Objective -To develop and validate a set of practical prediction tools that reliably estimate the outcome of subarachnoid haemorrhage from ruptured intracranial aneurysms (SAH).Design -Cohort study with logistic regression analysis to combine predictors and treatment modality.Setting -Subarachnoid Haemorrhage International Trialists' (SAHIT) data repository, including randomised clinical trials, prospective observational studies, and hospital registries.Participants -Researchers collaborated to pool datasets of prospective observational studies, hospital registries, and randomised clinical trials of SAH from multiple geographical regions to develop and validate clinical prediction models.Main outcome measure -Predicted risk of mortality or functional outcome at three months according to score on the Glasgow outcome scale.Results -Clinical prediction models were developed with individual patient data from 10 936 patients and validated with data from 3355 patients after development of the model. In the validation cohort, a core model including patient age, premorbid hypertension, and neurological grade on admission to predict risk of functional outcome had good discrimination, with an area under the receiver operator characteristics curve (AUC) of 0.80 (95% confidence interval 0.78 to 0.82). When the core model was extended to a "neuroimaging model," with inclusion of clot volume, aneurysm size, and location, the AUC improved to 0.81 (0.79 to 0.84). A full model that extended the neuroimaging model by including treatment modality had AUC of 0.81 (0.79 to 0.83). Discrimination was lower for a similar set of models to predict risk of mortality (AUC for full model 0.76, 0.69 to 0.82). All models showed satisfactory calibration in the validation cohort. Conclusion -The prediction models reliably estimate the outcome of patients who were managed in various settings for ruptured intracranial aneurysms that caused subarachnoid haemorrhage. The predictor items are readily derived at hospital admission. The web based SAHIT prognostic calculator (http://sahitscore.com) and the related app could be adjunctive tools to support management of patients. IntroductionSubarachnoid haemorrhage from a ruptured intracranial aneurysm (SAH) is a relatively uncommon but severe subtype of stroke that is associated with a sudden dramatic onset in otherwise apparently healthy individuals and often results in poor outcomes. On average, a third of affected individuals do not survive; at least one in five of those who do survive are unable to regain functional independence.1 SAH is unlike the more common ischaemic stroke as it affects younger adults (median age 55) and therefore results in disproportionately many years of lost productive life.1 2 Predicting the outcome of this condition can be challenging given the considerable heterogeneity in the characteristics of affected individuals and their clinical course and variability in morphology of the aneurysm. Reliance on clinical intuition alone might be insufficient for accur...

While clinical prediction models for aSAH use a few simple predictors, there are substantial methodological problems with the models and none have had external validation. This precludes the use of existing models for clinical or research purposes. We recommend further studies to develop and validate reliable clinical prediction models for aSAH.

FRESH is the first clinical tool to prognosticate long-term outcome after spontaneous SAH in a multidimensional manner. Ann Neurol 2016;80:46-58.