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Blood Biomarkers to Predict Long-Term Mortality after Ischemic Stroke.

Laura RamiroLaura AbrairaManuel QuintanaPaula García-RodríguezEstevo SantamarinaJose Álvarez-SabínJosep ZaragozaMaría Hernández-PérezXavier UstrellBlanca LaraMikel TerceñoAlejandro BustamanteJoan Montaner
Published in: Life (Basel, Switzerland) (2021)
Stroke is a major cause of disability and death globally, and prediction of mortality represents a crucial challenge. We aimed to identify blood biomarkers measured during acute ischemic stroke that could predict long-term mortality. Nine hundred and forty-one ischemic stroke patients were prospectively recruited in the Stroke-Chip study. Post-stroke mortality was evaluated during a median 4.8-year follow-up. A 14-biomarker panel was analyzed by immunoassays in blood samples obtained at hospital admission. Biomarkers were normalized and standardized using Z-scores. Multiple Cox regression models were used to identify clinical variables and biomarkers independently associated with long-term mortality and mortality due to stroke. In the multivariate analysis, the independent predictors of long-term mortality were age, female sex, hypertension, glycemia, and baseline National Institutes of Health Stroke Scale (NIHSS) score. Independent blood biomarkers predictive of long-term mortality were endostatin > quartile 2, tumor necrosis factor receptor-1 (TNF-R1) > quartile 2, and interleukin (IL)-6 > quartile 2. The risk of mortality when these three biomarkers were combined increased up to 69%. The addition of the biomarkers to clinical predictors improved the discrimination (integrative discriminative improvement (IDI) 0.022 (0.007-0.048), p < 0.001). Moreover, endostatin > quartile 3 was an independent predictor of mortality due to stroke. Altogether, endostatin, TNF-R1, and IL-6 circulating levels may aid in long-term mortality prediction after stroke.
Keyphrases
  • cardiovascular events
  • atrial fibrillation
  • risk factors
  • healthcare
  • acute ischemic stroke
  • blood pressure
  • emergency department
  • mental health
  • health information
  • quality improvement
  • drug induced