MIPACT-AFib研究数据

数据集获取（Figshare）

• 源站标题：Table1_Evaluation of atrial fibrillation using wearable device signals and home blood pressure data in the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) Study: A Subgroup Analysis (MIPACT-AFib).docx
• DOI：10.3389/fcvm.2023.1243574.s001
• 发布日期：2023-12-20
• 文件数：1 个
• 文件总体积：686.51 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table1_Evaluation_of_atrial_fibrillation_using_wearable_device_signals_and_home_blood_pressure_data_in_the_Michigan_Predictive_Activity_Clinical_Trajectories_in_Health_MIPACT_Study_A_Subgroup_Analysis_MIPACT-AFib_docx/24872604

命令行下载

curl -L -o "Table1_Evaluation of atrial fibrillation using wearable device signals and home blood pressure data in the Michigan Predictive Activity & Clinical Tra.docx" "https://ndownloader.figshare.com/files/43764492"

文件清单

• Table1_Evaluation of atrial fibrillation using wearable device signals and home blood pressure data in the Michigan Predictive Activity & Clinical Tra.docx（686.51 KB）

文件总体积 686.51 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 rising adoption of wearable technology increases the potential to identify arrhythmias. However, specificity of these notifications is poorly defined and may cause anxiety and unnecessary resource utilization. Herein, we report results of a follow-up screening protocol for incident atrial fibrillation/flutter (AF) within a large observational digital health study.
MethodsThe MIPACT Study enrolled 6,765 adult patients who were provided an Apple Watch and blood pressure (BP) monitors. From March to July 2019, participants were asked to contact the study team for any irregular heart rate (HR) notification. They were assessed using structured questionnaires and asked to provide 6 Apple Watch EKGs. Those with arrhythmias or non-diagnostic EKGs were sent 7-day monitors. The EHR was reviewed after 3 years to determine if participants developed arrhythmias.
Results86 participants received notifications and met inclusion criteria. Mean age was 50.5 (SD 16.9) years, and 46 (53.3%) were female. Of 76 participants assessed by the study team, 32 (42.1%) reported anxiety surrounding notifications. Of 59 participants who sent at least 1 EKG, 52 (88.1%) were in sinus rhythm, 3 (5.1%) AF, 2 (3.4%) indeterminate, and 2 (3.4%) sinus bradycardia. Cardiac monitor demonstrated AF in 2 of 3 participants with AF on Apple Watch EKGs. 2 contacted their PCPs and were diagnosed with AF. In total, 5 cases of AF were diagnosed with 1 additional case identified during EHR review.
ConclusionWearable devices produce alarms that can frequently be anxiety provoking. Research is needed to determine the implications of these alarms and appropriate follow-up.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2023.1243574.s001（source: https://frontiersin.figshare.com/articles/dataset/Table1_Evaluation_of_atrial_fibrillation_using_wearable_device_signals_and_home_blood_pressure_data_in_the_Michigan_Predictive_Activity_Clinical_Trajectories_in_Health_MIPACT_Study_A_Subgroup_Analysis_MIPACT-AFib_docx/24872604）。