囊性纤维化患者的心脏与血管健康

该数据集来自 clinicaltrials.gov,关注囊性纤维化(CF)患者的心血管(CV)风险,涉及肱动脉血流介导的扩张评估与脉搏波分析

Liverpool Heart and Chest Hospital NHS Foundation TrustLiverpool Heart and Chest Hospital NHS Foundation Trust
clinicaltrials
浏览 11
时间序列预测Cystic Fibrosis (CF)Cardiovascular (CV) RiskPulse Wave Analysis
Heart and Blood Vessels Health in People With Cystic Fibrosis

基本信息

模态
时间序列
任务类型
预测
创建/更新时间
2026-10-12

资源简介

囊性纤维化患者的心脏与血管健康

该数据集来自 clinicaltrials.gov,关注囊性纤维化(CF)患者的心血管(CV)风险,涉及肱动脉血流介导的扩张评估与脉搏波分析。囊性纤维化是一种常染色体隐性遗传病,由氯离子转运缺陷引起,导致上皮分泌物脱水并形成炎症与感染的循环,主要累及肺部但影响多个器官。在 CFTR 调节剂出现前,治疗以气道清除、抗感染和营养优化等支持性手段为主;高效调节剂疗法(HEMT)引入后,整体健康状况与预后显著改善。该数据可用于研究 CF 患者的心血管风险及相关血管功能评估(来源 clinicaltrials.gov)。

【数据内容】

源站描述(机器翻译,仅供参考):病症:囊性纤维化(CF)、心血管(CV)风险、肱动脉血流介导的扩张评估、脉搏波分析。背景:囊性纤维化(CF)是一种常染色体隐性遗传病,其特征是氯离子转运缺陷,导致上皮分泌物脱水,从而形成炎症/感染的循环。这最明显地表现在肺部,但会影响多个器官部位。在囊性纤维化跨膜受体(CFTR)调节剂出现之前,疾病的主要治疗是支持性的,重点在于气道清除、积极治疗和预防感染,以及优化营养和其他常见合并症,如囊性纤维化糖尿病(CFD)。由于引入了高效调节剂疗法(HEMT),整体健康状况和预后显著改善。这些近期的

【主题与分类】

主题标签:Cystic Fibrosis (CF)、Cardiovascular (CV) Risk、Pulse Wave Analysis。本站分类:循环系统疾病/其他。

【获取与许可】

源站页面:https://clinicaltrials.gov/study/NCT07692594

官方服务

专家保障全链路合规授权与标注交付

陕川双基地 × 超十家三甲医院直连

从合规授权、采集治理、专业标注到数据集交付,我们提供全链路闭环服务,让高质量临床数据即拿即用。

⚡️ 需要原始数据或标注支持?联系我们获取定制方案。

帮我联系

下载信息

注册下载

需要注册 Kaggle 账号并登录后下载,适合需要跟踪下载记录和使用 API 的用户。

暂未开放

公开下载

无需注册即可直接获取公开样本或文档,适合快速预览和评估数据集质量。

免登录

有偿下载

公开数据集受托下载与技术交付服务。

提供高速下载与技术交付服务(收技术服务费,非数据销售)

暂未开放

当前数据集主要来源为 Kaggle 公开托管,完整影像包建议通过原始链接或 Kaggle API 获取。

使用方式

数据集获取

源站:https://clinicaltrials.gov/study/NCT07692594

标准获取流程:

  1. 访问源站链接,注册/登录账号;
  2. 进入数据集详情页,按页面指引申请权限或直接下载;
  3. 部分平台提供 SDK,例如:
    • OpenDataLab: pip install openxlab 后 openxlab.dataset.get(...)
    • ModelScope: pip install modelscope 后 modelscope download
    • 天池: 登录后页面提供直链下载
      以源站「数据下载/使用文档」页面为准。

数据说明

Conditions: Cystic Fibrosis (CF), Cardiovascular (CV) Risk, Flow-mediated Dilation Evaluation of the Brachial Artery, Pulse Wave Analysis. Background: Cystic fibrosis (CF) is an autosomal recessive disorder characterised by a chloride ion transport defect, leading to dehydrated epithelial secretions, resulting in an inflammatory/infection cycle. This manifests itself most obviously in the lungs but affects multiple organ sites. Before the development of Cystic Fibrosis Transmembrane Receptor (CFTR) modulators, the mainstay of disease was supportive with a focus on airway clearance, aggressive treatment and prevention of infection and optimisation of nutrition and other common co-morbidities like cystic fibrosis diabetes (CFD) . As a result of the introduction of highly effective modulator therapies (HEMT), overall wellbeing and prognostic outlook has improved significantly. These recent step-changes in prognostic outlook come after small but persistent improvements over the last three decades, such that prior to the introduction of CFTR modulator therapy, the median life expectancy had reached approximately 50 years of age, but now expected it to be longer. The latest HEMT in the market is Elexacaftor/Tezacaftor/Ivacaftor (ETI). Licensed in 2020, it is a ground-breaking development of treatment. About 90% of people with CF worldwide are eligible for the medication according to their genotypes, drastically improving these people’s intra- and extra-pulmonary outcomes. Cardiovascular disease (CVD) is the leading cause of death globally [5]. CVD prevention is essential as there are modifiable risk factors that can be controlled and reducing the occurrence of cardiovascular disease morbidity and mortality. Although people with CF possess numerous traditional CVD risk factors, including a high-fat-high-calorie diet and CF diabetes, historically very few cases are presented with cardiovascular disease in CF. PwCF were considered not at high risk of CVD development because of the shorter life expectancy, low body mass index (BMI) and low blood pressure. However, the risk profile is changing rapidly for people

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。

数据缺失?

依托陕西、四川两大基地,我们与超过十家三甲医院建立直接合作关系,覆盖合规授权、采集治理、专业标注、数据交付的全流程,为AI医疗团队提供即拿即用的高质量临床数据。

⚡️ 需要数据支持或标注服务?立即联系我们获取专业方案。

获取专属数据定制方案