Assessment of the Risk of Rupture of Intracranial Aneurysms using Threedimensional Cerebral Digital Subtraction Angiography

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1 The Journal of International Medical Research 2010; 38: [first published online as 38(5) 5] Assessment of the Risk of Rupture of Intracranial Aneurysms using Threedimensional Cerebral Digital Subtraction Angiography J YU, Q WU, F-Q MA, J XU AND J-M ZHANG Department of Cerebral Surgery, The Second Affiliated Hospital, Zhejiang University College of Medicine, Hangzhou, China The aim of this study was to develop an improved binary logistic regression model for predicting the risk of intracranial aneurysm rupture. A cohort of patients (n = 37) with aneurysms underwent threedimensional digital subtraction angiography examination to measure several morphological parameters of the aneurysm. The aspect ratio (height/neck size) and the size ratio (length/mean diameter of parent vessel) were also calculated. All the morphological parameters combined with the aneurysm location and the patient s baseline data were used to derive a backward binary logistic regression model. In order to validate the model, it was applied to another independent cohort of 19 patients with aneurysms. The model had sensitivity, specificity and accuracy of 84.6%, 66.7% and 78.9%, respectively. This binary logistic regression model of aneurysm rupture risk identified the status of an aneurysm with high accuracy and could form the basis of more complex models in the future. KEY WORDS: INTRACRANIAL ANEURYSM; RISK ASSESSMENT; BINARY LOGISTIC REGRESSION; THREE-DIMENSIONAL CEREBRAL DIGITAL SUBTRACTION ANGIOGRAPHY Introduction Intracranial aneurysms are pathological enlargements of brain arteries that are most commonly located at the Circle of Willis and are morphologically divided into three groups: saccular, olivary and intramural. 1 The majority (95%) of intracranial aneurysms are saccular, which is the group investigated in the present study. Spontaneous subdural haemorrhage secondary to the rupture of intracranial aneurysms can be life-threatening and cause deformity with a mortality and morbidity rate of 50 60%. 2 4 With the development of modern imaging techniques, unruptured intracranial aneurysms (UIA) are detected more frequently than in the past. Controversy exists, however, regarding how to treat these patients because of a number of issues. First, although intracranial aneurysms are common, only a small percentage of them rupture; it is reported that the prevalence of UIAs is 1 6%, 5 11 but aneurysmal subarachnoid haemorrhage 1785

2 (SAH) has an annual prevalence of only 0.01%. 12 Secondly, both microsurgery and intravascular interventional therapy are invasive and associated with some risks to the patient. 13 Thirdly, microsurgery and intravascular interventional therapy have different long-term outcomes. 13 Finally, although there has been some progress in understanding the natural history of UIAs, knowledge remains inadequate. In addition, there are many patient-related complicating factors, such as age, basic health status and their desire to avoid surgery. Thus, the risk of a specific size, shape and location of an aneurysm in the context of the natural history of this pathology must be balanced with the possible complications caused by the treatment. It is very important to be able to predict accurately the status of an aneurysm so that neurosurgeons can target interventions to those patients with the greatest risk of rupture. Previous study has indicated that the size and shape of an aneurysm can be used to predict the risk of rupture. 2 Only comparison of the size between the ruptured and unruptured aneurysms has been studied, however, in an attempt to find a critical size (i.e. aneurysm size just prior to rupture) Recently, Dhar et al. 17 undertook a detailed study of the morphological characteristics of 45 saccular aneurysms (25 unruptured, 20 ruptured) through application of threedimensional digital subtraction angiography (DSA). They introduced two new parameters: (i) the maximum height/mean vascular diameter (size ratio; SR); and (ii) the angle of inclination between the intracranial aneurysm and its neck plane. Then they developed a mathematical model to predict rupture risk using multivariate logistic regression analysis and receiver operating characteristic analyses were performed on each parameter. Unfortunately, they did not analyse the validity of the model. It is especially important for the clinical decision-making processes used prior to neurosurgery to find the factors and to establish a model that can be used to predict the rupture risk of intracranial aneurysms. The first mathematical model for predicting the morbidity and mortality of intracranial aneurysms was introduced by Richardson et al. 18 in They presented a discriminative function by which mortality could be predicted in a type of operative approach to anterior communicating artery (ACoA) aneurysms. Subsequently, they used the same function to evaluate the prognostic factors in a series of posterior communicating artery (PCoA) aneurysms. Since the publication of their work, 18 mathematical modelling of aneurysms has been used to understand the biophysical phenomena that contribute to aneurysm growth and rupture. 19 Biomathematical models can incorporate various physical and dynamic phenomena that may provide insight into the potential for aneurysmal rupture and, possibly, help to predict the probability of rupture. 20 The present study describes the biomorphometric properties of aneurysms combined with general clinical data from patients in a prospective clinical series. By performing binary logistic regression analysis, a statistical technique similar to the previously described discriminative analysis method, a relational equation was derived that describes the rupture potential for cerebral aneurysms within this cohort. To assess the validity of this equation, it was then applied to an independent cohort of aneurysm patients to determine the status of each patient s aneurysm. This is the first attempt to verify a potentially clinically applicable mathematical formula that 1786

