Department of Neurology, West China Hospital, Sichuan University, Chengdu, China.
Int J Stroke. 2023 Mar;18(3):338-345. doi: 10.1177/17474930221106858. Epub 2022 Jul 6.
Prediction models/scores may help to identify patients at high risk of symptomatic intracerebral hemorrhage (sICH) after intravenous thrombolysis. We aimed to validate and compare the performance of different prediction models for sICH after thrombolysis using direct model estimation in the Virtual International Stroke Trials Archive (VISTA).
We searched PubMed for potentially eligible prediction models from inception to 1 June 2019. Simple and practical models/scores were validated in VISTA. The primary outcome was sICH based on two criteria (National Institute of Neurological Diseases and Stroke, NINDS; Safe Implementation of Thrombolysis in Stroke-Monitoring Study, SITS-MOST) and the secondary outcome was parenchymal hematoma (PH). The discrimination performance of each model was evaluated using area under the curve (AUC) and calibration was evaluated by Hosmer-Lemeshow goodness-of-fit tests.
We found 13 prediction models and five models (HAT, MSS, SPAN-100, GRASPS and THRIVE) were finally validated in VISTA. A total of 1884 participants were eligible for our study, of whom the proportion with sICH was 4.6% (87/1884) per NINDS and 3.9% (73/1884) per SITS-MOST, and with PH was 11.3% (213/1884). MSS and GRASPS had the greatest predictive ability for sICH (NINDS criteria: MSS AUC 0.7, 95% CI 0.63-0.77, < 0.001; GRASPS AUC 0.69, 95% CI 0.63-0.76, < 0.001; SITS-MOST criteria: MSS, AUC 0.76, 95% CI 0.68-0.85, < 0.001; GRASPS, AUC 0.79, 95% CI 0.71-0.87, < 0.001). Similar results were found for PH (MSS AUC 0.68, 95% CI 0.64-0.73, = 0.017; GRASPS AUC 0.68, 95% CI 0.63-0.72, = 0.017). The calibration of each model was almost good.
MSS and GRASPS had good discrimination and calibration for sICH and PH after thrombolysis as assessed in VISTA. These two models could be used in clinical practice and clinical trials to identity individuals with high risk of sICH.
预测模型/评分有助于识别静脉溶栓后发生症状性颅内出血（sICH）的高危患者。我们旨在通过虚拟国际卒中试验档案（VISTA）中的直接模型评估，验证和比较不同溶栓后 sICH 预测模型的性能。
我们从 VISTA 中检索了从成立到 2019 年 6 月 1 日的潜在合格预测模型。对简单实用的模型/评分进行了验证。主要结局为基于两个标准（国立神经病学与卒中研究院，NINDS；溶栓治疗后卒中监测研究，SITS-MOST）的 sICH，次要结局为实质血肿（PH）。使用曲线下面积（AUC）评估每个模型的判别性能，并通过 Hosmer-Lemeshow 拟合优度检验评估校准。
我们共发现了 13 个预测模型，其中 5 个模型（HAT、MSS、SPAN-100、GRASPS 和 THRIVE）最终在 VISTA 中得到了验证。共有 1884 名参与者符合我们的研究标准，其中根据 NINDS 标准，sICH 的比例为 4.6%（87/1884），根据 SITS-MOST 标准，sICH 的比例为 3.9%（73/1884），PH 的比例为 11.3%（213/1884）。MSS 和 GRASPS 对 sICH 具有最大的预测能力（NINDS 标准：MSS AUC 0.7，95%CI 0.63-0.77，<0.001；GRASPS AUC 0.69，95%CI 0.63-0.76，<0.001；SITS-MOST 标准：MSS，AUC 0.76，95%CI 0.68-0.85，<0.001；GRASPS，AUC 0.79，95%CI 0.71-0.87，<0.001）。对于 PH 也得到了类似的结果（MSS AUC 0.68，95%CI 0.64-0.73，=0.017；GRASPS AUC 0.68，95%CI 0.63-0.72，=0.017）。每个模型的校准几乎都很好。
MSS 和 GRASPS 在 VISTA 中评估的溶栓后 sICH 和 PH 具有良好的判别和校准能力。这两种模型可用于临床实践和临床试验，以识别 sICH 风险较高的个体。
