Risk factor analysis and prediction model construction of aneurysmal subarachnoid hemorrhage complicated by acute hydrocephalus
Objective To evaluate the risk factors for the development of acute hydrocephalus (AHC) after aneurysmal subarachnoid hemorrhage (aSAH) and to construct a prediction model.Methods The clinical data of patients with aSAH treated in the department of neurosurgery in the First Hospital of Qinhuangdao f...
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Editorial Office of New Medicine
2024-09-01
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| Series: | Yixue xinzhi zazhi |
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| Online Access: | https://yxxz.whuznhmedj.com/futureApi/storage/attach/2409/Xhjz0UFuBLW3aT8fkyY4i3skAcVKPREfLhNiX6IT.pdf |
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| author | ZHANG Long CHUAI Ruiyu WANG Guilin TIAN Yu YANG Dawei |
| author_facet | ZHANG Long CHUAI Ruiyu WANG Guilin TIAN Yu YANG Dawei |
| author_sort | ZHANG Long |
| collection | DOAJ |
| description | Objective To evaluate the risk factors for the development of acute hydrocephalus (AHC) after aneurysmal subarachnoid hemorrhage (aSAH) and to construct a prediction model.Methods The clinical data of patients with aSAH treated in the department of neurosurgery in the First Hospital of Qinhuangdao from January 2015 to January 2024 were retrospectively analyzed. The patients were randomly divided into the training set and the validation set in a 7:3 ratio and were also divided into the AHC group and non-AHC group according to whether they developed AHC or not. The training set was used to construct an AHC risk prediction model, while the validation set was used to validate the AHC risk prediction model. The reliability and stability of the risk prediction model were verified by the receiver operating characteristic curve (ROC), its area under the curve (AUC), calibration curve, and decision curve.Results A total of 1,062 patients with aSAH were included, among whom 324 patients developed AHC, with an incidence rate of 30.51%. The training set and validation set had 744 and 318 patients, respectively. Multivariate Logistic regression showed that age ≥60 years [OR=3.067, 95%CI (1.710, 5.499)], entering the ventricles [OR=7.039, 95%CI (3.792, 13.068)], Fisher grade IV [OR=3.371, 95%CI (1.335, 8.514)], Hunt-Hess grade IV [OR=6.198, 95%CI (2.218, 17.324)] and high level of neuron-specific enolase [OR=1.746, 95%CI (1.581, 1.928)] were independent risk factors for aSAH patients developing AHC (P |
| format | Article |
| id | doaj-art-283a965c631f4f63b3919555df965d62 |
| institution | Kabale University |
| issn | 1004-5511 |
| language | zho |
| publishDate | 2024-09-01 |
| publisher | Editorial Office of New Medicine |
| record_format | Article |
| series | Yixue xinzhi zazhi |
| spelling | doaj-art-283a965c631f4f63b3919555df965d622024-12-24T08:35:25ZzhoEditorial Office of New MedicineYixue xinzhi zazhi1004-55112024-09-01349999100810.12173/j.issn.1004-5511.2024060776534Risk factor analysis and prediction model construction of aneurysmal subarachnoid hemorrhage complicated by acute hydrocephalusZHANG LongCHUAI RuiyuWANG GuilinTIAN YuYANG DaweiObjective To evaluate the risk factors for the development of acute hydrocephalus (AHC) after aneurysmal subarachnoid hemorrhage (aSAH) and to construct a prediction model.Methods The clinical data of patients with aSAH treated in the department of neurosurgery in the First Hospital of Qinhuangdao from January 2015 to January 2024 were retrospectively analyzed. The patients were randomly divided into the training set and the validation set in a 7:3 ratio and were also divided into the AHC group and non-AHC group according to whether they developed AHC or not. The training set was used to construct an AHC risk prediction model, while the validation set was used to validate the AHC risk prediction model. The reliability and stability of the risk prediction model were verified by the receiver operating characteristic curve (ROC), its area under the curve (AUC), calibration curve, and decision curve.Results A total of 1,062 patients with aSAH were included, among whom 324 patients developed AHC, with an incidence rate of 30.51%. The training set and validation set had 744 and 318 patients, respectively. Multivariate Logistic regression showed that age ≥60 years [OR=3.067, 95%CI (1.710, 5.499)], entering the ventricles [OR=7.039, 95%CI (3.792, 13.068)], Fisher grade IV [OR=3.371, 95%CI (1.335, 8.514)], Hunt-Hess grade IV [OR=6.198, 95%CI (2.218, 17.324)] and high level of neuron-specific enolase [OR=1.746, 95%CI (1.581, 1.928)] were independent risk factors for aSAH patients developing AHC (Phttps://yxxz.whuznhmedj.com/futureApi/storage/attach/2409/Xhjz0UFuBLW3aT8fkyY4i3skAcVKPREfLhNiX6IT.pdfaneurysmal subarachnoid hemorrhagehydrocephalusventricular ruptureneuron specific enolaserisk factorsprediction model |
| spellingShingle | ZHANG Long CHUAI Ruiyu WANG Guilin TIAN Yu YANG Dawei Risk factor analysis and prediction model construction of aneurysmal subarachnoid hemorrhage complicated by acute hydrocephalus Yixue xinzhi zazhi aneurysmal subarachnoid hemorrhage hydrocephalus ventricular rupture neuron specific enolase risk factors prediction model |
| title | Risk factor analysis and prediction model construction of aneurysmal subarachnoid hemorrhage complicated by acute hydrocephalus |
| title_full | Risk factor analysis and prediction model construction of aneurysmal subarachnoid hemorrhage complicated by acute hydrocephalus |
| title_fullStr | Risk factor analysis and prediction model construction of aneurysmal subarachnoid hemorrhage complicated by acute hydrocephalus |
| title_full_unstemmed | Risk factor analysis and prediction model construction of aneurysmal subarachnoid hemorrhage complicated by acute hydrocephalus |
| title_short | Risk factor analysis and prediction model construction of aneurysmal subarachnoid hemorrhage complicated by acute hydrocephalus |
| title_sort | risk factor analysis and prediction model construction of aneurysmal subarachnoid hemorrhage complicated by acute hydrocephalus |
| topic | aneurysmal subarachnoid hemorrhage hydrocephalus ventricular rupture neuron specific enolase risk factors prediction model |
| url | https://yxxz.whuznhmedj.com/futureApi/storage/attach/2409/Xhjz0UFuBLW3aT8fkyY4i3skAcVKPREfLhNiX6IT.pdf |
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