Blessing N R Jaja1,2,3, Gustavo Saposnik4,2,3, Hester F Lingsma5, Erin Macdonald2, Kevin E Thorpe6, Muhammed Mamdani3,6, Ewout W Steyerberg5,7, Andrew Molyneux8, Airton Leonardo de Oliveira Manoel1,2, Bawarjan Schatlo9, Daniel Hanggi10, David Hasan11, George K C Wong12, Nima Etminan10, Hitoshi Fukuda13, James Torner14, Karl L Schaller15, Jose I Suarez16, Martin N Stienen17, Mervyn D I Vergouwen18, Gabriel J E Rinkel18, Julian Spears1,19, Michael D Cusimano1,3,19, Michael Todd20, Peter Le Roux21, Peter Kirkpatrick22, John Pickard22, Walter M van den Bergh23, Gordon Murray24, S Claiborne Johnston25, Sen Yamagata13, Stephan Mayer26, Tom A Schweizer1,2,3,19, R Loch Macdonald1,2,3,19. 1. Division of Neurosurgery, St Michael's Hospital, Toronto, ON, Canada. 2. Neuroscience Research Program of the Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, ON, Canada. 3. Institute of Medical Science, University of Toronto, ON, Canada. 4. Division of Neurology, St Michael's Hospital, Toronto, ON, Canada. 5. Department of Public Health, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, Netherlands. 6. Department of Health Policy, Management and Evaluation, University of Toronto, ON, Canada. 7. Department of Medical Statistics, Leiden University Medical Centre, Leiden, Netherlands. 8. Division of Endovascular Neurosurgery, Department of Neurosurgery, University of Oxford, Oxford, UK. 9. Department of Neurosurgery, University Hospital Göttingen, Germany. 10. Department of Neurosurgery, University Hospital Mannheim, Medical Faculty Mannheim, University of Heidelberg Theodor-Kutzer-Ufer 1-3, Germany. 11. Department of Neurosurgery, University of Iowa Carver College of Medicine, Iowa City, IA, USA. 12. Division of Neurosurgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China. 13. Department of Neurosurgery, Kurashiki Central Hospital, Kurashiki-city, Okayama, Japan. 14. Department of Epidemiology, University of Iowa College of Public Health, Iowa City, IA, USA. 15. Department of Clinical Neurosciences, Hôpitaux, Universitaire de Genève, Geneva, Switzerland. 16. Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, MD 21287, USA. 17. Department of Neurosurgery, University Hospital Zurich, Frauenklinikstrasse 10, 8091 Zürich, Switzerland. 18. Brain Centre Rudolf Magnus, Department of Neurology and Neurosurgery, room G03-228, University Medical Centre Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands. 19. Department of Surgery, University of Toronto, ON, Canada. 20. Department of Anesthesia, University of Iowa, Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA, USA. 21. The Brain and Spine Center, Lankenau Medical Center, Wynnewood, PA, USA. 22. Division of Neurosurgery, Department of Clinical Neurosciences, Addenbrooke's Hospital, University of Cambridge, Cambridge CB2 0QQ, UK. 23. Department of Critical Care, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands. 24. Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK. 25. Dell School of Medicine, University of Texas, Austin, TX, USA. 26. Division of Critical Care Neurology, Columbia University College of Physicians and Surgeons, New York, USA.
Abstract
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. 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.
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. 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.
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