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PLOS ONE  2013 

Cortisol as a Prognostic Marker of Short-Term Outcome in Chinese Patients with Acute Ischemic Stroke

DOI: 10.1371/journal.pone.0072758

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Background Early prediction of outcome is important for allocation of therapeutic strategies. Endocrine alterations of the hypothalamus–pituitary–axis are one of the first stress-induced alterations after cerebral ischemia. We therefore evaluated the prognostic value of serum cortisol in Chinese patients with an acute ischemic stroke. Methods In a prospective observational study, serum cortisol was measured using a solid-phase, competitive chemiluminescent enzyme immunoassay on admission in serum of 226 consecutive Chinese patients with an acute ischemic stroke. The prognostic value of serum cortisol to predict the functional outcome, mortality within 90 days, was compared with clinical variables (e.g., advanced age and the National Institutes of Health Stroke Scale [NHISS] score) and with other known predictors. Results Patients with a poor outcome and nonsurvivors had significantly increased serum cortisol levels on admission (P<0.0001, P<0.0001). There was a positive correlation between levels of cortisol and the NIHSS (r = 0.298, P<0.0001), glucose levels (r = 0.324, P<0.0001) and infarct volume (r = 0.328, P<0.0001). Cortisol was an independent prognostic marker of functional outcome and death [odds ratio 3.44 (2.58–6.23) and 4.21 (1.89–9.24), respectively, P<0.0001 for both, adjusted for age, the NIHSS and other predictors] in patients with ischemic stroke. In receiver operating characteristic curve analysis, cortisol could improve the NIHSS score in predicting short-term functional outcome (Area under the curve [AUC] of the combined model, 0.87; 95% CI, 0.82–0.92; P = 0.01) and mortality (AUC of the combined model, 0.90; 95% CI, 0.84–0.95; P = 0.01). Conclusion Cortisol can be seen as an independent short-term prognostic marker of functional outcome and death in Chinese patients with acute ischemic stroke even after correcting confounding factors. Combined model can add significant additional predictive information to the clinical score of the NIHSS.


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