Association of Lipoprotein(a) Levels With Myocardial Infarction in Patients With Low-Attenuation Plaque.

BACKGROUND: Lipoprotein(a) (Lp[a]) is associated with an increased risk of myocardial infarction (MI). However, the mechanism underlying this association has yet to be fully elucidated. OBJECTIVES: This multicenter study aimed to investigate whether association between Lp(a) and MI risk is reinforced by the presence of low-attenuation plaque (LAP) identified by coronary computed tomography angiography (CCTA). METHODS: In a derivation cohort, a total of 5,607 patients with stable chest pain suspected of coronary artery disease who underwent CCTA and Lp(a) measurement were prospectively enrolled. In validation cohort, 1,122 patients were retrospectively collected during the same period. High Lp(a) was defined as Lp(a) ≥50 mg/dL. The primary endpoint was a composite of time to fatal or nonfatal MI. Associations were estimated using multivariable Cox proportional hazard models. RESULTS: During a median follow-up of 8.2 years (Q1-Q3: 7.2-9.3 years), the elevated Lp(a) levels were associated with MI risk (adjusted HR [aHR]: 1.91; 95% CI: 1.46-2.49; P < 0.001). There was a significant interaction between Lp(a) and LAP (Pinteraction <0.001) in relation to MI risk. When stratified by the presence or absence of LAP, Lp(a) was associated with MI in patients with LAP (aHR: 3.03; 95% CI: 1.92-4.76; P < 0.001). Mediation analysis revealed that LAP mediated 73.3% (P < 0.001) for the relationship between Lp(a) and MI. The principal findings remained unchanged in the validation cohort. CONCLUSIONS: Elevated Lp(a) augmented the risk of MI during 8 years of follow-up, especially in patients with LAP identified by CCTA. The presence of LAP could reinforce the relationship between Lp(a) and future MI occurrence.

Machine learning prediction model for the rupture status of middle cerebral artery aneurysm in patients with hypertension: a Chinese multicenter study.

Background: Hypertension is a common comorbidity in patients with unruptured intracranial aneurysms and is closely associated with the rupture of aneurysms. However, only a few studies have focused on the rupture risk of aneurysms comorbid with hypertension. This retrospective study aimed to construct prediction models for the rupture of middle cerebral artery (MCA) aneurysm associated with hypertension using machine learning (ML) algorithms, and the constructed models were externally validated with multicenter datasets. Methods: We included 322 MCA aneurysm patients comorbid with hypertension who were being treated in four hospitals. All participants underwent computed tomography angiography (CTA), and aneurysm morphological features were measured. Clinical characteristics included sex, age, smoking, and hypertension history. Based on the clinical and morphological characteristics, the training datasets (n=277) were used to fit the ML algorithms to construct prediction models, which were externally validated with the testing datasets (n=45). The prediction performances of the models were assessed by receiver operating characteristic (ROC) curves. Results: The areas under the ROC curve (AUCs) of the k-nearest-neighbor (KNN), neural network (NNet), support vector machine (SVM) and logistic regression (LR) models in the training datasets were 0.83 [95% confidence interval (CI): 0.78-0.88], 0.87 (95% CI: 0.82-0.92), 0.91 (95% CI: 0.88-0.95), and 0.83 (95% CI: 0.77-0.88), respectively, and in the testing datasets were 0.74 (95% CI: 0.59-0.89), 0.82 (95% CI: 0.69-0.94), 0.73 (95% CI: 0.58-0.88), and 0.76 (95% CI: 0.61-0.90), respectively. The aspect ratio (AR) was ranked as the most important variable in the ML models except for NNet. Further analysis showed that the AR had good diagnostic performance, with AUC values of 0.75 in the training datasets and 0.77 in the testing datasets. Conclusions: The ML models performed reasonably accurately in predicting MCA aneurysm rupture comorbid with hypertension. AR was demonstrated as the leading predictor for the rupture of MCA aneurysm with hypertension.

Rupture Risk Assessment for Anterior Communicating Artery Aneurysms Using Decision Tree Modeling.

