Development and validation of a nomogram to predict risk of cystic biliary atresia in children with hepatic hilar cysts

doi:10.3877/cma.j.issn.1673-5250.2023.02.008

Objective

To establish a prediction model based on ultrasonic features combined with biochemical parameters to predict the risk for cystic biliary atresia (CBA) in neonates and infants with hepatic hilar cysts (HHC).

Methods

A total of 134 children aged under 120 days with HHC who received Kasai operation in Guangzhou Women and Children′s Medical Center from January 2016 to March 2022 were selected as research subjects. According to results of intraoperative laparoscopic cholangiography and histopathological examination of Kasai operation, they were divided into the CBA group (n=54) and choledochal cyst (CC) group (n=80) by retrospective method. The age, gender, preoperative ultrasonographic features and biochemical indicators of two groups were retrospectively collected. Logistic regression analysis method was used to identify the independent predictors of CBA in children with HHC and a nomogram was developed by using R software with package " rms". The performance of the nomogram was assessed by receiver operator characteristic curve (ROC) and area under curve (AUC), calibration curve, Hosmer-Lemeshow test and decision curve analysis (DCA). Bootstrap was performed for internal model validation of the nomogram. There were no statistical differences in age of ultrasonography for HHC and gender constituent ratio between two groups (P>0.05). The procedures followed in this study were in accordance with the Helsinki Declaration of the World Medical Association revised in 2013.

Results

①There were significant differences in the maximum length diameter of HHC, the maximum width diameter of HHC, incidence of abnormal gallbladder morphology, portal fibrous plaque, enlargement of portal lymph node, intrahepatic bile duct dilatation, and biliary sludge in HHC, as well as serum total bilirubin (TBIL), direct bilirubin (DBIL), γ-glutamyltransferase (GGT) levels between two groups (P<0.05). ②Multivariate logistic analysis indicated that the maximum length diameter of HHC, abnormal gallbladder morphology, intrahepatic bile duct dilatation and serum DBIL level all were independent influencing factors of CBA in children with HHC (OR=0.871, 70.251, 0.007, 1.089; 95%CI: 0.780-0.972, 2.445-2 018.581, 0-0.530, 1.026-1.156; P=0.014, 0.013, 0.025, 0.005), and a nomogram was developed based on these four prognostic factors. ③The AUC of this nomogram was 0.996 (95%CI: 0.991-1.000, P<0.001) for predicting the risk of CBA in children with HHC, indicating that the prediction model had a good discrimination between CBA and CC, and the optimal cut-off value of this model for predicting the risk of CBA in children with HHC was 0.512, with a sensitivity of 97.5% and a specificity of 96.3%. ④The P-value of the Hosmer-Lemeshow test for this model was 0.992. The calibration curve of this model showed that the calibration curve was very close to the ideal curve, the predicted incidence of CBA in HHC children by this model was consistent with the actual incidence and this model had a good calibration. ⑤DCA curve showed that the model was significantly higher than the two extreme curves of CBA in all HHC children and neighter CBA in HHC children, indicating that the clinical net benefit of predicting CBA in HHC children by this model was significant. ⑥Internal validation of the model using bootstrap method showed that the AUC of predicting CBA in children with HHC was 0.983 (95%CI: 0.976-1.000, P<0.001).

Conclusions

In this study, we constructed a nomogram model to predict the risk of CBA in children with HHC based on four indexes: the maximum length diameter of HHC, abnormal gallbladder morphology, intrahepatic bile duct dilatation and serum DBIL level. The model has good discrimination and calibration, and clinical adaptability. Because this study is only a single-center study, and the model has not been validated externally, it still needs large-sample, multi-center studies to validate the predict efficiency of this model for predicting CBA in children with HHC.

