Citations

Abstract Background aneurysmal subarachnoid hemorrhage (aSAH) is neurological emergency associated with substantial mortality and disability. Current grading systems such as the modified Fisher Scale (mFS) and World Federation of Neurological Societies (WFNS) score, rely on semiquantitative and examination based assessments. Hence, they demonstrate limited predictive precision. The enhanced subarachnoid hemorrhage (eSAH) score is a simplified quantitative model integrating age, Glasgow Coma Scale (GCS), and cisternal subarachnoid hemorrhage volume (SAHV) to predict clinical outcomes after aSAH. Methods We performed a retrospective multicenter cohort study that included 1088 patients across three tertiary-care centers the United States. Predictive performance for unfavorable functional outcome, in-hospital mortality and delayed cerebral ischemia (DCI) was evaluated using receiver operating characteristic (ROC) analysis and area under the curve (AUC). Comparative analyses were performed and compared to the WFNS and mFS grading systems. Results the eSAH score demonstrated excellent discrimination for unfavorable functional outcome at discharge ( AUC 0.89 ) and in-hospital mortality (AUC 0.87). The DCI subscore demonstrated good discriminatory performance for predicting DCI (AUC 0.77). Compared with conventional grading systems, this was superior to both the WFNS (AUC 0.75) and the mFS ( AUC 0.70). increasing eSAH scores were additionally associated with progressively higher rates of mortality and unfavorable functional outcomes. Conclusion the eSAH score demonstrates strong external validity, reproducibility and superior predictive performance compared with conventional grading systems in a large multicenter cohort. These findings support the clinical utility of quantitative hemorrhage burden integration for early risk stratification in patients with aSAH.

Introduction: Spinal cord infarction (SCI) is a rare but serious neurological condition that accounts for 1-2% of all ischemic strokes and can lead to significant disability. Despite its clinical importance, existing studies are limited by small sample sizes that lack national generalizability, and little is known about post-discharge events such as hospital readmissions. We aimed to evaluate factors associated with all-cause hospital readmissions among hospitalizations with primary diagnosis of SCI, utilizing the Nationwide Readmissions Database (NRD). Methods: NRD data were abstracted from 2016 to 2022 including all hospitalizations of adult patients (≥18 years) with a primary diagnosis of SCI. Multiple-predictor logistic regression models were estimated to assess the association of demographic and clinical variables with all-cause 90-day readmissions. We included specific comorbidities often used in comorbidity scoring systems and added conditions or complications that are known to be associated with SCI. Statistically significant covariates from the unadjusted model were retained in the multiple-predictor model. Odds ratios (OR) with associated 95% Confidence intervals (CI) and p-values were reported. Results: From 2016 to 2022, there were an estimated 3321 hospitalizations with a primary diagnosis of SCI, out of which 27% (95% CI: 24-30%) were readmitted within 90 days. Demographic factors that were associated with lower odds of readmission included private insurance (vs. Medicare) and high-income quartile (Fig. 1). [AH1] [AAs2] Clinical factors that were associated with increased odds of readmission included autonomic dysfunction (OR = 5.44, 95% CI: 1.86 to 15.94), renal disease (OR = 3.57, 95% CI: 1.83 to 6.96), liver disease (OR = 2.40, 95% CI: 1.34 to 4.32), full clinical associations shown in Figure 2. Conclusion: We identified several clinical and demographic factors that were significantly associated with increased all-cause readmissions after SCI hospitalizations. Autonomic dysfunction and renal disease were among the top predictors of readmissions. We uncovered socioeconomic disparities in readmissions rates following SCI. This data may assist vascular neurologists, physicians and healthcare administrators in reducing readmissions after SCI and addressing these disparities. Additionally, our results can guide future studies focusing on the management of this rare entity.