Background: Although anterior communicating artery (ACoA) aneurysms have a higher risk of rupture than aneurysms in other locations, whether to treat unruptured ACoA aneurysms incidentally found is a dilemma because of treatment-related complications. Machine learning models have been widely used in the prediction of clinical medicine. In this study, we aimed to develop an easy-to-use decision tree model to assess the rupture risk of ACoA aneurysms. Methods: This is a retrospective analysis of rupture risk for patients with ACoA aneurysms from two medical centers. Morphologic parameters of these aneurysms were measured and evaluated. Univariate analysis and multivariate logistic regression analysis were performed to investigate the risk factors of aneurysm rupture. A decision tree model was developed to assess the rupture risk of ACoA aneurysms based on significant risk factors. Results: In this study, 285 patients were included, among which 67 had unruptured aneurysms and 218 had ruptured aneurysms. Aneurysm irregularity and vessel angle were independent predictors of rupture of ACoA aneurysms. There were five features, including size ratio, aneurysm irregularity, flow angle, vessel angle, and aneurysm size, selected for decision tree modeling. The model provided a visual representation of a decision tree and achieved a good prediction performance with an area under the receiver operating characteristic curve of 0.864 in the training dataset and 0.787 in the test dataset. Conclusion: The decision tree model is a simple tool to assess the rupture risk of ACoA aneurysms and may be considered for treatment decision-making of unruptured intracranial aneurysms.

Irregular Pulsation of Intracranial Aneurysm Detected by Four-Dimensional CT Angiography and Associated With Small Aneurysm Rupture: A Single-Center Prospective Analysis.

Objectives: Predicting the risk of rupture of small intracranial aneurysms remains challenging. The irregular pulsation of aneurysms detected by four-dimensional CT angiography (4D-CTA) could be an imaging marker of aneurysm vulnerability. We aimed to investigate the association of irregular pulsation with small aneurysm rupture. Materials and Methods: This was a prospective study on intracranial aneurysms detected by 4D-CTA from October 2017 to January 2020. A total of 242 consecutive patients with 316 aneurysms were enrolled. Irregular pulsation was defined as a temporary focal protuberance on more than 3 consecutive frames of the 20 phases in the RR interval. Small aneurysms were defined as those <7 mm. Univariate and multivariate analyses were performed to determine the independent predictors of small aneurysm rupture. Results: A total of 169 patients with 217 small intracranial aneurysms were included. Fourteen (6.5%) of the aneurysms had ruptured, and 77 (35.5%) had irregular pulsation. There were no significant differences in age, sex, hypertension, smoking, diabetes, drinking, or hyperlipidemia between the ruptured and unruptured aneurysm groups. The univariate analysis showed that smaller vessel size (p = 0.008), larger size ratio (p = 0.003), larger aspect ratio (p = 0.006), larger flow angle (p = 0.001), large vessel angle (p = 0.004), middle cerebral artery aneurysms (p = 0.046), anterior cerebral artery/posterior communicating artery/posterior circulation aneurysm (p = 0.006), irregular aneurysm (p = 0.001), and t presence of irregular pulsation (p = 0.001) were associated with small aneurysm rupture. The multivariate analysis showed that the presence of irregular pulsation (p = 0.003), anterior cerebral artery/posterior communicating artery/posterior circulation aneurysms (p = 0.014), and larger flow angle (p = 0.006) was independently associated with aneurysm rupture. Multivariate analysis of predictors of the irregular pulsation of small aneurysms showed that the aneurysm rupture (p = 0.022), irregular aneurysm (p < 0.001), and large size ratio (p = 0.005) were independently associated with the presence of irregular pulsation. Conclusions: The ruptured small aneurysms more often had irregular pulsation. The irregular pulsation was independently associated with aneurysm rupture and may help evaluate the risk of rupture of small intracranial aneurysms.

Classifying Ruptured Middle Cerebral Artery Aneurysms With a Machine Learning Based, Radiomics-Morphological Model: A Multicentral Study.

