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目的：建立具有临床实用性的自发性脑出血血肿扩大(HE)的预测模型。方法：回顾性分析2018年5月1日-2021年5月31日苏州大学附属第二医院283例自发性脑出血患者的临床及影像学资料。将单因素分析中有意义的变量(P<0.05)纳入到多因素Logistic分析中，采用逐步回归法进行分析，筛选出HE预测模型的独立危险因素，再运用R语言绘制列线图。然后再采用Bootstrap法对本研究的数据重复抽样1 000次进行内部验证。以受试者工作特征曲线、临床决策曲线及校准曲线评价模型的鉴别能力、临床实用性及可靠性。结果：HE模型纳入了红细胞分布宽度的标准差(RDW-SD)、初始血肿体积、漩涡征、岛征、混杂征。HE模型的受试者工作特征曲线下面积(AUC)值为0.867,灵敏度为0.728,特异度为0.876,约登指数为0.605。内部验证提示该模型的C-index为0.859,标准误差为0.014。初步的外部验证提示HE模型均有良好的预测能力。结论：基于临床-影像学资料，纳入RDW-SD、初始血肿体积、漩涡征、岛征、混杂征构建的脑出血血肿扩大HE模型具有良好的预测效能。
Abstract：Objective: To establish a clinical practical prediction model for hematoma expansion(HE) in spontaneous intracerebral hemorrhage. Methods: The clinical and imaging data of 283 patients with spontaneous intracerebral hemorrhage from May 1, 2018 to May 31, 2021 in the Second Affiliated Hospital of Soochow University were retrospectively analyzed. Significant variables in univariate analysis(P<0.05) were included in multivariate logistic analysis, and stepwise regression was used to screen independent risk factors for prediction model of HE. R language was applied to draw Nomogram. Bootstrap method was used to repeat the sampling of the data in this study 1 000 times for internal verification. Receiver operating characteristic curve, clinical decision curve, and calibration curve were used to evaluate the discriminating ability, clinical practicability and reliability of the model. Results: HE model incorporated standard deviation of red blood distribution width(RDW-SD), initial hematoma volume, vortex sign, island sign, and mixed sign. The area under receiver operating characteristic curve(AUC) value of the HE model was 0.867. The sensitivity and specificity were 0.728 and 0.876, respectively, with Youden index of 0.605. The internal verification showed that the C-index of the model was 0.859, and the standard error was 0.014. The preliminary external verification indicate that HE model had a good predictive ability. Conclusion: The HE model constructed based on clinical-imaging data, including RDW-SD, initial hematoma volume, vortex sign, island sign, and mixed sign, had a good predictive efficacy.
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基本信息:
中图分类号:R743.34
引用信息:
[1]张涛,孔祥宇,李子聪,等.基于临床-影像学资料构建自发性脑出血血肿扩大预测模型[J].巴楚医学,2023,6(01):75-81.
基金信息:
2018年江苏省研究生培养创新工程研究生科研与实践创新计划(No:SJCX18_0855); 中核医疗2021年度“核医科技创新”项目(No:ZHYLZD2021006)
2022-06-24
2022
2022-11-05
2022-11-15
2022
1
2023-03-28
2023-03-28