Bicvic Antonela, Scherrer Natalie, Schweizer Juliane, Fluri Felix, Christ-Crain Mirjam, De Marchis Gian Marco, Luft Andreas R, Katan Mira
Department of Neurology, University Hospital of Zurich, Zurich, Switzerland.
Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Eur Stroke J. 2022 Jun;7(2):158-165. doi: 10.1177/23969873221090798. Epub 2022 Apr 20.
We investigated 92 blood biomarkers implicated in the pathophysiological pathways of ischemic injury, inflammation, hemostasis, and regulation of vascular resistance to predict post-stroke mortality.
Based on the most promising markers, we aimed to create a novel Biomarker Panel Index (BPI) for risk stratification.
In this prospective study, we measured 92 biomarkers in 320 stroke patients. The primary outcome measure was mortality within 90 days. We estimated the association of each biomarker using logistic regression adjusting for multiple testing. The most significant 16 biomarkers were used to create the BPI. We fitted regression models to estimate the association and the discriminatory accuracy of the BPI with mortality and stroke etiology.
Adjusted for demographic and vascular covariates, the BPI remained independently associated with mortality (odds ratio (OR) 1.68, 95% confidence interval (CI): 1.29-2.18) and cardioembolic stroke etiology (OR 1.38, 95% CI: 1.10-1.74), and improved the discriminatory accuracy to predict mortality (area under the receiver operating characteristic curve (AUC) 0.93, 95% CI: 0.89-0.96) and cardioembolic stroke etiology (AUC 0.70, 95% CI: 0.64-0.77) as compared to the best clinical prediction models alone (AUC 0.89, 95% CI: 0.84-0.94 and AUC 0.66, 95% CI: 0.60-0.73, respectively).
We identified a novel BPI improving risk stratification for mortality after ischemic stroke beyond established demographic and vascular risk factors. Furthermore, the BPI is associated with underlying cardioembolic stroke etiology. These results need external validation.
我们研究了92种与缺血性损伤、炎症、止血及血管阻力调节的病理生理途径相关的血液生物标志物，以预测卒中后死亡率。
基于最具前景的标志物，我们旨在创建一种用于风险分层的新型生物标志物组合指数（BPI）。
在这项前瞻性研究中，我们测量了320例卒中患者的92种生物标志物。主要结局指标为90天内的死亡率。我们使用逻辑回归并针对多重检验进行校正来估计每种生物标志物的相关性。使用最显著的16种生物标志物来创建BPI。我们拟合回归模型以估计BPI与死亡率及卒中病因的相关性和鉴别准确性。
在对人口统计学和血管协变量进行校正后，BPI仍与死亡率（优势比（OR）1.68，95%置信区间（CI）：1.29 - 2.18）和心源性栓塞性卒中病因（OR 1.38，95% CI：1.10 - 1.74）独立相关，并且与单独使用最佳临床预测模型相比，提高了预测死亡率（受试者工作特征曲线下面积（AUC）0.93，95% CI：0.89 - 0.96）和心源性栓塞性卒中病因（AUC 0.70，95% CI：0.64 - 0.77）的鉴别准确性（分别为AUC 0.89，95% CI：0.84 - 0.94和AUC 0.66，95% CI：0.60 - 0.73）。
我们确定了一种新型BPI，它在已有的人口统计学和血管危险因素之外，改善了缺血性卒中后死亡率的风险分层。此外，BPI与潜在的心源性栓塞性卒中病因相关。这些结果需要外部验证。
