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Prediction of Outcome After Aneurysmal Subarachnoid Haemorrhage Using Data from Patient Admission by Christian Rubbert; Kaustubh R. Patil; Kerim Beseoglu; Christian Mathys; Rebecca May; Marius G. Kaschner; Benjamin Sigl; Nikolas A. Teichert; Johannes Boos; Bernd Turowski; Julian Caspers is a Medicine article available to read on EtoBox.

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## Objectives: The pathogenesis leading to poor functional outcome after aneurysmal subarachnoid haemorrhage (asah) is multifactorial and not fully understood. we evaluated a machine learning approach based on easily determinable clinical and ct perfusion (ctp) features in the course of patient admission to predict the functional outcome 6 months after ictus. ## Methods: Out of 630 consecutive subarachnoid haemorrhage patients (2008-2015), 147 (mean age 54.3, 66.7% women) were retrospectively included (inclusion: asah, admission within 24 h of ictus, ctp within 24 h of admission, documented modified rankin scale (mrs) grades after 6 months. exclusion: occlusive therapy before first ctp, previous asah, ctp not evaluable). a random forests model with conditional inference trees was optimised and trained on sex, age, world federation of neurosurgical societies (wfns) and modified fisher grades, aneurysm in anterior vs. posterior circulation, early external ventricular drainage (evd), as well as mtt and t maximum, mean, standard deviation (sd), range, 75th quartile and interquartile range to predict dichotomised mrs (≤ 2; > 2). performance was assessed using the balanced accuracy over

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Author

Christian Rubbert; Kaustubh R. Patil; Kerim Beseoglu; Christian Mathys; Rebecca May; Marius G. Kaschner; Benjamin Sigl; Nikolas A. Teichert; Johannes Boos; Bernd Turowski; Julian Caspers

Published

2018

Language

EN

Field

Medicine (Health Sciences)