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Original research
Morphological and hemodynamic analysis of posterior communicating artery aneurysms prone to rupture: a matched case–control study
  1. Guoli Duan1,
  2. Nan Lv1,
  3. Jianhua Yin2,
  4. Jinyu Xu1,
  5. Bo Hong1,
  6. Yi Xu1,
  7. Jianmin Liu1,
  8. Qinghai Huang1
  1. 1Department of Neurosurgery, Changhai Hospital, Second Military Medical University, Shanghai, China
  2. 2Department of Epidemiology, Second Military Medical University, Shanghai, China
  1. Correspondence to Professor Jianmin Liu, Department of Neurosurgery, Changhai Hospital, Second Military Medical University, 168 Changhai Rd, Shanghai 200433, China; chstroke{at}163.com Dr Q Huang ocinhqh@163.com

Abstract

Objectives We evaluated the correlation between posterior communicating artery (PcomA) aneurysm rupture and morphological and hemodynamic parameters to assess related rupture risk indices.

Methods Six patients with PcomA aneurysms that ultimately ruptured (cases) were studied after initially being included in a prospective database including their three-dimensional (3D) imaging before rupture. For each case, four incidental stable unruptured aneurysms (controls) were randomly selected and matched based on clinical factors. The 3D images from all patients were reconstructed to establish the patient-specific model. Six morphologic parameters and three hemodynamic parameters were measured and calculated. A conditional logistic regression analysis was used to assess the individual risk of rupture.

Results The analysis demonstrated a larger aneurysm size (p=0.001), higher aspect ratio (p=0.018), ellipticity index (p<0.001), undulation index (p=0.005), percentage of low wall shear stress (WSS) area (LSA%) (p=0.010), and a lower normalized WSS (p=0.005) in the case group. The multivariate conditional logistic regression analysis demonstrated that only normalized WSS was significantly associated with the rupture of PcomA aneurysms (OR 0.151; 95% CI 0.025 to 0.914; p=0.040).

Conclusions Hemodynamics and morphology are closely associated with aneurysm rupture, and WSS may be a more reliable parameter characterizing the rupture status of PcomA aneurysms.

  • Aneurysm
  • Blood Flow
  • Angiography
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Introduction

Hemodynamic and morphological factors play an important role in the rupture of intracranial aneurysms. It might be reasonable to consider the morphological and hemodynamic characteristics of the aneurysm when assessing the risk of aneurysm rupture.1 However, morphological changes might lead to hemodynamic changes after aneurysm rupture, so the reliability of the results of previous studies based on ruptured aneurysms has been questioned.2 An assessment based on an acquired aneurysm prone to rupture and parent artery morphology followed by an analysis of the hemodynamic characteristics might be a more reasonable assessment; however, ethics and difficulties in case collection limit these analyses. We therefore performed a matched case–control study of patients with posterior communicating artery (PcomA) aneurysms to characterize the relationship between morphological and hemodynamic parameters and rupture risk.

Materials and methods

The Institutional Review Board of our hospital approved this retrospective study and the requirement for informed consent was obtained. In addition, we did not conduct research outside the country of residence.

Patients and study design

From January 2009 to December 2013 we examined six patients with PcomA aneurysms that eventually ruptured within 7 days after initial inclusion in the prospective database of our hospital. CT angiography (CTA; Somatomas 128, Erlangen, Germany) was performed for each patient before rupture. For each case, four patients (controls) with an incidental PcomA aneurysm that remained unruptured overrun 6 months after inclusion (mean follow-up time 25.7 months, range 9–51 months) were randomly selected from the same database and matched according to clinical factors (gender, age, history of hypertension, use of antiplatelet drugs). The age range of the patients was 43–86 years (mean 60.5 years) and all the patients were women.

Imaging

The raw imaging data for four patients in the case group and seven participants in the control group were obtained from the DICOM file and the CTA images were processed using Mimics V.9.22 Software (Materialise Interactive Medical Image Control System, Belgium). Three-dimensional (3D) images were obtained from the remaining 19 patients by digital subtraction angiography (DSA; Artis zee Biplane VC14, Siemens) at our hospital.

