儿童白血病脐带血移植原发性植入失败预测模型的构建

doi:10.12372/jcp.2025.24e0304

Abstract

Abstract:

Objective To build a risk prediction model for primary graft failure (PGF) after umbilical cord blood transplantation (UCBT) in pediatric leukemia based on over-sampling. Methods Patients with leukemia who received umbilical cord blood transplantation from January 2017 to December 2022 were retrospectively analyzed. According to the presence or absence of PGF, the patients were divided into graft failure group and graft success group. Based on the over-sampling algorithm to expand the positive group data, the random forest, neural network and logistic regression were used to construct the mode. The stability of the algorithm was evaluated by using the 5-fold cross-validation method. The model was evaluated by using AUC, precision, recall and F1-score. Results A total of 92 leukemia patients were enrolled, PGF occurred in 10 patients (10.9%). ROSE and SMOTE algorithm demonstrate good stability in 5-fold cross-validation method. In the data set processed by ROSE algorithm, all models have good prediction effect, and the best performance is the neural network model. Juvenile myelomonocytic leukemia, HLA matching<9/10, RIC, no Periengraftment syndrome and EBV infection within 42 days were independent risk factors for PGF. Conclusion Multiple factors may cause PGF after umbilical cord blood transplantation in pediatric leukemia. ROSE-Neural Network model has good predictive ability, which can help doctors to identify patients at high risk of PGF early, provide personalized treatment, and improve the prognosis of children.

Key words: leukemia, umbilical cord blood transplantation, primary graft failure, prediction model, child

CLC Number:

ZHANG Zhiqi, XIONG Ruolan, LI Bohan, JI Qi, WANG Qingwei, LU Jun, LI Jie, XIAO Peifang, HU Shaoyan. Construction of risk prediction model for primary graft failure after umbilical cord blood transplantation in pediatric leukemia[J].Journal of Clinical Pediatrics, 2025, 43(7): 511-518.

Figures/Tables 6

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References 23

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项目	植入失败组（n=10）	植入成功组（n=82）	统计量	P
性别[n(%)]			χ²=2.34	0.126
男	8（80）	40（48.8）
女	2（20）	42（51.2）
年龄[M(P₂₅~P₇₅)/岁]	2.47（1.98~5.52）	3.46（1.73~5.69）	Z=0.95	0.34
疾病类型[n(%)]			χ²=9.10	0.011
JMML	5（50）	8（9.8）
ALL	3（30）	33（40.2）
AML	2（20）	41（50）
HLA[n(%)]			χ²=4.04	0.044
≥9/10	2（20）	44（53.7）
<9/10	8（80）	38（46.3）
供受者血型[n(%)]			χ²=0.35	0.556
相合	4（40）	21（25.6）
不相合	6（60）	61（74.4）
预处理方案强度[n(%)]			χ²=5.83	0.016
MAC	3（30）	60（73.2）
RIC	7（70）	22（26.8）
白消安剂量[M(P₂₅~P₇₅)]/mg·kg^-1	9.74（9.6~12.86）	12.8（9.89~12.8）	Z=1.55	0.121
环磷酰胺剂量[M(P₂₅~P₇₅)]/mg·kg^-1	120（103.94~120.51）	120（115.08~120）	Z=0.42	0.674
MNC输注量[M(P₂₅~P₇₅)]×10⁵·kg^-1	9.31（6.42~11.09）	7.04（5.62~9.23）	Z=1.56	0.120
CD34⁺细胞输注量[M(P₂₅~P₇₅)]×10⁷·kg^-1	3.48（1.74~4.64）	2.54（1.88~3.37）	Z=1.20	0.231
Peri-ES[n(%)]			χ²=9.18	0.002
有	2（20）	60（73.2）
无	8（80）	22（26.8）
42天内EBV感染[n(%)]			χ²=14.93	<0.001
有	4（40）	2（2.4）
无	6（60）	80（97.6）