3 predicts aneurysm status in an independent patient population. Patients and methods PATIENTS AND DATA COLLECTION This study included a retrospective review of a prospectively maintained database of patients presenting with SAH due to aneurysm rupture at The Second Affiliated Hospital, Zhejiang University College of Medicine (Hangzhou, China) between 2007 and The diagnosis and evaluation of the patients was based on three-dimensional rotational DSA. The region of interest, consisting of the identified aneurysm(s) and the parent vessel, was identified and several biomorphometric parameters were obtained in planes parallel to the direction of flow within the parent vessel. All angiograms were analysed by two investigators (J.-M.Z. and Q.W.) who were blinded to the specific diagnoses and to all patients clinical data including rupture status of the aneurysms. Clinical data, including age, gender and basic health status, including hypertension status, were recorded for each patient and all the recorded parameters were used in the backward stepwise binary logistic regression analyses. A data set was then collected prospectively from an independent cohort of patients with aneurysms and who presented between 2007 and The model was then applied to this independent cohort by one of the authors (F.-Q.M.) who was blinded to the rupture status of this cohort s aneurysms. The log-odds risk of rupture for each aneurysm in the cohort was then obtained. Specificity, sensitivity and accuracy to predict aneurysm status using the binary logistic regression model were calculated. The study was approved by the Medical Ethical Committee of the Second Affiliated Hospital, Zhejiang University College of Medicine. All patients were informed about the study and gave their written consent to participate. DEFINITIONS OF THE PARAMETERS MEASURED As shown in Fig. 1, the following morphological parameters of the aneurysm were recorded: (i) size defined in accordance with Raghavan et al. 21 as the maximum perpendicular height of the intracranial aneurysm (i.e. the maximum perpendicular distance of the dome from the neck plane); (ii) length the maximum (not necessarily perpendicular) distance from the centroid of the aneurysm neck to any point on the aneurysm dome; (iii) aspect ratio (AR) the maximum perpendicular height-tomean neck diameter ratio, where the mean neck diameter is twice the mean distance from the neck centroid to the edge of the neck; 22,23 and (iv) aneurysm-to-vessel size ratio (SR) in accordance with Dhar et al., 17 incorporating the geometries of the intracranial aneurysm and its parent vessel and defined as maximum aneurysm height/mean vessel diameter. STATISTICAL ANALYSES Statistical analyses were carried out using the SPSS statistical package, version 15.0 (SPSS Inc., Chicago, IL, USA) for Windows. Independent t-tests and χ 2 -tests were used to compare the mean values for continuous data and categorical data, respectively. Backward binary logistic regression was then used to generate the model. A two-sided P- value < 0.05 was considered to be statistically significant. Results Between 2007 and 2009, a total of 37 patients with 37 aneurysms (24 ruptured and 13 unruptured) presented to the Second 1787

4 Maximum height (H max ) Perpendicular height (H) FLOW D 1 Mean neck diameter (D) D 2 1.5D 1 FIGURE 1: Schematic diagram of an intracranial aneurysm showing the morphological parameters that were measured using three-dimensional digital subtraction angiography (H, size [perpendicular height]; D, mean neck diameter; AR, aspect ratio [H/D]; H max, length [maximum height]; D v, mean diameter of the vessel measured at D 1 and D 2 [{D 1 + D 2 }/2]; SR, aneurysm-to-vessel size ratio [H max /D v ]) Affiliated Hospital, Zhejiang University College of Medicine and were used to generate the initial binary logistic regression model. Patients demographic characteristics and data on the location of the aneurysms which were used to generate the initial binary logistic regression model are presented in Table 1. Patients were aged between 36 and 76 years (mean ± SD 57 ± 9.5 years) and included 14 males and 23 females. The distributions of age, gender and hypertension status in patients with ruptured aneurysms were not significantly different to those in patients with unruptured aneurysms. No significant differences were observed for the number of ruptured versus unruptured aneurysms between men and women, hypertensive and non-hypertensive patients, or based on aneurysm location. Table 2 shows the comparison of the selected morphological parameters between ruptured and unruptured aneurysm patients. The mean length, size, AR and SR for the ruptured aneurysms were all significantly greater than for the unruptured aneurysms (P < 0.05). The mean neck size was also larger for the group with ruptured versus those with unruptured aneurysms but this difference did not reach statistical significance. Backward binary logistic regression involved the following parameters: age, gender, hypertension, location, size, length, AR and SR. The final equation that generated the greatest correlation with the initial database was: Logit = Loc(4) Loc(3) Loc(2) Loc(1) SR AR , where Loc(1), (2), (3) and (4) correspond to ACoA, PCoA, internal carotid artery (ICA) and middle 1788

