基于临床实验室生物标志物预测Omicron变异株感染合并心肌损伤住院患者入住ICU的预后模型数据集

数据集获取（Figshare）

• 源站标题：DataSheet_1_Development and validation of a prognostic model based on clinical laboratory biomarkers to predict admission to ICU in Omicron variant-infected hospitalized patients complicated with myocardial injury.docx
• DOI：10.3389/fimmu.2024.1268213.s001
• 发布日期：2024-02-01
• 文件数：1 个
• 文件总体积：106.43 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Development_and_validation_of_a_prognostic_model_based_on_clinical_laboratory_biomarkers_to_predict_admission_to_ICU_in_Omicron_variant-infected_hospitalized_patients_complicated_with_myocardial_injury_docx/25127048

命令行下载

curl -L -o "DataSheet_1_Development and validation of a prognostic model based on clinical laboratory biomarkers to predict admission to ICU in Omicron variant-in.docx" "https://ndownloader.figshare.com/files/44358446"

文件清单

• DataSheet_1_Development and validation of a prognostic model based on clinical laboratory biomarkers to predict admission to ICU in Omicron variant-in.docx（106.43 KB）

文件总体积 106.43 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

AimsThe aim of this study was to develop and validate a prognostic model based on clinical laboratory biomarkers for the early identification of high-risk patients who require intensive care unit (ICU) admission among those hospitalized with the Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and complicated with myocardial injury (MI).
MethodsThis single-center study enrolled 263 hospitalized patients with confirmed Omicron variant infection and concurrent MI. The patients were randomly divided into training and validation cohorts. Relevant variables were collected upon admission, and the least absolute shrinkage and selection operator (LASSO) was used to select candidate variables for constructing a Cox regression prognostic model. The model’s performance was evaluated in both training and validating cohorts based on discrimination, calibration, and net benefit.
ResultsOf the 263 eligible patients, 210 were non-ICU patients and 53 were ICU patients. The prognostic model was built using four selected predictors: white blood cell (WBC) count, procalcitonin (PCT) level, C-reactive protein (CRP) level, and blood urea nitrogen (BUN) level. The model showed good discriminative ability in both the training cohort (concordance index: 0.802, 95% CI: 0.716–0.888) and the validation cohort (concordance index: 0.799, 95% CI: 0.681–0.917). For calibration, the predicted probabilities and observed proportions were highly consistent, indicating the model’s reliability in predicting outcomes. In the 21-day decision curve analysis, the model had a positive net benefit for threshold probability ranges of 0.2 to 0.8 in the training cohort and nearly 0.2 to 1 in the validation cohort.
ConclusionIn this study, we developed a clinically practical model with high discrimination, calibration, and net benefit. It may help to early identify severe and critical cases among Omicron variant-infected hospitalized patients with MI.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fimmu.2024.1268213.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Development_and_validation_of_a_prognostic_model_based_on_clinical_laboratory_biomarkers_to_predict_admission_to_ICU_in_Omicron_variant-infected_hospitalized_patients_complicated_with_myocardial_injury_docx/25127048）。