OBJECTIVE: Radiomics and morphological features were associated with aneurysms rupture. However, the multicentral study of their predictive power for specific-located aneurysms rupture is rare. We aimed to determine robust radiomics features related to middle cerebral artery (MCA) aneurysms rupture and evaluate the additional value of combining morphological and radiomics features in the classification of ruptured MCA aneurysms. METHODS: A total of 632 patients with 668 MCA aneurysms (423 ruptured aneurysms) from five hospitals were included. Radiomics and morphological features of aneurysms were extracted on computed tomography angiography images. The model was developed using a training dataset (407 patients) and validated with the internal (152 patients) and external validation (73 patients) datasets. The support vector machine method was applied for model construction. Optimal radiomics, morphological, and clinical features were used to develop the radiomics model (R-model), morphological model (M-model), radiomics-morphological model (RM-model), clinical-morphological model (CM-model), and clinical-radiomics-morphological model (CRM-model), respectively. A comprehensive nomogram integrating clinical, morphological, and radiomics predictors was generated. RESULTS: We found seven radiomics features and four morphological predictors of MCA aneurysms rupture. The R-model obtained an area under the receiver operating curve (AUC) of 0.822 (95% CI, 0.776, 0.867), 0.817 (95% CI, 0.744, 0.890), and 0.691 (95% CI, 0.567, 0.816) in the training, temporal validation, and external validation datasets, respectively. The RM-model showed an AUC of 0.848 (95% CI, 0.810, 0.885), 0.865 (95% CI, 0.807, 0.924), and 0.721 (95% CI, 0.601, 0.841) in the three datasets. The CRM-model obtained an AUC of 0.856 (95% CI, 0.820, 0.892), 0.882 (95% CI, 0.828, 0.936), and 0.738 (95% CI, 0.618, 0.857) in the three datasets. The CRM-model and RM-model outperformed the CM-model and M-model in the internal datasets (p < 0.05), respectively. But these differences were not statistically significant in the external dataset. Decision curve analysis indicated that the CRM-model obtained the highest net benefit for most of the threshold probabilities. CONCLUSION: Robust radiomics features were determined related to MCA aneurysm rupture. The RM-model exhibited good ability in classifying ruptured MCA aneurysms. Integrating radiomics features into conventional models might provide additional value in ruptured MCA aneurysms classification.

Aneurysm morphological prediction of intracranial aneurysm rupture in elderly patients using four-dimensional CT angiography.

OBJECTIVE: The natural history of unruptured intracranial aneurysms (UIAs) in elderly patients remains poorly understood, and the treatment of UIAs is controversial. The presence of irregular pulsation detected by four-dimensional CT angiography (4D-CTA) is associated with ruptured aneurysms. We aimed to investigate the morphological predictors of irregular pulsation of aneurysms in elderly patients. PATIENTS AND METHODS: We performed a prospective study of intracranial aneurysms detected by 4D-CTA. Elderly patients were defined as those more than 60 years of age. The irregular pulsation was defined as a focal protuberance during a cardiac cycle. We performed multivariate analyses to determine the associations of clinical characteristics and aneurysm morphologies with the irregular pulsation of aneurysms. RESULTS: A total of 128 elderly patients with 166 intracranial aneurysms was included. The irregular pulsation occurred in 71 (42.8%) aneurysms. The multivariate analysis showed that a large size ratio (p = 0.006), posterior circulation aneurysms (p = 0.033), the presence of a daughter dome (p = 0.006), and aneurysm rupture (p = 0.032) were independently associated with the irregular pulsation. The multivariate analysis of predictors of irregular pulsation of unruptured aneurysms showed that size ratio (p = 0.01) and the presence of a daughter dome (p = 0.016) were independent predictors of irregular pulsation. CONCLUSION: A large size ratio, posterior circulation aneurysms, the presence of a daughter dome, and aneurysm rupture were independent predictors of the irregular pulsation of aneurysms in elderly patients. The morphological characteristics detected by 4D-CTA may be helpful to evaluate the risk of rupture of aneurysms.

A nomogram to predict rupture risk of middle cerebral artery aneurysm.

