Key Data Collection Specifications and Variable Standards for Measuring the Clinical Management and Outcomes of Patients With Valvular Heart Disease in China:Based on the Multi-center Clinical Research Innovation Platform of"China-DVD2 Study"
10.3969/j.issn.1000-3614.2025.03.003
- VernacularTitle:瓣膜性心脏病临床研究数据采集规范和变量标准——基于"老年瓣膜性心脏病标准评估体系及优化治疗路径研究"大数据平台
- Author:
Zhe LI
1
;
Haiyan XU
1
;
Yongjian WU
1
Author Information
1. 中国医学科学院 北京协和医学院 国家心血管病中心 阜外医院 结构性心脏病中心,北京 100037
- Collective Name:on behalf of China-DVD2 Study Group
- Publication Type:Journal Article
- Keywords:
valvular heart disease;
standardized variable set;
clinical trial;
information data platform;
image data
- From:
Chinese Circulation Journal
2025;40(3):219-226
- CountryChina
- Language:Chinese
-
Abstract:
Objectives:Utilizing an advanced and high-capacity data acquisition system tailored for elderly patients with valvular heart disease,we aimed to establish standardized data acquisition protocols and define variable sets for a clinical research big data platform dedicated to this condition.Guided by clinical treatment decisions,we implemented a standardized and uniform evaluation process for valvular heart disease.This initiative might culminate in the creation of the first comprehensive database for elderly valvular heart disease,encompassing clinical information,functional assessments,multimodal imaging,treatment modalities,and follow-up data.Methods:Variables were selected based on past clinical studies and literature,both domestic and international,and considering key guidelines,advancements in valvular heart disease,and China's clinical practices.Standards for data variables,imaging techniques,data acquisition,multi-channel verification,and quality control were established.Variables are then classified by their properties and generation time.Results:This comprehensive set of variables encompassed a total of 567 relevant variables,which were generated throughout the entire diagnosis and treatment process of patients with valvular heart disease,starting from admission and extending to those required for clinical research.The variable set was divided into six distinct parts:clinical information(encompassing demographics,admission details,clinical manifestations,comorbidities,medical history,physical examination,auxiliary examinations,and comprehensive assessment of frailty in the elderly),echocardiography,cardiac computed tomography(CT),treatment(including surgery,valve intervention therapy,non-valve intervention therapy,and medication therapy),clinical outcomes,and follow-up.Specifically,it included 14 variables related to demographic and admission information;22 variables encompassing clinical manifestations and physical examination;63 variables concerning medical history and comorbidities;49 variables for inspection and examination;15 variables for the comprehensive evaluation of frail elderly individuals;55 variables of echocardiography,including complete dynamic images at 4 time points,6 cross-sections per point,and corresponding Doppler spectra);131 variables for cardiac CT,including baseline and postoperative raw image uploads(encompassing non-enhanced electrocardiography[ECG]-gated cardiac CT scans,ECG-gated full-phase CT angiography(CTA)scans,and large-scale non-ECG-gated spiral CTA scans);51 variables for surgical diagnosis and treatment;72 variables for valve intervention therapy;33 variables for non-valvular interventional therapy;27 variables for medication information;and 35 variables for clinical outcomes.The follow-up integration comprises 140 variables,along with the upload of raw echocardiography and CT images.Conclusions:Through the establishment of data collection standards and variable standards for the big data platform of valvular heart disease,the standardization and normalization of comprehensive evaluation,diagnosis and treatment,and clinical research of patients with valvular heart disease can be achieved.As a national level big data research platform that can be sustainably used for clinical research and innovative development of valvular heart disease,it will provide support for long-term monitoring of valvular heart disease,real-world data analysis,clinical research(observational and randomized controlled trials),technological innovation,drug and device development,and post market evaluation.
