Sharma, Rohan

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.