Development and Validation of a Predictive Model for Spontaneous Hemorrhagic Transformation After Ischemic Stroke.
Development and Validation of a Predictive Model for Spontaneous Hemorrhagic Transformation After Ischemic Stroke.
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缺血性中风后自发性出血性转化预测模型的开发和验证
DOI：
10.3389/fneur.2021.747026
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发表时间：
2021
影响因子：
3.4
通讯作者：
Liu M
中科院分区：
文献类型：
--
作者：
Wei C;Liu J;Guo W;Jin Y;Song Q;Wang Y;Ye C;Li J;Zhang S;Liu M
背景资料：急性缺血性卒中（AIS）再灌注治疗后的出血性转化（HT）已得到充分研究;然而，缺乏针对自发性HT（sHT）的研究。自发性HT同样重要，其发生率相对较高，可能与神经系统恶化相关。我们的目的是开发和验证一种简单实用的模型来预测AIS后的sHT（SHAIS），并将SHAIS评分的预测值与再灌注后HT模型对sHT的预测值进行比较。方法：前瞻性筛选发病24小时内入院的AIS患者，以开发和验证SHAIS评分。主要结局为住院期间（发病后30天内）的sHT，次要结局为症状性sHT和脑实质血肿（PH）。筛选临床信息、实验室和神经影像学数据以构建SHAIS评分。我们选择了六种常用的量表来预测再灌注治疗后的HT，并使用Delong检验将其对sHT的预测能力与SHAIS评分进行比较。结果如下：衍生队列包括539例患者（平均年龄68.1岁;男性，61.4%），其中91（16.9%）的患者发生sHT，其中25.3%（23/91）为症状性sHT，62.6%（57/91）为PH。SHAIS评分标准为0 ~ 11分（房颤、NIHSS评分≥ 10分、大脑中动脉供血区低密度> 1/3、高密度动脉征、前循环梗死）。受试者工作特征曲线下面积（AUC）为0.86（95% CI 0.82-0.91，p < 0.001）总体sHT，0.85（95% CI 0.76-0.92，p < 0.001），症状性sHT为0.89（95% CI 0.85-0.94，p < 0.001）。没有发现SHAIS评分的错误校准的证据来预测总体sHT（p = 0.19）、症状性sHT（p = 0.44）和PH（p = 0.22）。与推导队列相比，内部（n = 245）和外部验证队列（n = 200）描述了相似的预测性能。SHAIS评分预测sHT的AUC高于六个预先存在的模型中的任何一个（p < 0.05）。结论：SHAIS评分提供了一个易于使用的模型来预测sHT，这可以帮助提供者做出关于高出血风险治疗的决策，并就HT的基线风险为患者和家庭提供咨询，使期望与可能的结果保持一致。
Background: Hemorrhagic transformation (HT) after reperfusion therapy for acute ischemic stroke (AIS) has been well studied; however, there is scarce research focusing on spontaneous HT (sHT). Spontaneous HT is no less important with a relatively high incidence and could be associated with neurological worsening. We aimed to develop and validate a simple and practical model to predict sHT after AIS (SHAIS) and compared the predictive value of the SHAIS score against the models of post-Reperfusion HT for sHT. Methods: Patients with AIS admitted within 24 h of onset were prospectively screened to develop and validate the SHAIS score. The primary outcome was sHT during hospitalization (within 30 days after onset), and the secondary outcomes were symptomatic sHT and parenchymal hematoma (PH). Clinical information, laboratory, and neuroimaging data were screened to construct the SHAIS score. We selected six commonly used scales for predicting HT after reperfusion therapy and compared their predictive ability for sHT with the SHAIS score using Delong's test. Results: The derivation cohort included 539 patients (mean age, 68.1 years; men, 61.4%), of whom 91 (16.9%) patients developed sHT with 25.3% (23/91) being symptomatic sHT and 62.6% (57/91) being PH. Five variables (atrial fibrillation, NIHSS score ≥ 10, hypodensity > 1/3 of middle cerebral artery territory, hyperdense artery sign, and anterior circulation infarction) composed the SHAIS score, which ranged from 0 to 11 points. The area under the receiver-operating characteristic curve (AUC) was 0.86 (95% CI 0.82–0.91, p < 0.001) for the overall sHT, 0.85 (95% CI 0.76–0.92, p < 0.001) for symptomatic sHT, and 0.89 (95% CI 0.85–0.94, p < 0.001) for PH. No evidence of miscalibration of the SHAIS score was found to predict the overall sHT (p = 0.19), symptomatic sHT (p = 0.44), and PH (p = 0.22). The internal (n = 245) and external validation cohorts (n = 200) depicted similar predictive performance compared to the derivation cohort. The SHAIS score had a higher AUC to predict sHT than any of the six pre-Existing models (p < 0.05). Conclusions: The SHAIS score provides an easy-to-use model to predict sHT, which could help providers with decision-making about treatments with high bleeding risk, and to counsel patients and families on the baseline risk of HT, aligning expectations with probable outcomes.
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影响因子：
2.6
作者：
Kalinin MN;Khasanova DR;Ibatullin MM
通讯作者：
Ibatullin MM
DOI：
10.1016/j.jstrokecerebrovasdis.2013.06.005
发表时间：
2014-04-01
影响因子：
2.5
作者：
D'Amelio, Marco;Terruso, Valeria;Aridon, Paolo
通讯作者：
Aridon, Paolo
影响因子：
1.9
作者：
Chen, Guojuan;Wang, Anxin;Wang, Yilong
通讯作者：
Wang, Yilong
影响因子：
158.5
作者：
Hacke, Werner;Kaste, Markku;Toni, Danilo
通讯作者：
Toni, Danilo
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
影响因子：
4.8
作者：
Liu J;Wang Y;Jin Y;Guo W;Song Q;Wei C;Li J;Zhang S;Liu M
通讯作者：
Liu M