失代偿期肝硬化患者自发性细菌性腹膜炎非侵入性诊断列线图数据表

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Establishment and Validation of a Non-Invasive Diagnostic Nomogram to Identify Spontaneous Bacterial Peritonitis in Patients With Decompensated Cirrhosis.docx
• DOI：10.3389/fmed.2021.797363.s001
• 发布日期：2022-01-31
• 文件数：1 个
• 文件总体积：477.78 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Establishment_and_Validation_of_a_Non-Invasive_Diagnostic_Nomogram_to_Identify_Spontaneous_Bacterial_Peritonitis_in_Patients_With_Decompensated_Cirrhosis_docx/19096802

命令行下载

curl -L -o "Data_Sheet_1_Establishment and Validation of a Non-Invasive Diagnostic Nomogram to Identify Spontaneous Bacterial Peritonitis in Patients With Decompe.docx" "https://ndownloader.figshare.com/files/33936698"

文件清单

• Data_Sheet_1_Establishment and Validation of a Non-Invasive Diagnostic Nomogram to Identify Spontaneous Bacterial Peritonitis in Patients With Decompe.docx（477.78 KB）

文件总体积 477.78 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

BackgroundSpontaneous bacterial peritonitis (SBP) is a common and life-threatening infection in patients with decompensated cirrhosis (DC), and it is accompanied with high mortality and morbidity. However, early diagnosis of spontaneous bacterial peritonitis (SBP) is not possible because of the lack of typical symptoms or the low patient compliance and positivity rate of the ascites puncture test. We aimed to establish and validate a non-invasive diagnostic nomogram to identify SBP in patients with DC.
MethodData were collected from 4,607 patients with DC from July 2015 to December 2019 in two tertiary hospitals in Chongqing, China (A and B). Patients with DC were divided into the SBP group (995 cases) and the non-SBP group (3,612 cases) depending on whether the patients had SBP during hospitalization. About 70% (2,685 cases) of patients in hospital A were randomly selected as the traindata, and the remaining 30% (1,152 cases) were used as the internal validation set. Patients in hospital B (770 cases) were used as the external validation set. The univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to screen variables, and logistic regression was used to determine independent predictors to construct a nomogram to identify patients with SBP. Area under curve (AUC), calibration curve, and dynamic component analysis (DCA) were carried out to determine the effectiveness of the nomogram.
ResultThe nomogram was composed of seven variables, namely, mean red blood cell hemoglobin concentration (odds ratio [OR] = 1.010, 95% CI: 1.004–1.016), prothrombin time (OR = 1.038, 95% CI: 1.015–1.063), lymphocyte percentage (OR = 0.955, 95% CI: 0.943–0.967), prealbumin (OR = 0.990, 95% CI: 0.987–0.993), total bilirubin (OR = 1.003 95% CI: 1.002–1.004), abnormal C-reactive protein (CRP) level (OR = 1.395, 95% CI: 1.107–1.755), and abnormal procalcitonin levels (OR = 1.975 95% CI: 1.522–2.556). Good discrimination of the model was observed in the internal and external validation sets (AUC = 0.800 and 0.745, respectively). The calibration curve result indicated that the nomogram was well-calibrated. The DCA curve of the nomogram presen

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2021.797363.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Establishment_and_Validation_of_a_Non-Invasive_Diagnostic_Nomogram_to_Identify_Spontaneous_Bacterial_Peritonitis_in_Patients_With_Decompensated_Cirrhosis_docx/19096802）。