Impact analysis of primary and secondary research in radiology journals.

INTRODUCTION: Primary and secondary studies are considered the two major research categories. In this study, we examined the scientific and social media impact of primary and secondary publication types in papers published radiological journals during 2010-2020. MATERIALS AND METHODS: PubMed publication type tags were used to filter original articles and systematic review and meta-analysis (SR/MA) articles. Clarivate Web of Science was utilized to obtain a list of all radiology journals from the category "Radiology, Nuclear Medicine and Imaging" in Science Citation Index Expanded (SCIE). Automated approach was developed for programmatic extraction of bibliometric and Altmetric yearly citations of each included article using Dimensions API and Altmetric API with Python. Statistical analysis was performed to compare the citation rates between primary and secondary research articles. RESULTS: A total of 96,684 published articles from 2010 to 2020 were identified and their meta-data collected. The mean 2-year citation count following publication year was 5.8 for primary research and 10.2 for SR/MA articles (p < 0.001). Between 2010 and 2020, the mean number of citations per SR/MA article was 51.3 compared to 30.5 per primary research article (p < 0.001). Mean Altmetric score was 8.2 in SR/MA compared to 3.7 for primary research articles (p < 0.001). CONCLUSION: Secondary research studies have been increasing in impact in both academia and social media compared to primary research. Our results highlight the importance and impact of systematic reviews and meta-analysis articles as a scientifically influential study type in radiology.

Prediction of delayed cerebral ischemia followed aneurysmal subarachnoid hemorrhage. A machine-learning based study.

INTRODUCTION: Delayed Cerebral Ischemia (DCI) is a significant complication following aneurysmal subarachnoid hemorrhage (aSAH) that can lead to poor outcomes. Machine learning techniques have shown promise in predicting DCI and improving risk stratification. METHODS: In this study, we aimed to develop machine learning models to predict the occurrence of DCI in patients with aSAH. Patient data, including various clinical variables and co-factors, were collected. Six different machine learning models, including logistic regression, multilayer perceptron, decision tree, random forest, gradient boosting machine, and extreme gradient boosting (XGB), were trained and evaluated using performance metrics such as accuracy, area under the curve (AUC), precision, recall, and F1 score. RESULTS: After data augmentation, the random forest model demonstrated the best performance, with an AUC of 0.85. The multilayer perceptron neural network model achieved an accuracy of 0.93 and an F1 score of 0.85, making it the best performing model. The presence of positive clinical vasospasm was identified as the most important feature for predicting DCI. CONCLUSIONS: Our study highlights the potential of machine learning models in predicting the occurrence of DCI in patients with aSAH. The multilayer perceptron model showed excellent performance, indicating its utility in risk stratification and clinical decision-making. However, further validation and refinement of the models are necessary to ensure their generalizability and applicability in real-world settings. Machine learning techniques have the potential to enhance patient care and improve outcomes in aSAH, but their implementation should be accompanied by careful evaluation and validation.

Common design and data elements reported in active mechanical thrombectomy trials focusing on distal medium vessel occlusions and minor strokes: a systematic review.

BACKGROUND: Distal medium vessel occlusions (DMVOs) and minor strokes represent emerging frontiers in mechanical thrombectomy (MT). Although several randomized clinical trials (RCTs) are underway, the design characteristics of these trials and the specific questions they aim to address have not been extensively explored. This current study sought to investigate the design and data elements reported in active prospective DMVO and minor stroke studies. METHODS: The ClinicalTrials.gov database was searched for ongoing prospective studies assessing the role of MT in patients with DMVOs or minor strokes. The Nested Knowledge AutoLit platform was utilized to categorize reported outcomes and inclusion/exclusion criteria. Frequencies of reported data elements were extracted from study protocols. RESULTS: A total of 10 (8 DMVO and 2 minor stroke) studies enrolling 3520 patients were included. All DMVO studies employ different criteria regarding target occlusion locations. Five DMVO studies use stent retrievers as the first-line thrombectomy technique (62.5%, 5/8), while three studies allow any MT techniques, generally at the operator's discretion. Four DMVO studies permit intravenous thrombolysis (IVT) utilization in both intervention and control arms (50%, 4/8). The DISTALS trial excludes patients receiving IVT, while the DUSK trial and Tigertriever registry only enroll patients who are ineligible for IVT or for whom IVT failed to achieve reperfusion. DMVO studies exhibit notable heterogeneity in symptom onset duration thresholds for inclusion (<6 hours: 2 studies; <12 hours: 2 studies; <24 hours: 3 studies). Minor stroke trials employ similar inclusion criteria and outcome measures except for symptom duration thresholds for inclusion (8 hours for ENDOLOW and 23 hours for MOSTE). CONCLUSIONS: There is considerable heterogeneity among active DMVO trials regarding potential target DMVO locations and time thresholds for inclusion based on the last known well time. Furthermore, our review indicates that the utility of aspiration thrombectomy in DMVOs and the advantages of MT without IVT over IVT alone will remain largely unexplored even after completion of active DMVO trials.

