Development and validation of outcome prediction models for aneurysmal subarachnoid haemorrhage: the SAHIT multinational cohort study

SAHIT collaboration, Blessing N R Jaja, Gustavo Saposnik, Hester F. Lingsma, Erin Macdonald, Kevin E Thorpe, Muhammed Mamdani, Ewout W. Steyerberg, Andrew J. Molyneux, Airton Leonardo de Oliveira Manoel, Bawarjan Schatlo, Daniel Hanggi, David M Hasan, George K C Wong, Nima Etminan, Hitoshi Fukuda, James Torner, Karl L Schaller, Jose I Suarez, Martin N StienenMervyn D I Vergouwen, Gabriel J E Rinkel, Julian Spears, Michael D Cusimano, Michael Todd, Peter Le Roux, Peter J Kirkpatrick, John D. Pickard, Walter M van den Bergh, Gordon Murray, S Claiborne Johnston, Sen Yamagata, Stephan A Mayer, Tom A Schweizer, R. Loch Macdonald

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)j5745
JournalBMJ (Clinical research ed.)
Volume360
DOIs
Publication statusPublished - 18 Jan 2018

Keywords

  • Journal Article

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