5 TABLE 1: Patient demographic characteristics and aneurysm location data, stratified by intracranial aneurysm rupture status, which were used to generate the initial binary logistic regression model Intracranial aneurysm status Ruptured Unruptured Statistical Clinical measure n = 24 n = 13 significance a Age (years) NS < Gender NS Male 11 3 Female Hypertension NS Yes 15 4 No 9 9 Aneurysm location NS ACoA 5 3 PCoA 11 2 ICA 2 5 MCA 4 2 BA 2 1 Data show number of patients. a Pearson χ 2 -test or Fisher s exact test. ACoA, anterior communicating artery; PCoA, posterior communicating artery; ICA, internal carotid artery; MCA, middle cerebral artery; BA, basilar artery; NS, not statistically significant (P > 0.05). carotid artery (MCA), respectively; when one of them was 1, the other were all 0 (Table 3). The significance of the binary logistic regression was tested by χ 2 analysis which TABLE 2: Differences in the selected morphological parameters of the ruptured and unruptured intracranial aneurysms measured by three-dimensional digital subtraction angiography Intracranial aneurysm status Ruptured Unruptured Statistical Parameter n = 24 n = 13 significance a Neck size 3.77 ± ± 0.79 NS Length 6.96 ± ± 0.94 P = Size 6.59 ± ± 0.94 P = Aspect ratio (AR) 1.79 ± ± 0.72 P = Size ratio (SR) 2.81 ± ± 0.6 P = Data show mean ± SD. a Statistical analysis by t-test. 1789

6 TABLE 3: Coding of the intracranial aneurysm location used in the backward binary logistic regression analysis to produce a model for predicting intracranial aneurysm rupture risk (n = 37 patients) Coding of the parameter Location No. of aneurysms Loc(1) Loc(2) Loc(3) Loc(4) Loc(5) ACoA PCoA ICA MCA BA Loc(1), (2), (3), (4), (5) correspond to anterior communicating artery (ACoA), posterior communicating artery (PCoA), internal carotid artery (ICA), middle carotid artery (MCA) and basilar artery (BA), respectively. generated a probability value of P < This model was then used prospectively in risk assessment of intracranial aneurysm rupture in an independent cohort of 19 aneurysms in 19 patients who presented between 2007 and The model was able correctly to predict aneurysm status in 15 of the 19 aneurysms. The sensitivity, specificity and accuracy for the model in this cohort were 84.6%, 66.7% and 78.9%, respectively (Table 4). Discussion The mathematical model generated in one cohort of patients (n = 37) in the present study was shown to be able accurately to determine the rupture status of aneurysms within a second, independent, prospective cohort of patients (n = 19), with sensitivity of 84.6%, specificity of 66.7% and accuracy of 78.9%. The mathematical analysis was consistent with the previous research of Hademenos et al., 15 who explored the relationship between the anatomy and morphology of aneurysms and their rupture in 74 patients. The sensitivity, specificity and accuracy demonstrated in their study were 76.3%, 61.8% and 69.4%, respectively. Published data from the International Study of Unruptured Intracranial Aneurysms TABLE 4: Effectiveness of the backward binary logistic regression model in risk assessment of intracranial aneurysm rupture Predicted status Aneurysm No. of Data source status patients Ruptured Unruptured Accuracy (%) Initial data cohort Ruptured Unruptured Total Predictive data cohort Ruptured Unruptured Total Total Ruptured Unruptured Total

