动脉瘤性蛛网膜下腔出血预后列线图数据表

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Development and Clinical Translation of a Perioperative Nomogram Incorporating Free Fatty Acids to Predict Poor Outcome of Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment.PDF
• DOI：10.3389/fneur.2021.629997.s001
• 发布日期：2021-07-26
• 文件数：1 个
• 文件总体积：1.25 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Development_and_Clinical_Translation_of_a_Perioperative_Nomogram_Incorporating_Free_Fatty_Acids_to_Predict_Poor_Outcome_of_Aneurysmal_Subarachnoid_Hemorrhage_Following_Endovascular_Treatment_PDF/15052776

命令行下载

curl -L -o "Data_Sheet_1_Development and Clinical Translation of a Perioperative Nomogram Incorporating Free Fatty Acids to Predict Poor Outcome of Aneurysmal Sub.PDF" "https://ndownloader.figshare.com/files/28949499"

文件清单

• Data_Sheet_1_Development and Clinical Translation of a Perioperative Nomogram Incorporating Free Fatty Acids to Predict Poor Outcome of Aneurysmal Sub.PDF（1.25 MB）

文件总体积 1.25 MB（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

Objective: A reliable prediction of clinical outcome is important for clinicians to set appropriate medical strategies in treating patients with aneurysmal subarachnoid hemorrhage (aSAH). In this study, we aim to establish a perioperative nomogram involving serum lipid signatures for predicting poor outcomes at 3 months in patients with aSAH following endovascular therapy.
Methods: Data of patients with aSAH receiving endovascular therapy were collected. Univariable and multivariable analyses were performed to screen independent predictors related to unfavorable outcomes defined by the modified Rankin Scale (mFS) ≥3. A novel nomogram based on these significant features was conducted. The clinical application of this nomogram was assessed by decision curve analysis (DCA) and clinical impact curve.
Results: A total number of patients included in this study were 213 (average age 58.9 years, 65.7% female), representing a poor 3-month outcome rate of 48.8%. Free fatty acid (FFA) levels on admission were efficient in predicting poor outcomes compared with other contents in serum lipids. Univariable and multivariable analyses revealed advanced age (P = 0.034), poor Hunt Hess (HH) (odds ratio, OR = 3.7, P Conclusion: Nomogram involving age, HH grade, mFS, aneurysm location, and serum FFA levels was sufficient to provide an individualized prediction of 3-month poor outcome for each patient with aSAH who underwent endovascular therapy.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2021.629997.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Development_and_Clinical_Translation_of_a_Perioperative_Nomogram_Incorporating_Free_Fatty_Acids_to_Predict_Poor_Outcome_of_Aneurysmal_Subarachnoid_Hemorrhage_Following_Endovascular_Treatment_PDF/15052776）。