Prediction of the functional outcome of acute recurrent cerebral ischemic hemispheric stroke
DOI:
https://doi.org/10.14739/2310-1237.2018.3.151859Keywords:
acute stroke, prognosisAbstract
The aim of the work is to develop criteria for predicting the functional outcome of the acute period of recurrent cerebral ischemic hemispheric stroke (RCIHS) based on a comprehensive clinical and paraclinical examinations.Materials and methods. An open, comparative, prospective, cohort study of 111 patients (49 (44.1 %) women and 62 (55.9 %) men, age 72.0 (64.0; 77.0) years) with acute RCIHS was performed. All patients underwent a detailed clinical and neurological examination using National Institutes of Health Stroke Scale, mRS (modifid Rankin scale), computed tomography of the brain,
hematological and biochemical investigation.
Results. Based on a univariate logistic regression analysis, it was found that the functional outcome of the acute RCIHS is
inflenced by the following parameters: age of patient (OR 1.06, 95 % CI 1.00–1.11, P = 0.0284), contralateral localization of RCIHS (OR 2.86 95 % CI 1.05–7.79, P = 0.0391), the baseline NIHSS score (OR 1.76. 95 % CI 1.38–2.26; P < 0. 0001), the infarct volume (OR 1.03, 95 % CI 1.00–1.05, P = 0.0221). It was determined that localization of RCIHS in the contralateral hemisphere was associated with the unfavorable functional outcome on the 21 st day of the disease (χ² = 4.44, P = 0.0352). The independent inflence of the initial level of glycemia on the functional outcome of the acute period of RCIHS in patients without diabetes (OR 2.39 95 % CI 1.32–4.33, P = 0.0042) was revealed.
Conclusions. The independent predictor of the unfavorable functional outcome of the acute period of the RCIHS (mRS ≥3 at 21st day of disease) was score by NIHSS >10 at baseline (sensitivity 73.3 %, specifiity 90.5 %). The mathematical model, which included the baseline levels of neurological defiit was developed and it could determine the prognosis of the functional outcome of the acute period of RCIHS with an accuracy of 87.5 % (AUC = 0.92, P ˂ 0.05).
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