Rotational angiograms were performed 1 s after a 7 s contrast injection with 15 mL contrast agent and a 200° rotation; a total of 133 frames were captured. The corresponding 133 images were reconstructed into a 3D model using the syngo X Workplace workstation, and the image was then exported as a stereolithography (STL) file. The pulsatile velocity waveform was obtained from a normal subject using transcranial Doppler (TCD). The flow spectrum envelope was then captured to obtain average blood flow velocity waveforms throughout the entire cardiac cycle using Matlab V.7.0 software (MathWorks, Natick, Massachusetts, USA).

Patient-specific modeling of aneurysm

The STL file was imported into GEOMAGIC STUDIO V.9.0 software (Geomagic, USA) for segmenting and surface smoothing. The surface data were imported into ICEM CFD V.14.0 (ANSYS, Lebanon, New Hampshire, USA) to generate volume grids for fluid dynamics computation. Each model was meshed to create 1 800 000–2 000 000 finite volume tetrahedral and wall prism elements (for accurate boundary layer resolution). The simulation was performed using CFX V.11.0 software (ANSYS). The vessel was assumed to be rigid with no-slip boundary conditions. We treated blood as a Newtonian fluid with an assumption of laminar and incompressible blood flow. The density and dynamic viscosity of the blood were specified as ρ=1050 kg/m³ and μ=0.00 345 Pa×s, respectively. The vessel inlet was imposed by pulsatile velocity profile obtained using TCD. The outlet was modeled as an opening boundary condition with zero static pressure. Navier–Stokes formulations were the governing equations underlying these calculations. We discretized the entire cardiac cycle of 0.8 s using a time step of 0.001 s for numeric simulation. Three cardiac cycles were simulated to ensure numeric stability and the last cycle was considered as output.

Parameter definition and calculation

The morphological parameters examined in the study included aneurysm size, aspect ratio (AR), size ratio (SR), undulation index (UI), non-sphericity index (NSI), and ellipticity index (EI), as described by Dhar.3

All hemodynamic parameters were calculated as the average value of one cardiac cycle, including oscillatory shear index (OSI), the percentage of low WSS area (LSA%), and normalized wall shear stress (WSS) (ratio of WSS-aneurysm sac and WSS-parent artery), and the contour maps for both the case and control groups demonstrated the qualitative representation of WSS averaged over one cardiac cycle, as previously described.4

Statistical analysis

The Shapiro–Wilks W test was used to calculate the departure from a normal distribution and to determine whether a parameter was normally distributed. The means and SDs (for normally distributed data) or deviation median and IQR (for abnormally distributed data) of all morphological and hemodynamic parameters were calculated for the case and control groups. A two-tailed independent Student t test (for normally distributed data) or Mann–Whitney U test (for abnormally distributed data) was performed to assess the statistical significance of the observed differences between the parameters in the two groups. The probability values from the two tests were calculated, and statistical significance was considered as p<0.05.

A matched case–control analysis using univariate conditional logistic regression was performed to further examine significant parameters (p<0.05). For each statistically significant parameter we performed linear regression analysis with other significant parameters, followed by multivariate conditional logistic regression (stepwise elimination, sls=0.25, sle=0.15). For each variable, we evaluated the increased likelihood of rupture, with statistical significance set at p<0.05, and determined the associated OR when the null hypothesis was rejected.

Statistical analyses were performed using Matlab V.7.0 (MathWorks) and SAS V.9.1 (SAS Institute, Cary, North Carolina, USA).

Results

Univariate analyses between case and control groups

The distributions of WSS for the case and control groups are shown in figure 1. The contour maps show the qualitative representation of the WSS averaged over a cycle for cases and controls. The results of univariate analyses are detailed in table 1. The results of the Shapiro–Wilks W test showed that aneurysm size, NSI, and EI were normally distributed.

Table 1

Results of univariate statistical analysis for all parameters examined in the case and control groups

Figure 1

Wall shear stress (WSS) distribution of the results for (A) the case group and (B) the control group. The contour maps are qualitative representations of the WSS averaged over an entire cardiac cycle. The five vertical pictures were included in one matched group.

