急性胰腺炎中急性肾损伤预测模型的系统综述与荟萃分析（表1）

数据集获取（Figshare）

• 源站标题：Table 1_Predictive models for acute kidney injury in acute pancreatitis: a systematic review and meta-analysis.docx
• DOI：10.3389/fmed.2025.1699717.s001
• 发布日期：2026-02-05
• 文件数：1 个
• 文件总体积：53.74 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Predictive_models_for_acute_kidney_injury_in_acute_pancreatitis_a_systematic_review_and_meta-analysis_docx/31267264

命令行下载

curl -L -o "Table 1_Predictive models for acute kidney injury in acute pancreatitis: a systematic review and meta-analysis.docx" "https://ndownloader.figshare.com/files/61665193"

文件清单

• Table 1_Predictive models for acute kidney injury in acute pancreatitis: a systematic review and meta-analysis.docx（53.74 KB）

文件总体积 53.74 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

BackgroundThe utilization of predictive models facilitates the identification of patients at risk, thereby enabling the implementation of individualized interventions. Despite the growing use of predictive models to estimate the likelihood of AKI in AP, concerns persist regarding their effectiveness in clinical settings and the rigor and relevance of forthcoming research. The objective of this study is to systematically review and evaluate predictive models for AKI in AP.
MethodsA comprehensive search of relevant databases was conducted, encompassing China National Knowledge Infrastructure (CNKI), Wanfang, VIP, Chinese Medical Association, PubMed, Web of Science, Scopus, and Cochrane Library, with the search extending from database inception to 26 November 2024. The data from a number of selected studies was extracted using the CHARMS form, while the quality of predictive modeling studies was assessed using the PROBAST tool. A meta-analysis of AUC for predictive models and relevant predictors (≥2) was conducted using Stata 17.0 and MedCalc software.
ResultsThe total number of studies included in the review was 17, with a total of 9,949 patients and 37 predictive models. Of these, 32 models underwent internal validation, with an area under the curve (AUC) > 0.7. The overall risk of bias was high across all 17 studies, yet the overall applicability was deemed satisfactory. The results of the meta-analysis indicated that the pooled AUC for internal validation across 20 predictive models for AKI in AP was 0.790 (95% CI = 0.761–0.818); and the pooled external validation AUC for five models was 0.766 (95% CI = 0.684–0.845). The overall risk of bias was high across all 17 studies, with significant heterogeneity observed. However, the overall applicability was deemed satisfactory.
ConclusionThe predictive model for AKI complicating AP demonstrates moderate predictive efficacy. Nevertheless, given the elevated risk of bias in the majority of studies and the absence of adequate external validation, its clinical applicability merits further investigation.
Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD420251008769, identifier CRD420251008769.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2025.1699717.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Predictive_models_for_acute_kidney_injury_in_acute_pancreatitis_a_systematic_review_and_meta-analysis_docx/31267264）。