Convexity subarachnoid haemorrhage: a practical guide.

Atraumatic convexity subarachnoid haemorrhage describes spontaneous bleeding into the convexities of the brain sulci without parenchymal involvement. Its many causes include reversible cerebral vasoconstriction syndrome, cerebral sinus venous thrombosis, posterior reversible encephalopathy syndrome and (in older people) cerebral amyloid angiopathy. We describe the clinical and radiological features of non-traumatic convexity subarachnoid haemorrhage with its various presentations, causes, treatments and prognoses, and use clinical vignettes to highlight important clinical points and pitfalls.

Network Mapping of Time to Antithrombotic Therapy Among Patients With Ischemic Stroke and Transient Ischemic Attack (TIA).

Background: There is emphasis on timely administration of thrombolysis and clot retrieval but not antithrombotic therapy within 48 h for ischemic stroke (frequency of 64% in Australia and 97% in North America). We planned to assess the time metrics and variables associated with delaying antithrombotics (antiplatelet and anticoagulant therapy) administration. Methods: This was a retrospective study at Monash Health over 12 months in 2015. We plotted the cumulative event and mapped the key drivers (dimensionless variable Shapley value/SV) of antithrombotics. Results: There were 42 patients with transient ischemic attack/TIA and 483 with ischemic stroke [mean age was 71.8 ± 15.4; 56.0% male; nil by mouth (NBM) 74.5 and 49.3% of patients received "stat" (immediate and one off) dose antithrombotics]. The median time to imaging for the patients who did not have stroke code activated was 2.3 h (IQR 1.4-3.7), from imaging to dysphagia screen was 14.6 h (IQR 6.2-20.3), and from stopping NBM to antithrombotics was 1.7 h (IQR 0-16.5). TIA patients received antithrombotics earlier than those with ischemic stroke (90.5 vs. 86.5%, p = 0.01). Significant variables in regression analysis for time to antithrombotics were time to dysphagia screen (ß 0.20 ± 0.03, SV = 3.2), nasogastric tube (ß 19.8 ± 5.9, SV = -0.20), Alteplase (ß 8.6 ± 3.6, SV = -1.9), stat dose antithrombotic (ß -18.9 ± 2.9, SV = -10.8) and stroke code (ß -5.9 ± 2.5, SV = 2.8). The partial correlation network showed that the time to antithrombotics increased with delay in dysphagia screen (coefficient = 0.33) and decreased if "stat" dose of antithrombotics was given (coefficient = -0.32). Conclusion: The proportion of patients receiving antithrombotics within 48 h was higher than previously reported in Australia but remained lower than the standard achieved in North American hospitals. Our process map and network analysis show avenues to shorten the time to antithrombotic.

Predicting Disability after Ischemic Stroke Based on Comorbidity Index and Stroke Severity-From the Virtual International Stroke Trials Archive-Acute Collaboration.

BACKGROUND AND AIM: The availability and access of hospital administrative data [coding for Charlson comorbidity index (CCI)] in large data form has resulted in a surge of interest in using this information to predict mortality from stroke. The aims of this study were to determine the minimum clinical data set to be included in models for predicting disability after ischemic stroke adjusting for CCI and clinical variables and to evaluate the impact of CCI on prediction of outcome. METHOD: We leverage anonymized clinical trial data in the Virtual International Stroke Trials Archive. This repository contains prospective data on stroke severity and outcome. The inclusion criteria were patients with available stroke severity score such as National Institutes of Health Stroke Scale (NIHSS), imaging data, and outcome disability score such as 90-day Rankin Scale. We calculate CCI based on comorbidity data in this data set. For logistic regression, we used these calibration statistics: Nagelkerke generalised R2 and Brier score; and for discrimination we used: area under the receiver operating characteristics curve (AUC) and integrated discrimination improvement (IDI). The IDI was used to evaluate improvement in disability prediction above baseline model containing age, sex, and CCI. RESULTS: The clinical data among 5,206 patients (55% males) were as follows: mean age 69 ± 13 years, CCI 4.2 ± 0.8, and median NIHSS of 12 (IQR 8, 17) on admission and 9 (IQR 5, 15) at 24 h. In Model 2, adding admission NIHSS to the baseline model improved AUC from 0.67 (95% CI 0.65-0.68) to 0.79 (95% CI 0.78-0.81). In Model 3, adding 24-h NIHSS to the baseline model resulted in substantial improvement in AUC to 0.90 (95% CI 0.89-0.91) and increased IDI by 0.23 (95% CI 0.22-0.24). Adding the variable recombinant tissue plasminogen activator did not result in a further change in AUC or IDI to this regression model. In Model 3, the variable NIHSS at 24 h explains 87.3% of the variance of Model 3, follow by age (8.5%), comorbidity (3.7%), and male sex (0.5%). CONCLUSION: Our results suggest that prediction of disability after ischemic stroke should at least include 24-h NIHSS and age. The variable CCI is less important for prediction of disability in this data set.