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Related Concept Videos

Mitral Valve Prolapse II: Assessment and Management01:22

Mitral Valve Prolapse II: Assessment and Management

IntroductionA range of clinical features characterizes Mitral Valve Prolapse (MVP), but it is important to note that many individuals with MVP are asymptomatic and may remain so throughout their lives. For those who do exhibit symptoms, the following are the key clinical features:Palpitations: This is a common symptom where individuals feel an irregular or rapid heartbeat. Palpitations in MVP are often due to arrhythmias such as premature ventricular contractions or supraventricular tachycardia.
Aortic Regurgitation II: Clinical Features and Diagnostic Tests01:22

Aortic Regurgitation II: Clinical Features and Diagnostic Tests

Aortic valve regurgitation (AR) occurs when the aortic valve fails to close properly, allowing blood to flow backward from the aorta into the left ventricle. This backflow can result in two distinct clinical presentations: acute and chronic AR, each characterized by its own set of symptoms and physical findings.Acute Aortic RegurgitationAcute AR presents with a sudden onset of severe symptoms. Patients typically experience profound dyspnea (shortness of breath), chest pain, and signs of left...
Mitral Regurgitation III: Medical Management01:25

Mitral Regurgitation III: Medical Management

Mitral regurgitation (MR) is characterized by retrograde blood circulation from the left ventricle into the left atrium due to inadequate mitral valve closure. The severity of the condition, symptoms, and underlying cause determine treatment strategies.Monitoring and Pharmacological TreatmentPatients with mild to moderate MR typically do not need immediate intervention but regular monitoring to assess progression and guide treatment. Patients with mild MR should have an echocardiogram every 3-5...
Mitral Regurgitation II: Clinical Features and Diagnostic Tests01:23

Mitral Regurgitation II: Clinical Features and Diagnostic Tests

Mitral regurgitation (MR) is a valvular heart disorder in which the mitral valve fails to close tightly, allowing blood to leak backward into the heart. Understanding the clinical manifestations, assessment, diagnostic findings, and medical management of MR is crucial to effectively managing affected patients.Clinical Manifestations of Mitral RegurgitationMitral regurgitation can be acute or chronic, each presenting differently and requiring different approaches:1. Acute Mitral...

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Related Experiment Video

Updated: May 28, 2026

Chronic Ovine Model of Right Ventricular Failure and Functional Tricuspid Regurgitation
08:43

Chronic Ovine Model of Right Ventricular Failure and Functional Tricuspid Regurgitation

Published on: March 17, 2023

Prognostic Modeling of Tricuspid Valve Regurgitation Outcomes Using Machine Learning-Based Survival Analysis.

Sepehr Janghorbani1,2, Pablo Villar Calle3, Prianca Tawde4

  • 1Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, NY 10021, USA.

Journal of Clinical Medicine
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict survival in tricuspid regurgitation (TR) patients. Right heart structural issues and myocardial fibrosis are key mortality predictors, guiding personalized treatment for this common valvular heart condition.

Keywords:
deep learningmachine learningsurvival analysistricuspid regurgitationvalvular disease

Related Experiment Videos

Last Updated: May 28, 2026

Chronic Ovine Model of Right Ventricular Failure and Functional Tricuspid Regurgitation
08:43

Chronic Ovine Model of Right Ventricular Failure and Functional Tricuspid Regurgitation

Published on: March 17, 2023

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Tricuspid regurgitation (TR) is a prevalent valvular heart condition linked to high mortality rates.
  • Current risk prediction and intervention timing for TR are suboptimal due to limited patient-specific insights.
  • Machine learning (ML) offers a promising avenue for enhancing TR patient risk stratification and outcome prediction.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting survival curves in patients with moderate to severe TR.
  • To compare the performance of different ML approaches, including Random Survival Forests (RSF), DeepSurv, and Cox proportional hazards (Cox PH) models.
  • To identify key clinical and imaging predictors of mortality in the TR patient cohort.

Main Methods:

  • Survival prediction models were developed using clinical and cardiac magnetic resonance (CMR) imaging data from 949 TR patients.
  • Comparative analysis of Cox PH, RSF, and DeepSurv models was performed using concordance index (C-index) and time-dependent AUC.
  • Kaplan-Meier analysis and multivariable Cox regression identified significant mortality predictors.

Main Results:

  • The RSF model demonstrated superior predictive performance (C-index 78%, AUC 82%), outperforming DeepSurv and Cox PH.
  • ML models effectively distinguished between low- and high-risk patient groups based on predicted survival curves.
  • Significant predictors of mortality included advanced age, smoking, right heart structural abnormalities (dilation, hypertrophy, atrial enlargement), and myocardial fibrosis.

Conclusions:

  • Machine learning models, particularly RSF and DeepSurv, provide valuable tools for personalized risk stratification in TR patients.
  • Right heart structural abnormalities and myocardial fibrosis are critical determinants of mortality in TR.
  • Integrating AI-driven survival prediction into clinical practice can improve decision-making and personalize TR management.