老年心力衰竭ICU患者30天住院死亡率预测模型数据（Table_1）

数据集获取（Figshare）

• 源站标题：Table_1_A novel web-based calculator to predict 30-day all-cause in-hospital mortality for 7,202 elderly patients with heart failure in ICUs: a multicenter retrospective cohort study in the United States.docx
• DOI：10.3389/fmed.2023.1237229.s001
• 发布日期：2023-09-15
• 文件数：1 个
• 文件总体积：20.20 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_A_novel_web-based_calculator_to_predict_30-day_all-cause_in-hospital_mortality_for_7_202_elderly_patients_with_heart_failure_in_ICUs_a_multicenter_retrospective_cohort_study_in_the_United_States_docx/24145047

命令行下载

curl -L -o "Table_1_A novel web-based calculator to predict 30-day all-cause in-hospital mortality for 7,202 elderly patients with heart failure in ICUs: a multic.docx" "https://ndownloader.figshare.com/files/42359520"

文件清单

• Table_1_A novel web-based calculator to predict 30-day all-cause in-hospital mortality for 7,202 elderly patients with heart failure in ICUs: a multic.docx（20.20 KB）

文件总体积 20.20 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

Background and aimsHeart failure (HF) is a significant cause of in-hospital mortality, especially for the elderly admitted to intensive care units (ICUs). This study aimed to develop a web-based calculator to predict 30-day in-hospital mortality for elderly patients with HF in the ICU and found a relationship between risk factors and the predicted probability of death.
Methods and resultsData (N = 4450) from the MIMIC-III/IV database were used for model training and internal testing. Data (N = 2,752) from the eICU-CRD database were used for external validation. The Brier score and area under the curve (AUC) were employed for the assessment of the proposed nomogram. Restrictive cubic splines (RCSs) found the cutoff values of variables. The smooth curve showed the relationship between the variables and the predicted probability of death. A total of 7,202 elderly patients with HF were included in the study, of which 1,212 died. Multivariate logistic regression analysis showed that 30-day mortality of HF patients in ICU was significantly associated with heart rate (HR), 24-h urine output (24h UOP), serum calcium, blood urea nitrogen (BUN), NT-proBNP, SpO2, systolic blood pressure (SBP), and temperature (P 2 of 93%, SBP of 137 mmHg, and a temperature of 36.4°C.
ConclusionDecreased temperature, decreased SpO2, decreased 24h UOP, increased NT-proBNP, increased serum BUN, increased or decreased SBP, fast HR, and increased or decreased serum calcium increase the predicted probability of death. The web-based nomogram developed in this study showed good performance in predicting 30-day in-hospital mortality for elderly HF patients in the ICU.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2023.1237229.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_A_novel_web-based_calculator_to_predict_30-day_all-cause_in-hospital_mortality_for_7_202_elderly_patients_with_heart_failure_in_ICUs_a_multicenter_retrospective_cohort_study_in_the_United_States_docx/24145047）。