Department of Neurology Hospital de Egas Moniz, Centro Hospitalar Lisboa Ocidental Lisbon Portugal.
Centro Clínico Académico de Lisboa (CCAL), NOVA Medical School (MNS) Lisbon Portugal.
J Am Heart Assoc. 2023 Nov 21;12(22):e030021. doi: 10.1161/JAHA.123.030021. Epub 2023 Nov 10.
Blood biomarkers are a potential tool for early stroke diagnosis. We aimed to perform a pilot and exploratory study on untargeted blood biomarkers in patients with suspected stroke by using mass spectrometry analysis.
This was a prospective observational study of consecutive patients with suspected stroke admitted within 6 hours of last being seen well. Blood samples were collected at admission. Patients were divided into 3 groups: ischemic stroke (IS), intracerebral hemorrhage (ICH), and stroke mimics. Quantitative analysis from mass spectrometry data was performed using a supervised approach. Biomarker-based prediction models were developed to differentiate IS from ICH and ICH+stroke mimics. Models were built aiming to minimize misidentification of patients with ICH as having IS. We included 90 patients, one-third within each subgroup. The median age was 71 years (interquartile range, 57-81 years), and 49 participants (54.4%) were women. In quantitative analysis, C3 (complement component 3), ICAM-2 (intercellular adhesion molecule 2), PLGLA (plasminogen like A), STXBP5 (syntaxin-binding protein 5), and IGHV3-64 (immunoglobulin heavy variable 3-64) were the 5 most significantly dysregulated proteins for both comparisons. Biomarker-based models showed 88% sensitivity and 89% negative predictive value for differentiating IS from ICH, and 75% sensitivity and 95% negative predictive value for differentiating IS from ICH+stroke mimics. ICAM-2, STXBP5, PLGLA, C3, and IGHV3-64 displayed the highest importance score in our models, being the most informative for identifying patients with stroke.
In this proof-of-concept and exploratory study, our biomarker-based prediction models, including ICAM-2, STXBP5, PLGLA, C3, and IGHV3-64, showed 75% to 88% sensitivity for identifying patients with IS, while aiming to minimize misclassification of ICH. Although our methodology provided an internal validation, these results still need validation in other cohorts and with different measurement techniques.
血液生物标志物是早期中风诊断的一种潜在工具。我们旨在通过质谱分析对疑似中风患者进行非靶向性血液生物标志物的初步探索性研究。
这是一项连续纳入发病后 6 小时内就诊的疑似中风患者的前瞻性观察性研究。入院时采集血样。患者分为 3 组：缺血性中风（IS）、颅内出血（ICH）和中风模拟组。采用有监督方法对质谱数据进行定量分析。建立基于生物标志物的预测模型以区分 IS 与 ICH 和 ICH+中风模拟组。模型的建立旨在尽量减少将 ICH 患者误诊为 IS。我们纳入了 90 例患者，每组各三分之一。中位年龄为 71 岁（四分位距 57-81 岁），49 名患者（54.4%）为女性。在定量分析中，C3（补体成分 3）、ICAM-2（细胞间黏附分子 2）、PLGLA（纤溶酶原样 A）、STXBP5（突触结合蛋白 5）和 IGHV3-64（免疫球蛋白重链可变区 3-64）是两种比较中差异最显著的 5 个失调蛋白。基于生物标志物的模型在区分 IS 与 ICH 时的灵敏度为 88%，阴性预测值为 89%，在区分 IS 与 ICH+中风模拟组时的灵敏度为 75%，阴性预测值为 95%。ICAM-2、STXBP5、PLGLA、C3 和 IGHV3-64 在我们的模型中显示出最高的重要性评分，是识别中风患者最有价值的信息。
在这项概念验证和探索性研究中，我们的基于生物标志物的预测模型包括 ICAM-2、STXBP5、PLGLA、C3 和 IGHV3-64，在识别 IS 患者时具有 75%至 88%的灵敏度，同时尽量减少 ICH 的误诊。尽管我们的方法提供了内部验证，但这些结果仍需在其他队列和使用不同测量技术中进行验证。