Disability-free outcomes after mechanical thrombectomy: A systematic review and meta-analysis of the randomized controlled trials.

BACKGROUND: The modified Rankin Scale (mRS) score of ≤2 (functional independence) has been the most common primary endpoint of modern mechanical thrombectomy (MT) trials. However, unlike mRS 0-1, mRS score of 2 indicates disability. An important proportion of the mRS 2 patients are home dependent and report a significant decrease in their quality of life. PURPOSE: To investigate excellent outcome (mRS 0-1) rates after MT. METHODS: We systematically searched Ovid MEDLINE, Ovid EMBASE, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science databases. Randomized controlled trials (RCTs) investigating the role of modern MT in acute ischemic stroke were screened. Posterior circulation and large-core infarct trials were excluded from the study. The data regarding excellent outcomes (mRS: 0-1), functional independence (mRS: 0-2), and reperfusion were collected. RESULTS: Twenty-two RCTs comprising 5692 patients were included in the meta-analysis. The overall mRS 0-1 rate was 31.24% (95% CI = 26.95-36.2). The rate of successful reperfusion was 81.8% (95% CI = 77.93-85.86). MT achieved significantly higher rates of mRS 0-1 compared to standard care alone (OR = 2.04; 95% CI = 1.64-2.55; P-value < 0.001), with no heterogeneity detected among studies (I2= 0%; P-value = 0.52). The direct MT and MT plus intravenous thrombolytic treatment groups' excellent outcome rates were comparable (OR = 0.98; 95% CI = 0.82-1.18; P-value = 0.863). Also, aspiration and stent retriever thrombectomy techniques provided similar excellent outcome rates (OR = 0.76; 95% CI = 0.55-1.05; P-value = 0.141). CONCLUSIONS: Our results prove the additional benefit of MT over standard care, using a stricter definition for favorable functional outcome. Nearly one-third of patients presenting with large artery occlusion and treated with MT had no disability at 90 days. While this is remarkable, our results also indicate that reperfusion alone is often not enough to prevent disability and underline the need for better neuroprotection strategies.

Journal selection guide for radiology case reports.

Case reports and interesting images are valuable contributions to the radiology literature as they provide unique insights into uncommon conditions and rare presentations. Additionally, they serve as a rapidly expanding live image atlas and, therefore, can help radiologists to improve their diagnostics skills. However, due to high rejection rates and an increasing number of predatory publishers, publishing radiology case reports remains a daunting task for junior researchers. To overcome these challenges and ensure timely dissemination of their research findings, authors should consider several factors when selecting a target journal for case report publications. In this primer, we have summarized key considerations in journal selection and highlighted reputable journals that welcome radiology case reports.

Region-specific interobserver agreement of the Alberta Stroke Program Early Computed Tomography Score: A meta-analysis.

