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中华临床医师杂志(电子版) ›› 2025, Vol. 19 ›› Issue (07) : 504 -512. doi: 10.3877/cma.j.issn.1674-0785.2025.07.004

临床研究

中青年脑卒中患者应激障碍风险预测模型的构建与验证
皇立媛1, 浦洁1,(), 王苏贵2, 陈婷婷1, 朱德慧1, 胡雪1   
  1. 1 223003 江苏 淮安,徐州医科大学附属淮安医院神经内科
    2 223003 江苏 淮安,徐州医科大学附属淮安医院泌尿外科
  • 收稿日期:2025-05-23 出版日期:2025-07-30
  • 通信作者: 浦洁

Development and validation of a risk prediction model for post-traumatic stress disorder in young and middle-aged stroke patients

Liyuan Huang1, Jie Pu1,(), Sugui Wang2, Tingting Chen1, Dehui Zhu1, Xue Hu1   

  1. 1 Department of Neurology, Huai’an Hospital Affiliated with Xuzhou Medical University, Huai’an 223003, China
    2 Department of Urology, Huai’an Hospital Affiliated with Xuzhou Medical University, Huai’an 223003, China
  • Received:2025-05-23 Published:2025-07-30
  • Corresponding author: Jie Pu
引用本文:

皇立媛, 浦洁, 王苏贵, 陈婷婷, 朱德慧, 胡雪. 中青年脑卒中患者应激障碍风险预测模型的构建与验证[J/OL]. 中华临床医师杂志(电子版), 2025, 19(07): 504-512.

Liyuan Huang, Jie Pu, Sugui Wang, Tingting Chen, Dehui Zhu, Xue Hu. Development and validation of a risk prediction model for post-traumatic stress disorder in young and middle-aged stroke patients[J/OL]. Chinese Journal of Clinicians(Electronic Edition), 2025, 19(07): 504-512.

目的

探讨中青年脑卒中患者应激障碍(PTSD)的影响因素,构建风险预测列线图模型,并验证其预测效能。

方法

便利抽样法选取2023年9月至2024年12月在徐州医科大学附属淮安医院神经内科治疗的514例中青年脑卒中患者为研究对象。采用一般资料调查表、事件影响量表-修订版、日常生活活动能力评定表、领悟社会支持量表和心理韧性量表收集相关资料。通过单因素分析和Logistic回归分析筛选发生PTSD的影响因素,构建列线图预测模型并对其进行验证。

结果

文化程度、吞咽障碍、偏瘫、ADL是中青年脑卒中患者发生PTSD的独立影响因素(P<0.05)。基于影响因素构建预测模型,Hosmer-Lemeshow拟合度检验结果显示,χ2=1.468,P=0.690,训练集受试者操作特征曲线下面积(AUC)为0.838,95%CI为0.792~0.880,最佳临界值为0.239,敏感度为78.69%,特异度为76.89%;测试集AUC为0.822,95%CI为0.715~0.882,最佳临界值为0.290,敏感为77.08%,特异度为75.52%。

结论

该研究构建的风险预测模型可以有效预测PTSD的发生,有利于医护人员早期识别中青年脑卒中患者发生PTSD的高危人群和制定个性化的干预策略。

Objective

To identify the influencing factors of post-traumatic stress disorder (PTSD) in young and middle-aged stroke patients, construct a risk prediction nomogram model, and validate its predictive accuracy.

Methods

A total of 514 young and middle-aged stroke patients treated in the Department of Neurology, Huai'an Hospital Affiliated to Xuzhou Medical University from September 2023 to December 2024 were recruited as research participants using convenience sampling. Data were collected using the General Information Questionnaire, Impact of Event Scale-Revised (IES-R), Activities of Daily Living (ADL) Scale, Perceived Social Support Scale, and Connor-Davidson Resilience Scale. Univariate analysis and logistic regression analysis were conducted to identify the influencing factors of PTSD. Subsequently, a nomogram-based prediction model was developed and validated.

