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Is prognostication possible in patients with aneurysmal subarachnoid haemorrhage post endovascular treatment?

Asadi, Hamed, Dowling, Richard, Yan, Bernard and Mitchell, Peter 2016, Is prognostication possible in patients with aneurysmal subarachnoid haemorrhage post endovascular treatment?, Translational biomedicine, vol. 7, no. 1, pp. 1-10, doi: 10.21767/2172-0479.100045.

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Title Is prognostication possible in patients with aneurysmal subarachnoid haemorrhage post endovascular treatment?
Author(s) Asadi, HamedORCID iD for Asadi, Hamed orcid.org/0000-0003-2475-9727
Dowling, Richard
Yan, Bernard
Mitchell, Peter
Journal name Translational biomedicine
Volume number 7
Issue number 1
Article ID 45
Start page 1
End page 10
Total pages 10
Publisher iMedPub
Place of publication Wilmington, Del.
Publication date 2016
ISSN 2172-0479
Keyword(s) acute subarachnoid hemorrhage
cerebral aneurysm
machine learning
artificial neural network
regression model
decision tree
assisted decision making
Summary Introduction: Subarachnoid haemorrhage due to aneurysm rupture is a major cause of death and disability. Accurately predicting the outcome for those patients who have endovascular treatment from a set of predictive variables may identify high-risk patients and guide treatment approaches, leading to decreased morbidity.

Logistic regression models allow for the identification and validation of predictive variables. However, advanced machine learning algorithms offer an alternative, in particular, for large-scale multi-institutional data, with the advantage of easily incorporating newly available data to improve prediction performance.

Our aim was to design and compare different machine learning methods, capable of predicting the outcome of endovascular intervention in acute subarachnoid haemorrhage and aneurysm rupture.

Method: We conducted a retrospective study on a prospectively collected database of patients with acute subarachnoid haemorrhage due to aneurysm rupture who underwent endovascular intervention.

All demographic, clinical and procedural data was collated including information from follow up imaging studies. Using SPSS®, MATLAB® and RapidMiner®, classical statistics as well as machine learning algorithms were applied to design a supervised machine capable of classifying these predictors into potential good and poor outcomes.

It was attempted to predict the final patients’ outcome based on modified Rankin Scale (mRS), and a dichotomised outcome, good or bad, as well as mortality, recanalization rate and need for retreatment. Subsequently, these algorithms were trained, validated and tested using randomly divided data.

Results: We included 236 consecutive acute subarachnoid haemorrhage patients with ruptured intracerebral aneurysm treated by endovascular technique, with a mean age of 52.7 (SD=13.7). All the available demographic, procedural and clinical factors were included into the models.

The overall accuracy in predicting the exact mRS was just below 50%, which increased to above 75% in prediction of the dichotomised (good or bad) outcome, and approximately 85% in prediction of mortality.

Prediction of recanalization had an overall accuracy of just below 50%; however, there was an approximately 90% accuracy in prediction of those patients requiring retreatment.

Discussion: We showed promising accuracy of outcome prediction, using supervised machine learning algorithms in particular in prediction of final outcome as good or bad as well as the probability of needing retreatment in future, with potential for incorporation of larger multicenter datasets, likely further improving predictive accuracy.

Finally the filtered and optimized dataset was introduced into a decision induction module and a simplified prognostication tree was designed representing a pictorial relationship between the predictors and the final outcome in a relatively easy to interpret way.
Language eng
DOI 10.21767/2172-0479.100045
Field of Research 110999 Neurosciences not elsewhere classified
Socio Economic Objective 920118 Surgical Methods and Procedures
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, iMedPub
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30092710

Document type: Journal Article
Collections: Faculty of Health
School of Medicine
Open Access Collection
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.