预测冠状动脉造影患者造影剂相关急性肾损伤的术前列线图

数据集获取（Figshare）

• 源站标题：Table_1_An Online Pre-procedural Nomogram for the Prediction of Contrast-Associated Acute Kidney Injury in Patients Undergoing Coronary Angiography.pdf
• DOI：10.3389/fmed.2022.839856.s001
• 发布日期：2022-03-11
• 文件数：1 个
• 文件总体积：122.99 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_An_Online_Pre-procedural_Nomogram_for_the_Prediction_of_Contrast-Associated_Acute_Kidney_Injury_in_Patients_Undergoing_Coronary_Angiography_pdf/19343264

命令行下载

curl -L -o "Table_1_An Online Pre-procedural Nomogram for the Prediction of Contrast-Associated Acute Kidney Injury in Patients Undergoing Coronary Angiography.pdf" "https://ndownloader.figshare.com/files/34350944"

文件清单

• Table_1_An Online Pre-procedural Nomogram for the Prediction of Contrast-Associated Acute Kidney Injury in Patients Undergoing Coronary Angiography.pdf（122.99 KB）

文件总体积 122.99 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

BackgroundIdentifying high-risk patients for contrast-associated acute kidney injury (CA-AKI) helps to take early preventive interventions. The current study aimed to establish and validate an online pre-procedural nomogram for CA-AKI in patients undergoing coronary angiography (CAG).
MethodsIn this retrospective dataset, 4,295 patients undergoing CAG were enrolled and randomized into the training or testing dataset with a split ratio of 8:2. Optimal predictors for CA-AKI were determined by Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) algorithm. Nomogram was developed and deployed online. The discrimination and accuracy of the nomogram were evaluated by receiver operating characteristic (ROC) and calibration analysis, respectively. Clinical usefulness was estimated by decision curve analysis (DCA) and clinical impact curve (CIC).
ResultsA total of 755 patients (17.1%) was diagnosed with CA-AKI. 7 pre-procedural predictors were identified and integrated into the nomogram, including age, gender, hemoglobin, N-terminal of the prohormone brain natriuretic peptide, neutrophil-to-lymphocyte ratio, cardiac troponin I, and loop diuretics use. The ROC analyses showed that the nomogram had a good discrimination performance for CA-AKI in the training dataset (area under the curve, AUC = 0.766, 95%CI [0.737 to 0.794]) and testing dataset (AUC = 0.737, 95%CI [0.693 to 0.780]). The nomogram was also well-calibrated in both the training dataset (P = 0.965) and the testing dataset (P = 0.789). Good clinical usefulness was identified by DCA and CIC. Finally, this model was deployed in a web server for public use (https://duanbin-li.shinyapps.io/DynNomapp/).
ConclusionAn easy-to-use pre-procedural nomogram for predicting CA-AKI was established and validated in patients undergoing CAG, which was also deployed online.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2022.839856.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_An_Online_Pre-procedural_Nomogram_for_the_Prediction_of_Contrast-Associated_Acute_Kidney_Injury_in_Patients_Undergoing_Coronary_Angiography_pdf/19343264）。