BACKGROUND AND PURPOSE: The Alberta Stroke Program Early CT Score (ASPECTS) is a widely used scoring system for evaluating ischemic stroke to determine therapeutic strategy. However, there is variation in the interobserver agreement of ASPECTS. This systematic review and meta-analysis aimed to investigate the interobserver agreement of total and regional ASPECTS. METHODS: A comprehensive search was conducted in the Web of Sciences, PubMed, and Scopus databases to identify relevant studies. Inclusion criteria were studies of noncontrast CT performed within 24 hours of ischemic stroke in the middle cerebral artery territory. RESULTS: A total of 20 studies, with 3482 patients, reporting interobserver agreement of total and regional ASPECTS were included in the meta-analysis. The interobserver agreement for total ASPECTS in studies using Kappa coefficient (κ) analysis was substantial (κ = .67, 95% confidence interval [CI]: .57-.78). In studies using intraclass correlation coefficient (ICC) analysis, agreement was excellent (ICC = .84, 95% CI: .77-.90). Interobserver agreement was higher in studies in which the observer was unblinded to clinical scenario in both groups (κ = .74, 95% CI: .59-.89, and ICC = .82, 95% CI: .79-.85). Per-region analysis showed that the caudate nucleus had the highest agreement (κ = .68, 95% CI: .60-.76, and ICC = .84, 95% CI: .74-.93), while M2 and internal capsule in Kappa studies (κ = .45, 95% CI: .34-.55 and κ = .47, 95% CI: .28-.66), and M4 and internal capsule in ICC studies (ICC = .54, 95% CI: .43-.64 and ICC = .55, 95% CI: .18-.91) had the lowest agreement. CONCLUSION: This meta-analysis demonstrates substantial to excellent interobserver agreement for total ASPECTS, which supports using this method for stroke treatment. However, findings emphasize the need to consider interobserver agreement in specific regions of ASPECTS for treatment decisions.

National Institutes of Health grant funding for cerebrovascular diseases.

BACKGROUND: Federal research funding is highly sought after but may be challenging to attain. A clear understanding of funding for specific diseases, such as cerebrovascular disorders, might help researchers regarding which National Institutes of Health (NIH) institutes fund research into specific disorders and grant types. OBJECTIVE: To examine the current scope of NIH grant funding for cerebrovascular conditions. METHODS: The NIH-developed RePORTER was used to extract active NIH-funded studies related to cerebrovascular diseases through January 2023. Duplicate studies were removed, and projects were manually screened and labeled in subcategories as clinical and basic science and as research subcategories. Extracted data included total funding, grant types, institutions that received funding, and diseases studied. Python (version 3.9) and SciPy library were used for statistical analyses. RESULTS: We identified 1232 cerebrovascular projects across seven diseases with US$699 952 926 in total funding. The cerebrovascular diseases with the greatest number of grants were ischemic stroke (705, or 57.2% of all funded projects), carotid disease (193, or 15.7%), and hemorrhagic stroke (163, or 13.2%). R01 grants were the most common mechanism of funding (632 grants, or 51.3%). The National Institute of Neurological Disorders and Stroke (NINDS) funded the most projects (504 projects; US$325 536 405), followed by the National Heart, Lung, and Blood Institute (NHLBI) (376 projects; US$216 784 546). CONCLUSION: Cerebrovascular disease receives roughly US$700 million in NIH funding. Ischemic stroke accounts for the majority of NIH-funded cerebrovascular projects, and R01 grants are the most common funding mechanism. Notably, NHLBI provides a large proportion of funding, in addition to NINDS.

Quantitative image signature and machine learning-based prediction of outcomes in cerebral cavernous malformations.

PURPOSE: There is increasing interest in novel prognostic tools and predictive biomarkers to help identify, with more certainty, cerebral cavernous malformations (CCM) susceptible of bleeding if left untreated. We developed explainable quantitative-based machine learning models from magnetic resonance imaging (MRI) in a large CCM cohort to demonstrate the value of artificial intelligence and radiomics in complementing natural history studies for hemorrhage and functional outcome prediction. MATERIALS AND METHODS: One-hundred-eighty-one patients from a prospectively registered cohort of 366 adults with CCM were included. Fluid attenuated inversion recovery (FLAIR) T2-weighted brain images were preprocessed, and CCM and surrounding edema were segmented before radiomic feature computation. Minority class oversampling, dimensionality reduction and feature selection methods were applied. With prospective hemorrhage as primary outcome, machine learning models were built, cross-validated, and compared using clinico-radiologic, radiomic, and combined features. SHapley Additive exPlanations (SHAP) was used for interpretation to determine the radiomic features with most contribution to hemorrhage prediction. RESULTS: The highest performances in hemorrhage predictions on the test set were combining radiomic and clinico-radiological features with an area under the curve (AUC) of 83% using linear regression and selected features, and an F1 score of 61% and 85% sensitivity using K-nearest neighbors with principal component analysis (PCA). Multilayer perceptron had the best performance predicting modified Rankin Scale ≥ 2 with an AUC of 74% using PCA derived features. For interpretation of the selected radiomic signature XGBoost model, Shapley additive explanations highlighted 6 radiomic features contributing the most to hemorrhage prediction. CONCLUSION: Quantitative image-based modeling using machine learning has the potential to highlight novel imaging biomarkers that predict hemorrhagic and functional outcomes, ensuring more precise and personalized care for CCM patients.

