April 1, 2022Hepatology Communications81 citationsOpen Access

Predicting NAFLD prevalence in the United States using National Health and Nutrition Examination Survey 2017–2018 transient elastography data and application of machine learning

MNMazen Noureddin FNFady Ntanios DMDeepa Malhotra

Structured PICO

Can machine learning models using standard demographic and clinical data predict NAFLD prevalence in the US population?

Population

Adults ≥20 years of age with valid transient elastography measurements from the National Health and Nutrition Examination Survey (NHANES) database (2017–2018). Excluded: high alcohol consumption, viral hepatitis, or human immunodeficiency virus.

Intervention

Machine learning models (six different approaches) using demographic and clinical data

Outcome

Prevalence of NAFLD and NAFLD with fibrosis at different stages, and predictive performance of machine learning models (AUROC)

Machine learning models using standard clinical data can effectively identify NAFLD, which has a high prevalence in the US, particularly among those with T2DM.

Abstract

Abstract This cohort analysis investigated the prevalence of nonalcoholic fatty liver disease (NAFLD) and NAFLD with fibrosis at different stages, associated clinical characteristics, and comorbidities in the general United States population and a subpopulation with type 2 diabetes mellitus (T2DM), using the National Health and Nutrition Examination Survey (NHANES) database (2017–2018). Machine learning was explored to predict NAFLD identified by transient elastography (FibroScan ® ). Adults ≥20 years of age with valid transient elastography measurements were included; those with high alcohol consumption, viral hepatitis, or human immunodeficiency virus were excluded. Controlled attenuation parameter ≥302 dB/m using Youden’s index defined NAFLD; vibration‐controlled transient elastography liver stiffness cutoffs were ≤8.2, ≤9.7, ≤13.6, and >13.6 kPa for F0–F1, F2, F3, and F4, respectively. Predictive modeling, using six different machine‐learning approaches with demographic and clinical data from NHANES, was applied. Age‐adjusted prevalence of NAFLD and of NAFLD with F0–F1 and F2–F4 fibrosis was 25.3%, 18.9%, and 4.4%, respectively, in the overall population and 54.6%, 32.6%, and 18.3% in those with T2DM. The highest prevalence was among Mexican American participants. Test performance for all six machine‐learning models was similar (area under the receiver operating characteristic curve, 0.79–0.84). Machine learning using logistic regression identified male sex, hemoglobin A1c, age, and body mass index among significant predictors of NAFLD ( P ≤ 0.01). Conclusion: Data show a high prevalence of NAFLD with significant fibrosis (≥F2) in the general United States population, with greater prevalence in participants with T2DM. Using readily available, standard demographic and clinical data, machine‐learning models could identify subjects with NAFLD across large data sets.

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