老年髋部骨折患者术后谵妄预测列线图表1数据集

数据集获取（Figshare）

• 源站标题：Table_1_Development and Validation of a Nomogram for Predicting Postoperative Delirium in Patients With Elderly Hip Fracture Based on Data Collected on Admission.DOCX
• DOI：10.3389/fnagi.2022.914002.s001
• 发布日期：2022-06-16
• 文件数：1 个
• 文件总体积：16.98 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_Validation_of_a_Nomogram_for_Predicting_Postoperative_Delirium_in_Patients_With_Elderly_Hip_Fracture_Based_on_Data_Collected_on_Admission_DOCX/20077175

命令行下载

curl -L -o "Table_1_Development and Validation of a Nomogram for Predicting Postoperative Delirium in Patients With Elderly Hip Fracture Based on Data Collected o.DOCX" "https://ndownloader.figshare.com/files/35912486"

文件清单

• Table_1_Development and Validation of a Nomogram for Predicting Postoperative Delirium in Patients With Elderly Hip Fracture Based on Data Collected o.DOCX（16.98 KB）

文件总体积 16.98 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

Delirium is a common postoperative complication in elderly hip fracture patients that seriously affects patients’ lives and health, and early delirium risk prediction, and targeted measures can significantly reduce the incidence of delirium. The purpose of this study was to develop and validate a nomogram for the prediction of postoperative delirium (POD) in elderly hip fracture patients. A total of 328 elderly patients with hip fractures enrolled retrospectively in department 1 of our hospital were randomly divided into the training set (n = 230) and the internal validation set (n = 98). The least absolute shrinkage and selection operator (LASSO) regression analysis was used for feature variable selection, and multivariate logistic regression with a backward stepwise method was used to construct a nomogram in the training set. The discrimination efficacy and calibration efficacy of the nomogram were evaluated through the receiver operating characteristic (ROC) curve and calibration curve, respectively. The clinical usefulness was estimated through decision curve analysis (DCA) and clinical impact curve (CIC) analysis. Another validation set from department 2 of our hospital, containing 76 elderly patients with hip fractures, was used for external validation of the nomogram. A total of 43 (13.1%) and 12 (15.8%) patients had POD in department 1 and department 2, respectively. The nomogram was constructed by three predictors, including dementia, chronic obstructive pulmonary disease (COPD), and albumin level. The nomogram showed good discrimination efficacy and calibration efficacy, with the AUC of 0.791 (95% CI, 0.708–0.873), 0.820 (95% CI, 0.676–0.964), and 0.841 (95% CI, 0.717–0.966) in the training set, the internal validation set, and the external validation set, respectively. Both DCA and CIC demonstrated that this nomogram has good clinical usefulness. The nomogram constructed by dementia, COPD, and albumin level can be conveniently used to predict POD in patients with elderly hip fractures.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fnagi.2022.914002.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_Validation_of_a_Nomogram_for_Predicting_Postoperative_Delirium_in_Patients_With_Elderly_Hip_Fracture_Based_on_Data_Collected_on_Admission_DOCX/20077175）。