Prognostic Metrics Associated with Inflammation and Atherosclerosis Signaling Evaluate the Burden of Adverse Clinical Outcomes in Ischemic Stroke Patients

Abstract

Background: Conventional prognostic risk factors can only partly explain the adverse clinical outcomes after ischemic stroke. We aimed to establish a set of prognostic metrics and evaluate its public health significance on the burden of adverse clinical outcomes of ischemic stroke. Methods: All patients were from the China Antihypertensive Trial in Acute Ischemic Stroke (CATIS). We established prognostic metrics of ischemic stroke from 20 potential biomarkers in a propensity-score-matched extreme case sample (n = 146). Pathway analysis was conducted using Ingenuity Pathway Analysis. In the whole CATIS population (n = 3575), we evaluated effectiveness of these prognostic metrics and estimated their population-attributable fractions (PAFs) related to the risk of clinical outcomes. The primary outcome was a composite outcome of death or major disability (modified Rankin Scale score ≥3) at 3 months after stroke. Results: Matrix metalloproteinase-9 (MMP-9), S100A8/A9, high-sensitivity C-reactive protein (hsCRP), and growth differentiation factor-15 (GDF-15) were selected as prognostic metrics for ischemic stroke. Pathway analysis showed significant enrichment in inflammation and atherosclerosis signaling. All 4 prognostic metrics were independently associated with poor prognosis of ischemic stroke. Compared with patients having 1 or 0 high-level prognostic metrics, those with 4 had higher risk of primary outcome (OR: 3.84, 95%CI: 2.67-5.51; PAF: 37.4%, 95%CI: 19.5%-52.9%). Conclusion: The set of prognostic metrics, enriching in inflammation and atherosclerosis signaling, could effectively predict the prognosis at 3 months after ischemic stroke and would provide additional information for the burden of adverse clinical outcomes among patients with ischemic stroke.