Study on Standard Evaluation System and Optimal Treatment Path of Senile Valvular Heart Disease

NCT05044338 | Unknown DMC

• 1 other identifier: 2020YFC2008100
• Study Type: observational
• Target: 6,000
• Locations: 1 country
• Sites: 1

Brief Summary

The key technology research and standard evaluation system of elderly heart valve disease evaluation is to further establish a domestic multi center and large sample full information big data platform of elderly heart valve disease based on the previous Chinese elderly valve disease cohort and clinical research platform and the national valve disease surgery data platform.

Conditions

Valvular Heart Disease

Outcome Measures

Primary Outcomes (1)

• all-cause mortality

  all-cause mortality

  Time Frame: 2years

Interventions

imaging DIAGNOSTIC_TEST

(1) Build the first full information big data cloud platform for diagnosis, treatment and clinical research of senile valvular disease in China (2) To establish a cohort of elderly patients with valvular disease in China and a complete database covering clinical information, functional, imaging evaluation, treatment and follow-up; (3) To analyze the clinical characteristics, functional status and imaging characteristics of senile valvular disease in China, and to understand the current situation of diagnosis and treatment; To establish a whole process standardized comprehensive evaluation system of clinical indicators of senile valvular disease combined with a variety of imaging for Chinese people.

Eligibility Criteria

• Age: 65 Years+
• Sex: all
• Age Groups: Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Inpatients in each research center

You may qualify if:

• Patients with heart valve disease who are ≥ 65 years old and diagnosed by echocardiography meet the requirements
• One of the following criteria:
• (1)Severe mitral stenosis: valve orifice area \< 1.0cm2;
• (2)Severe mitral regurgitation: constriction neck width ≥ 7mm, or regurgitation per beat (regurgitation volume)≥ 60ml, or reflux beam area / left atrial area \> 50%; Or effective reflux port area (eroa) ≥ 0.4cm2;
• (3)Severe aortic stenosis: mean cross valve pressure difference ≥ 40mmhg, or maximum jet velocity ≥ 4m / s, orValve orifice area \< 1.0cm2, it should be differentiated from left ventricular outflow tract stenosis;
• (4)Severe aortic regurgitation: constriction neck width \> 6mm, or each pulsation regurgitation ≥ 60ml, or regurgitation Flow fraction ≥ 50%, or jet width ≥ 65% of left ventricular outflow tract, or effective reflux orifice area (eroa) ≥0.3cm2；
• (5)Severe tricuspid stenosis: mean cross valve pressure difference ≥ 5mmhg;
• (6)Severe tricuspid regurgitation: constriction neck width ≥ 7mm, or effective regurgitation orifice area (eroa)≥0.4cm2；
• (7)Severe pulmonary stenosis: the forward blood flow velocity is ≥ 4m / s, which should be differentiated from right ventricular outflow tract stenosis; Severe pulmonary valve regurgitation: massive regurgitation, wide constriction neck

You may not qualify if:

• Patients unwilling to accept registration and follow-up;
• Patients with mental illness who cannot cooperate with information collection and follow-up

Sponsors & Collaborators

• Yongjian Wu: lead

Study Sites (1)

Fuwai Hospital, National Center for Cardiovascular Diseases，Chinese Academy of Medical Sciences

Beijing, Beijing Municipality, 100037, China

RECRUITING

Related Publications (3)

• Mao Y, Liu Y, Zhai M, Jin P, Chen F, Zhang G, Wei L, Liu J, Guo Y, Wu Y, Yang J. Predictors of Paravalvular Leakage After Transcatheter Aortic Valve Replacement in Patients With BAV: A Machine Learning Model. JACC Adv. 2026 Jan 19;5(2):102566. doi: 10.1016/j.jacadv.2025.102566. Online ahead of print.

  PMID: 41558358 DERIVED

• Mao Y, Liu Y, Zhai M, Jin P, Li W, Chen F, Yang Y, Zhang G, Liu J, Guo Y, Pan X, Wu Y, Yang J. Clinical significance of machine learning algorithm in predicting PPM during TAVR in small annuli. Cardiovasc Interv Ther. 2026 Jan 5. doi: 10.1007/s12928-025-01215-5. Online ahead of print.

  PMID: 41491442 DERIVED

• Mao Y, Liu Y, Zhai M, Jin P, Li W, Dong X, Chen F, Wang X, Wang Y, Zhang G, Li H, Yang Y, Zhang H, Liu J, Guo Y, Wu Y, Xue Y, Zhang J, Frangi A, Yang J. Precision TAVR quantification- AI-accelerated TAVR planning reduces assessment time by 80% in bicuspid aortic stenosis. Eur Heart J Imaging Methods Pract. 2025 Dec 5;3(4):qyaf153. doi: 10.1093/ehjimp/qyaf153. eCollection 2025 Oct.

  PMID: 41445779 DERIVED

MeSH Terms

Conditions

Heart Valve Diseases

Interventions

Diagnostic Imaging

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases

Intervention Hierarchy (Ancestors)

Diagnostic Techniques and Procedures; Diagnosis

Study Officials

• yongjian wu, doctor

  Fuwai Hospital, National Center for Cardiovascular Diseases，Chinese Academy of Medical Sciences

  STUDY CHAIR

Central Study Contacts

haiyan xu, doctor

CONTACT

+86 13681012249; xuhaiyan@fuwaihospital.org

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER GOV
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Chief physician

Study Record Dates

• First Submitted: September 12, 2021
• First Posted: September 14, 2021
• Study Start: September 1, 2021
• Primary Completion: November 1, 2023
• Study Completion: November 1, 2023
• Last Updated: September 16, 2021

Record last verified: 2021-09

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)