脓毒症相关急性肾损伤死亡率风险机器学习预测模型数据集（早期与晚期CRRT启动评估）

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Machine learning prediction models for mortality risk in sepsis-associated acute kidney injury: evaluating early versus late CRRT initiation.docx
• DOI：10.3389/fmed.2024.1483710.s001
• 发布日期：2025-01-22
• 文件数：1 个
• 文件总体积：1.54 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_learning_prediction_models_for_mortality_risk_in_sepsis-associated_acute_kidney_injury_evaluating_early_versus_late_CRRT_initiation_docx/28253501

命令行下载

curl -L -o "Data Sheet 1_Machine learning prediction models for mortality risk in sepsis-associated acute kidney injury: evaluating early versus late CRRT initiat.docx" "https://ndownloader.figshare.com/files/51838964"

文件清单

• Data Sheet 1_Machine learning prediction models for mortality risk in sepsis-associated acute kidney injury: evaluating early versus late CRRT initiat.docx（1.54 MB）

文件总体积 1.54 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

BackgroundSepsis-associated acute kidney injury (S-AKI) has a significant impact on patient survival, with continuous renal replacement therapy (CRRT) being a crucial intervention. However, the optimal timing for CRRT initiation remains controversial.
MethodsUsing the MIMIC-IV database for model development and the eICU database for external validation, we analyzed patients with S-AKI to compare survival rates between early and late CRRT initiation groups. Propensity score matching was performed to address potential selection bias. Subgroup analyses stratified patients by disease severity using SOFA scores (low ≤10, medium 11–15, high >15) and creatinine levels (low ≤3 mg/dL, medium 3–5 mg/dL, high >5 mg/dL). Multiple machine learning models were developed and evaluated to predict patient prognosis, with Shapley Additive exPlanations (SHAP) analysis identifying key prognostic factors.
ResultsAfter propensity score matching, late CRRT initiation was associated with improved survival probability, but led to increased hospital and ICU stays. Subgroup analyses showed consistent trends favoring late CRRT across all SOFA categories, with the most pronounced effect in high SOFA scores (>15, p = 0.058). The GBM model demonstrated robust predictive performance (average C-index 0.694 in validation and test sets). SHAP analysis identified maximum lactate levels, age, and minimum SpO2 as the strongest predictors of mortality, while CRRT timing showed relatively lower impact on outcome prediction.
ConclusionWhile later initiation of CRRT in S-AKI patients was associated with improved survival, this benefit comes with increased healthcare resource utilization. The clinical parameters, rather than CRRT timing, are the primary determinants of patient outcomes, suggesting the need for a more personalized approach to CRRT initiation based on overall illness severity.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2024.1483710.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_learning_prediction_models_for_mortality_risk_in_sepsis-associated_acute_kidney_injury_evaluating_early_versus_late_CRRT_initiation_docx/28253501）。