Development and validation of a multivariable risk prediction model for hepatic steatosis in patients with HIV infection

<bold>Objective: </bold>Early detection of hepatic steatosis in people with HIV (PWH) could prevent progression and inflammation. The aim was to develop and validate a multivariable risk prediction model for hepatic steatosis in German PWH. <bold>Design: </bold>In this cohort study, 282 PWH were prospectively enrolled, and hepatic steatosis was defined via controlled attenuation parameter (CAP; >= 275 dB/m) using vibration-controlled transient elastography. <bold>Methods: </bold>Three multivariable logistic regression models were conducted. Missing values were imputed with multiple imputation. Cut-offs were derived based on Youden-Indices. Performance was assessed via discriminatory and calibrative ability and accuracy via Brier Skill Score. Sensitivity, specificity, and predictive values were calculated. Internal validation was performed via bootstrapping. <bold>Results: </bold>The prevalence of hepatic steatosis was 35.3% (100/282). Univariate analyses revealed associations with age, waist circumference, BMI, hypertension, hyperlipidemia and gamma-gt. In multivariable analyses, male sex [odds ratio (OR) 2.07, 95% confidence interval (CI) 1.42-3.00, P = 0.001] and BMI (OR 1.27, 95% CI 1.18-1.36, P < 0.001) were identified as independent predictors of hepatic steatosis. The naive and optimism-corrected c -statistic of 79% showed a good discriminatory ability, the calibration was well with a slight tendency for overestimation for predicted probabilities above 70%. At the cutoff of 1.95, the specificity was 71% and the negative-predictive value 82.3%. Twenty-seven percent of the 282 patients would be misclassified, 17% as false positives and 10% as false negatives. <bold>Conclusion: </bold>The developed prediction model contributes to the lack of validated noninvasive tools to predict hepatic steatosis in people with HIV. Future studies should include more candidate predictors and externally validate the model.