TY - JOUR
T1 - Development and validation of outcome prediction models for aneurysmal subarachnoid haemorrhage
T2 - The SAHIT multinational cohort study
AU - Jaja, Blessing N.R.
AU - Saposnik, Gustavo
AU - Lingsma, Hester F.
AU - Macdonald, Erin
AU - Thorpe, Kevin E.
AU - Mamdani, Muhammed
AU - Steyerberg, Ewout W.
AU - Molyneux, Andrew
AU - Manoel, Airton Leonardo De Oliveira
AU - Schatlo, Bawarjan
AU - Hanggi, Daniel
AU - Hasan, David
AU - Wong, George K.C.
AU - Etminan, Nima
AU - Fukuda, Hitoshi
AU - Torner, James
AU - Schaller, Karl L.
AU - Suarez, Jose I.
AU - Stienen, Martin N.
AU - Vergouwen, Mervyn D.I.
AU - Rinkel, Gabriel J.E.
AU - Spears, Julian
AU - Cusimano, Michael D.
AU - Todd, Michael
AU - Le Roux, Peter
AU - Kirkpatrick, Peter
AU - Pickard, John
AU - Van Den Bergh, Walter M.
AU - Murray, Gordon
AU - Johnston, S. Claiborne
AU - Yamagata, Sen
AU - Mayer, Stephan
AU - Schweizer, Tom A.
AU - Macdonald, R. Loch
N1 - Publisher Copyright:
© 2018 Published by the BMJ Publishing Group Limited.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85040935546&partnerID=8YFLogxK
U2 - 10.1136/bmj.j5745
DO - 10.1136/bmj.j5745
M3 - Article
C2 - 29348138
AN - SCOPUS:85040935546
SN - 0959-8146
VL - 360
JO - BMJ
JF - BMJ
M1 - j5745
ER -