• Luis A Rodriguez, Stephen C Shiboski, Patrick T Bradshaw, Alicia Fernandez, David Herrington, Jingzhong Ding, Ryan D Bradley, and Alka M Kanaya.
• Department of Epidemiology & Biostatistics, University of California, San Francisco, 550 16th Street, 2nd Floor, Box 0560, San Francisco, CA, 94143, USA. Luis.Rodriguez@ucsf.edu.
• J Gen Intern Med. 2021 Sep 1; 36 (9): 264826552648-2655.

BackgroundMany adults have risk factors for non-alcoholic fatty liver disease (NAFLD). Screening all adults with risk factors for NAFLD using imaging is not feasible.ObjectiveTo develop a practical scoring tool for predicting NAFLD using participant demographics, medical history, anthropometrics, and lab values.DesignCross-sectional.ParticipantsData came from 6194 white, African American, Hispanic, and Chinese American participants from the Multi-Ethnic Study of Atherosclerosis cohort, ages 45-85 years.Main MeasuresNAFLD was identified by liver computed tomography (≤ 40 Hounsfield units indicating > 30% hepatic steatosis) and data on 14 predictors was assessed for predicting NAFLD. Random forest variable importance was used to identify the minimum subset of variables required to achieve the highest predictive power. This subset was used to derive (n = 4132) and validate (n = 2063) a logistic regression-based score (NAFLD-MESA Index). A second NAFLD-Clinical Index excluding laboratory predictors was also developed.Key ResultsNAFLD prevalence was 6.2%. The model included eight predictors: age, sex, race/ethnicity, type 2 diabetes, smoking history, body mass index, gamma-glutamyltransferase (GGT), and triglycerides (TG). The NAFLD-Clinical Index model excluded GGT and TG. In the NAFLD-MESA model, the derivation set achieved an AUCNAFLD-MESA = 0.83 (95% CI, 0.81 to 0.86), and the validation set an AUCNAFLD-MESA = 0.80 (0.77 to 0.84). The NAFLD-Clinical Index model was AUCClinical = 0.78 [0.75 to 0.81] in the derivation set and AUCClinical = 0.76 [0.72 to 0.80] in the validation set (pBonferroni-adjusted < 0.01).ConclusionsThe two models are simple but highly predictive tools that can aid clinicians to identify individuals at high NAFLD risk who could benefit from imaging.© 2021. Society of General Internal Medicine.

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