Background We automated subarachnoid hemorrhage volume (SAHV) calculation with artificial intelligence (SAHVAI) and created 3‐dimensional volumetric images (SAHVAI‐3D) using noncontrast head computed tomography data for patients with aneurysmal SAH. We also defined 4‐dimensional SAHVAI (SAHVAI‐4D), representing SAHV over time. We aimed to compare automated SAHVAI values and computational times to manual SAHV measurement methods, explore the potential of imaging biomarkers to identify at‐risk brain regions for delayed cerebral ischemia and explore potential insights in future neurotherapeutic interventions for patient recovery after SAH. Methods A training set of 10 consecutive patients with aneurysmal SAH was used to manually compute SAHV, SAHVAI‐3D, and SAHVAI‐4D, including 92 noncontrast computed tomography scans (182 slices each). The SAHVAI deep learning algorithm generated automated SAHV values in cubic centimeters. A 3‐dimensional SAH brain map was created for each patient for the SAHVAI and manual evaluations. Blood volumetric outputs were analyzed and compared to neurologic outcomes at discharge, including delayed cerebral ischemia events, symptomatic vasospasm, and areas with the thickest SAH blood concentration. Results SAHVAI quantified SAHV in a mean of 6.7 seconds per scan, significantly faster than the manual method, which took >60 minutes per scan (Fisher exact test, P <0.001). SAHVAI demonstrated an accuracy of 99.8%, Dice score of 0.701, false‐positive rate of 0.0005, and negative predictive value of 0.999. The mean absolute error between SAHVAI and manual methods was 5.67 mL. SAHVAI‐3D brain map and total SAHV at admission were inversely associated with Glasgow Coma Scale (R 2 = 0.23, P = 0.017) and directly associated with length of hospital stay (R 2 = 0.175, P = 0.004), especially in regions with dense blood concentration. Conclusion SAHVAI‐3D and SAHVAI‐4D brain mapping techniques represent innovative imaging biomarkers for SAH. These advancements enable rapid evaluation and targeted interventions, potentially improving patient care in SAH management.

Background We developed a simplified ABC/2‐derived method to estimate total subarachnoid hemorrhage volume (SAHV) on noncontrast computed tomography in patients with aneurysmal SAH and compared the clinical and radiographic outcomes. Methods and Results In this retrospective observational cohort study, we analyzed 277 patients with SAH admitted to our Comprehensive Stroke Center between 2012 and 2022. We derived a mathematical model (model 1) by measuring SAH basal cisternal blood volume using an ABC/2‐derived ellipsoid formula (A=width/thickness, B=length, C=vertical extension) on head noncontrast computed tomography in 5 major SAH cisternal compartments. We compared model 1 against a manual segmentation method (model 2) on noncontrast computed tomography. Data were analyzed using logistic regression analysis, t test, receiver operator characteristic curves, and area under the curve analysis. There was no significant difference in cisternal SAHV analysis between the 2 models ( P =0.14). Mean SAHV by the simplified method was 7.0 mL (95% CI, 5.89–8.09) for good outcome and 16.6 mL (95% CI, 13.49–19.77) for poor outcome. Patients with delayed cerebral ischemia had higher SAHV, with a cutoff value of 10 mL. Conclusions Our simplified ABC/2‐derived method to estimate SAHV is comparable to manual segmentation and can be performed in low‐resource settings. Higher total SAHV was associated with worse outcomes and higher risk of delayed cerebral ischemia. A potential dose–response relationship was observed, with SAHV >10 mL predicting worse outcomes and higher risk of DCI.

Background Intraventricular hemorrhage is a calamitous type of stroke where bleeding into the ventricular system can be defined as: primary, if confined within the ventricles; or secondary, due to intracerebral hemorrhage extending from adjacent parenchyma. Intraventricular blood clot can lead to secondary insult and inflammatory responses that culminates in hydrocephalus as the most common cause of death. Purpose THerein, we report a patient with a high modified Graeb scale and low Glasgow coma scale. She spontaneously recanalized her fourth ventricle, decompressed her reticular activating system with remarkable spontaneous bilateral eye opening, and a consequently experienced a halfway drop in her mGS. Results This is the first reported case of a spontaneous recanalization of 4th ventricle obstruction secondary to IVH without intervention and subsequent dramatic neurological improvement. We believe that the apixaban primarily preserved the liquid state of hemorrhage and her presumed elevated ICP was sufficient to push out the liquified blood in the 4th ventricle into the upper spinal canal , recanalizing the 4th ventricle by continuously creating downward CSF pressure waves. Given the RAS location around the 4th ventricle, we hypothesize spontaneous decompression from the clot lysis triggered the RAS activation with sudden arousal manifested as spontaneous bilateral eyes opening. Hence, we refer to this as the reticular activating system reactivation after ventricular hemorrhage evacuation, or simply the “REVIVE” phenomenon. Conclusion This dramatic improvement from coma to awake state is worthy of recognition for future neurotherapeutic interventions.