Machine learning prediction of hospital discharge disposition for inpatients with acute ischemic stroke following mechanical thrombectomy in the United States.

BACKGROUND AND PURPOSE: Predicting patient recovery and discharge disposition following mechanical thrombectomy remains a challenge in patients with ischemic stroke. Machine learning offers a promising prognostication approach assisting in personalized post-thrombectomy care plans and resource allocation. As a large national database, National Inpatient Sample (NIS), contain valuable insights amenable to data-mining. The study aimed to develop and evaluate ML models predicting hospital discharge disposition with a focus on demographic, socioeconomic and hospital characteristics. MATERIALS AND METHODS: The NIS dataset (2006-2019) was used, including 4956 patients diagnosed with ischemic stroke who underwent thrombectomy. Demographics, hospital characteristics, and Elixhauser comorbidity indices were recorded. Feature extraction, processing, and selection were performed using Python, with Maximum Relevance - Minimum Redundancy (MRMR) applied for dimensionality reduction. ML models were developed and benchmarked prior to interpretation of the best model using Shapley Additive exPlanations (SHAP). RESULTS: The multilayer perceptron model outperformed others and achieved an AUROC of 0.81, accuracy of 77 %, F1-score of 0.48, precision of 0.64, and recall of 0.54. SHAP analysis identified the most important features for predicting discharge disposition as dysphagia and dysarthria, NIHSS, age, primary payer (Medicare), cerebral edema, fluid and electrolyte disorders, complicated hypertension, primary payer (private insurance), intracranial hemorrhage, and thrombectomy alone. CONCLUSION: Machine learning modeling of NIS database shows potential in predicting hospital discharge disposition for inpatients with acute ischemic stroke following mechanical thrombectomy in the NIS database. Insights gained from SHAP interpretation can inform targeted interventions and care plans, ultimately enhancing patient outcomes and resource allocation.

Middle meningeal artery embolization for chronic subdural hematoma: A single-center experience and predictive modeling of outcomes.

BACKGROUND: Remarkable interest is rising around middle meningeal artery embolization (MMAE) as an emerging alternative therapy for chronic subdural hematoma (cSDH). The study aims to highlight a large center experience and the variables associated with treatment failure and build experimental machine learning (ML) models for outcome prediction. MATERIAL AND METHODS: A 2-year experience in MMAE for managing patients with chronic subdural hematoma was analyzed. Descriptive statistical analysis was conducted using imaging and clinical features of the patients and cSDH, which were subsequently used to build predictive models for the procedure outcome. The modeling evaluation metrics were the area under the ROC curve and F1-score. RESULTS: A total of 100 cSDH of 76 patients who underwent MMAE were included with an average follow-up of 6 months. The intervention had a per procedure success rate of 92%. Thrombocytopenia had a highly significant association with treatment failure. Two patients suffered a complication related to the procedure. The best performing machine learning models in predicting MMAE failure achieved an ROC-AUC of 70%, and an F1-score of 67%, including all patients with or without surgical intervention prior to embolization, and an ROC-AUC of 82% and an F1-score of 69% when only patients who underwent upfront MMAE were included. CONCLUSION: MMAE is a safe and minimally invasive procedure with great potential in transforming the management of cSDH and reducing the risk of surgical complications in selected patients. An ML approach with larger sample size might help better predict outcomes and highlight important predictors following MMAE in patients with cSDH.

Embolization of arteriovenous malformations of head and neck: A systematic review.