Key words: Biliary atresia, cystic, Choledochal cyst, Hepatic hilar cysts, Prediction model, Nomogram, Child

图4 1例皮肤、巩膜黄染3⁺个月和大便白陶土样CBA患儿(女性，118 d龄)肝、胆超声胆囊纵切面图像，提示胆囊形态僵硬、充盈好、囊壁凹凸不平呈波浪样(白色箭头所示)，胆总管区一无回声囊腔(蓝色箭头所示)，肝实质呈肝硬化改变

表1 2组HHC患儿临床资料比较

组别	例数	HHC超声检查时日龄[d，M(Q₁，Q₃)]	性别[例数(%)]		HHC最大长径[cm，M(Q₁，Q₃)]	HHC最大横径[cm，M(Q₁，Q₃)]	空腹胆囊形态[例数(%)]
组别	例数	HHC超声检查时日龄[d，M(Q₁，Q₃)]	男	女	HHC最大长径[cm，M(Q₁，Q₃)]	HHC最大横径[cm，M(Q₁，Q₃)]	正常	异常
CBA组	54	47(30，61)	24(44.4)	30(55.6)	1.1(0.5，1.5)	0.8(0.3，1.1)	1(1.9)	53(98.1)
CC组	80	60(30，87)	31(38.8)	49(61.2)	4.6(3.3，6.9)	3.1(2.0，4.2)	46(57.5)	34(42.5)
统计量		Z＝0.81	χ²＝0.43		Z＝8.58	Z＝8.65	χ²＝43.84
P值		0.418	0.511		<0.001	<0.001	<0.001
组别	例数	肝门区纤维斑块[例数(%)]	肝门淋巴结肿大[例数(%)]	肝内胆管扩张[例数(%)]	HHC内胆泥沉积[例数(%)]	血清TBIL浓度[μmol/L，M(Q₁，Q₃)]	血清DBIL浓度[μmol/L，M(Q₁，Q₃)]	血清GGT浓度[U/L，M(Q₁，Q₃)]
CBA组	54	32(59.3)	28(51.9)	2(3.7)	0(0)	150.9(121.0，167.1)	96.2(75.6，120.1)	424.5(244.8，676.8)
CC组	80	0(0)	0(0)	46(57.5)	46(57.5)	34.9(8.8，87.7)	7.0(2.0，54.6)	213.0(51.3，648.2)
统计量		χ²＝62.28	χ²＝52.44	χ²＝40.58	χ²＝47.28	Z＝7.33	Z＝7.66	Z＝3.20
P值		<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	0.001

表1 2组HHC患儿临床资料比较

组别	例数	HHC超声检查时日龄[d，M(Q₁，Q₃)]	性别[例数(%)]		HHC最大长径[cm，M(Q₁，Q₃)]	HHC最大横径[cm，M(Q₁，Q₃)]	空腹胆囊形态[例数(%)]
组别	例数	HHC超声检查时日龄[d，M(Q₁，Q₃)]	男	女	HHC最大长径[cm，M(Q₁，Q₃)]	HHC最大横径[cm，M(Q₁，Q₃)]	正常	异常
CBA组	54	47(30，61)	24(44.4)	30(55.6)	1.1(0.5，1.5)	0.8(0.3，1.1)	1(1.9)	53(98.1)
CC组	80	60(30，87)	31(38.8)	49(61.2)	4.6(3.3，6.9)	3.1(2.0，4.2)	46(57.5)	34(42.5)
统计量		Z＝0.81	χ²＝0.43		Z＝8.58	Z＝8.65	χ²＝43.84
P值		0.418	0.511		<0.001	<0.001	<0.001
组别	例数	肝门区纤维斑块[例数(%)]	肝门淋巴结肿大[例数(%)]	肝内胆管扩张[例数(%)]	HHC内胆泥沉积[例数(%)]	血清TBIL浓度[μmol/L，M(Q₁，Q₃)]	血清DBIL浓度[μmol/L，M(Q₁，Q₃)]	血清GGT浓度[U/L，M(Q₁，Q₃)]
CBA组	54	32(59.3)	28(51.9)	2(3.7)	0(0)	150.9(121.0，167.1)	96.2(75.6，120.1)	424.5(244.8，676.8)
CC组	80	0(0)	0(0)	46(57.5)	46(57.5)	34.9(8.8，87.7)	7.0(2.0，54.6)	213.0(51.3，648.2)
统计量		χ²＝62.28	χ²＝52.44	χ²＝40.58	χ²＝47.28	Z＝7.33	Z＝7.66	Z＝3.20
P值		<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	0.001

