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@ARTICLE{Rubbert:849902,
      author       = {Rubbert, Christian and Patil, Kaustubh and Beseoglu, Kerim
                      and Mathys, Christian and May, Rebecca and Kaschner, Marius
                      G. and Sigl, Benjamin and Teichert, Nikolas A. and Boos,
                      Johannes and Turowski, Bernd and Caspers, Julian},
      title        = {{P}rediction of outcome after aneurysmal subarachnoid
                      haemorrhage using data from patient admission},
      journal      = {European radiology},
      volume       = {28},
      number       = {12},
      issn         = {1432-1084},
      address      = {Berlin},
      publisher    = {Springer},
      reportid     = {FZJ-2018-04000},
      pages        = {4949–4958},
      note         = {Editing assistance of an earlier version of the manuscript:
                      Bonnie Hami, M.A. (Cleveland, OH, USA).},
      abstract     = {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 Tmax 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 the training and validation folds using 20 repeats of
                      10-fold cross-validation.RESULTS:In the final model, using
                      200 trees and the synthetic minority oversampling technique,
                      median balanced accuracy was $84.4\%$ (SD 0.7) over the
                      training folds and $70.9\%$ (SD 1.2) over the validation
                      folds. The five most important features were the modified
                      Fisher grade, age, MTT range, WFNS and early
                      EVD.CONCLUSIONS:A random forests model trained on easily
                      determinable features in the course of patient admission can
                      predict the functional outcome 6 months after aSAH with
                      considerable accuracy.KEY POINTS:• Features determinable
                      in the course of admission of a patient with aneurysmal
                      subarachnoid haemorrhage (aSAH) can predict the functional
                      outcome 6 months after the occurrence of aSAH. • The top
                      five predictive features were the modified Fisher grade,
                      age, the mean transit time (MTT) range from computed
                      tomography perfusion (CTP), the WFNS grade and the early
                      necessity for an external ventricular drainage (EVD). •
                      The range between the minimum and the maximum MTT may prove
                      to be a valuable biomarker for detrimental functional
                      outcome.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574)},
      pid          = {G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29948072},
      UT           = {WOS:000451353500004},
      doi          = {10.1007/s00330-018-5505-0},
      url          = {https://juser.fz-juelich.de/record/849902},
}