BACKGROUND: Cervicofacial arteriovenous malformations (AVMs) are a significant source of morbidity. Endovascular embolization has emerged as a promising treatment technique for these lesions. However, current literature on cervicofacial AVM embolization mostly consists of single-agent oriented case series, and to date, no comprehensive study has compared the outcomes of available embolic agents. PURPOSE: To investigate the performance of different embolic agents in the management of cervicofacial AVMs. METHODS: We systematically searched Ovid MEDLINE, Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials Scopus, and Web of Science. Studies providing data for the endovascular treatment of cervicofacial AVMs were included. The data regarding complication and cure rates were collected for each embolic agent. Pooled event rates were presented as descriptive statistics. RESULTS: Eleven studies comprising 204 patients were included in the review. The overall complete and partial cure rates were 62.2% (127/204) and 36.2% (74/204), respectively. Embolization failed to achieve significant improvement in only 1.6% (3/204) of the patients. The complete cure rates were 87.5% (75-100%) for n-Butyl cyanoacrylate (NBCA, glue), 80.5% (61-100%) for Onyx (Medtronic, MN, USA), and 51.5% (18-85%) for ethanol. The overall complication rate was 30% (61/204). The complication rates were 33% (12.5-53%) for ethanol, 14% (0-28%) for Onyx, and 0% for NBCA. CONCLUSIONS: Our systematic review supports that endovascular embolization is an effective treatment option for cervicofacial AVMs. In our review, the use of Onyx and NBCA was associated with consistently high complete cure rates and a promising safety profile. However, more research is needed to investigate the use of different embolic agents in the treatment of cervicofacial AVMs.

The Effect of the COVID-19 Pandemic on Academic Research Gender Disparities in Radiology.

RATIONALE AND OBJECTIVES: Gender disparities have long existed in radiology. The COVID-19 pandemic disrupted research activities worldwide and have impacted gender disparities across medical specialties. This study investigates the effect of the COVID-19 pandemic on gender disparities in radiology academic authorship. MATERIALS AND METHODS: A retrospective observational study was conducted using data from 110 843 global and 23 977 US radiology articles. The gender of authors was determined using an automated gender inference tool. Descriptive statistics were applied to explore authorship changes overall globally, in the US as well as across countries and states. RESULTS: Female first-authorship increased globally from 16.9% to 17.6% (p < 0.001), and in the US, from 19.0% to 19.6% (p = 0.19) in the peri-COVID period. The combined female percentage increased from 19.7% to 20.0% globally (p = 0.021), and from 20.2% to 21.1% in the US (p = 0.006). Country-level analysis revealed significant increases in female authorship in Colombia, Denmark, Egypt, France, India, and Japan, while New Zealand demonstrated a decrease in female authorship. In the US, Florida, Indiana, Louisiana, Massachusetts, and Ohio experienced increases in female authorship, whereas South Carolina demonstrated a decrease in female authorship. CONCLUSION: In contrast with other medical fields, the study demonstrates that radiology experienced a slight increase in female authorship in radiology research globally and in the US during the COVID period. While the pandemic may have influenced these findings, further research is needed to establish regional causal relationships and identify best practices for promoting gender equity in radiology research.

Influential Radiology Figures and Organizations in Social Media.

PURPOSE: With social media becoming a vibrant hub for the radiology community, highlighting expert leaders and trustful conduits of information in the virtual field is proving crucial. The aim of this study was to identify and describe the most prominent and influential figures and organizational accounts to follow in radiology. METHODS: Influence scores for the topic "radiology" on Twitter (now known as X) were computed using the Right Relevance machine learning service. Top influencers were classified according to gender, geography, physician degree, areas of influence, subspecialization, influence score, title, affiliated institution, dual degree, medical school origin, content type, and research activity. Statistical analysis was performed assessing variable correlations. RESULTS: In the top quartile of influential figures, 87% were physicians, 60% men, and 93% located in the United States. Prevalent backgrounds included neuroradiology (21%), abdominal imaging (12%), and artificial intelligence (11%). Of the top 100 figures, 81% were US graduates, 97% held medical degrees, and 28% had dual degrees. Fifty-eight percent provided educational content. A majority held leadership positions (58%) and academic professorship titles (70%). The median h index, publication number, and citation number were 14, 49, and 881, respectively. No significant correlation was noted between influence score and academic rank or research output. CONCLUSIONS: Virtual presence is becoming integral to health care professions and academic spheres, unfolding great potential for enhancing the sense of belonging, advocacy, recruitment, and fostering new relationships. Having a core of influential leaders and organizations to follow can serve as a resource for the community members and aspiring students building a positive connected basis for radiology's thriving future.

