Abstract
ObjectiveRebleeding 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.MethodsThe 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).ResultA 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).ConclusionWe presented two nomogram models, named CMH nomogram and CM nomogram, which could assist in identifying the RIAs with high risk of rebleeding.
Highlights
Intracranial aneurysms (IAs), a common cerebrovascular disease in the aging population, refer to the main cause of subarachnoid hemorrhage
We presented two nomogram models, named CMH nomogram and CM nomogram, which could assist in identifying the ruptured intracranial aneurysm (RIA) with high risk of rebleeding
411 appropriate patients with ruptured IAs were reviewed (Figure 1). 70 patients were identified as rebleeding after the admission ranged in age from 49 to 68 years
Summary
Intracranial aneurysms (IAs), a common cerebrovascular disease in the aging population, refer to the main cause of subarachnoid hemorrhage. Rebleeding is recognized as a catastrophic event with high mortality after aneurysmal subarachnoid hemorrhage (aSAH) (Rosenørn et al, 1987; Jaechan et al, 2015; Kienzler et al, 2016). A predicting model has not been built, or reliable factors have not been set to discriminate the RIAs at high risk of rebleeding. It is noteworthy that some existing studies reported that morphological characteristics could predict the risk of rebleeding after aSAH (Starke et al, 2011; Boogaarts et al, 2015). As indicated from our preliminary study, the hemodynamic characteristics could help discriminate the RIAs at high risk of rebleeding (Liu et al, 2019). Based on the mentioned facts, this study assumed that building a multidimensional predicting model can effectively discriminate the RIAs at high risk of rebleeding
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