EGAN评分预测卒中溶栓后症状性颅内出血数据集

基于655名急性缺血性卒中患者构建的EGAN评分模型,用于预测静脉溶栓后症状性颅内出血风险,包含4个临床预测因子。

Taylor & FrancisTaylor & Francis
DataCite Commons
2026-01-22 更新
浏览 17
表格Prognostic scoreintravenous thrombolysisalteplasesymptomatic intracranial hemorrhageacute ischemic stroke
A simple scoring to predict symptomatic intracranial hemorrhage after stroke thrombolysis: the EGAN score

基本信息

模态
表格
大小
0.000194989 GB
许可
CC BY 4.0
创建/更新时间
2026-01-22
版本
v1

资源简介

EGAN评分预测卒中溶栓后症状性颅内出血数据集

【一句话概览】

本数据集是 figshare 公开的结构化表格数据集,收录 2 个文件、合计 194.99 KB,主题涵盖 Prognostic score、intravenous thrombolysis、alteplase、symptomatic intracranial hemorrhage、acute ischemic stroke,可用于统计建模、特征工程与队列分析,CC BY 4.0 许可开放获取。

【数据集背景】

本数据集源站标题为《A simple scoring to predict symptomatic intracranial hemorrhage after stroke thrombolysis: the EGAN score》。配套论文《A simple scoring to predict symptomatic intracranial hemorrhage after stroke thrombolysis: the EGAN score》发表于 Neurological Research(2025),DOI:10.1080/01616412.2025.2495989。项目资助方:The authors are grateful to the ICF International and IDHS for providing the data set used to undertake this study, and Universitas Indonesia for funding this study through PUTI Grant with contract number (BA-647/UN2.RST/PPM.00.03.01/2021).。源站首次发布:2025-05-02。

【数据内容】

源站原始描述:Symptomatic intracranial hemorrhage (sICH) after intravenous thrombolysis represents a critical and fatal complication observed in acute ischemic stroke (AIS) patients. This study aims to establish a simple scoring model to predict sICH. We retrospectively conducted a cohort study of eligible AIS patients treated with rt-PA at a tertiary comprehensive stroke center from January 2018 to December 2022. Backward stepwise multivariable logistic regression provided the final model. The point score wa…

【数据结构与技术规格】

文件数 2 个,合计 194.99 KB。Supplementary_Material (1).xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet,103.39 KB。TRIPOD Guidelines Checklist_EGAN Score.docx:application/vnd.openxmlformats-officedocument.wordprocessingml.document,91.60 KB。数据集 DOI:10.6084/m9.figshare.28919987.v1。

【主题与分类】

源站学科分类:Medicine、Neuroscience、Biotechnology、Biological Sciences not elsewhere classified、Science Policy、Mental Health。主题标签:Prognostic score、intravenous thrombolysis、alteplase、symptomatic intracranial hemorrhage、acute ischemic stroke。

【适用方向】

结构化表格数据可直接用于统计建模、特征工程与队列分析。

【获取与许可】

源站页面:https://tandf.figshare.com/articles/dataset/A_simple_scoring_to_predict_symptomatic_intracranial_hemorrhage_after_stroke_thrombolysis_the_EGAN_score/28919987

使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。

规范引用:A simple scoring to predict symptomatic intracranial hemorrhage after stroke thrombolysis: the EGAN score. Taylor & Francis. Rilianto, Beny; Helda; Adisasmita, Asri C.; Rahmartani, Lhuri Dwianti; Pandhita, Gea; Kurniawan, Ricky Gusanto; et al

【使用注意】

· 源站最后修改于 2026-01-22,引用时请注意版本时效。

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下载信息

注册下载

需要注册 Kaggle 账号并登录后下载,适合需要跟踪下载记录和使用 API 的用户。

暂未开放

公开下载

无需注册即可直接获取公开样本或文档,适合快速预览和评估数据集质量。

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有偿下载

公开数据集受托下载与技术交付服务。

提供高速下载与技术交付服务(收技术服务费,非数据销售)

暂未开放

当前数据集主要来源为 Kaggle 公开托管,完整影像包建议通过原始链接或 Kaggle API 获取。

使用方式

数据集获取(Figshare)

命令行下载

curl -L -o "Supplementary_Material (1).xlsx" "https://ndownloader.figshare.com/files/54145385"
curl -L -o "TRIPOD Guidelines Checklist_EGAN Score.docx" "https://ndownloader.figshare.com/files/54145388"

文件清单

  • Supplementary_Material (1).xlsx(103.39 KB)
  • TRIPOD Guidelines Checklist_EGAN Score.docx(91.60 KB)

文件总体积 194.99 KB(源站 API 实测)。

数据加载示例(图像类)

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Symptomatic intracranial hemorrhage (sICH) after intravenous thrombolysis represents a critical and fatal complication observed in acute ischemic stroke (AIS) patients. This study aims to establish a simple scoring model to predict sICH.
We retrospectively conducted a cohort study of eligible AIS patients treated with rt-PA at a tertiary comprehensive stroke center from January 2018 to December 2022. Backward stepwise multivariable logistic regression provided the final model. The point score was generated from β-coefficients. The area under the curve (AUC) of the receiver operating characteristics (ROC) and the Hosmer–Lemeshow goodness-of-fit test were used to assess the discrimination and calibration of the model. The conditional probabilities were derived based on the Bayes theorem.
Of the included patients, sICH occurred in 26 (3.97%) of the 655. The EGAN score consisted of an early infarct sign (10 points), baseline glucose ≥200 mg/dL (11 points), atrial fibrillation (AF) (13 points), and an NIH Stroke Scale (NIHSS) score ≥10 (12 points). With a cut-off point of 13, the EGAN score demonstrated good discrimination (0.7453 [95% CI: 0.649–0.841]), sensitivity (80.77%), and specificity (58.19%), respectively, for identifying sICH.
This easy-to-use scoring model, based on predictors quickly obtained in clinical practices, offers a simple approach to screening for post-thrombolysis sICH and can serve as an alternative option in hospitals with limited resources for thrombolysis therapy.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.28919987.v1(source: https://tandf.figshare.com/articles/dataset/A_simple_scoring_to_predict_symptomatic_intracranial_hemorrhage_after_stroke_thrombolysis_the_EGAN_score/28919987)。

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