BACKGROUND: Determining the rupture risk of unruptured intracranial aneurysm is crucial for treatment strategy. The purpose of this study was to predict the rupture risk of middle cerebral artery (MCA) aneurysms using a machine learning technique. METHODS: We retrospectively reviewed 403 MCA aneurysms and randomly partitioned them into the training and testing datasets with a ratio of 8:2. A generalized linear model with logit link was developed using training dataset to predict the aneurysm rupture risk based on the clinical variables and morphological features manually measured from computed tomography angiography. To facilitate the clinical application, we further constructed an easy-to-use nomogram based on the developed model. RESULTS: Ruptured MCA aneurysm had larger aneurysm size, aneurysm height, perpendicular height, aspect ratio, size ratio, bottleneck factor, and height-width ratio. Presence of a daughter-sac was more common in ruptured than in unruptured MCA aneurysms. Six features, including aneurysm multiplicity, lobulations, size ratio, bottleneck factor, height-width ratio, and aneurysm angle, were adopted in the model after feature selection. The model achieved a relatively good performance with areas under the receiver operating characteristic curves of 0.77 in the training dataset and 0.76 in the testing dataset. The nomogram provided a visual interpretation of our model, and the rupture risk probability of MCA aneurysms can be directly read from it. CONCLUSION: Our model can be used to predict the rupture risk of MCA aneurysm.

Predictors of Poor-Grade Aneurysmal Subarachnoid Hemorrhage Caused by Anterior Communicating Artery Aneurysm.

BACKGROUND: Patients with poor-grade aneurysmal subarachnoid hemorrhage (aSAH) are considered to have a poor prognosis. However, the underlying reason for the association between the aneurysmal characteristics and poor-grade aSAH is still unclear. In the present study, we retrospectively evaluated the independent risk factors for patients with anterior communicating artery (ACoA) aneurysms with poor-grade aSAH. METHODS: From January 2009 to January 2016, 477 consecutive patients with ruptured ACoA aneurysms were included in the present study. Poor-grade aSAH was defined as a World Federation of Neurosurgical Society grade of IV or V, and good-grade aSAH was defined as a grade of I-III. Univariate and multivariable regression analyses were used to investigate the differences in aneurysm morphology and clinical characteristics between the 2 groups. RESULTS: On univariate analysis, older patients (P = 0.038), larger aneurysm size (P = 0.013), larger size ratio (P = 0.007), larger aspect ratio (P = 0.009), positive history of stroke (P = 0.001), and posterior projection aneurysms (P = 0.001) were associated with poor-grade aSAH. Multivariate analyses revealed that older patients (odds ratio [OR], 1.654; 95% confidence interval [CI], 1.004-2.728; P = 0.048), larger size ratio (OR, 1.280; 95% CI, 1.111-1.475; P = 0.001), positive history of stroke (OR, 6.051; 95% CI, 1.712-21.381; P = 0.005), and posterior projection aneurysms (OR, 2.718; 95% CI, 1.607-4.598; P < 0.001) were independently associated with poor-grade aSAH. CONCLUSIONS: Poor-grade aSAH was independently associated with older patients, a larger size ratio, a positive history of stroke, and posterior projection aneurysms in patients with a ruptured ACoA aneurysm. These parameters could contribute to screening for patients with the potential for poor-grade aSAH.

Prediction of Clinical Outcome at Discharge After Rupture of Anterior Communicating Artery Aneurysm Using the Random Forest Technique.

Background: Aneurysmal subarachnoid hemorrhage (SAH) is a devastating disease. Anterior communicating artery (ACoA) aneurysm is the most frequent location of intracranial aneurysms. The purpose of this study is to predict the clinical outcome at discharge after rupture of ACoA aneurysms using the random forest machine learning technique. Methods: A total of 607 patients with ruptured ACoA aneurysms were included in this study between December 2007 and January 2016. In addition to basic clinical variables, 12 aneurysm morphologic parameters were evaluated. A multivariate logistic regression analysis was performed to determine the independent predictors of poor outcome. Of the 607 patients, 485 patients were randomly selected for training and the remaining for internal testing. The random forest model was developed using the training data set. An additional 202 patients from February 2016 to December 2017 were collected for externally validating the model. The prediction performance of the random forest model was compared with two radiologists. Results: Patients' age (odds ratio [OR] = 1.04), ventilated breathing status (OR = 4.23), World Federation of Neurosurgical Societies (WFNS) grade (OR = 2.13), and Fisher grade (OR = 1.50) are significantly associated with poor outcome. None of the investigated morphological parameters of ACoA aneurysm is an independent predictor of poor outcome. The developed random forest model achieves sensitivities of 78.3% for internal test and 73.8% for external test. The areas under receiver operating characteristic (ROC) curve of the random forest model were 0.90 for the internal test and 0.84 for the external test. Both sensitivities and areas under ROC curves of our model are superior to those of two raters in both internal and external tests. Conclusions: The random forest model presents good performance in predicting the outcome after rupture of ACoA aneurysms, which may aid in clinical decision making.

