The Barrow Neurological Institute Grading Scale as a Predictor for Delayed Cerebral Ischemia and Outcome After Aneurysmal Subarachnoid Hemorrhage: Data From a Nationwide Patient Registry (Swiss SOS).

The Barrow Neurological Institute (BNI) scale is a novel quantitative scale measuring maximal subarachnoid hemorrhage (SAH) thickness to predict delayed cerebral ischemia (DCI). This scale could replace the Fisher score, which was traditionally used for DCI prediction. To validate the BNI scale. All patient data were obtained from the prospective aneurysmal SAH multicenter registry. In 1321 patients, demographic data, BNI scale, DCI, and modified Rankin Scale (mRS) score up to the 1-yr follow-up (1FU) were available for descriptive and univariate statistics. Outcome was dichotomized in favorable (mRS 0-2) and unfavorable (mRS 3-6). Odds ratios (OR) for DCI of Fisher 3 patients (n = 1115, 84%) compared to a control cohort of Fisher grade 1, 2, and 4 patients (n = 206, 16%) were calculated for each BNI grade separately. Overall, 409 patients (31%) developed DCI with a high DCI rate in the Fisher 3 cohort (34%). With regard to the BNI scale, DCI rates went up progressively from 26% (BNI 2) to 38% (BNI 5) and corresponding OR for DCI increased from 1.9 (1.0-3.5, 95% confidence interval) to 3.4 (2.1-5.3), respectively. BNI grade 5 patients had high rates of unfavorable outcome with 75% at discharge and 58% at 1FU. Likelihood for unfavorable outcome was high in BNI grade 5 patients with OR 5.9 (3.9-8.9) at discharge and OR 6.6 (4.1-10.5) at 1FU. This multicenter external validation analysis confirms that patients with a higher BNI grade show a significantly higher risk for DCI; high BNI grade was a predictor for unfavorable outcome at discharge and 1FU

Correction to: Ruptured PICA aneurysms: presentation and treatment outcomes compared to other posterior circulation aneurysms. A Swiss SOS study.

Incorrect authorgroup and authorname

Correction to: Ruptured posterior circulation aneurysms: epidemiology, patterns of care, and outcomes from the Swiss SOS national registry.

The name of Roy Thomas Daniel was incorrectly captured in the original manuscript

Development of a Complication- and Treatment-Aware Prediction Model for Favorable Functional Outcome in Aneurysmal Subarachnoid Hemorrhage Based on Machine Learning.

Current prognostic tools in aneurysmal subarachnoid hemorrhage (aSAH) are constrained by being primarily based on patient and disease characteristics on admission. To develop and validate a complication- and treatment-aware outcome prediction tool in aSAH. This cohort study included data from an ongoing prospective nationwide multicenter registry on all aSAH patients in Switzerland (Swiss SOS [Swiss Study on aSAH]; 2009-2015). We trained supervised machine learning algorithms to predict a binary outcome at discharge (modified Rankin scale [mRS] ≤ 3: favorable; mRS 4-6: unfavorable). Clinical and radiological variables on admission ("Early" Model) as well as additional variables regarding secondary complications and disease management ("Late" Model) were used. Performance of both models was assessed by classification performance metrics on an out-of-sample test dataset. Favorable functional outcome at discharge was observed in 1156 (62.0%) of 1866 patients. Both models scored a high accuracy of 75% to 76% on the test set. The "Late" outcome model outperformed the "Early" model with an area under the receiver operator characteristics curve (AUC) of 0.85 vs 0.79, corresponding to a specificity of 0.81 vs 0.70 and a sensitivity of 0.71 vs 0.79, respectively. Both machine learning models show good discrimination and calibration confirmed on application to an internal test dataset of patients with a wide range of disease severity treated in different institutions within a nationwide registry. Our study indicates that the inclusion of variables reflecting the clinical course of the patient may lead to outcome predictions with superior predictive power compared to a model based on admission data only