The Silent Stroke: Unveiling the Hidden Danger of Ultra-Early Mild AIS
Cerebrovascular disease, particularly Acute Ischemic Stroke (AIS) caused by cerebral atherosclerosis, remains a leading cause of illness and death in China. While intravenous thrombolysis within 6 hours of symptom onset significantly improves outcomes, the narrow therapeutic window demands rapid and accurate diagnosis. But here's where it gets controversial: distinguishing CT-negative ultra-early mild AIS from Transient Ischemic Attack (TIA) based solely on clinical presentation is notoriously difficult. Magnetic resonance imaging with diffusion-weighted imaging (MRI-DWI) is the gold standard, but its high cost and limited accessibility in primary hospitals often leave clinicians relying on computed tomography (CT), which has limited sensitivity for early ischemic changes, leading to potential delays in treatment.
And this is the part most people miss: Serum biomarkers, reflecting inflammation, endothelial dysfunction, and metabolic alterations, hold promise in aiding diagnosis. Markers like high-sensitivity C-reactive protein (hs-CRP), homocysteine (HCY), and lipid profiles have been linked to stroke risk and outcomes. Additionally, dynamic changes in neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) may reflect the acute inflammatory state following cerebral ischemia. However, their combined utility in differentiating CT-negative mild AIS from TIA in the hyperacute phase (<6 hours) remains underexplored.
This study aimed to develop and validate a clinical prediction model integrating NIHSS scores with readily available serum biomarkers to differentiate CT-negative mild AIS from TIA at an early stage. The model, incorporating NIHSS score, CRP, glucose, total cholesterol, triglycerides, and LDL, demonstrated robust discriminative ability, good calibration, and clinical utility. This raises a crucial question: Can this model, relying on widely available parameters, bridge the diagnostic gap in resource-limited settings where MRI is inaccessible, potentially accelerating thrombolysis and improving patient outcomes?
While the study's single-center design and modest sample size warrant further validation, its findings highlight the potential of combining clinical assessment with readily available biomarkers to enhance stroke diagnosis and treatment, particularly in settings with limited access to advanced imaging. The debate continues: How can we best leverage these findings to improve stroke care globally, ensuring timely and accurate diagnosis for all patients, regardless of resource availability?