Morphological risk model assessing anterior communicating artery aneurysm rupture: Development and validation.

OBJECTIVE: Prediction of the rupture risk in anterior communicating artery (ACoA) aneurysms remains challenging. We aimed to investigate the association of detailed morphologies with ACoA aneurysm rupture. PATIENT AND METHODS: 759 consecutive patients with ACoA aneurysms were identified from December 2007 to January 2016. An independent cohort was collected for validation from March 2017 to October 2019. Morphological parameters of the aneurysms were measured using CT angiography. Univariable and multivariable analyses were used to investigate the association of morphological characteristics with aneurysm rupture. Area under receiver operating characteristic curves (AUC) were used to assess the performance of the model. RESULTS: A total of 650 patients with 650 ACoA aneurysms were included for the derivation, and 41 patients with 41 ACoA aneurysms were included for the validation. Aneurysm size, neck size, aspect ratio, size ratio, vessel angle, anterior projection, dominant A1 segment, irregular shape, the presence of a daughter dome, vessel size, and aneurysm angle were risk factors for rupture. The multivariable analysis showed that a larger aneurysm, anterior projection of aneurysms, dominant A1 segment, and irregular aneurysms were associated with aneurysm rupture, whereas larger vessel size was inversely associated with rupture. The morphological risk score showed good discrimination of ruptured and unruptured aneurysms with an AUC of 0.73 in the derivation and an AUC of 0.80 in the validation, and good calibration in both cohorts, signifying a good fit. CONCLUSION: The morphological risk model may contribute to evaluating the risk of rupture of ACoA aneurysms.

Morphological predictors of middle cerebral artery bifurcation aneurysm rupture.

OBJECTIVE: Middle cerebral artery (MCA) bifurcation aneurysms are more likely to be associated with severe hemorrhage or hematoma in a clinical setting. We aimed to investigate the morphological predictors of MCA bifurcation aneurysm rupture. PATIENTS AND METHODS: We conducted a retrospective analysis of 317 patients with MCA aneurysms between January 2009 and October 2016. Aneurysm status was grouped into ruptured and unruptured groups. The MCA bifurcation was defined as the bifurcation of the main trunk (the origin of the M2 trunks). Aneurysm morphologies were determined using CT angiography. We performed univariate and multivariable regression analyses to investigate the association of morphological characteristics with ruptured MCA bifurcation aneurysms. RESULTS: A total of 268 (84.5 %) patients with 280 MCA bifurcation aneurysms were included. 207 (73.9 %) aneurysms had ruptured. In the univariate analysis, a larger aneurysm (p = 0.042), a larger size ratio (p = 0.001), a larger aspect ratio (p = 0.017), a greater bottleneck ratio (p = 0.047), an irregular aneurysm (p = 0.004) and the presence of a daughter dome (p = 0.002) were associated with aneurysm rupture. The multivariate analysis showed that a larger size ratio (OR 1.324, 95 % CI, 1.062-1.651; p = 0.013) and the presence of daughter dome (OR 2.462, 95 % CI, 1.123-5.398; p = 0.024) were independently associated with ruptured aneurysms. The threshold of the size ratio for discriminating ruptured and unruptured aneurysms was 2.53 (p < 0.001). CONCLUSIONS: The size ratio and the presence of a daughter dome were independent predictors of the rupture of MCA bifurcation aneurysms. These parameters may contribute to the evaluation of the risk of rupture of aneurysms.

Morphological and Hemodynamic Factors Associated with Ruptured Middle Cerebral Artery Mirror Aneurysms: A Retrospective Study.

