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Updated: Feb 14, 2026

A Low Mortality Rat Model to Assess Delayed Cerebral Vasospasm After Experimental Subarachnoid Hemorrhage
Published on: January 17, 2013
Mortality after Spontaneous Subarachnoid Hemorrhage: Causality and Validation of a Prediction Model
Yasser B Abulhasan1, Najayeb Alabdulraheem2, Gabrielle Simoneau3
1Neurological Intensive Care Unit, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Faculty of Medicine, Health Sciences Center, Kuwait University, Kuwait.
Objective:
To evaluate primary causes of death after spontaneous subarachnoid hemorrhage (SAH) and externally validate the HAIR score, a prognostication tool, in a single academic institution.
Methods:
We reviewed all patients with SAH admitted to our neuro-intensive care unit between 2010 and 2016. Univariate and multivariate logistic regressions were performed to identify predictors of in-hospital mortality. The HAIR score predictors were Hunt and Hess grade at treatment decision, age, intraventricular hemorrhage, and rebleeding within 24 hours. Validation of the HAIR score was characterized with the receiver operating curve, the area under the curve, and a calibration plot.
Results:
Among 434 patients with SAH, in-hospital mortality was 14.1%. Of the 61 mortalities, 54 (88.5%) had a neurologic cause of death or withdrawal of care and 7 (11.5%) had cardiac death. Median time from SAH to death was 6 days. The main causes of death were effect of the initial hemorrhage (26.2%), rebleeding (23%) and refractory cerebral edema (19.7%). Factors significantly associated with in-hospital mortality in the multivariate analysis were age, Hunt and Hess grade, and intracerebral hemorrhage. Maximum lumen size was also a significant risk factor after aneurysmal SAH. The HAIR score had a satisfactory discriminative ability, with an area under the curve of 0.89.
Conclusions:
The in-hospital mortality is lower than in previous reports, attesting to the continuing improvement of our institutional SAH care. The major causes are the same as in previous reports. Despite a different therapeutic protocol, the HAIR score showed good discrimination and could be a useful tool for predicting mortality.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Data Validation
Key parameters for method validation include:

