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

Abstract

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.