• JajaBlessing N RBNRDivision of Neurosurgery, St Michael's Hospital, Toronto, ON, Canada.Neuroscience Research Program of the Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, ON, Canada.Institute of Medical Science, University of Toronto, O, Gustavo Saposnik, Hester F Lingsma, Erin Macdonald, Kevin E Thorpe, Muhammed Mamdani, Ewout W Steyerberg, Andrew Molyneux, ManoelAirton Leonardo de OliveiraALODivision of Neurosurgery, St Michael's Hospital, Toronto, ON, Canada.Neuroscience Research Program of the Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, ON, Canada., Bawarjan Schatlo, Daniel Hanggi, David Hasan, WongGeorge K CGKCDivision of Neurosurgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China., Nima Etminan, Hitoshi Fukuda, James Torner, Karl L Schaller, Jose I Suarez, Martin N Stienen, VergouwenMervyn D IMDIBrain Centre Rudolf Magnus, Department of Neurology and Neurosurgery, room G03-228, University Medical Centre Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands., RinkelGabriel J EGJEBrain Centre Rudolf Magnus, Department of Neurology and Neurosurgery, room G03-228, University Medical Centre Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands., Julian Spears, Michael D Cusimano, Michael Todd, Le RouxPeterPThe Brain and Spine Center, Lankenau Medical Center, Wynnewood, PA, USA., Peter Kirkpatrick, John Pickard, Walter M van den Bergh, Gordon Murray, S Claiborne Johnston, Sen Yamagata, Stephan Mayer, Tom A Schweizer, R Loch Macdonald, and SAHIT collaboration.
• Division of Neurosurgery, St Michael's Hospital, Toronto, ON, Canada.
• BMJ. 2018 Jan 18; 360: j5745.

ObjectiveTo develop and validate a set of practical prediction tools that reliably estimate the outcome of subarachnoid haemorrhage from ruptured intracranial aneurysms (SAH).DesignCohort study with logistic regression analysis to combine predictors and treatment modality.SettingSubarachnoid Haemorrhage International Trialists' (SAHIT) data repository, including randomised clinical trials, prospective observational studies, and hospital registries.ParticipantsResearchers 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 MeasurePredicted risk of mortality or functional outcome at three months according to score on the Glasgow outcome scale.ResultsClinical 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.ConclusionThe 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.Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

20 keywords...

hide…