BPG is committed to discovery and dissemination of knowledge
Observational Study
Copyright ©The Author(s) 2025.
World J Gastroenterol. Sep 14, 2025; 31(34): 108807
Published online Sep 14, 2025. doi: 10.3748/wjg.v31.i34.108807
Table 1 Laboratory data and echocardiographic findings, n (%)
TR
P value
(1) Mild
(2) Moderate
(3) Severe
(1) vs (2)
(1) vs (3)
(2) vs (3)
Blood test results
Sex, male34 (64.2)45 (68.2)41 (68.3)NSNSNS
Age (year)68 (29-93)70 (25-91)68 (36-91)NSNSNS
AST (IU/L)23 (11-83)23 (7-772)27 (12-67)NSNSNS
ALT (IU/L)21 (6-107)20 (7-459)19 (6-71)NSNSNS
γ-GTP (IU/L)36 (8-562)39 (9-425)53 (11-377)NSNS0.039
Alb (g/dL)3.8 (2.1-4.9)3.8 (1.7-4.9)3.7 (1.4-4.4)NSNSNS
T-Bil (mg/dL)0.6 (0.2-1.9)0.8 (0.3-3.7)1.1 (0.3-4.1)NS0.025NS
Cre (mg/dL)0.86 (0.41-5.82)0.84 (0.38-8.93)1.01 (0.4-5.52)NSNS0.033
eGFR (mL/minute)57.8 (2.6-128.8)62.8 (4.5-153.0)49.2 (16.2-95.0)NSNSNS
Plt (× 104/µL)20.7 (7.9-49.6)19.8 (4.2-40.5)22.6 (12.7-32.7)NSNSNS
ALBI score-2.48 (-3.46 to -1.12)-2.48 (-3.44 to -0.83)-2.35 (-3.10 to -064)NSNSNS
MELD-XI score6.39 (-3.12 to 35.9)7.49 (-6.62 to 33.9)8.96 (-0.15 to 42.9)NS0.024NS
BNP (pg/dL)141 (11.8-2110)264 (6.6-3752)397 (35.0-3147)NS0.047NS
Echocardiographic findings
LVDd (mm)55.1 (37.4-82.9)56.6 (38.7-96.9)59.9 (37.4-78.9)NSNSNS
EF (%)36.8 (10.1-49.7)36.4 (10.8-49.8)34.7 (17.2-49.9)NSNSNS
E/E’15.7 (7.1-54.2)17.3 (2.8-50.7)15.5 (5.7-46.4)NSNSNS
LAD (mm)42.8 (25.8-68.9)47.0 (24.5-87.1)49.1 (28.5-69.7)NSNSNS
TRPG (mmHg)23.0 (9.7-49.0)33.9 (13.0-91.4)40.8 (22.2-70.9)< 0.01< 0.01< 0.01
IVC (mm)14.9 (5.5- 27.0)18.0 (2.2-28.2)23.0 (13.0-30.0)< 0.01< 0.010.012
Table 2 Performance metrics of the three-tier tricuspid regurgitation severity classification

Predicted severity
Recall
Mild
Moderate
Severe
Actual severityMild6730.375
Moderate11020.769
Severe0071
Precision0.8570.5880.583
F-measure0.5220.6670.737
Table 3 Predictive accuracy of computed tomography feature-based models with and without biochemical parameters
Model
Input data
AUC (95%CI)
Accuracy (%)
Sensitivity (%)
Specificity (%)
Image-only modelPara-umbilical CT feature maps0.78 (0.72-0.84)63.960.066.7
Multimodal modelCT features + γ-GTP, total bilirubin0.83 (0.78-0.88)68.570.067.8
Table 4 5-fold cross-validation and sensitivity analysis results
Analysis
Dataset configuration
Accuracy (%)
AUC (95%CI)
A 5-fold cross-validationFold 162.80.77 (0.69-0.85)
Fold 264.20.79 (0.71-0.87)
Fold 363.50.78 (0.70-0.86)
Fold 464.70.79 (0.71-0.87)
Fold 563.90.78 (0.70-0.86)
mean ± SD63.8 ± 0.70.78 ± 0.01
Table 5 Scanner-specific model performance after ComBat batch correction
CT scanner (manufacturer & model)
No. of cases (n)
Accuracy (%)
AUC (95%CI)
SOMATOM Definition Flash (Siemens, Munich, Germany)4564.40.78 (0.70-0.86)
Aquilion ONE (Canon Medical Systems, Otawara, Tochigi, Japan)4063.10.76 (0.68-0.84)
Aquilion 64 (Canon Medical Systems, Otawara, Tochigi, Japan)3562.90.77 (0.67-0.87)
Ingenuity Elite (Philips, Amsterdam, Netherlands)3264.00.79 (0.69-0.89)
Overall mean15263.60.78 (0.74-0.82)

  • Citation: Miida S, Kamimura H, Fujiki S, Kobayashi T, Endo S, Maruyama H, Yoshida T, Watanabe Y, Kimura N, Abe H, Sakamaki A, Yokoo T, Tsukada M, Numano F, Kashimura T, Inomata T, Fuzawa Y, Hirata T, Horii Y, Ishikawa H, Nonaka H, Kamimura K, Terai S. Image analysis of cardiac hepatopathy secondary to heart failure: Machine learning vs gastroenterologists and radiologists. World J Gastroenterol 2025; 31(34): 108807
  • URL: https://www.wjgnet.com/1007-9327/full/v31/i34/108807.htm
  • DOI: https://dx.doi.org/10.3748/wjg.v31.i34.108807