7 suggested that, for aneurysms that are < 10 mm in diameter at diagnosis, the cumulative rate of rupture was < 0.05% per year in patients with no history of SAH whereas it was approximately 11 times higher per year in patients who presented with a history of SAH. 24 The rupture rate per year in aneurysms that were 10 mm in diameter was approximately 1% in both groups regardless of the SAH history. 24 There has, however, been no model that can accurately predict the rupture status of intracranial aneurysms and the model in the present study is no exception as it cannot be used to predict the risk of aneurysm rupture in clinical practice. Also, the pathophysiology of aneurysms is extremely complicated, so more complicated models are required that may then be applied during the clinical decision-making process. Since the first mathematical model for predicting the morbidity and mortality associated with intracranial aneurysms was introduced by Richardson et al. 18 in 1966, quantitative models have been extensively used to explain the biological phenomena involved in the expansion and rupture of intracranial aneurysms. For example, Jou et al. 25 determined the vascular wall sheer tension of 26 aneurysm patients (eight ruptured, 18 unruptured) using computational flow dynamics and found that, for intracranial aneurysms of the ICA under the same flow rate, the mean wall shear stress was determined by aneurysm size. Ruptured aneurysms had a statistically significantly greater area under low wall shear stress than unruptured aneurysms. 25 Ohshima et al. 26 focused on round terminaltype aneurysms with the positioning of the neck orifice set according to the following three patterns in relation to the axis of the parent artery: the type-a neck orifice was positioned directly in line with the flow of the parent artery; the type-b neck orifice was shifted 1.5 mm offline toward the unilateral branch; and the type-c neck orifice was shifted 3 mm offline. It was found that type B accounted for the most ruptures (P < 0.05). Their study demonstrated that the neck orifice positioning of aneurysms was one of the risk factors that influenced risk of rupture. 26 The present study showed that aneurysm location was a factor in predicting aneurysm rupture and the backward binary logistic regression model developed in the present study quantified the relationship with location to predict aneurysm rupture status. This is consistent with previous studies that have indicated that the risk of rupture is related to location, although the exact relationship has not been determined. 2,15,27 29 An epidemiological study indicated that aneurysm size and location are related to the risk of aneurysm rupture. 30 Additionally, other studies have suggested that aneurysms located in the ACoA and PCoA are more likely to rupture than those located elsewhere. 2,15,31 The SR parameter used in the present study was found to be related to intracranial aneurysm status. It was recently introduced by Dhar et al., 17 who investigated the morphological characteristics of 45 saccular aneurysms (25 unruptured, 20 ruptured) using three-dimensional DSA. Other, simpler morphological parameters were also used in the present study but, in assessing the validity of the model and in agreement with Dhar et al., 17 it was concluded that a larger SR indicates a greater rupture risk. To determine whether the diameter of the parent artery is related to the risk of aneurysm rupture, Dhar et al. 17 excluded low-risk locations, such as cavernous ICA and the ICA adjacent to the ophthalmic artery, and found that even when vessel diameter was no longer 1791

8 significantly associated with rupture risk, SR remained associated. Carter et al. 30 analysed the reason for these trends by investigating 854 ruptured and 819 unruptured aneurysms. They found that the locations for ruptured lesions in decreasing order of mean aneurysm size were: ophthalmic artery, ICA bifurcation, basilar bifurcation, MCA bifurcation, PCoA, ACoA, posteroinferior cerebellar artery and distal locations ( distal defined as an intracranial aneurysm located distally on A2, P2, or M2 bifurcations). The intracranial aneurysms in the distal locations rarely grew to > 10 mm. From a correlation with associated vessel sizes, they noted that the average sizes of ruptured intracranial aneurysms were smaller on smaller-sized vessels. To explain this trend, they suggested that aneurysms arising from smaller vessels had thinner walls and, according to Laplace s Law, would experience much greater wall tension when subjected to the same pressures as intracranial aneurysms with greater wall thicknesses. Thus, Carter et al. 30 stated that the relative resistance to rupture of two aneurysms of the same size, but arising from vessels with different sizes and different wall thicknesses, may be different. In general, vessel size and wall thickness decrease behind branching points and the more distally an intracranial aneurysm is located in the arterial tree, the smaller its wall thickness and parent vessel diameter will be. Aneurysm size is another risk factor that can be used to predict rupture. Although the present study did not directly demonstrate this, the model indicates that a larger aneurysm is associated with increased rupture risk. Many studies have indicated an important relationship between the aneurysm size and rupture risk, 15,29 and several have attempted to determine the threshold or critical size at which an aneurysm becomes likely to rupture. 3,24,32 34 Nevertheless, results to date have been extremely variable with a wide range of critical sizes being reported from 4 mm to > 10 mm. 15 Beck et al. 2 studied the size and location of ruptured and unruptured aneurysms and concluded that a critical size for aneurysm rupture could not be identified. Sekhar and Heros 35 suggested in a review that rupture can and does occur at any size. Taken together, these studies indicate that there is no definite size above which rupture will definitely occur and this might explain, in part, the observation that only parameters that include aneurysm height are significant predictors of rupture. In fact, a normal vessel might be regarded as an aneurysm with an extremely large neck size and the AR then becomes very small and the rupture risk very low according to the model, which is consistent with the actual situation. De Rooij et al. 36 reported that the prevalence of aneurysms increases with age, but more than half of their patients were < 55 years old. In the present study, patients > 55 years old accounted for 67.6% of the cohort, but age was not found to be significant in the model. This is consistent with other studies that have shown no significant difference between ruptured and unruptured patients in terms of age. 29 It is notable that age is an important factor in the prediction of UIA because it has a big influence on expected life-span, however it is not the only determining factor and should be considered along with location, size, risk of interventional procedure and the basic health status of the patient. There were some limitations to the present study. First, the ruptured aneurysm data could have been affected by vasospasm. It is well known that vasospasm affects the parent artery on which the haemorrhage occurs from approximately 5 days after the haemorrhage. 37 The vast majority of the 1792

