基于MIMIC-IV数据库的脓毒症患者接受体外膜氧合和/或持续性肾脏替代治疗死亡风险因素的回顾性队列研究

数据集获取（Figshare）

• 源站标题：Risk factors for mortality in patients with sepsis on extracorporeal membrane oxygenation and/or continuous renal replacement therapy: a retrospective cohort study based on MIMIC-IV database
• DOI：10.6084/m9.figshare.27965171.v1
• 发布日期：2024-12-05
• 文件数：2 个
• 文件总体积：5.88 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Risk_factors_for_mortality_in_patients_with_sepsis_on_extracorporeal_membrane_oxygenation_and_or_continuous_renal_replacement_therapy_a_retrospective_cohort_study_based_on_MIMIC-IV_database/27965171

命令行下载

curl -L -o "irnf_a_2436106_sm5809.tif" "https://ndownloader.figshare.com/files/50988304"
curl -L -o "irnf_a_2436106_sm5808.tif" "https://ndownloader.figshare.com/files/50988307"

文件清单

• irnf_a_2436106_sm5809.tif（1.37 MB）
• irnf_a_2436106_sm5808.tif（4.51 MB）

文件总体积 5.88 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

This study aimed to identify risk factors for mortality in septic patients undergoing extracorporeal membrane oxygenation (ECMO) and/or continuous renal replacement therapy (CRRT).
Data from the MIMIC-IV database were retrospectively reviewed for 24,502 septic patients treated with ECMO or CRRT between 2008 and 2019. After applying inclusion and exclusion criteria, 70 patients receiving ECMO, 513 receiving CRRT, and 22 receiving both were included in the final analysis. Univariate and multivariate stepwise Cox regression analyses were performed to identify independent risk factors for mortality. Model performance was assessed using receiver operating characteristic (ROC) curve analysis. We also provided model-agnostic explanations for each Cox regression model.
For septic patients on ECMO, prothrombin time (per 1-s increase, HR 1.037, 95% CI 1.007–1.068, p = .015) was the key independent risk factor. For septic patients undergoing CRRT, SOFA score (per one-point increase, HR 1.100, 95% CI 1.055–1.147, p p = .015) was the sole independent risk factor. ROC analysis showed satisfactory model performance (AUC > 0.75).
For septic patients requiring ECMO, prothrombin time was the key independent risk factor. For those needing CRRT, SOFA score was the most significant independent risk factor. Prior history of hypertension was the primary independent risk factor for septic patients needing both CRRT and ECMO.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.27965171.v1（source: https://tandf.figshare.com/articles/dataset/Risk_factors_for_mortality_in_patients_with_sepsis_on_extracorporeal_membrane_oxygenation_and_or_continuous_renal_replacement_therapy_a_retrospective_cohort_study_based_on_MIMIC-IV_database/27965171）。