Table 3.

Performance assessment of the noninvasive indices and models for the prediction of MAFLD in the Western China cohort

Variables	AUC (95% CI)	SEN (95% CI)	SPE (95% CI)	PPV (95% CI)	NPV (95% CI)	Cutoff value
METS-IR	0.896 (0.888–0.905)	0.876 (0.859–0.892)	0.760 (0.745–0.774)	0.630 (0.609–0.650)	0.929 (0.919–0.939)	35.223
TyG	0.776 (0.762–0.789)	0.685 (0.662–0.708)	0.736 (0.721–0.751)	0.547 (0.525–0.570)	0.834 (0.820–0.847)	8.680
TyG-BMI	0.903 (0.895–0.911)	0.836 (0.817–0.854)	0.811 (0.798–0.825)	0.674 (0.653–0.695)	0.914 (0.904–0.924)	211.515
TyG-WC	0.873 (0.864–0.883)	0.865 (0.848–0.882)	0.724 (0.709–0.739)	0.594 (0.574–0.614)	0.920 (0.909–0.930)	714.871
TyG-WtHR	0.866 (0.856–0.876)	0.904 (0.889–0.919)	0.667 (0.651–0.683)	0.559 (0.539–0.578)	0.937 (0.927–0.947)	4.198
HSI	0.873 (0.863–0.883)	0.842 (0.824–0.860)	0.736 (0.721–0.751)	0.598 (0.578–0.619)	0.909 (0.898–0.920)	33.032
VAI	0.773 (0.759–0.786)	0.755 (0.733–0.776)	0.657 (0.641–0.673)	0.506 (0.486–0.527)	0.852 (0.838–0.865)	1.426
FLI	0.879 (0.869–0.888)	0.848 (0.830–0.866)	0.751 (0.736–0.765)	0.613 (0.593–0.634)	0.914 (0.903–0.924)	25.876
LAP	0.854 (0.843–0.864)	0.810 (0.790–0.829)	0.740 (0.725–0.755)	0.592 (0.571–0.613)	0.893 (0.882–0.905)	28.720
ZJU	0.900 (0.891–0.908)	0.865 (0.848–0.882)	0.773 (0.759–0.787)	0.640 (0.620–0.661)	0.925 (0.915–0.935)	34.549
FSI	0.872 (0.863–0.882)	0.904 (0.889–0.919)	0.676 (0.660–0.692)	0.565 (0.546–0.585)	0.938 (0.928–0.948)	-2.408
K-NAFLD	0.836 (0.825–0.847)	0.810 (0.790–0.829)	0.715 (0.700-0.731)	0.570 (0.550–0.591)	0.890 (0.878–0.902)	-2.589

Abbreviations: AUC Area under the receiver operating characteristic curve, SPE specificity, SEN Sensitivity, NPV Negative predictive value, PPV Positive predictive value