Automated CT angiography collateral scoring in anterior large vessel occlusion stroke: A multireader study.

BACKGROUND: Computed tomography (CT) angiography collateral score (CTA-CS) is an important clinical outcome predictor following mechanical thrombectomy for ischemic stroke with large vessel occlusion (LVO). The present multireader study aimed to evaluate the performance of e-CTA software for automated assistance in CTA-CS scoring. MATERIALS AND METHODS: Brain CTA images of 56 patients with anterior LVO were retrospectively processed. Twelve readers of various clinical training, including junior neuroradiologists, senior neuroradiologists, and neurologists graded collateral flow using visual CTA-CS scale in two sessions separated by a washout period. Reference standard was the consensus of three expert readers. Duration of reading time, inter-rater reliability, and statistical comparison of readers' performance metrics were analyzed between the e-CTA assisted and unassisted sessions. RESULTS: e-CTA assistance resulted in significant increase in mean accuracy (58.6% to 67.5%, p = 0.003), mean F1 score (0.574 to 0.676, p = 0.002), mean precision (58.8% to 68%, p = 0.007), and mean recall (58.7% to 69.9%, p = 0.002), especially with slight filling deficit (CTA-CS 2 and 3). Mean reading time was reduced across all readers (103.4 to 59.7 s, p = 0.001), and inter-rater agreement in CTA-CS assessment was increased (Krippendorff's alpha 0.366 to 0.676). Optimized occlusion laterality detection was also noted with mean accuracy (92.9% to 96.8%, p = 0.009). CONCLUSION: Automated assistance for CTA-CS using e-CTA software provided helpful decision support for readers in terms of improving scoring accuracy and reading efficiency for physicians with a range of experience and training backgrounds and leading to significant improvements in inter-rater agreement.

Operator assessment versus core laboratory adjudication of recanalization following endovascular treatment of acute ischemic stroke: a systematic review and meta-analysis.

BACKGROUND: Successful recanalization after endovascular thrombectomy serves as the primary endpoint in clinical trials and is a crucial predictor of long-term outcomes. Radiographic outcomes for various interventions have been shown to vary based on the type of interpreter, including the site interventionalist compared with an independent reader. OBJECTIVE: To compare angiographic outcomes in stroke thrombectomy procedures based on the type of reader. METHODS: A systematic literature search was conducted in Medline, EMBASE, Scopus, and Web-of-Science through February 2022. We included primary studies that reported core laboratory-read and operator angiographic outcomes after mechanical thrombectomy for ischemic stroke. Furthermore, study-defined successful recanalization data were collected. RESULTS: Eight studies were included with 4797 patients, 51.2% of whom were male. Four thousand, four hundred and thirty-one patients had core readings, and 4211 had operator readings. Study-defined successful recanalization was significantly higher for operator (84%, 3543/4211) examinations than for core laboratory-read (78.4%, 3476/4431) examinations (p<0.001; OR=1.462, 95% CI 1.175 to 1.819). The modified Thrombolysis in Cerebral Infarction (mTICI) scale score of ≥2 b was higher for operator (85%, 3341/3929) examinations than for core laboratory-read (78.6%, 3107/3952) examinations (p<0.001; OR=1.349, 95% CI 1.071 to 1.701). mTICI 3 was significantly higher for operator (54.6%, 1000/1832) examinations than for core laboratory-read (39.9%, 731/1832) examinations (p<0.001; OR=1.823, 95% CI 1.598 to 2.081). CONCLUSION: Operator angiographic reads are statistically significantly higher than core laboratory-read readings following stroke thrombectomy, especially for complete recanalization. These differences should be considered when interpreting reports of angiographic outcomes after thrombectomy.

Common Data Elements Analysis of Mechanical Thrombectomy Clinical Trials for Acute Ischemic Stroke with Large Core Infarct.

