionsWe 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.
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