Application of unruptured aneurysm scoring systems to a cohort of ruptured aneurysms: are we underestimating rupture risk?

Evaluation of PHASES Score for Predicting Rupture of Intracranial Aneurysms: Significance of Aneurysm Size

BACKGROUND

Recent data have identified that certain risk factors for rupture differ between small and larger intracranial aneurysms (IAs). Such differing risk factors make up 5 out of the 6 predictor variables used in the PHASES score, which raises the question on whether IA size has a significant effect on the score's performance.

METHODS

Patients who were diagnosed with an IA incidentally or due to a subarachnoid hemorrhage between 2015 and 2023 were selected for potential inclusion. The median IA size of the cohort was chosen as the cutoff point to categorize small and large (6 mm). The PHASES score was calculated for all patients, and a receiver operating characteristic curve analysis was performed to evaluate the classification accuracy of PHASES in predicting rupture for small and large IAs.

RESULTS

A total of 677 IAs were included. Among the IAs, 400 (58.9%) presented as UIAs and 279 (41.0%) as subarachnoid hemorrhage. The average PHASES score was 2.9 and 6.5 for small (n = 322) and large (n = 355) IAs, respectively. The PHASES score performed significantly lower for predicting rupture in smaller IAs (area under the curve: 0.634) compared with the larger (area under the curve: 0.741) (P = 0.00083).

CONCLUSIONS

PHASES was shown to underperform on small IAs. The decision to treat small unruptured IAs remains highly controversial, and the development of a new score to estimate the annual rupture rate while accounting for IA morphology is of great need. Our findings can help encourage future researchers to develop such a score.

Predictors for Rupture of Small (<7mm) Intracranial Aneurysms: A Systematic Review and Meta-Analysis

INTRODUCTION

Identifying predictors for rupture of small intracranial aneurysms (sIAs) have become a growing topic in the literature given the relative paucity of data on their natural history. The authors performed a meta-analysis to identify reliable predictors.

METHODS

PubMed, Scopus, and Web of Science were used to systematically extract references which involved at least 10 IAs <7mm which including a control group experiencing no rupture. All potential predictors reported in the literature were evaluated in the meta-analysis.

RESULTS

Fifteen studies yielding 4,739 sIAs were included in the meta-analysis. Four studies were prospective and 11 were retrospective. Univariate analysis identified 7 predictors which contradicted or are absent in the current scoring systems, while allowing to perform subgroup analysis for further reliability: patient age (MD -1.97, 95%CI -3.47-0.48; P = 0.01), the size ratio (MD 0.40, 95%CI 0.26-0.53; P < 0.00001), the aspect ratio (MD 0.16, 95%CI 0.11-0.22; P < 0.00001), bifurcation point (OR 3.76, 95%CI 2.41-5.85; P < 0.00001), irregularity (OR 2.95, 95%CI 1.91-4.55; P < 0.00001), the pressure loss coefficient (MD -0.32, 95%CI -0.52-0.11; P = 0.002), wall sheer stress (Pa) (MD -0.16, 95%CI -0.28-0.03; P = 0.01). All morphology related predictors listed above have been confirmed as independent predictors via multivariable analysis among the individual studies.

CONCLUSIONS

Morphology related predictors are superior to the classic patient demographic predictors present in most scoring systems. Given that morphology predictors take time to measure, our findings may be of great interest to developers seeking to incorporate artificial intelligence into the treatment decision-making process.

Evaluation of unruptured aneurysm scoring systems and ratios in subarachnoid hemorrhage patients with multiple intracranial aneurysms

Background

This study aims to appraise aneurysm scores and ratios' ability to discriminate between ruptured aneurysms and unruptured intracranial aneurysms (UIAs) in subarachnoid hemorrhage (SAH) patients harboring multiple intracranial aneurysms (MICAs). We, then, investigate the most frequent risk factors associated with MICAs.

Methods

We retrospectively applied unruptured intracranial aneurysm treatment score (UIATS) and population hypertension age size of aneurysm earlier SAH from another aneurysm site of aneurysm (PHASES) score, aspect, and dome-to-neck ratio to the 59 consecutive spontaneous SAH patients with MICAs admitted between January 2000 and December 2015 to the Department of Neurosurgery of the University Hospital Center "Hôpital des Spécialités" of Rabat (Morocco). Patients with at least two intracranial aneurysms (IAs) confirmed on angiography were included in the study.

