Construction and validation of a predictive model for chronic hydrocephalus in patients with acute cerebral infarction after thrombolytic therapy
10.3760/cma.j.cn115455-20230619-00668
- VernacularTitle:急性脑梗死溶栓治疗后慢性脑积水预测模型的构建及验证
- Author:
Weiwei LI
1
;
Bingrui ZHAO
Author Information
1. 临汾市人民医院神经内科，临汾 041000
- Keywords:
Brain infarction;
Hydrocephalus;
Thrombolytic therapy;
Risk factors
- From:
Chinese Journal of Postgraduates of Medicine
2024;47(12):1089-1093
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish and validate a predictive model for chronic hydrocephalus in patients with acute cerebral infarction after thrombolytic therapy.Methods:The clinical data of 288 patients with acute cerebral infarction admitted to Linfen People′s Hospital from January 2020 to December 2022 were retrospectively collected, based on whether the patients had chronic hydrocephalus or not, they were divided into the hydrocephalus group (62 cases) and the control group (226 cases). The clinical characteristics of the two groups were compared, and the risk factors for chronic hydrocephalus in patients with acute cerebral infarction after thrombolysis treatment were analyzed by multivariate Logistic regression. Based on relevant risk factors, the predictive model for chronic hydrocephalus in patients with acute cerebral infarction after thrombolytic therapy were established and validated it by R4.0.3 software.Results:The age, rate of diabetes mellitus and massive cerebral infarction in the hydrocephalus group were higher than those in the control group: (70.81 ± 10.34) years old vs. (63.46 ± 10.34) years old, 50.00%(31/62) vs. 16.81%(38/226), 38.71%(24/62) vs. 15.93%(36/226), there were statistical differences ( P<0.05). The results of multivariate Logistic regression analysis showed that the age>65 years old, diabetes mellitus and massive cerebral infarction were the risk factors of chronic hydrocephalus after thrombolytic therapy for acute cerebral infarction ( P<0.05). A prediction model and randomly divided the dataset into a training set (202 cases) and a validation set (86 cases) by R4.0.3 software, the area under curve (AUC) of the receiver operating characteristic curve in the training set was 0.774 (95% CI 0.694 - 0.855), and the AUC of the validation set was 0.807 (95% CI 0.664 - 0.950). In the validation set, the model was subjected to the Hosmer-Lemeshow Goodness-of-Fit test, χ2 = 8.35, P = 0.400, indicating that the model had good reliability. Conclusions:The present predictive model has high value in predicting chronic hydrocephalus in patients with acute cerebral infarction after thrombolytic therapy, and can be used to identify patients at high risk of chronic hydrocephalus.