BACKGROUND: Clinical trials addressing large core acute ischemic stroke (AIS) are ongoing across multiple international groups. Future development of clinical guidelines depends on meta-analyses of these trials calling for a degree of homogeneity of elements across the studies. This common data element study aims to provide an overview of key features of pertinent large core infarct trials. METHODS: PubMed and ClinicalTrials.gov databases were screened for published and ongoing clinical trials assessing mechanical thrombectomy in patients with AIS with large core infarct. Nested Knowledge AutoLit living review platform was utilized to categorize primary and secondary outcomes as well as inclusion and exclusion criteria for patient selection in the trials. RESULTS: The most reported data element was ASPECTS score but with varied definitions of what constitutes large core. Non-utility-weighted modified Rankin score (mRS) was reported in 6/7 studies as the primary outcome, while the utility-weighted mRS was the outcome of interest in the TESLA trial, all of them at the 3 months mark, with only LASTE looking for mRS shift at the 6 months mark. Secondary outcomes had more variations. Mortality is reported separately only in 4/7 trials, all at the 3­month mark. Additionally, the TENSION trial reported the frequency of serious adverse events, including mortality, at the 1­week and 12-month mark. DISCUSSION: Overall, in large core trials there is a large degree of heterogeneity in the collected data elements. Differences in definition and timepoints render reaching a unified standard difficult, which hinders high quality meta-analyses and cohesive evidence-driven synthesis.

Long-term outcomes of flow diversion for unruptured intracranial aneurysms: a systematic review and meta-analysis.

BACKGROUND: Flow diverters have been widely used in clinical practice for more than a decade. However, most outcome data are limited to 1 year timepoints. This study aims to offer meta-analysis data on long-term (>1 year) safety and effectiveness results for patients with aneurysms treated with flow diverters. METHODS: PubMed, Web of Science, Embase, and SCOPUS were searched up to February 24, 2022 using the AutoLit platform. We included primary studies assessing the long-term outcomes for flow diverter devices to manage unruptured internal carotid artery aneurysms with a follow-up period of >1 year. The meta-analysis was carried out using Comprehensive Meta-Analysis software (CMA). RESULTS: Eleven studies were included in the meta-analysis. The pooled occlusion rates after flow diversion treatment for unruptured intracranial brain aneurysms were 77%, 87.4%, 84.5%, 89.4%, 96% for 1 year, 1-2 years, 2 years, 3 years, and 5 years follow-up, respectively. The in-stent stenosis rate was 4.8% and the retreatment rate for the long-term follow-up period was 5%. No delayed rupture of the aneurysm was reported, and there was one case of delayed ischemic stroke. The sensitivity analysis of the prospective studies showed a complete occlusion rate of 83.5% and 85.2% for 1 and 3 years of follow-up, respectively. CONCLUSION: Flow diverters are safe and effective in short- and long-term follow-up and rarely cause serious delayed side effects.

The Prophylactic Use of Glycoprotein 2b/3a Inhibitors in the Endovascular Treatment of Intracranial Aneurysms: A Systematic Review and Meta-Analysis.

BACKGROUND: There has been a growing interest in the use of Glycoprotein 2b/3a (GP2B3A) inhibitors in neuroendovascular procedures. However, clinical evidence for their prophylactic use is still sparse. In this review, we aimed to assess the safety and efficacy of prophylactic GP2B3A inhibitor use and to compare the performance of GP2B3A inhibitors with oral dual antiplatelet (DAP) treatment in intracranial aneurysm patients treated with stent-assisted coil embolization or flow diversion. METHODS: A systematic literature search was conducted in Ovid MEDLINE, Ovid Embase, Scopus, Web of Science, and Cochrane Central Register of Clinical Trials databases. Data collected included hemorrhagic and thromboembolic complication rates, mortality, good functional outcome, and rupture status. A random-effects model was fit for each outcome measure. RESULTS: Thirteen studies comprising 1429 patients were included. The overall hemorrhagic complication rate of the GP2B3A cohort was 3.98% (95% confidence interval [CI] = 1.58-7.42). The subgroup analysis comparing ruptured versus unruptured aneurysms in which GP2B3A antagonists were used did not show a significant difference in hemorrhagic complication rates (P-value = 0.504). Compared with the DAP group, the GP2B3A inhibitor cohort had significantly lower hemorrhagic complication rates (odds ratio = 0.33; 95% CI = 0.13-0.85; P-value = 0.022). The thromboembolic complication rates were 6.63% (95% CI = 3.44-10.75) for the GP2B3A inhibitor group and 10.4% (95% CI = 7-13.8) for the DAP group. However, the difference was not statistically significant (odds ratio = 0.52; 95% CI = 0.22-1.24; P-value = 0.142). CONCLUSIONS: Our results support that GP2B3A inhibitors are safe and effective in preventing ischemic complications associated with the endoluminal devices. Additionally, our findings indicate that GP2B3A inhibitors can be utilized as prophylactic agents regardless of the rupture status.

