Table 1.
Demographics, clinical features, and noninvasive fibrosis assessment.
Fig 1.
Flow diagram for the study.
Table 2.
Multinomial Regression Analysis of features associated with CIRR, CIRR-HCC or HCC.
Fig 2.
The SHAP values for the selected variables that improve training accuracy in the nested cross validation.
SHAP values higher than 0.0 move the classifier to predict death while points with lower SHAP values move the classifier to predict alive. The red color indicates values were high and blue indicates values were low. In the case of age, higher values (red) indicating older patients moved the classifier in the direction of predicting death, while lower values (blue) of albumin moved the classifier to predicting death. The variables considered included age, sex, bmi, race, ethnicity, NFS, AAR, AP, APRI, BARD, alt, ast, diabetes, hypertension, and dyslipidemia.
Table 3.
The AUC performance of models along with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the random forest classifier and FIB-4 on the test set. The FIB-4 threshold of 2.67 is used for FIB-4 classification.
Fig 3.
AUC comparison of random forest and FIB-4 at predicting mortality within five years of meeting MASLD criteria on holdout test set.
The models are significantly different at p < 0.05 (Chi-Square).
Fig 4.
Kaplan Meier plots of random forest (RF) and FIB-4 high and low risk categories on holdout test set.
The RF and FIB-4 Low Risk models are significantly different at p < 0.05 (Log-Rank Test), while the High Risk models are not significantly different.