Dataset Information

Rebleeding of Ruptured Intracranial Aneurysm After Admission: A Multidimensional Nomogram Model to Risk Assessment.

ABSTRACT:

Objective

Rebleeding is recognized as the main cause of mortality after intracranial aneurysm rupture. Though timely intervention can prevent poor prognosis, there is no agreement on the surgical priority and choosing medical treatment for a short period after rupture. The aim of this study was to investigate the risk factors related to the rebleeding after admission and establish predicting models for better clinical decision-making.

Methods

The patients with ruptured intracranial aneurysms (RIAs) between January 2018 and September 2020 were reviewed. All patients fell to the primary and the validation cohort by January 2020. The hemodynamic parameters were determined through the computational fluid dynamics simulation. Cox regression analysis was conducted to identify the risk factors of rebleeding. Based on the independent risk factors, nomogram models were built, and their predicting accuracy was assessed by using the area under the curves (AUCs).

Result

A total of 577 patients with RIAs were enrolled in this present study, 86 patients of them were identified as undergoing rebleeding after admission. Thirteen parameters were identified as significantly different between stable and rebleeding aneurysms in the primary cohort. Cox regression analysis demonstrated that six parameters, including hypertension [hazard ratio (HR), 2.54; P = 0.044], bifurcation site (HR, 1.95; P = 0.013), irregular shape (HR, 4.22; P = 0.002), aspect ratio (HR, 12.91; P < 0.001), normalized wall shear stress average (HR, 0.16; P = 0.002), and oscillatory stress index (HR, 1.14; P < 0.001) were independent risk factors related to the rebleeding after admission. Two nomograms were established, the nomogram including clinical, morphological, and hemodynamic features (CMH nomogram) had the highest predicting accuracy (AUC, 0.92), followed by the nomogram including clinical and morphological features (CM nomogram; AUC, 0.83), ELAPSS score (AUC, 0.61), and PHASES score (AUC, 0.54). The calibration curve for the probability of rebleeding showed good agreement between prediction by nomograms and actual observation. In the validation cohort, the discrimination of the CMH nomogram was superior to the other models (AUC, 0.93 vs. 0.86, 0.71 and 0.48).

Conclusion

We presented two nomogram models, named CMH nomogram and CM nomogram, which could assist in identifying the RIAs with high risk of rebleeding.

SUBMITTER: Liu Q

PROVIDER: S-EPMC8440913 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:Ruptured aneurysms, the commonest cause of nontraumatic subarachnoid hemorrhage, can be catastrophic; the mortality and morbidity of affected patients being very high. Some risk factors, such as smoking, hypertension and female sex have been identified, whereas others, such as hemodynamics, imaging, and genomics, remain unclear. Currently, no accurate model that includes all factors for predicting such rupture is available. We plan to use data from a large cohort of Chinese individuals to set up a multidimensional model for predicting risk of rupture of unruptured intracranial aneurysms (UIAs). METHODS:The China Intracranial Aneurysm Project-2 (CIAP-2) will comprise screening of a cohort of 500 patients with UIA (From CIAP-1) and focus on hemodynamic factors, high resolution magnetic resonance imaging (HRMRI) findings, genetic factors, and biomarkers. Possible risk factors for rupture of UIA, including genetic factors, biomarkers, HRMRI, and hemodynamic factors, will be analyzed. The first project of the China Intracranial Aneurysm Project (CIAP-1; chaired by the Department of Neurosurgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China) will prospectively collect a cohort of 5000 patients with UIA from 20 centers in China, and collect baseline information for each patient. Multidimensional data will be acquired in follow-up assessments. Statistically significant clinical features in the UIA cohort will also be analyzed and integrated into the model for predicting risk of UIA rupture. After the model has been set up, the resultant evidence-based prediction will provide a preliminary theoretical basis for treating aneurysms at high risk of rupture. DISCUSSION:This study will explore the risk of rupture of aneurysms and develop a scientific multidimensional model for predicting rupture of unruptured intracranial aneurysms. Clinical Trials registration A Study on a Multidimensional Prediction Model for Rupture Risk of Unruptured Intracranial Aneurysms (CIAP-2), NCT03133624. Registered: 16 April 2017. https://clinicaltrials.gov/ct2/show/NCT03133624.