COVID-19疫情期间心血管急诊入院与院内死亡率动态数据

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Dynamics of Emergency Cardiovascular Hospital Admissions and In-Hospital Mortality During the COVID-19 Pandemic: Time Series Analysis and Impact of Socioeconomic Factors.docx
• DOI：10.3389/fcvm.2022.827212.s001
• 发布日期：2022-06-09
• 文件数：1 个
• 文件总体积：6.41 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Dynamics_of_Emergency_Cardiovascular_Hospital_Admissions_and_In-Hospital_Mortality_During_the_COVID-19_Pandemic_Time_Series_Analysis_and_Impact_of_Socioeconomic_Factors_docx/20036858

命令行下载

curl -L -o "Data_Sheet_1_Dynamics of Emergency Cardiovascular Hospital Admissions and In-Hospital Mortality During the COVID-19 Pandemic: Time Series Analysis and.docx" "https://ndownloader.figshare.com/files/35786717"

文件清单

• Data_Sheet_1_Dynamics of Emergency Cardiovascular Hospital Admissions and In-Hospital Mortality During the COVID-19 Pandemic: Time Series Analysis and.docx（6.41 MB）

文件总体积 6.41 MB（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

AimsThis study aimed to evaluate the decline in urgent cardiovascular hospital admissions and in-hospital mortality during the COVID pandemic in two successive waves, and to evaluate differences by sex, age, and deprivation index subgroups.
Methods and ResultsWe obtained acute cardiovascular hospital episodes during the years 2019–2020 from region-wide data on public healthcare usage for the population of Catalonia (North-East Spain). We fitted time models to estimate the incidence rate ratios (IRRs) of the acute coronary syndrome (ACS) and acute heart failure (HF) admissions during the first pandemic wave, the between-waves period, and the second wave compared with the corresponding pre-COVID-19 periods and to test for the interaction with sex, age, and area-based socioeconomic level. We evaluated the effect of COVID-19 period on in-hospital mortality. ACS (n = 8,636) and HF (n = 27,566) episodes were defined using primary diagnostic ICD-10 codes. ACS and HF admissions decreased during the first wave (IRR = 0.66, 95%CI: 0.58–0.76 and IRR = 0.61, 95% CI: 0.55–0.68, respectively) and during the second wave (IRR = 0.80, 95%CI: 0.72–0.88 and IRR = 0.76, 95%CI: 0.69–0.84, respectively); acute HF admissions also decreased in the period between waves (IRR: 0.81, 95%CI: 0.74–0.89). The impact was similar in all sex and socioeconomic subgroups and was higher in older patients with ACS. In-hospital mortality was higher than expected only during the first wave.
ConclusionDuring the first wave of the COVID-19 pandemic, there was a marked decline in urgent cardiovascular hospital admissions that were attenuated during the second wave. Both the decline and the attenuation of the effect have been similar in all subgroups regardless of age, sex, or socioeconomic status. In-hospital mortality for ACS and HF episodes increased during the first wave, but not during the second wave.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2022.827212.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Dynamics_of_Emergency_Cardiovascular_Hospital_Admissions_and_In-Hospital_Mortality_During_the_COVID-19_Pandemic_Time_Series_Analysis_and_Impact_of_Socioeconomic_Factors_docx/20036858）。