BACKGROUND: Mirror intracranial aneurysms with different rupture status is a useful model to investigate features associated with aneurysm rupture. Morphological and hemodynamic analyses of ruptured middle cerebral artery (MCA) mirror aneurysms are rarely reported. The purpose of this study was to determine the morphological or hemodynamic characteristics associated with ruptured MCA mirror aneurysms. METHODS: We performed a retrospective analysis of consecutive 317 patients with MCA aneurysms. Ruptured MCA mirror aneurysms (1 ruptured and mirror unruptured aneurysm) were included. In the matched pairs of ruptured and unruptured mirror aneurysms, 13 morphological parameters were measured using 3-dimensional computed tomography angiography and 6 hemodynamic parameters were evaluated using high-resolution computational fluid dynamic simulations. The association of morphological and hemodynamic characteristics with the rupture of MCA mirror aneurysms was determined. RESULTS: A total of 20 (6.31%) patients with 40 MCA mirror aneurysms were included in this study. There were significant differences in morphological and hemodynamic parameters between the ruptured and unruptured mirror aneurysms. Irregular aneurysms were 3 times more common in the ruptured aneurysms than in the unruptured aneurysms. A larger aneurysm (P = 0.025), a higher aneurysm (P = 0.020), a larger size ratio (P = 0.009), a higher bottleneck ratio (P = 0.033), an irregular aneurysm (P = 0.022), a higher maximum intra-aneurysmal wall shear stress (WSS; P = 0.020), and a lower normalized average WSS (P = 0.008) were associated with MCA mirror aneurysm rupture. CONCLUSIONS: Larger aneurysms, a larger size ratio, irregular aneurysms, a lower spatial average WSS, and a higher maximum WSS may contribute to evaluating the risk of rupture of MCA aneurysms independent of patient characteristics.

Prediction of rupture risk in anterior communicating artery aneurysms with a feed-forward artificial neural network.

OBJECTIVES: Anterior communicating artery (ACOM) aneurysms are the most common intracranial aneurysms, and predicting their rupture risk is challenging. We aimed to predict this risk using a two-layer feed-forward artificial neural network (ANN). MATERIALS AND METHOD: 594 ACOM aneurysms, 54 unruptured and 540 ruptured, were reviewed. A two-layer feed-forward ANN was designed for ACOM aneurysm rupture-risk analysis. To improve ANN efficiency, an adaptive synthetic (ADASYN) sampling approach was applied to generate more synthetic data for unruptured aneurysms. Seventeen parameters (13 morphological parameters of ACOM aneurysm measured from these patients' CT angiography (CTA) images, two demographic factors, and hypertension and smoking histories) were adopted as ANN input. RESULTS: Age, vessel size, aneurysm height, perpendicular height, aneurysm neck size, aspect ratio, size ratio, aneurysm angle, vessel angle, aneurysm projection, A1 segment configuration, aneurysm lobulations and hypertension were significantly different between the ruptured and unruptured groups. Areas under the ROC curve for training, validating, testing and overall data sets were 0.953, 0.937, 0.928 and 0.950, respectively. Overall prediction accuracy for raw 594 samples was 94.8 %. CONCLUSION: This ANN presents good performance and offers a valuable tool for prediction of rupture risk in ACOM aneurysms, which may facilitate management of unruptured ACOM aneurysms. KEY POINTS: • A feed-forward ANN was designed for the prediction of rupture risk in ACOM aneurysms. • Two demographic parameters, 13 morphological aneurysm parameters, and hypertension/smoking history were acquired. • An ADASYN sampling approach was used to improve ANN quality. • Overall prediction accuracy of 94.8 % for the raw samples was achieved.

Larger size ratio associated with the rupture of very small (≤3†mm) anterior communicating artery aneurysms.

BACKGROUND: Anterior communicating artery (AcoA) aneurysms have a high rupture risk, and ruptured AcoA aneurysms tend to be smaller than other intracranial aneurysms. We aimed to determine the incidence and morphologic predictors of aneurysm rupture of very small AcoA aneurysms. METHODS: We conducted a retrospective analysis of 519 consecutive patients with single AcoA aneurysms between December 2007 and February 2015 in our hospital. Aneurysm morphologies were re-measured using CT angiography images. Very small aneurysms were defined as those with a maximum size ≤3 mm, and small aneurysms were defined as those with a maximum size ≤5 mm. Multivariate regression analyses were used to determine the association between aneurysm morphology and aneurysm rupture status. RESULTS: Of the 474 ruptured AcoA aneurysms, 134 (28.3%) aneurysms were very small and 278 (58.6%) aneurysms were small. In the univariate analysis for very small aneurysms, larger aneurysm size (p=0.037), larger size ratio (p=0.002), higher aneurysm height (p=0.038), smaller vessel size (p=0.012), and dominant A1 segment configuration (p=0.011) were associated with aneurysm rupture. Multivariate analysis revealed that a larger size ratio was independently associated with the rupture status of the very small aneurysms (OR 3.69, 95% CI 1.5 to 9.0; p=0.004), and larger aneurysm size, larger size ratio, and dominant A1 segment configuration were associated with the rupture of small aneurysms. CONCLUSIONS: About one-third of ruptured AcoA aneurysms were very small. A larger size ratio, rather than other aneurysm morphologies, was independently associated with the rupture of very small AcoA aneurysms.