Interpretable Machine Learning Modeling for Ischemic Stroke Outcome Prediction.

Background and Purpose: Mechanical thrombectomy greatly improves stroke outcomes. Nonetheless, some patients fall short of full recovery despite good reperfusion. The purpose of this study was to develop machine learning (ML) models for the pre-interventional prediction of functional outcome at 3 months of thrombectomy in acute ischemic stroke (AIS), using clinical and auto-extractable radiological information consistently available upon first emergency evaluation. Materials and Methods: A two-center retrospective cohort of 293 patients with AIS who underwent thrombectomy was analyzed. ML models were developed to predict dichotomized modified Rankin score at 90 days (mRS-90) using clinical and imaging features, both separately and combined. Conventional and experimental imaging biomarkers were quantified using automated image-processing software from non-contract computed tomography (CT) and computed tomography angiography (CTA). Shapley Additive Explanation (SHAP) was applied for model interpretability and predictor importance analysis of the optimal model. Results: Merging clinical and imaging features returned the best results for mRS-90 prediction. The best performing classifier was Extreme Gradient Boosting (XGB) with an area under the receiver operating characteristic curve (AUC) = 84% using selected features. The most important classifying features were age, baseline National Institutes of Health Stroke Scale (NIHSS), occlusion side, degree of brain atrophy [primarily represented by cortical cerebrospinal fluid (CSF) volume and lateral ventricle volume], early ischemic core [primarily represented by e-Alberta Stroke Program Early CT Score (ASPECTS)], and collateral circulation deficit volume on CTA. Conclusion: Machine learning that is applied to quantifiable image features from CT and CTA alongside basic clinical characteristics constitutes a promising automated method in the pre-interventional prediction of stroke prognosis. Interpretable models allow for exploring which initial features contribute the most to post-thrombectomy outcome prediction overall and for each individual patient outcome.

Bibliometric Trends in Open Surgical and Endovascular Cerebrovascular Research.

The last decade has witnessed a major expansion in endovascular interventions concurrent with a contraction of open neurovascular surgeries. Whether research efforts have also shifted from open to endovascular neurosurgery is an effect that has not been explored extensively. Understanding the bibliometric trend is important for researchers, funding agencies, and publishing journals. The aim of this review is to explore this potential shift. We compared the bibliometrics of open cerebrovascular and endovascular research articles published in two neurosurgical journals (Journal of Neurosurgery, Neurosurgery) and two neuroradiological journals (Journal of Neurointerventional Surgery, American Journal of Neuroradiology). Data were collected between September 26, 2021, and October 18, 2021. Articles published in 2011, 2013, 2015, 2017, and 2019 from the journals were screened. Neurovascular articles were classified into open surgical, endovascular, or mixed. Bibliometric parameters were collected via SCOPUS and journals' websites. A total of 8,018 articles were screened, of which 1,551 were included (16.2% open, 62.2% endovascular, 21.5% mixed). Most articles were related to aneurysms (76%). Open-access status correlated with increased citations (p<0.001) and Altmetric (p<0.001), which measures online activity. Comparing 2011 and 2019, the article distribution (open/endovascular/mixed) has changed significantly (χ2 test, p=0.002), with open articles dropping from 23.6% (68/288) to 12.9% (44/342) and endovascular articles rising from 56.6% (163/288) to 65.8% (225/342). Using the Kruskal-Wallis test, the citation distribution is different across the three groups in 2019 (p<0.001), favoring endovascular articles, but not in the other years. Our study suggests a trend of diminishing open neurovascular research output and increasing endovascular research output, in terms of both the number of articles and the citations. More time for citation accumulation may be required to verify this trend.