9 patients, however, presented for evaluation and had imaging via rotational angiography within 24 h of the initial haemorrhage. Thus, vasospasm was not expected to have had a significant effect on the data. It would have been useful to have compared the diameter of the parent artery with the published literature. Secondly, there is the influence of the rupture on the morphology of the aneurysm. To date, there has been no specific research into this, although some studies have indicated that the size and shape of an aneurysm are not affected that much by rupture. 21,23,38 The reason for the lack of such studies might be that an intracranial aneurysm is rarely imaged both before and after SAH. Thirdly, one of the main limiting factors of the present study was the comparatively small size of the patient population, so bias error might have been large; this is related to the low prevalence rate of UIA in China. Finally, when evaluating the risk of aneurysmal rupture, the natural history of the patient in combination with histology and haemodynamics should be considered. In the present study, using a new binary logistic regression model of aneurysm rupture and basic biomorphometric data and relationships obtained from DSA, it was possible to identify accurately the status of an aneurysm with a total accuracy of 78.9%, a sensitivity of 84.6% for the ruptured group and a specificity of 66.7% for the unruptured group in a prospectively obtained independently-derived cohort. The mathematical model provides a basis from which more complex models may be derived. Future studies combining dynamic flow characteristics and the bioelastic properties of tissue may further enhance these models. Conflicts of interest The authors had no conflicts of interest to declare in relation to this article. Received for publication 17 May 2010 Accepted subject to revision 19 May 2010 Revised accepted 2 September 2010 Copyright 2010 Field House Publishing LLP References 1 Weir B: Unruptured intracranial aneurysms: a review. J Neurosurg 2001; 96: Beck J, Rohde S, Berkefeld J, et al: Size and location of ruptured and unruptured intracranial aneurysms measured by 3- dimensional rotational angiography. Surg Neurol 2006; 65: Orz Y, Kobayashi S, Osawa M, et al: Aneurysm size: a prognostic factor for rupture. Br J Neurosurg 1997; 11: Rosenorn J, Eskesen V, Schmidt K, et al: Clinical features and outcome in 1076 patients with ruptured intracranial saccular aneurysms: a prospective consecutive study. Br J Neurosurg 1987; 1: Cohen MM: Cerebrovascular accidents, a study of two hundred one cases. AMA Arch Pathol 1955; 60: Crompton MR: Mechanism of growth and rupture in cerebral berry aneurysms. Br Med J 1966; 1: de la Monte SM, Moore GW, Monk MA, et al: Risk factors for the development and rupture of intracranial berry aneurysms. Am J Med 1985; 78: Housepian EM, Pool JL: A systematic analysis of intracranial aneurysms from the autopsy file of the Presbyterian Hospital, 1914 to J Neuropathol Exp Neurol 1958; 17: Inagawa T, Ishikawa S, Aoki H, et al: Aneurysmal subarachnoid hemorrhage in Izumo City and Shimane Prefecture of Japan. Incidence. Stroke 1988; 19: McCormick WF, Acosta-Rua GJ: The size of intracranial saccular aneurysms. An autopsy study. J Neurosurg 1970; 33: Stehbens WE: Aneurysms and anatomical variation of cerebral arteries. Arch Pathol 1963; 75: Fogelholm R, Hernesniemi J, Vapalahti M: Impact of early surgery on outcome after aneurismal subarachnoid hemorrhage: a population-based study. Stroke 1993; 24: Winn HR, Spetzler R, Meyer F, et al: The natural history of unruptured saccular cerebral aneurysms. In: Youmans Neurological Surgery, 5th edn (Winn HR, ed). New York; WB Saunders, 2003; pp

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