Jabal, Mohamed Sobhi

Background The Woven EndoBridge (WEB) device represents an innovative solution for cerebral aneurysm occlusion, particularly for challenging wide-neck bifurcation aneurysms. However, factors affecting sustained occlusion remain poorly understood. We utilized machine learning to attempt to identify predictors of favorable long-term outcomes following WEB treatment. Methods In this multicenter retrospective study, we collected patient demographics, aneurysm characteristics, procedural details, and clinical outcomes. The primary endpoint was improvement in occlusion status, defined as maintained Raymond-Roy Occlusion Classification (RROC) grade 1, or improvement from grade 2 to 1, or from grade 3 to either 2 or 1 on final angiographic follow up. The dataset was split into training (75%) and validation (25%) sets. The CatBoost algorithm was selected based on performance metrics, with Shapley Additive exPlanations (SHAP) values calculated to determine feature importance. Furthermore, a multivariable binomial logistic regression model was performed to validate machine learning findings. Results Among 720 aneurysms from 36 hospitals, 84% showed improvement in occlusion at follow up. Both machine learning and multivariable logistic regression identified aneurysm height as the most consistent correlate of nonimprovement (odds ratio (OR) 0.90 per mm, p = 0.022). In the CatBoost model, the highest-ranking features by SHAP included aneurysm height, patient age, treatment acuity, ACom location, WEB-SLS device, bifurcation anatomy, aneurysm multiplicity, baseline modified Rankin Scale, access route, and partial thrombosis. Conclusions Machine-learning and regression analyses identified consistent predictors of occlusion improvement after WEB treatment, with aneurysm height most strongly linked to nonimprovement. These insights may guide patient selection and follow up. Findings require cautious interpretation and external validation in larger cohorts.

Purpose Aneurysmal rupture and subarachnoid hemorrhage (SAH) have an exceptionally high mortality and morbidity burden. The aim of this study was to develop interpretable machine learning models for predicting short-term poor outcomes defined by the National Inpatient Sample Subarachnoid Hemorrhage Outcome Measure (NIS-SOM). Methods The National Inpatient Sample (NIS) database was queried from 2008 to 2018 to identify patients diagnosed with SAH who had undergone endovascular coiling or clipping for intracranial aneurysm. Demographic, comorbidity, risk factor, and hospital characteristic variables were recorded. Variables were preprocessed, and the feature space was reduced to include the most important features. To predict poor outcomes, machine learning models were trained and cross-validated before being evaluated on a separate testing set. Shapley Additive exPlanations of the best performing model was used for general and local model interpretation. Results Among 18,149 admissions (mean age 55 ± 14 years, 68.8% women), 52.9% had a poor outcome. Test-set AUCs ranged 0.74–0.80; a multilayer perceptron performed best (AUC 0.80, precision 0.74, recall 0.82). SHAP ranked the ten most influential variables: age, neurological comorbidity, paralysis, Medicare insurance, smoking status, Elixhauser burden, fluid-electrolyte disorders, weight loss, arrhythmia, and heart failure. Conclusions The modeling predicted nationwide aSAH prognosis with decent accuracy and highlighted clinical, socioeconomic, and system-level drivers of determinants of poor short-term outcome. These results support the potential of explainable ML tools as complementary tools for early risk stratification, guiding resource allocation, and informing prospective multi-center validation and implementation studies.

Background The impact of certain comorbidities on mechanical thrombectomy (MT) outcomes remains largely unexplored. Diabetes mellitus (DM) and admission hyperglycemia have been associated with poor clinical outcomes for patients treated with MT. In this study, we sought to investigate the effects of DM and admission hyperglycemia on MT outcomes. Methods Following PRISMA guidelines, a systematic literature search was conducted in Medline, Embase, Scopus, and Web of Science databases. Data regarding successful recanalization (modified Thrombolysis in Cerebral Infarction [mTICI] ≥2b), functional independence (modified Rankin Scale [mRS] 0–2), excellent outcomes (mRS 0–1), symptomatic intracranial hemorrhage (sICH), and mortality were extracted from the included studies. The pooled odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were calculated using random effects model. Results Twenty-one studies comprising 9708 patients were included. A total of 2311 patients (24%) had a history of DM, and 2026 patients (21%) had admission hyperglycemia. Admission hyperglycemia was associated with significantly lower odds of mTICI ≥2b (OR = 0.7, 95% CI = 0.55–0.89), mRS 0–2 (OR = 0.47, 95% CI = 0.41–0.53), and mRS 0–1 (OR = 0.43, 95% CI = 0.34–0.55) as compared to normoglycemic state. Patients with hyperglycemia had significantly higher rates of sICH (OR = 2.05, 95% CI = 1.66–2.54) and mortality (OR = 1.99, 95% CI = 1.58–2.52) than normoglycemic patients. Diabetes mellitus was associated with significantly high rates of mortality (OR = 1.74, 95% CI = 1.31–2.3) and lower rates of mRS 0–2 (OR = 0.60, 95% CI = 0.48–0.76) in sensitivity analyses. Conclusion Our results indicate that admission blood glucose levels and DM can negatively affect MT outcomes. Further research should focus on optimizing MT outcomes for these patients.

Background As the use of flow diverters has expanded in recent years, predicting successful outcomes has become more challenging for certain aneurysms. Objective To provide neurointerventionalists with an understanding of the available machine learning algorithms for predicting the success of flow diverters in occluding aneurysms. Methods This study followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the four major medical databases (PubMed, Embase, Scopus, Web of Science) were screened. The study included original research articles that evaluated the predictive abilities of various machine learning algorithms for determining the success of flow diverters in achieving aneurysm occlusion. Results Five studies out of 217 were included based on our criteria. The included studies used various variables (patient demographics, aneurysm and parent artery characteristics, flow diverter and hemodynamic-related features, and angiographic parametric imaging) to predict flow diverter treatment outcomes. The machine learning algorithms used, along with their respective accuracy rates, were as follows: logistic regression (61% and 85%), support vector machine (88%), Gaussian support vector machine (90%), linear support vector machine (85%), decision tree (80%), random forest (87%), k-nearest neighbors (83% and 85%), XGBoost (87%), CatBoost (86%), deep neural networks (77.9%), and recurrent neural networks (74%).Two studies trained the machine learning models with both all features and the most significant features. Both studies observed that the accuracy of machine learning models decreased by removing the insignificant features. Conclusion The current literature indicates that machine learning algorithms can be trained to predict the success of flow diverters with an accuracy of up to 90%.

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.

AbstractBackground and PurposeThe 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.MethodsA 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.ResultsA 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.ConclusionThis 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.

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 (I 2 = 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.

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.

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.