图5 预测HHC患儿发生CBA风险的列线图模型注：HHC为肝门区囊肿，CBA为囊肿型胆道闭锁，DBIL为直接胆红素

表2 影响HHC患儿发生CBA的单因素logistic回归分析结果

影响因素	B	SE	Wald值	P值	OR值	OR值95%CI
年龄	－0.005	0.006	0.742	0.389	0.995	0.984~1.006
性别	0.235	0.357	0.431	0.511	1.265	0.628~2.547
HHC最大长径	－0.131	0.023	33.435	<0.001	0.877	0.839~0.917
HHC最大横径	－0.249	0.046	28.866	<0.001	0.780	0.712~0.854
空腹胆囊形态异常	4.273	1.034	17.060	<0.001	71.707	9.442~544.568
肝门区纤维斑块	22.494	7 105.180	<0.001	0.997	5.874×10⁹	—^a
肝门淋巴结肿大	22.327	7 595.757	<0.001	0.998	4.971×10⁹	—^a
肝内胆管扩张	－3.560	0.755	22.224	<0.001	0.028	0.006~0.125
HHC内胆泥沉积	－21.666	5 926.130	<0.001	0.997	0.000	—^a
血清TBIL浓度	0.026	0.004	35.891	<0.001	1.027	1.018~1.036
血清DBIL浓度	0.040	0.006	39.373	<0.001	1.041	1.028~1.054
血清GGT浓度	0	0	0.876	0.349	1.000	1.000~1.001

表2 影响HHC患儿发生CBA的单因素logistic回归分析结果

影响因素	B	SE	Wald值	P值	OR值	OR值95%CI
年龄	－0.005	0.006	0.742	0.389	0.995	0.984~1.006
性别	0.235	0.357	0.431	0.511	1.265	0.628~2.547
HHC最大长径	－0.131	0.023	33.435	<0.001	0.877	0.839~0.917
HHC最大横径	－0.249	0.046	28.866	<0.001	0.780	0.712~0.854
空腹胆囊形态异常	4.273	1.034	17.060	<0.001	71.707	9.442~544.568
肝门区纤维斑块	22.494	7 105.180	<0.001	0.997	5.874×10⁹	—^a
肝门淋巴结肿大	22.327	7 595.757	<0.001	0.998	4.971×10⁹	—^a
肝内胆管扩张	－3.560	0.755	22.224	<0.001	0.028	0.006~0.125
HHC内胆泥沉积	－21.666	5 926.130	<0.001	0.997	0.000	—^a
血清TBIL浓度	0.026	0.004	35.891	<0.001	1.027	1.018~1.036
血清DBIL浓度	0.040	0.006	39.373	<0.001	1.041	1.028~1.054
血清GGT浓度	0	0	0.876	0.349	1.000	1.000~1.001

表3 影响HHC患儿发生CBA相关因素的多因素非条件logistic回归分析结果

影响因素	B	SE	Wald值	P值	OR值	OR值95%CI
HHC最大长径	－0.138	0.056	6.090	0.014	0.871	0.780~0.972
空腹胆囊形态异常	4.252	1.713	6.159	0.013	70.251	2.445~2 018.581
肝内胆管扩张	－5.034	2.244	5.030	0.025	0.007	0~0.530
血清DBIL浓度	0.086	0.030	7.874	0.005	1.089	1.026~1.156
常数项	－4.674	2.869	2.654	0.103	0.009	—

表3 影响HHC患儿发生CBA相关因素的多因素非条件logistic回归分析结果

影响因素	B	SE	Wald值	P值	OR值	OR值95%CI
HHC最大长径	－0.138	0.056	6.090	0.014	0.871	0.780~0.972
空腹胆囊形态异常	4.252	1.713	6.159	0.013	70.251	2.445~2 018.581
肝内胆管扩张	－5.034	2.244	5.030	0.025	0.007	0~0.530
血清DBIL浓度	0.086	0.030	7.874	0.005	1.089	1.026~1.156
常数项	－4.674	2.869	2.654	0.103	0.009	—

图6 预测HHC患儿发生CBA风险的列线图模型预测HHC患儿发生CBA的ROC曲线注：HHC为肝门区囊肿，CBA为囊肿型胆道闭锁，ROC曲线为受试者工作特征曲线，AUC为曲线下面积

图7 预测HHC患儿发生CBA风险的列线图模型的校准曲线图注：HHC为肝门区囊肿，CBA为囊肿型胆道闭锁

图8 预测HHC患儿发生CBA风险的列线图模型的DCA曲线注：HHC为肝门区囊肿，CBA为囊肿型胆道闭锁，DCA决策曲线分析曲线

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