脊髓损伤后并发神经源性膀胱功能障碍的风险预测模型构建

doi:10.3969/j.issn.1006-5725.2025.13.017

Abstract

Abstract:

Objective To analyze the risk factors of neurogenic bladder dysfunction （NB） in patients with spinal cord injury， and establish a risk prediction model of NB in patients with spinal cord injury by decision tree algorithm. Method Clinical data of 176 patients with spinal cord injury admitted from April 2022 to July 2024 were retrospectively analyzed. Patients with spinal cord injury were divided into disorder group and non-disorder group according to whether they were complicated by NB. Multivariate Logistic regression analysis was used to screen the risk factors of NB. Modeler software was used to construct the decision tree model of spinal cord injury patients with concurrent NB， and the 5-fold cross-validation method was used to internally verify the model， and the prediction efficiency of the model was compared. Results Among 176 patients with spinal cord injury， 42 patients had concurrent NB， the incidence of NB was 23.86%. Logistic regression analysis showed that the level of spinal cord injury （T₁₀—L₂）， degree of spinal cord injury （complete injury）， course of disease （≥ 6 months）， bladder compliance （abnormal）， urinary system infection （yes） and detrusor sphincter disorder （yes） were all independent risk factors for NB in patients with spinal cord injury （P < 0.05）. Probability forecasting model P = 1/［1 + e- ^{（-6.008+0.791*X1+3.117*X2+1.492*X3+1.270*X4+1.516*X5+2.158*X6）}］， models to predict the overall accuracy is 80.5%； The prediction accuracy of the model is 71.7% through the cross-verification of 5 fold. Decision tree model showed that the degree of spinal cord injury had the greatest effect on the complication of NB in patients with spinal cord injury， and the information gain was 0.46. ROC results showed that the AUC values of NB predicted by the two models were close （0.873 vs. 0.852， Z = 0.875， P = 0.469）. Conclusion The level of spinal cord injury， degree of spinal cord injury， course of disease， bladder compliance， urinary system infection， detrusor sphincter disorder can all predict the risk of NB. The decision tree model constructed in this study can effectively predict the risk probability of NB in patients with spinal cord injury， and medical staff can make targeted plans according to the above factors to reduce the risk of NB.

Key words: spinal cord injury, neurogenic bladder dysfunction, decision tree algorithm, risk prediction model

CLC Number:

R694

Xianqun TAN,Fenglin ZHANG,Guangyan ZOU,Xidong CHEN. Establishment of a risk prediction model for neurogenic bladder dysfunction after spinal cord injury[J]. The Journal of Practical Medicine, 2025, 41(13): 2058-2064.

Figures/Tables 6

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Fig.2

References 27

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变量	障碍组（n = 42）	非障碍组（n = 134）	χ²值	P值	变量	障碍组（n = 42）	非障碍组（n = 134）	χ²值	P值
性别					脊髓损伤平面
男性	25	72	0.434	0.510	C_{3 ~ 8}	9	42	17.423	0.000
女性	17	62			T_{1 ~ 9}	12	68
年龄					T₁₀ ~ L₂	21	24
< 60岁	27	95	0.657	0.418	脊髓损伤程度
≥ 60岁	15	39			完全损伤	13	9	17.173	0.000
婚姻状态					不完全损伤	29	125
有配偶	34	112	0.156	0.693	脑损伤
无配偶	8	22			有	8	15	1.736	0.188
居住地					无	34	119
乡村	19	71	0.769	0.381	病程
城镇	23	63			< 6个月	16	89	10.658	0.001
文化程度					≥ 6个月	26	45
初中以下	17	48	0.298	0.585	膀胱顺应
初中及以上	25	86			正常	10	69	9.906	0.002
BMI					异常	32	65
≥ 24 kg/m²	11	29	0.377	0.539	泌尿系统感染
< 24 kg/m²	31	105			有	30	55	11.822	0.001
饮酒					无	12	79
有	21	63	0.114	0.735	排尿方式
无	21	71			留置尿管	10	28	3.582	0.310
抽烟史					间歇导尿	12	36
有	18	65	0.410	0.522	叩击/腹压排尿	16	40
无	24	69			自行排尿	4	30
合并慢性病					逼尿肌括约肌失调
有	10	25	0.533	0.465	是	27	49	10.014	0.002
无	32	109			否	15	85
损伤原因					最大尿道闭合压
重物砸伤	18	59	0.164	0.921	正常	25	82	0.037	0.847
高空坠落	14	47			异常	17	52
其他	10	28

因素	回归系数	标准误	Wald值	P值	OR	95%CI
X1：脊髓损伤平面	0.791	0.315	6.297	0.012	2.206	1.189 ~ 4.093
X2：脊髓损伤程度	3.117	0.759	16.879	0.000	22.588	5.105 ~ 99.950
X3：病程	1.492	0.479	9.685	0.002	4.445	1.737 ~ 11.376
X4：膀胱顺应	1.270	0.489	6.732	0.009	3.560	1.364 ~ 9.289
X5：泌尿系统感染	1.516	0.493	9.478	0.002	4.556	1.735 ~ 11.962
X6：逼尿肌括约肌失调	2.158	0.551	15.354	0.000	8.650	2.940 ~ 25.451
常量	-6.008	0.947	40.276	0.000	0.002

重要变量	信息增益
脊髓损伤程度	0.46
泌尿系统感染	0.29
逼尿肌括约肌失调	0.27
脊髓损伤平面	0.25
病程	0.21
膀胱顺应	0.14

模型	AUC	灵敏度/%	特异度/%	约登指数	95%CI	SE	P值
多因素	0.873	85.71	75.88	0.586	0.814 ~ 0.918	0.030	< 0.001
决策树	0.852	80.95	72.39	0.543	0.789 ~ 0.894	0.033	< 0.001

Establishment of a risk prediction model for neurogenic bladder dysfunction after spinal cord injury

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