Age and morphology of posterior communicating artery aneurysms

Associations between morphological parameters and ruptured anterior communicating artery aneurysm: A propensity score-matched, single center, case-control study

BACKGROUND

To identify an association between morphological parameters and the rupture risk of anterior communicating artery (ACoA) aneurysms using propensity score matching (PSM).

METHODS

Data for 109 patients with ACoA aneurysms treated from January 2018 to October 2021 were reviewed; 94 patients were enrolled. The geometrical parameters of the ACoA aneurysms were measured and calculated using three-dimensional reconstructed digital subtraction angiography images. The aneurysms' morphological parameters were analyzed using a propensity score for six factors (age, sex, excess alcohol intake, smoking, hypertension, diabetes mellitus). Univariate logistic regression was used to analyze the relationship between the aneurysms' morphological parameters and rupture risk.

RESULTS

Twenty-five patients each with or without ruptured aneurysms were selected. After matching, no statistically significant differences were seen between the groups in their baseline characteristics. Aneurysm neck size (p = 0.038) was higher in the unruptured group than that in the ruptured group, and the dome-to-neck ratio (D/N; p = 0.009) and aspect ratio (AR; p = 0.003) were higher in the ruptured group than those in the unruptured group. Univariable logistic regression analysis demonstrated that ACoA aneurysm rupture was associated with AR (odds ratio: 8.047; 95% confidence interval: 1.569-41.213; p = 0.012) and D/N (odds ratio: 4.253; 95% confidence interval: 1.228-14.731; p = 0.022). The areas under the receiver operating characteristic curves for AR and D/N were 0.746 and 0.715, respectively.

CONCLUSIONS

After PSM, ACoA aneurysms with higher AR and D/N, and smaller neck size were more likely to rupture. AR may be a much more important predictor of aneurysm rupture than other predictors.

Geometric Features Associated with Middle Cerebral Artery Bifurcation Aneurysm Formation: A Matched Case-Control Study

OBJECTIVES

The pathogenesis of intracranial aneurysms is multifactorial and includes genetic, environmental, and anatomic influences. We aimed to identify image-based morphological parameters that were associated with middle cerebral artery (MCA) bifurcation aneurysms.

MATERIALS AND METHODS

We evaluated three-dimensional morphological parameters obtained from CT angiography (CTA) or digital subtraction angiography (DSA) from 317 patients with unilateral MCA bifurcation aneurysms diagnosed at the Brigham and Women's Hospital and Massachusetts General Hospital between 1990 and 2016. We chose the contralateral unaffected MCA bifurcation as the control group, in order to control for genetic and environmental risk factors. Diameters and angles of surrounding parent and daughter vessels of 634 MCAs were examined.

RESULTS

Univariable and multivariable statistical analyses were performed to determine statistical significance. Sensitivity analyses with smaller (≤ 3 mm) aneurysms only and with angles excluded, were also performed. In a multivariable conditional logistic regression model we showed that smaller diameter size ratio (OR 0.0004, 95% CI 0.0001-0.15), larger daughter-daughter angles (OR 1.08, 95% CI 1.06-1.11) and larger parent-daughter angle ratios (OR 4.24, 95% CI 1.77-10.16) were significantly associated with MCA aneurysm presence after correcting for other variables. In order to account for possible changes to the vasculature by the aneurysm, a subgroup analysis of small aneurysms (≤ 3 mm) was performed and showed that the results were similar.

CONCLUSIONS

Easily measurable morphological parameters of the surrounding vasculature of the MCA may provide objective metrics to assess MCA aneurysm formation risk in high-risk patients.

A Two-Gene-Based Diagnostic Signature for Ruptured Intracranial Aneurysms

Background: Ruptured intracranial aneurysm (IA) is a disease with high mortality. Despite the great progress in treating ruptured IA, methods for risk assessment of ruptured IA remain limited. Methods: In this study, we aim to develop a robust diagnostic model for ruptured IA. Gene expression profiles in blood samples of 18 healthy persons and 43 ruptured IA patients were obtained from the Gene Expression Omnibus (GEO). Differential expression analysis was performed using limma Bioconductor package followed by functional enrichment analysis via clusterProfiler Bioconductor package. Immune cell compositions in ruptured IA and healthy samples were assessed through the CIBERSORT tool. Protein-protein interaction (PPI) was predicted based on the STRING database. Logistic regression model was used for the construction of predictive model for distinguishing ruptured IA and healthy samples. Real-time quantitative polymerase chain reaction (RT-qPCR) was performed to validate the gene expression between the ruptured IA and healthy samples. Results: A total of 58 differentially expressed genes (DEGs) were obtained for ruptured IA patients compared with healthy controls. Functional enrichment analysis showed that the DEGs were enriched in biological processes related to neutrophil activation, neutrophil degranulation, and cytokine-cytokine receptor interaction. Notably, immune analysis results proved that the rupture of IA might be related to immune cell distribution. We further identified 24 key genes as hub genes using the PPI networks. The logistic regression model trained based on the 24 key genes ultimately retained two genes, i.e., IL2RB and CCR7, which had great potential for risk assessment for rupture of IA. The RT-qPCR further validated that compared with the healthy samples, the expression levels of IL2RB and CCR7 were decreased in ruptured IA samples. Conclusions: This study might be helpful for cohorts who have a high risk of ruptured IA for early diagnosis and prevention of the disease.

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask

Coincident with the tsunami of COVID-19–related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.