Patient phenotypes associated with outcomes after aneurysmal subarachnoid
hemorrhage: a principal component analysis.
Author(s): Ibrahim GM(1), Morgan BR, Macdonald RL.
Affiliation(s): Author information:
(1)From the Division of Neurosurgery, St Michael's Hospital, Labatt Family Centre of
Excellence in Brain Injury and Trauma Research, Keenan Research Centre of the Li
Ka Shing Knowledge Institute of St Michael's Hospital, Toronto, Ontario, Canada
(G.M.I., B.R.M.); Department of Surgery (G.M.I., R.L.M.) and Institute of Medical
Science (G.M.I.), University of Toronto, Toronto, Ontario, Canada; and Department
of Medical Imaging, Hospital for Sick Children, Toronto, Ontario, Canada (G.M.I.,
B.R.M.).
Publication date & source: 2014, Stroke. , 45(3):670-6
BACKGROUND AND PURPOSE: Predictors of outcome after aneurysmal subarachnoid
hemorrhage have been determined previously through hypothesis-driven methods that
often exclude putative covariates and require a priori knowledge of potential
confounders. Here, we apply a data-driven approach, principal component analysis,
to identify baseline patient phenotypes that may predict neurological outcomes.
METHODS: Principal component analysis was performed on 120 subjects enrolled in a
prospective randomized trial of clazosentan for the prevention of angiographic
vasospasm. Correlation matrices were created using a combination of Pearson,
polyserial, and polychoric regressions among 46 variables. Scores of significant
components (with eigenvalues>1) were included in multivariate logistic regression
models with incidence of severe angiographic vasospasm, delayed ischemic
neurological deficit, and long-term outcome as outcomes of interest.
RESULTS: Sixteen significant principal components accounting for 74.6% of the
variance were identified. A single component dominated by the patients' initial
hemodynamic status, World Federation of Neurosurgical Societies score,
neurological injury, and initial neutrophil/leukocyte counts was significantly
associated with poor outcome. Two additional components were associated with
angiographic vasospasm, of which one was also associated with delayed ischemic
neurological deficit. The first was dominated by the aneurysm-securing procedure,
subarachnoid clot clearance, and intracerebral hemorrhage, whereas the second had
high contributions from markers of anemia and albumin levels.
CONCLUSIONS: Principal component analysis, a data-driven approach, identified
patient phenotypes that are associated with worse neurological outcomes. Such
data reduction methods may provide a better approximation of unique patient
phenotypes and may inform clinical care as well as patient recruitment into
clinical trials.
CLINICAL TRIAL REGISTRATION URL: http://www.clinicaltrials.gov. Unique
identifier: NCT00111085.
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