二尖瓣修复手术患者死亡与再住院风险预测数据表

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Personalized risk prediction of mortality and rehospitalization for heart failure in patients undergoing mitral valve repair surgery.pdf
• DOI：10.3389/fcvm.2024.1470987.s001
• 发布日期：2024-11-01
• 文件数：1 个
• 文件总体积：885.29 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Personalized_risk_prediction_of_mortality_and_rehospitalization_for_heart_failure_in_patients_undergoing_mitral_valve_repair_surgery_pdf/27425589

命令行下载

curl -L -o "Data Sheet 1_Personalized risk prediction of mortality and rehospitalization for heart failure in patients undergoing mitral valve repair surgery.pdf" "https://ndownloader.figshare.com/files/50131224"

文件清单

• Data Sheet 1_Personalized risk prediction of mortality and rehospitalization for heart failure in patients undergoing mitral valve repair surgery.pdf（885.29 KB）

文件总体积 885.29 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

BackgroundAccurately assessing the postoperative mortality and rehospitalization for heart failure risks in patients undergoing mitral valve repair surgery is of significant importance for individualized medical strategies.
ObjectiveWe sought to develop and validate a risk assessment system for the prediction of mortality and rehospitalization for heart failure.
MethodsPersonalized risk prediction system of mortality and rehospitalization for heart failure was developed. For developing a prediction system with death as the outcome, there were 965 patients (70%) and 413 patients (30%) were included in the the derivation cohort and the validation cohort. For developing a prediction system with rehospitalization for heart failure as the outcome, there were 927 patients (70%) and 398 patients (30%) were included in the derivation cohort and the validation cohort. There were 42 routine clinical variables used to develop the models. The performance evaluation of the model is based on the area under the curve (AUC). Evaluate the improvement with Euro Score II according to NRI and IDI net reclassification improvement (NRI) and integrated discrimination improvement (IDI).
ResultsThe median follow-up time was 685 days, the incidence of death was 3.85% (n = 53), and the incidence of rehospitalization for heart failure was 10.01% (n = 138). The AUC values of the mortality prediction model in the derivation and validation cohorts were 0.825 (0.764–0.886) and 0.808 (0.699–0.917), respectively. The AUC values of the rehospitalization for heart failure prediction model in the derivation and validation cohorts were 0.794 (0.756–0.832) and 0.812 (0.758–0.866), respectively. NRI and IDI showed that the mortality prediction model exhibited superior performance than the Euro Score II. The mortality and rehospitalization for heart failure risk prediction models effectively stratified patients into different risk subgroups.
ConclusionThe developed and validated models exhibit satisfactory performance in prediction of all-cause mortality and rehospitalization for heart failure after mitral valve repair surgery.
Clinical Trial Registrationhttp://www.clinicaltrials.gov, Unique identifier

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2024.1470987.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Personalized_risk_prediction_of_mortality_and_rehospitalization_for_heart_failure_in_patients_undergoing_mitral_valve_repair_surgery_pdf/27425589）。