Results

Fifty-nine patients were harboring 128 IAs. The most frequent patient-level risk factors were arterial hypertension (AHT) 30.5 % (n = 18) and smoking status 22.0 % (n = 13). A PHASES score recommended treatment in 52 of 60 ruptured aneurysms and in six of 68 UIAs with a sensitivity of 31.67% and a specificity of 76.47%. UIATS recommended treatment in 26 of 62 ruptured aneurysms and in 35 of 55 UIAs with a sensitivity of 41.9% and a specificity of 63.6%. Aspect ratio recommended treatment in 60 of 60 ruptured aneurysms and in 63 of 68 UIAs with a sensitivity of 100% and a specificity of 88.24%. Dome-to-neck ratio recommended treatment in 45 of 60 ruptured aneurysms and in 48 of 68 UIAs with a sensitivity of 80% and a specificity of 63.24%. The aspect ratio (area under the curve [AUC] = 0.953) AUC > 0.8 has a higher discriminatory power between ruptured aneurysms and UIAs.

Conclusion

AHT and smoking status were the most common risk factors for intracranial multiple aneurysms and the aspect ratio and PHASES score were the most powerful discrimination tools between ruptured aneurysms and the UIAs.

Trends in the Age of Patients Treated for Unruptured Intracranial Aneurysms from 1990 to 2020

BACKGROUND

The decision for treatment for unruptured intracranial aneurysms (UIAs) is often difficult. Innovation in endovascular devices have improved the benefit-to-risk profile especially for elderly patients; however, the treatment guidelines from the past decade often recommend conservative management. It is unknown how these changes have affected the overall age of the patients selected for treatment. Herein, we aimed to study potential changes in the average age of the patients that are being treated over time.

METHODS

A systematic search of the literature was performed to identify all studies describing the age of the UIAs that were treated by any modality. Scatter diagrams with trend lines were used to plot the age of the patients treated over time and assess the presence of a potential significant trend via statistical correlation tests.

RESULTS

A total of 280 studies including 83,437 UIAs treated between 1987 and 2021 met all eligibility criteria and were entered in the analysis. Mean age of the patients was 55.5 years, and 70.7% were female. There was a significant increasing trend in the age of the treated patients over time (Spearman r: 0.250; P < 0.001), with a 1-year increase in the average age of the treated patients every 5 years since 1987.

CONCLUSIONS

The present study indicates that based on the treated UIA patient data published in the literature, older UIAs are being treated over time. This trend is likely driven by safer treatments while suggesting that re-evaluation of certain UIA treatment decision scores may be of great interest.

StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm

Intracranial Aneurysms (IA) present a complex challenge for neurosurgeons as the risks associated with surgical intervention, such as Subarachnoid Hemorrhage (SAH) mortality and morbidity, may outweigh the benefits of aneurysmal occlusion in some cases. Hence, there is a critical need for developing techniques that assist physicians in assessing the risk of aneurysm rupture to determine which aneurysms require treatment. However, a reliable IA rupture risk prediction technique is currently unavailable. To address this issue, this study proposes a novel approach for aneurysm segmentation and multidisciplinary rupture prediction using 2D Digital Subtraction Angiography (DSA) images. The proposed method involves training a fully connected convolutional neural network (CNN) to segment aneurysm regions in DSA images, followed by extracting and fusing different features using a multidisciplinary approach, including deep features, geometrical features, Fourier descriptor, and shear pressure on the aneurysm wall. The proposed method also adopts a fast correlation-based filter approach to drop highly correlated features from the set of fused features. Finally, the selected fused features are passed through a Decision Tree classifier to predict the rupture severity of the associated aneurysm into four classes: Mild, Moderate, Severe, and Critical. The proposed method is evaluated on a newly developed DSA image dataset and on public datasets to assess its generalizability. The system's performance is also evaluated on DSA images annotated by expert neurosurgeons for the rupture risk assessment of the segmented aneurysm. The proposed system outperforms existing state-of-the-art segmentation methods, achieving an 85 % accuracy against annotated DSA images for the risk assessment of aneurysmal rupture.

