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.
|Original language||English (US)|
|State||Published - 2018|
Bibliographical noteFunding Information:
Contributors: RLM initiated the SAHIT Collaboration, monitored data collection for the study, supervised the study, and revised and approved the final version of the manuscript. He is guarantor. BNRJ cleaned and merged the datasets, co-designed the analysis plan, performed the statistical analysis, and drafted and revised the paper. HFL and EWS co-designed the analysis plan, contributed statistical analysis codes, and revised the draft paper. The other authors and all members of the SAHIT collaboration designed the study, contributed data, and revised the draft paper. Funding: This work was supported by a grant from the Canadian Institutes for Health Research, Personnel Award from the Heart and Stroke Foundation of Canada, an early researcher award from the Ontario Ministry of Research and Innovation, and a special overseas scholarship to BNRJ from the Rivers State Government of Nigeria. Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for the submitted work; RLM receives grant support from the Physicians Services Incorporated Foundation, Brain Aneurysm Foundation, Canadian Institutes for Health Research, and the Heart and Stroke Foundation of Canada and is chief scientific officer of Edge Therapeutics; GS is supported by the distinguished clinician scientist award from Heart and Stroke Foundation of Canada (HSFC); SM is a consultant to Actelion Pharmaceuticals; PLeR is a member of the scientific advisory board of Edge Therapeutics and Cerebrotech and provides regular consultation for Integra LifeSciences, Depuy-Synthes, Codman, and Neurologica; no other relationships or activities that could appear to have influenced the submitted work. Ethical approval: The study was approved by the research ethics board of the St Michael’s Hospital, Toronto, Canada. Data sharing: Patient level data and statistical codes are available from the corresponding author. Participants consent was not obtained and the study involved de-identified data; hence, the risk of patient identification is low. Transparency: The lead author (RLM) affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
© 2018 Published by the BMJ Publishing Group Limited.