Development and validation of machine learning models for esophagogastric variceal bleeding risk in hepatocellular carcinoma patients

Table 1 Clinical characteristics of patients in the entire cohort

Variables	Total (n = 188)	Non-bleeding (n = 97)	Bleeding (n = 91)	P value
Sex (male)	163 (86.70)	81 (83.51)	82 (90.11)	0.183
Cirrhosis (yes)	176 (93.62)	86 (88.66)	90 (98.90)	0.004
Hepatitis B (yes)	171 (90.96)	89 (91.75)	82 (90.11)	0.695
Smoking (yes)	119 (63.30)	61 (62.89)	58 (63.74)	0.904
Drinking (yes)	98 (52.13)	45 (46.39)	53 (58.24)	0.104
Diabetes (yes)	25 (13.30)	10 (10.31)	15 (16.48)	0.213
Hypertension (yes)	25 (13.30)	17 (17.53)	8 (8.79)	0.078
China liver cancer staging III-IV (yes)	92 (48.94)	33 (34.02)	59 (64.84)	< 0.001
Intrahepatic metastasis (yes)	123 (65.43)	54 (55.67)	69 (75.82)	0.004
Extrahepatic metastasis (yes)	20 (10.64)	8 (8.25)	12 (13.19)	0.272
Portal vein tumor thrombus (yes)	86 (45.74)	31 (31.96)	55 (60.44)	< 0.001
Ascites (yes)	109 (57.98)	33 (34.02)	76 (83.52)	< 0.001
Spontaneous bacterial peritonitis (yes)	32 (17.02)	7 (7.22)	25 (27.47)	< 0.001
AFP < 400 µg/L	74 (39.36)	40 (41.24)	34 (37.36)	0.587
Maximum tumor diameter ≥ 5 cm	109 (57.98)	46 (47.42)	63 (69.23)	0.002
Multiple tumors	128 (68.09)	56 (57.73)	72 (79.12)	0.002
Age (year)	56.90 ± 10.88	58.48 ± 11.12	55.21 ± 10.42	0.039
Tumor burden score	13.25 ± 3.34	11.89 ± 3.10	14.69 ± 2.98	< 0.001
Portal vein diameter (mm)	6.88 (4.46, 9.35)	5.59 (3.83, 7.96)	7.87 (5.76, 10.31)	< 0.001
Spleen anteroposterior diameter (cm)	15.00 (14.00, 17.00)	14.00 (13.00, 15.00)	16.50 (15.00, 18.00)	< 0.001
Spleen craniocaudal length (cm)	12.20 (10.70, 13.91)	11.30 (9.86, 12.60)	13.20 (11.95, 14.75)	< 0.001
Spleen thickness (cm)	4.90 (4.20, 5.82)	4.40 (3.80, 5.30)	5.40 (4.69, 6.15)	< 0.001
Splenic vein diameter (mm)	8.00 (6.00, 10.00)	7.00 (6.00, 8.00)	10.00 (8.00, 12.00)	< 0.001
ALT (U/L)	41.75 (24.58, 68.82)	44.20 (27.40, 77.80)	36.60 (22.60, 59.10)	0.112
AST (U/L)	57.30 (36.77, 98.80)	56.60 (37.60, 95.00)	57.50 (36.75, 119.85)	0.532
TBIL (µmol/L)	20.14 (13.97, 32.25)	18.70 (12.90, 29.30)	22.11 (15.50, 32.80)	0.145
IBIL (µmol/L)	11.10 (8.47, 14.83)	11.20 (8.30, 15.70)	10.90 (8.50, 14.10)	0.665
DBIL (µmol/L)	7.93 (5.30, 16.29)	6.80 (4.82, 13.60)	9.50 (5.95, 18.40)	0.010
Albumin (g/L)	32.90 (28.20, 39.05)	38.10 (35.00, 41.80)	28.30 (26.45, 31.40)	< 0.001
MELD score	9.80 (8.00, 12.65)	8.47 (7.31, 10.71)	11.41 (9.32, 13.82)	< 0.001
Fibrosis-4 index	5.79 (3.45, 10.89)	4.74 (2.93, 8.51)	8.40 (4.33, 13.88)	< 0.001

Normally distributed quantitative data were presented as mean ± SD. Non-normally distributed continuous variables were reported as median (Q1, Q3). Categorical data were presented as n (%). AFP: Alpha-fetoprotein; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; TBIL: Total bilirubin; DBIL: Direct bilirubin; IBIL: Indirect bilirubin; MELD: Model for End-Stage Liver Disease.

Table 2 Multivariate logistic regression analysis

Variable	β	SE	Waldχ2	OR	95%CI	P value
Ascites	1.371	0.610	2.246	3.939	1.190-13.031	0.025
Tumor burden score	0.283	0.111	2.555	1.327	1.068-1.648	0.011
Splenic vein diameter	0.590	0.140	4.207	1.804	1.370-2.374	< 0.01
Albumin	-0.188	0.049	-3.867	0.829	0.754-0.912	< 0.01

Table 3 Performance evaluation metrics of six machine learning models on the validation set

Model	Brier	Accuracy	Precision	Sensitivity	Specificity	F1 score	Youden’s index	Cut-off
DT	0.114	0.875	0.857	0.889	0.862	0.873	0.751	0.200
RF	0.106	0.893	0.920	0.852	0.931	0.885	0.783	0.590
XGBoost	0.112	0.875	0.917	0.815	0.931	0.863	0.746	0.590
LightGBM	0.136	0.839	0.909	0.741	0.931	0.816	0.672	0.670
SVM	0.105	0.893	0.889	0.889	0.897	0.889	0.785	0.550
ANN	0.104	0.893	0.957	0.815	0.966	0.880	0.780	0.650

DT: Decision tree; RF: Random forest; XGBoost: Extreme gradient boosting; LightGBM: Light gradient boosting machine; SVM: Support vector machine; ANN: Artificial neural network.