Predicting intraprocedural rupture and thrombus formation during coiling of ruptured anterior communicating artery aneurysms.

BACKGROUND: Intraprocedural rupture and thrombus formation are serious complications during coiling of ruptured intracranial aneurysms, and they more often occur in patients with anterior communicating artery (ACoA) aneurysms. OBJECTIVE: To identify independent predictors of intraprocedural rupture and thrombus formation during coiling of ruptured ACoA aneurysms. METHODS: Between January 2008 and February 2015, 254 consecutive patients with 255 ACoA aneurysms were treated with coiling. We retrospectively reviewed intraoperative angiograms and medical records to identify intraprocedural rupture and thrombus formation, and re-measured aneurysm morphologies using CT angiography images. Multivariate logistic regression models were used to determine independent predictors of intraprocedural rupture and thrombus formation. RESULTS: Of the 231 patients included, intraprocedural rupture occurred in 10 (4.3%) patients, and thrombus formation occurred in 15 (6.5%) patients. Patients with smaller aneurysms more often experienced intraprocedural rupture than those with larger aneurysms (3.5±1.3 mm vs 5.7±2.3 mm). Multivariate analysis showed that smaller ruptured aneurysms (p=0.003) were independently associated with intraprocedural rupture. The threshold of aneurysm size separating rupture and non-rupture groups was 3.5 mm. Multivariate analysis showed that a history of hypertension (p=0.033), aneurysm neck size (p=0.004), and parent vessel angle (p=0.023) were independent predictors of thrombus formation. The threshold of parent vessel angle separating thrombus and non-thrombus groups was 60.0°. CONCLUSIONS: Ruptured aneurysms <3.5 mm were associated with an increased risk of intraprocedural rupture, and parent vessel angle <60.0°, wider-neck aneurysms, and a history of hypertension were associated with increased risk of thrombus formation during coiling of ruptured ACoA aneurysms.

Sex differences in aneurysm morphologies and clinical outcomes in ruptured anterior communicating artery aneurysms: a retrospective study.

OBJECTIVES: Ruptured anterior communicating artery (ACoA) aneurysms occur more frequently in men. The purpose of the study was to investigate sex difference in aneurysm morphologies and clinical outcomes in patients with ruptured ACoA aneurysms. SETTING: A tertiary referral hospital. PARTICIPANTS: A total of 574 consecutive patients with ACoA aneurysms were admitted to our hospital from December 2007 to February 2015. In all, 474 patients (257 men and 217 women) with ruptured ACoA aneurysms were included in the study. MAIN OUTCOME MEASURES: Aneurysm morphologies were measured using computed tomographic angiography and clinical outcomes were measured with Glasgow coma score at discharge. RESULTS: The aneurysm sizes (p=0.001), aneurysm heights (p=0.011), size ratios (p<0.001), flow angles (p=0.047) and vessel angles (p=0.046) were larger in the male patients than in the females. The female patients more often had larger vessel sizes (p=0.002). Multivariate logistic analysis revealed that significant differences in aneurysm morphologies between men and women were aneurysm size (OR 1.1, 95% CI 1.0 to 1.3; p=0.036), aneurysm height (OR 0.8, 95% CI 0. to 0.9; p=0.006) and size ratio (OR 1.4, 95% CI 0.5 to 1.7; p=0.001). There were no statistically significant differences in the outcomes between men and women (OR 1.0, 95% CI 0.6 to 1.7, p=0.857). CONCLUSIONS: The men were independently associated with larger aneurysm sizes, greater aneurysm heights and larger size ratios. Sex was not a risk factor for poor outcome in patients with ruptured ACoA aneurysms.