Using the Student t test or Mann–Whitney U test, we detected a significantly larger aneurysm size (p=0.001), higher AR (p=0.018), EI (p<0.001), UI (p=0.005) and LSA% (p=0.010), and a lower normalized WSS (p=0.005) in the case group compared with the control group. There were no statistically significant differences between the two groups in NSI (p=0.481), SR (p=0.066), or OSI (p=0.897).

Conditional logistic regression analysis

Univariate conditional logistic regression analysis was performed to assess the significant parameters: aneurysm size, AR, EI, UI, normalized WSS, and LSA%. The analysis showed that aneurysm size and normalized WSS were statistically significant. For these two parameters, linear regression analysis yielded an R2 value of 0.317 (p=0.001), which implies that there is a statistical correlation between the two parameters. The scatterplots are presented in figure 2.

Figure 2

Scatterplots showing the distribution of key parameters in the case and control groups (red squares represent case values and the straight line represents the linear regression equation of the form Y=8.6292–3.7765X).

The results of the multivariate conditional logistic regression analysis of these two parameters (aneurysm size and normalized WSS) showed that only normalized WSS was retained as an independently significant parameter (p=0.040) and the associated OR was 0.151 (95% CI 0.025 to 0.914; parameter estimate −1.893, SE 0.920, χ2 4.233).

To better assess whether low normalized WSS was independently associated with rupture, for each case we matched one stable unruptured aneurysm as control matched for aneurysm size (difference in size between each pair of matched case–control aneurysms was <0.5 mm), and the results showed that the aneurysm which was prone to rupture had a lower WSS (0.433±0.242 vs 0.821±0.195, p=0.005).

Discussion

In this study we examined three hemodynamic parameters and six morphological parameters using a matched case–control study based on six PcomA aneurysms prone to rupture. The results showed that WSS was significantly associated with rupture of PcomA aneurysms.

Assessment of the risk of aneurysm rupture has been challenging for neurosurgeons, and the screening of high-risk factors for aneurysm rupture and timely intervention are the most rational strategy treatments for unruptured aneurysms. However, the risk of rupture of intracranial aneurysms is different depending on the location. The ISUIA study classified PcomA aneurysms as posterior circulation aneurysms, with a higher risk of rupture than anterior circulation aneurysms.5 However, Clarke et al6 conducted a systematic review and showed that, after excluding the cases in the ISUIA study, PcomA aneurysms have a rupture risk similar to that of other anterior circulation aneurysms (0.46% and 0.49%, respectively); however, for posterior circulation aneurysms, the risk of rupture remains as high as 1.9% per year. The results of a recent study on the natural history of unruptured aneurysms in Japan suggested that, although the risk of rupture for small aneurysms (<7 mm) is 0.4% per year, based on the aneurysm location, the risk of PcomA aneurysm rupture is higher at 1.72% per year.7

Hemodynamics have an important role in the pathogenesis, progression, and rupture of cerebral aneurysms. WSS is a well-studied hemodynamic parameter associated with aneurysm rupture.8 ,9 Although a few researchers have suggested that aneurysms with high WSS were more likely to rupture, more studies support lower WSS as a risk factor for aneurysm rupture. Xiang et al10 compared the hemodynamics and morphology of 38 ruptured aneurysms and 81 unruptured aneurysms and found that lower WSS was a risk factor for aneurysm rupture; indeed, as the WSS increased, the risk of aneurysm rupture decreased. In a previous hemodynamic study on mirror PcomA aneurysms, we also showed that, compared with unruptured aneurysms, ruptured aneurysms have a lower WSS and a larger low WSS area.4 In another study11 we observed that unruptured PcomA aneurysms with oculomotor nerve palsy have lower WSS than ruptured aneurysms. Hence, oculomotor nerve palsy is a clinical symptom of the sudden increase in the PcomA aneurysm sac, and PcomA aneurysms with oculomotor nerve palsy have a higher risk of rupture. These studies further support lower WSS as a risk factor for aneurysm rupture. In the present case–control study we also showed that PcomA aneurysms prone to rupture have a lower WSS compared with stable unruptured aneurysms. Pereira et al12 conducted a case–control study based on four small aneurysms prone to rupture and showed that there were no differences in the mean, maximum and minimum WSS of the aneurysmal wall between aneurysms prone to rupture and stable unruptured aneurysms, but the WSS cumulative distribution function was significant and might be a potential predictor of small-sized aneurysm rupture.