Toward Grading Subarachnoid Hemorrhage Risk Prediction: A Machine Learning-Based Aneurysm Rupture Score

OBJECTIVE

Existing approaches neither provide an accurate prediction of subarachnoid hemorrhage (SAH) nor offer a quantitative comparison among a group of its risk factors. To evaluate the population, hypertension, age, size, earlier subarachnoid hemorrhage, and location (PHASES) and unruptured intracranial aneurysm treatment score (UIATS) scores and develop an Artificial Intelligence-based 5-year and lifetime aneurysmal rupture criticality prediction (ARCP) score for a set of risk factors.

METHODS

We design various location-specific and ensemble learning models to develop lifetime rupture risk, employ the longitudinal data to develop a linear regression-based model to predict an aneurysm's growth score, and use the Apriori algorithm to identify risk factors strongly associated with SAH. We develop ARCP by integrating output of Apriori algorithm and ML models and compare with PHASES and UIATS scores along with the scores of a multidisciplinary team of neurosurgeons.

RESULTS

The PHASES and UIATS scores show sensitivities of 22% and 35% and specificities of 76% and 79%, respectively. Location-specific models show precision and recall of 93% and 90% for the middle cerebral artery, 83% and 80% for the anterior communicating artery, and 80% and 80% for the supraclinoid internal carotid artery, respectively. The ensemble method shows both precision and recall of 80%. The validation of the models shows that ARCP performs better than our control group of neurosurgeons. Data-driven knowledge produces comparisons among 61 risk factor combinations, 11 ranked minor, 8 moderate, and 41 severe, and 1 of which is a critical factor.

CONCLUSIONS

The PHASES and UIATS are weak predictors, and the ARCP score can identify, and grade, risk factors associated with SAH.

RNA Expression Signatures of Intracranial Aneurysm Growth Trajectory Identified in Circulating Whole Blood

After detection, identifying which intracranial aneurysms (IAs) will rupture is imperative. We hypothesized that RNA expression in circulating blood reflects IA growth rate as a surrogate of instability and rupture risk. To this end, we performed RNA sequencing on 66 blood samples from IA patients, for which we also calculated the predicted aneurysm trajectory (PAT), a metric quantifying an IA's future growth rate. We dichotomized dataset using the median PAT score into IAs that were either more stable and more likely to grow quickly. The dataset was then randomly divided into training (n = 46) and testing cohorts (n = 20). In training, differentially expressed protein-coding genes were identified as those with expression (TPM > 0.5) in at least 50% of the samples, a q-value < 0.05 (based on modified F-statistics with Benjamini-Hochberg correction), and an absolute fold-change ≥ 1.5. Ingenuity Pathway Analysis was used to construct networks of gene associations and to perform ontology term enrichment analysis. The MATLAB Classification Learner was then employed to assess modeling capability of the differentially expressed genes, using a 5-fold cross validation in training. Finally, the model was applied to the withheld, independent testing cohort (n = 20) to assess its predictive ability. In all, we examined transcriptomes of 66 IA patients, of which 33 IAs were "growing" (PAT ≥ 4.6) and 33 were more "stable". After dividing dataset into training and testing, we identified 39 genes in training as differentially expressed (11 with decreased expression in "growing" and 28 with increased expression). Model genes largely reflected organismal injury and abnormalities and cell to cell signaling and interaction. Preliminary modeling using a subspace discriminant ensemble model achieved a training AUC of 0.85 and a testing AUC of 0.86. In conclusion, transcriptomic expression in circulating blood indeed can distinguish "growing" and "stable" IA cases. The predictive model constructed from these differentially expressed genes could be used to assess IA stability and rupture potential.

Profiling of Circulating Gene Expression Reveals Molecular Signatures Associated with Intracranial Aneurysm Rupture Risk

BACKGROUND

Following detection, rupture risk assessment for intracranial aneurysms (IAs) is critical. Towards molecular prognostics, we hypothesized that circulating blood RNA expression profiles are associated with IA risk.

METHODS

We performed RNA sequencing on 68 blood samples from IA patients. Here, patients were categorized as either high or low risk by assessment of aneurysm size (≥ 5 mm = high risk) and Population, Hypertension, Age, Size, Earlier subarachnoid hemorrhage, Site (PHASES) score (≥ 1 = high risk). Modified F-statistics and Benjamini-Hochberg false discovery rate correction was performed on transcripts per million-normalized gene counts. Protein-coding genes expressed in ≥ 50% of samples with a q value < 0.05 and an absolute fold-change ≥ 2 were considered significantly differentially expressed. Bioinformatics in Ingenuity Pathway Analysis was performed to understand the biology of risk-associated expression profiles. Association was assessed between gene expression and risk via Pearson correlation analysis. Linear discriminant analysis models using significant genes were created and validated for classification of high-risk cases.

