LACE指数预测急性心肌梗死患者30天再入院高风险数据表

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_LACE Index to Predict the High Risk of 30-Day Readmission in Patients With Acute Myocardial Infarction at a University Affiliated Hospital.PDF
• DOI：10.3389/fcvm.2022.925965.s001
• 发布日期：2022-07-11
• 文件数：1 个
• 文件总体积：66.01 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_LACE_Index_to_Predict_the_High_Risk_of_30-Day_Readmission_in_Patients_With_Acute_Myocardial_Infarction_at_a_University_Affiliated_Hospital_PDF/20280885

命令行下载

curl -L -o "Data_Sheet_1_LACE Index to Predict the High Risk of 30-Day Readmission in Patients With Acute Myocardial Infarction at a University Affiliated Hospita.PDF" "https://ndownloader.figshare.com/files/36223530"

文件清单

• Data_Sheet_1_LACE Index to Predict the High Risk of 30-Day Readmission in Patients With Acute Myocardial Infarction at a University Affiliated Hospita.PDF（66.01 KB）

文件总体积 66.01 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

BackgroundThe LACE index (length of stay, acuity of admission, comorbidity index, and emergency room visit in the past 6 months) has been used to predict the risk of 30-day readmission after hospital discharge in both medical and surgical patients. This study aimed to utilize the LACE index to predict the risk of 30-day readmission in hospitalized patients with acute myocardial infraction (AMI).
MethodsThis was a retrospective study. Data were extracted from the hospital’'s electronic medical records of patients admitted with AMI between 2015 and 2019. LACE index was built on admission patient demographic data, and clinical and laboratory findings during the index of admission. The multivariate logistic regression was performed to determine the association and the risk prediction ability of the LACE index, and 30-day readmission were analyzed by receiver operator characteristic curves with C-statistic.
ResultsOf the 3,607 patients included in the study, 5.7% (205) were readmitted within 30 days of discharge from the hospital. The adjusted odds ratio based on logistic regression of all baseline variables showed a statistically significant association with the LACE score and revealed an increased risk of readmission within 30 days of hospital discharge. However, patients with high LACE scores (≥10) had a significantly higher rate of emergency revisits within 30 days from the index discharge than those with low LACE scores. Despite this, analysis of the receiver operating characteristic curve indicated that the LACE index had favorable discrimination ability C-statistic 0.78 (95%CI; 0.75–0.81). The Hosmer–Lemeshow goodness- of-fit test P value was p = 0.920, indicating that the model was well-calibrated to predict risk of the 30-day readmission.
ConclusionThe LACE index demonstrated the good discrimination power to predict the risk of 30-day readmissions for hospitalized patients with AMI. These results can help clinicians to predict the risk of 30-day readmission at the early stage of hospitalization and pay attention during the care of high-risk patients. Future work is to be focused on additional factors to predict the risk of 30-day readmissions; they should be

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2022.925965.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_LACE_Index_to_Predict_the_High_Risk_of_30-Day_Readmission_in_Patients_With_Acute_Myocardial_Infarction_at_a_University_Affiliated_Hospital_PDF/20280885）。