Morphology is an important factor affecting hemodynamics. Moreover, the computational fluid dynamics analysis is time-consuming. Identifying morphologic parameters associated with aneurysm rupture is therefore important. A series of parameters have been previously studied and reported (including aneurysm size, AR, SR), and size has been found to be the most important geometrical factor used in day-to-day clinical practice to guide whether to treat or manage expectantly, along with several other clinical factors such as life expectancy, family history, and smoking.3 ,13 ,14 In the present study we demonstrated that aneurysm size was associated with rupture. Although larger aneurysms have a higher probability of rupture, small or micro aneurysm ruptures also frequently occur, suggesting that estimation of the risk of aneurysm rupture based only on aneurysm size is insufficient.

The pathogenesis, progression, and rupture of cerebral aneurysms might be influenced by factors beyond morphology and hemodynamics. Kaspera et al15 found that the risk of anterior communicating artery aneurysm formation is determined by several independent clinical, morphological, and hemodynamic factors. The strongest independent risk factors include smoking, asymmetry of A1 segments >40%, low blood flow pulsatility, and angle between A1 and A2 segments ≤100°. Matsukawa et al16 retrospectively reviewed 134 consecutive patients with PcomA aneurysms (39 ruptured) and compared the morphological and clinical characteristics. The results showed that age <60 years, history of hypertension, lateral direction of the aneurysmal dome, and bleb formation were significantly associated with aneurysm rupture. To reduce the impact of other factors in the present study, four controls were matched for each case based on clinical risk factors such as gender, age, history of hypertension, and the use of antiplatelet drugs.

Interestingly, univariate conditional logistic regression analysis showed that aneurysm size and normalized WSS were significant factors whereas multivariate conditional logistic regression analysis showed only normalized WSS as an independently significant parameter. This difference probably reflects the linear relationship between these two parameters and might also explain the interaction between morphology and hemodynamics. In this study we matched one stable incidental unruptured aneurysm as control for each case based on aneurysm size and the results showed that, compared with stable unruptured aneurysms, aneurysms which were prone to rupture had a lower WSS, which is further proof that WSS may be a more reliable parameter for characterizing the rupture status of PcomA aneurysms.

There are some limitations in the present study. First, although we used aneurysm models based on patient-specific CTA or DSA images, we did not consider patient-specific boundary conditions which might cause deviations in the research. Second, ethics and difficulties in case collection resulted in a small sample size of six matched groups; future studies should use a larger number of cases to confirm the results. Another concern is that the aneurysms examined in the present study were all located on the PcomA, and the high variability in the PcomA might affect the results of this research. Furthermore, there are many risk factors for intracranial aneurysm rupture including clinical risk factors, morphology, patient-specific hemodynamics, and blood biochemical status. Evaluation of the risk of aneurysm rupture from a morphological and hemodynamic perspective might therefore be inadequate, even if we obtain an image of aneurysms prone to rupture. The combination of these factors and increased sample size will be important in future studies analyzing the interactions between morphology and hemodynamics. Moreover, a multifactor composite aneurysm rupture risk prediction model should be established.

Acknowledgments

The authors would like to thank the Shanghai Supercomputer Center for providing the calculation software.

References

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Footnotes

  • GD and NL contributed equally to the work and should be considered as co-first authors.

  • Correction notice This article has been corrected since it was published online first. Dr Huang has been added as the second corresponding author.

  • Contributors GD and QH contributed equally to the experimental design. NL and JX contributed equally to data collection. JY, YX and GD contributed equally to data analysis. QH, BH and JL contributed equally to the preparation of the manuscript.

  • Funding This study has received funding from the National Natural Science Foundation of China (Grant No. 81171092), Shanghai Education Commission Innovation Fund (Grant No. 14ZZ081), Major Joint Research Projects of Shanghai Health Bureau (Unnumbered). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

  • Competing interests None.

  • Patient consent Obtained.

  • Ethics approval Ethics approval was obtained from the Institutional Review Board of Changhai Hospital, affiliated with the Second Military Medical University.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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