RESULTS

We analyzed transcriptomes of 68 IA patients. In these cases, 31 IAs were large (≥ 5 mm), while 26 IAs had a high PHASES score. Based on size, 36 genes associated with high-risk IAs, and two were correlated with the size measurement. Alternatively, based on PHASES score, 76 genes associated with high-risk cases, and nine of them showed significant correlation to the score. Similar ontological terms were associated with both gene profiles, which reflected inflammatory signaling and vascular remodeling. Prediction models based on size and PHASES stratification were able to correctly predict IA risk status, with > 80% testing accuracy for both.

CONCLUSIONS

Here, we identified genes associated with IA risk, as quantified by common clinical metrics. Preliminary classification models demonstrated feasibility of assessing IA risk using whole blood expression.

Target Ultra and Nano coils in the endovascular treatment of small intracranial aneurysms (ULTRA Registry)

OBJECTIVE

The ULTRA Registry is a national multicenter prospective study designed to assess aneurysm occlusion rates and safety profiles of the Target Ultra and Nano coils in the treatment of small intracranial aneurysms (IAs).

METHODS

Patients with small (≤ 5 mm) ruptured and unruptured IAs were treated exclusively with Target Ultra and Nano coils. The primary endpoints were the initial rate of complete or near-complete aneurysm occlusion, aneurysm recurrence, and need for retreatment. Secondary endpoints were device- and procedure-related adverse events, hemorrhage from the coiled aneurysm at any time during follow-up, and clinical outcomes.

RESULTS

The ULTRA Registry included 100 patients with a mean ± SD age of 56 ± 11.6 years, of whom 75 were women and 48 presented after aneurysm rupture. The mean aneurysm size was (3.5 ± 0.9) × (2.8 ± 0.9) × (3.0 ± 1.0) mm, and the mean packing density was 34.4% ± 16.7%. Posttreatment complete or near-complete occlusion reported by an independent imaging core laboratory was seen in 92% of patients at baseline and in 87%, 87%, and 83% of patients at first, second, and final follow-up, respectively. At first, second, and final follow-up, 10%, 11%, and 15%, respectively, of patients were deemed to require retreatment. There were three procedural-related ischemic strokes and one intracranial hemorrhage from wire perforation of a parent artery not involved by the aneurysm. There were no coil-related adverse events, including no intraoperative aneurysm ruptures and no known aneurysm ruptures after coiling.

CONCLUSIONS

This assessment of aneurysm occlusion rates and safety profiles in ULTRA Registry study participants demonstrates excellent safety and efficacy profiles for Target Ultra and Nano coils in the treatment of small IAs.

Identification of ruptured intracranial aneurysms using the aneurysm-specific prediction score in patients with multiple aneurysms with subarachnoid hemorrhages- a Chinese population based external validation study

Background

For patients with aneurysmal subarachnoid hemorrhages (SAHs) and multiple intracranial aneurysms (MIAs), a simple and fast imaging method that can identify ruptured intracranial aneurysms (RIAs) may have great clinical value. We sought to use the aneurysm-specific prediction score to identify RIAs in patients with MIAs and evaluate the aneurysm-specific prediction score.

Methods

Between May 2018 and May 2021, 134 patients with 290 MIAs were retrospectively analyzed. All patients had an SAH due to IA rupture. CT angiography (CTA) was used to assess the maximum diameter, shape, and location of IAs to calculate the aneurysm-specific prediction score. Then, the aneurysm-specific prediction score was applied to RIAs in patients with MIAs.

Results

The IAs with the highest aneurysm-specific prediction scores had not ruptured in 17 (12.7%) of the 134 patients with 290 MIAs. The sensitivity, specificity, false omission rate, diagnostic error rate, and diagnostic accuracy of the aneurysm-specific prediction score were higher than those of the maximum diameter, shape, and location of IAs.

Conclusions

The present study suggests that the aneurysm-specific prediction score has high diagnostic accuracy in identifying RIAs in patients with MIAs and SAH, but that it needs further evaluation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-022-02727-w.