5折交叉验证	随机森林		神经网络		logistic回归
5折交叉验证	ROSE	SMOTE	ROSE	SMOTE	ROSE	SMOTE
Fold1	1.00	0.91	0.92	0.91	0.87	0.74
Fold2	0.96	0.95	0.91	0.87	0.92	0.96
Fold3	0.96	0.87	1.00	0.95	0.72	0.83
Fold4	0.96	1.00	0.96	0.95	0.57	0.91
Fold5	0.96	0.91	1.00	0.96	0.87	0.91

模型	集合	方法	项	AUC	precision	recall	F1-score
随机森林	训练集	ROSE	值	0.97	1.00	0.97	0.99
			95%CI	0.94~1	0.93~1	0.92~0.99	0.95~0.99
			P	-	<0.001	<0.001	<0.001
	测试集		值	0.98	1.00	0.98	0.99
			95%CI	0.94~1	0.84~1	0.88~1	0.96~0.99
			P	-	<0.001	<0.001	<0.001
	训练集	SMOTE	值	0.96	0.93	0.96	0.94
			95%CI	0.92~0.99	0.82~0.98	0.89~0.98	0.89~0.97
			P	-	<0.001	<0.001	<0.001
	测试集		值	0.94	0.88	0.94	0.91
			95%CI	0.88~1	0.68~0.97	0.82~0.98	0.85~0.94
			P	-	<0.001	<0.001	<0.001
神经网络	训练集	ROSE	值	0.99	1.00	0.97	0.99
			95%CI	0.99~1	0.93~1	0.92~0.99	0.96~0.99
			P	-	<0.001	<0.001	<0.001
	测试集		值	1.00	1.00	0.98	0.99
			95%CI	0.99~1	0.84~1	0.88~1	0.96~1
			P	-	<0.001	<0.001	<0.001
	训练集	SMOTE	值	0.96	0.93	0.94	0.93
			95%CI	0.93~0.99	0.82~0.97	0.87~0.97	0.88~0.96
			P	-	<0.001	<0.001	<0.001
	测试集		值	0.98	0.88	0.94	0.91
			95%CI	0.95~1	0.68~0.97	0.82~0.98	0.85~0.94
			P	-	<0.001	<0.001	<0.001
logistic回归	训练集	ROSE	值	0.90	0.85	0.81	0.83
			95%CI	0.84-0.95	0.73-0.92	0.73-0.87	0.76-0.88
			P	-	<0.001	<0.001	<0.001
	测试集		值	0.92	0.89	0.83	0.86
			95%CI	0.85~0.99	0.70~0.97	0.69~0.91	0.80~0.90
			P	-	<0.001	<0.001	<0.001
	训练集	SMOTE	值	0.96	0.91	0.91	0.91
			95%CI	0.94~0.99	0.79~0.97	0.84~0.95	0.86~0.95
			P	-	<0.001	<0.001	<0.001
	测试集		值	0.92	0.68	0.82	0.74
			95%CI	0.84~1	0.46~0.84	0.67~0.91	0.66~0.81
			P	-	0.11	<0.001	<0.001

方法	集合	模型对比	统计量	P
ROSE	训练集	随机森林与神经网络	-1.81	0.07
		神经网络与logistic	3.34	0.00
		随机森林与logistic	2.45	0.01
	测试集	随机森林与神经网络	-0.97	0.33
		神经网络与logistic	2.09	0.04
		随机森林与logistic	1.51	0.13
SMOTE	训练集	随机森林与神经网络	-0.77	0.44
		神经网络与logistic	0.42	0.67
		随机森林与logistic	-0.66	0.51
	测试集	随机森林与神经网络	0.79	0.43
		神经网络与logistic	-1.00	0.32
		随机森林与logistic	0.63	0.53

项目	回归系数	标准误	χ²值	P	OR	95%CI
HLA<9/10	-1.61	0.73	4.90	0.027	0.20	0.05~0.83
预处理方案强度	-4.62	1.13	16.66	<0.001	0.01	0.001~0.09
围植入综合征	-3.15	0.75	17.55	<0.001	0.04	0.01~0.19
42天内感染EBV	3.87	1.38	7.84	0.005	47.73	3.19~714.97
JMML	4.62	1.17	15.54	<0.001	101.64	10.21~1 011.37

Construction of risk prediction model for primary graft failure after umbilical cord blood transplantation in pediatric leukemia

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