期刊
METABOLIC BRAIN DISEASE
卷 38, 期 2, 页码 657-670出版社
SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s11011-022-01116-z
关键词
Ischemic stroke; Cell adhesion molecules; VCAM-1; NIHSS; Biomarker; Mortality
This study aimed to investigate the association between cellular adhesion molecules (CAMs) and acute ischemic stroke (IS), as well as to identify biomarkers for predicting short-term mortality after IS. The results showed that a combination of baseline IS severity and soluble vascular cellular adhesion molecule 1 (sVCAM-1) levels could early predict the prognosis of IS patients. Additionally, this study suggests that VCAM-1 might be a potential target for therapeutic strategies in IS.
The aim was to investigate the association between plasma levels of cellular adhesion molecules (CAMs) and risk factors, subtypes, severity and short-term mortality of acute ischemic stroke (IS), and to identify a panel of biomarkers to predict short-term mortality after IS. The prospective study evaluated 132 IS patients within 24 h of their hospital admission. The baseline IS severity was assessed using the National Institutes Health Stroke Scale (NIHSS) and categorized as mild (NIHSS < 5), moderate (NIHSS 5-14) and severe (NIHSS >= 15). After three-month follow-up, the disability was assessed using the modified Rankin Scale (mRS); moreover, the patients were classified as survivors and non-survivors. Baseline inflammatory and anti-inflammatory cytokines and soluble CAMs were evaluated. Twenty-nine (21.9%) IS patients were non-survivors and showed higher NIHSS and soluble vascular cellular adhesion molecule 1 (sVCAM-1) than the survivors. The sVCAM-1 levels positively correlated with age, homocysteine, severity, and disability. The model #3 combining sVCAM-1 and NIHSS showed better results to predict short-term mortality with an area under the curve receiving operating characteristics (AUC/ROC) of 0.8841 [95% confidence interval (CI): 0.795-0.941] than the models with sVCAM-1 and NIHSS alone, with positive predictive value of 68.0%, negative predictive value of 91.3%, and accuracy of 86.5%. In conclusion, the combined model with baseline severity of IS and sVCAM-1 levels can early predict the prognosis of IS patients who may benefit with therapeutic measures of personalized therapy that taken into account these biomarkers. Moreover, this result suggests that VCAM-1 might be a potential target for the therapeutic strategies in IS.
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