Results

Education level, dysphagia, hemiplegia, and ADL score were identified as independent influencing factors for PTSD in young and middle-aged stroke patients (P<0.05). The Hosmer-Lemeshow goodness-of-fit test indicated an adequate model fit (χ2=1.468, P=0.690). The area under the receiver operating characteristic curve (AUC) for the training set was 0.838 (95% confidence interval [CI]: 0.792~0.880), with the optimal cutoff value at 0.239, achieving a sensitivity of 78.69% and specificity of 76.89%. For the test set, the AUC was 0.822 (95%CI: 0.715~0.882), with the optimal cutoff value at 0.290, resulting in a sensitivity of 77.08% and specificity of 75.52%.

Conclusion

The risk prediction model developed in this study is capable of effectively forecasting the incidence of PTSD, thereby assisting medical professionals in the early identification of high-risk groups among young and middle-aged stroke patients and the development of personalized intervention strategies.

表1 中青年脑卒中患者PTSD的单因素分析(n=360)
项目 未发生组(n=238) 发生组(n=122) 统计值 P
年龄[例(%)] χ2=0.453 0.501
18~44岁 40(16.81) 24(19.67)
45~59岁 198(83.19) 98(80.33)
性别[例(%)] χ2=4.814 0.028
男性 161(67.65) 96(78.69)
女性 77(32.35) 26(21.31)
文化程度[例(%)] χ2=12.824 0.002
初中及以下 138(57.98) 90(73.77)
高中或中专 71(29.83) 29(23.77)
大学及以上 29(12.19) 3(2.46)
婚姻状况[例(%)] χ2=3.157 0.206
未婚 7(2.94) 1(0.82)
已婚 229(96.22) 118(96.72)
离异 2(0.84) 3(2.46)
家庭人均月收入[例(%)] χ2=2.956 0.228
<3000元 36(15.13) 22(18.03)
3000~5000元 127(53.36) 72(59.02)
>5000元 75(31.51) 28(22.95)
性格类型[例(%)] χ2=0.009 0.932
内向 38(15.97) 19(15.57)
外向 200(84.03) 103(84.43)
卒中类型[例(%)] χ2=0.040 0.842
缺血 199(83.61) 101(82.79)
出血 39(16.39) 21(17.21)
饮酒史[例(%)] χ2=1.052 0.305
146(61.34) 68(55.74)
92(38.66) 54(42.26)
高血压史[例(%)] χ2=0.836 0.361
156(65.55) 74(60.66)
82(34.45) 48(39.34)
糖尿病史[例(%)] χ2=2.572 0.109
193(81.09) 90(73.77)
45(18.91) 32(26.23)
吞咽障碍[例(%)] χ2=55.029 <0.001
216(90.76) 70(57.38)
22(9.24) 52(42.62)
偏瘫[例(%)] χ2=74.986 <0.001
220(92.44) 65(53.28)
18(7.56) 57(46.72)
语言障碍[例(%)] χ2=5.026 0.025
204(85.71) 93(76.23)
34(14.29) 29(23.77)
肢体活动障碍[例(%)] χ2=3.098 0.078
79(33.19) 52(42.62)
159(66.81) 70(57.38)
ADL[例(%)] χ2=20.299 <0.001
生活自理 103(43.28) 35(28.69)
轻度障碍 65(27.31) 27(22.13)
中度障碍 46(19.33) 27(22.13)
重度障碍 24(10.08) 33(27.05)
社会支持[分,MP25P75)] 59.00(45.00,62.00) 60.00(48.00,68.00) Z=-2.075 0.039
心理韧性[分,MP25P75)] 59.00(53.00,64.00) 58.00(51.75,62.00) Z=-2.105 0.036
表2 自变量赋值方式
表3 中青年脑卒中患者PTSD的Logistic回归分析
图1 中青年脑卒中患者PTSD风险预测模型列线图 注:ADL为日常生活活动能力评定表;PTSD为应激障碍
图2 中青年脑卒中患者PTSD预测模型ROC曲线。图a为训练集模型ROC曲线;图b为测试集模型ROC曲线
图3 中青年脑卒中患者PTSD预测模型校准曲线。图a为训练集模型校准曲线;图b为测试集模型校准曲线
图4 中青年脑卒中患者PTSD预测模型决策曲线。图a为训练集模型决策曲线;图b为测试集模型决策曲线
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