Research Article
Exploring Neurological and Cardiac Biomarkers in Acute Ischemic Stroke: A Correlation with Stroke Severity and Prognosis
Khan Y* and Gaikwad A
Department of Medicine, Internal Medicine. MRCP UK, Swastik hospital Jabalpur, Madhya Pradesh, India
*Corresponding author: Yasmin Khan, Department of Medicine, Internal medicine, MRCP UK, Swastik hospital, Jabalpur, Madhya Pradesh, India. E-mail Id: Yasu7868@gmail.com
Article Information:Submission: 29/07/2025; Accepted: 19/08/2025; Published: 23/08/2025
Copyright: © 2025 Khan Y, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Background: Biomarkers play a crucial role in predicting clinical outcomes in acute ischemic stroke. This study evaluates the predictive ability of S100B, neuron-specific enolase (NSE), troponin, and N-terminal pro-brain natriuretic peptide (NT-proBNP) for stroke severity, mortality, and functional outcomes in a cohort of acute ischemic stroke patients.
Methods: A retrospective analysis was conducted on 80 acute ischemic stroke patients admitted between February 2023 and January 2024, with a follow-up period of three months. Multiple linear regression assessed the relationship between biomarkers and stroke severity using the National Institutes of Health Stroke Scale (NIHSS). Logistic regression determined predictors of mortality, while ordinal logistic regression evaluated functional outcomes using the modified Rankin Scale (mRS) at three months. Kaplan-Meier survival analysis and Cox proportional hazards models analyzed time-to-mortality. Receiver Operating Characteristic (ROC) curve analysis assessed the discriminatory power of biomarkers in predicting mortality.
Results: Among the biomarkers analyzed, NT-proBNP showed the strongest correlation with NIHSS scores, indicating its potential as a predictor of stroke severity. S100B and NSE exhibited weaker associations, while troponin levels had minimal correlation with clinical severity. The overall mortality rate was 56.25%, with significantly higher NT-proBNP levels observed in non-survivors. These findings suggest that while NT-proBNP may serve as a useful prognostic marker, a combination of clinical assessment and biomarker evaluation is necessary for accurate risk stratification in AIS patients.
Conclusion: NT-proBNP emerged as a strong predictor of stroke severity and mortality, highlighting its potential role in AIS prognosis
Methods: A retrospective analysis was conducted on 80 acute ischemic stroke patients admitted between February 2023 and January 2024, with a follow-up period of three months. Multiple linear regression assessed the relationship between biomarkers and stroke severity using the National Institutes of Health Stroke Scale (NIHSS). Logistic regression determined predictors of mortality, while ordinal logistic regression evaluated functional outcomes using the modified Rankin Scale (mRS) at three months. Kaplan-Meier survival analysis and Cox proportional hazards models analyzed time-to-mortality. Receiver Operating Characteristic (ROC) curve analysis assessed the discriminatory power of biomarkers in predicting mortality.
Results: Among the biomarkers analyzed, NT-proBNP showed the strongest correlation with NIHSS scores, indicating its potential as a predictor of stroke severity. S100B and NSE exhibited weaker associations, while troponin levels had minimal correlation with clinical severity. The overall mortality rate was 56.25%, with significantly higher NT-proBNP levels observed in non-survivors. These findings suggest that while NT-proBNP may serve as a useful prognostic marker, a combination of clinical assessment and biomarker evaluation is necessary for accurate risk stratification in AIS patients.
Conclusion: NT-proBNP emerged as a strong predictor of stroke severity and mortality, highlighting its potential role in AIS prognosis
Keywords:Acute Ischemic Stroke [AIS]; Biomarkers; Stroke Severity; Mortality Prediction; NT-proBNP; ROC Curve Analysis; Survival Analysis; Functional Outcomes; NIHSS Score; mRS.