Prediction of Hemorrhagic Transformation After Ischemic Stroke: Development and Validation Study of a Novel Multi-biomarker Model.
Prediction of Hemorrhagic Transformation After Ischemic Stroke: Development and Validation Study of a Novel Multi-biomarker Model.
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缺血性中风后出血性转化的预测：新型多生物标志物模型的开发和验证研究
DOI：
10.3389/fnagi.2021.667934
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发表时间：
2021
影响因子：
4.8
通讯作者：
Liu M
中科院分区：
文献类型：
--
作者：
Liu J;Wang Y;Jin Y;Guo W;Song Q;Wei C;Li J;Zhang S;Liu M
目的：我们旨在开发和验证一种新的多生物标志物模型，用于预测急性缺血性卒中（AIS）后出血性转化（HT）风险。方法：我们前瞻性纳入了2016年1月1日至2019年1月31日卒中后24小时内入院的AIS患者。在该队列中测量并分析了一组17种循环生物标志物。我们评估了单个循环生物标志物和多种生物标志物组合预测AIS后任何HT、症状性HT（sHT）和实质血肿（PH）的能力。然后在288例中国患者的独立队列中对多种生物标志物联合治疗的策略进行外部验证。结果如下：共纳入1207例AIS患者（727例男性;平均年龄，67.2 ± 13.9岁）作为衍生队列，其中179例患者（14.8%）发生HT。最终的多生物标志物模型包括来自不同途径的三种生物标志物[血小板、嗜中性粒细胞与淋巴细胞比率（NLR）和高密度脂蛋白（HDL）]，在推导队列（c统计量= 0·64，95% CI 0·60-0·69）和验证队列（c统计量= 0·70，95% CI 0·58-0·82）中均显示出预测HT的良好性能。将这三种生物标志物同时加入到具有常规危险因素的基本模型中，改善了HT重新分类的能力[净重新分类改善（NRI）65.6%，P < 0.001]，PH（NRI 64.7%，P < 0.001）和sHT（NRI 71.3%，P < 0.001）。结论：这种易于应用的多生物标志物模型在推导和外部验证队列中均具有良好的预测HT的性能。将生物标志物纳入临床决策可能有助于识别AIS后HT高风险患者，并值得进一步考虑。
Objectives: We aimed to develop and validate a novel multi-biomarker model for predicting hemorrhagic transformation (HT) risk after acute ischemic stroke (AIS). Methods: We prospectively included patients with AIS admitted within 24 h of stroke from January 1st 2016 to January 31st 2019. A panel of 17 circulating biomarkers was measured and analyzed in this cohort. We assessed the ability of individual circulating biomarkers and the combination of multiple biomarkers to predict any HT, symptomatic HT (sHT) and parenchymal hematoma (PH) after AIS. The strategy of multiple biomarkers in combination was then externally validated in an independent cohort of 288 Chinese patients. Results: A total of 1207 patients with AIS (727 males; mean age, 67.2 ± 13.9 years) were included as a derivation cohort, of whom 179 patients (14.8%) developed HT. The final multi-biomarker model included three biomarkers [platelets, neutrophil-to-lymphocyte ratios (NLR), and high-density lipoprotein (HDL)] from different pathways, showing a good performance for predicting HT in both the derivation cohort (c statistic = 0·64, 95% CI 0·60–0·69), and validation cohort (c statistic = 0·70, 95% CI 0·58–0·82). Adding these three biomarkers simultaneously to the basic model with conventional risk factors improved the ability of HT reclassification [net reclassification improvement (NRI) 65.6%, P < 0.001], PH (NRI 64.7%, P < 0.001), and sHT (NRI 71.3%, P < 0.001). Conclusion: This easily applied multi-biomarker model had a good performance for predicting HT in both the derivation and external validation cohorts. Incorporation of biomarkers into clinical decision making may help to identify patients at high risk of HT after AIS and warrants further consideration.
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作者：
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作者：
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通讯作者：
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