Evaluation of aneurysm rupture risk based upon flowrate-independent hemodynamic parameters: a multi-center pilot study

Low shear stress induces macrophage infiltration and aggravates aneurysm wall inflammation via CCL7/CCR1/TAK1/ NF-κB axis

BACKGROUND

This study aimed to elucidate the mechanism by which wall shear stress (WSS) influences vascular walls, accounting for the susceptibility of intracranial aneurysms (IAs) to rupture.

METHOD

We collected blood samples from the sacs of 24 ruptured and 28 unruptured IAs and analyzed the expression of chemokine CCL7 using enzyme-linked immunosorbent assay (ELISA). Univariate and multivariate logistic regression analyses were employed to assess clinical data, aneurysm morphology, and hemodynamics in both groups. Pearson correlation analysis investigated the relationship between CCL7 expression in aneurysm sac blood and WSS. Additionally, we established a bionic cell parallel plate co-culture shear stress model and a mouse low shear stress (LSS) model. The model was modulated using CCL7 recombinant protein, CCR1 inhibitor, and TAK1 inhibitor. We further evaluated CCL7 expression in endothelial cells and the levels of TAK1, NF-κB, IL-1β, and TNF-α in macrophages. Subsequently, the intergroup differences in expression were calculated.

RESULTS

CCL7 expression was significantly higher in the ruptured group compared to the unruptured group. Hemodynamic analysis indicated that WSS was an independent predictor of the risk of aneurysm rupture. A negative linear correlation was observed between CCL7 expression and WSS. Upon addition of CCL7 recombinant protein, upregulation of CCR1 expression and increased levels of p-TAK1 and p-p65 were observed. Treatment with CCR1 and TAK1 inhibitors reduced inflammatory cytokine expression in macrophages under LSS conditions. Overexpression of TAK1 significantly alleviated the inhibitory effects of CCR1 inhibitors on p-p65 and inflammatory cytokines.

CONCLUSION

LSS prompts endothelial cells to secrete CCL7, which, upon binding to the macrophage surface receptor CCR1, stimulates the release of macrophage inflammatory factors via the TAK1/NF-κB signaling pathway. This process exacerbates aneurysm wall inflammation and increases the risk of aneurysm rupture.

A web-based dynamic nomogram for rupture risk of posterior communicating artery aneurysms utilizing clinical, morphological, and hemodynamic characteristics

Background

Predicting rupture risk is important for aneurysm management. This research aimed to develop and validate a nomogram model to forecast the rupture risk of posterior communicating artery (PcomA) aneurysms.

Methods

Clinical, morphological, and hemodynamic parameters of 107 unruptured PcomA aneurysms and 225 ruptured PcomA aneurysms were retrospectively analyzed. The least absolute shrinkage and selection operator (LASSO) analysis was applied to identify the optimal rupture risk factors, and a web-based dynamic nomogram was developed accordingly. The nomogram model was internally validated and externally validated independently. The receiver operating characteristic (ROC) curve was used to assess the discrimination of nomogram, and simultaneously the Hosmer–Lemeshow test and calibration plots were used to assess the calibration. Decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the clinical utility of nomogram additionally.

Results

Four optimal rupture predictors of PcomA aneurysms were selected by LASSO and identified by multivariate logistic analysis, including hypertension, aspect ratio (AR), oscillatory shear index (OSI), and wall shear stress (WSS). A web-based dynamic nomogram was then developed. The area under the curve (AUC) in the training and external validation cohorts was 0.872 and 0.867, respectively. The Hosmer–Lemeshow p > 0.05 and calibration curves showed an appropriate fit. The results of DCA and CIC indicated that the net benefit rate of the nomogram model is higher than other models.

Conclusion

Hypertension, high AR, high OSI, and low WSS were the most relevant risk factors for rupture of PcomA aneurysms. A web-based dynamic nomogram thus established demonstrated adequate discrimination and calibration after internal and external validation. We hope that this tool will provide guidance for the management of PcomA aneurysms.

A predictive hemodynamic model based on risk factors for ruptured mirror aneurysms

Objectives

To identify hemodynamic risk factors for intracranial aneurysm rupture and establish a predictive model to aid evaluation.

Methods

We analyzed the hemodynamic parameters of 91 pairs of ruptured mirror aneurysms. A conditional univariate analysis was used for the continuous variables. A conditional multivariate logistic regression analysis was performed to identify the independent risk factors. Differences where p < 0.05 were statistically significant. A predictive model was established based on independent risk factors. Odds ratios (ORs) were used to score points. The validation cohort consisted of 189 aneurysms. Receiver operating characteristic curves were generated to determine the cutoff values and area under the curves (AUCs) of the predictive model and independent risk factors.

Results

The conditional multivariate logistic analysis showed that the low shear area (LSA) (OR = 70.322, p = 0.044, CI = 1.112–4,445.256), mean combined hemodynamic parameter (CHP) (>0.087) (OR = 3.171, p = 0.034, CI = 1.089–9.236), and wall shear stress gradient (WSSG) ratio (>893.180) (OR = 5.740, p = 0.003, CI = 1.950–16.898) were independent risk factors. A prediction model was established: 23^*LSA + 1^*CHP mean (>0.087: yes = 1, no = 0) + 2 ^* WSSG ratio (>893.180: yes = 1, no = 0). The AUC values of the predictive model, LSA, mean CHP (>0.087), and WSSG ratio (>893.180) were 0.748, 0.700, 0.654, and 0.703, respectively. The predictive model and LSA cutoff values were 1.283 and 0.016, respectively. In the validation cohort, the predictive model, LSA, CHP (>0.087), and WSSG ratio (>893.180) were 0.736, 0.702, 0.689, and 0.706, respectively.

Conclusions

LSA, CHP (>0.087), and WSSG ratio (>893.180) were independent risk factors for aneurysm rupture. Our predictive model could aid practical evaluation.