AIMC Topic: Heart Transplantation

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Automated segmentation of biventricular contours in tissue phase mapping using deep learning.

NMR in biomedicine Sep 2, 2021
Tissue phase mapping (TPM) is an MRI technique for quantification of regional biventricular myocardial velocities. Despite its potential, clinical use is limited due to the requisite labor-intensive manual segmentation of cardiac contours for all tim...
Middle Aged Deep Learning Heart Ventricles Heart Transplantation Magnetic Resonance Imaging, Cine Humans Female Aged Adult Male
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Using machine learning to improve survival prediction after heart transplantation.

Journal of cardiac surgery Aug 19, 2021
BACKGROUND: This study investigates the use of modern machine learning (ML) techniques to improve prediction of survival after orthotopic heart transplantation (OHT).
Retrospective Studies Adult Logistic Models Registries Heart Transplantation Humans Machine Learning
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State-of-the-art machine learning algorithms for the prediction of outcomes after contemporary heart transplantation: Results from the UNOS database.

Clinical transplantation Jun 29, 2021
PURPOSE: We sought to develop and validate machine learning (ML) models to increase the predictive accuracy of mortality after heart transplantation (HT).
Middle Aged Area Under Curve Heart Transplantation Humans Machine Learning Databases, Factual Algorithms Female Aged Adult Male
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State-of-the-art machine learning improves predictive accuracy of 1-year survival after heart transplantation.

ESC heart failure May 18, 2021
Algorithms Heart Transplantation Humans Machine Learning
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Machine Learning of 12-Lead QRS Waveforms to Identify Cardiac Resynchronization Therapy Patients With Differential Outcomes.

Circulation. Arrhythmia and electrophysiology Jun 14, 2020
BACKGROUND: Cardiac resynchronization therapy (CRT) improves heart failure outcomes but has significant nonresponse rates, highlighting limitations in ECG selection criteria: QRS duration (QRSd) ≥150 ms and subjective labeling of left bundle branch b...
Female Risk Factors Aged Male Ventricular Function, Left Humans Retrospective Studies Time Factors Electrocardiography Treatment Outcome Unsupervised Machine Learning Recovery of Function Stroke Volume Middle Aged Risk Assessment Disease Progression Diagnosis, Computer-Assisted Heart-Assist Devices Heart Transplantation Bundle-Branch Block Predictive Value of Tests Signal Processing, Computer-Assisted Heart Failure Cardiac Resynchronization Therapy
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Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines.

Circulation. Arrhythmia and electrophysiology Jun 20, 2019
BACKGROUND: Cardiac resynchronization therapy (CRT) has significant nonresponse rates. We assessed whether machine learning (ML) could predict CRT response beyond current guidelines.
Ohio Baltimore Time Factors Recovery of Function Predictive Value of Tests Patient Selection Heart Failure Cardiac Resynchronization Therapy Progression-Free Survival Practice Guidelines as Topic Heart-Assist Devices Heart Transplantation Stroke Volume Echocardiography Middle Aged Risk Assessment Disease Progression Risk Factors Aged Male Ventricular Function, Left Humans Machine Learning Decision Support Techniques Retrospective Studies Clinical Decision-Making Female
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An integrated molecular diagnostic report for heart transplant biopsies using an ensemble of diagnostic algorithms.

The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation Feb 6, 2019
BACKGROUND: We previously reported a microarray-based diagnostic system for heart transplant endomyocardial biopsies (EMBs), using either 3-archetype (3AA) or 4-archetype (4AA) unsupervised algorithms to estimate rejection. In the present study we ex...
Adult Male Middle Aged Risk Assessment Adolescent Cohort Studies Child, Preschool Child ROC Curve Predictive Value of Tests Machine Learning Algorithms Graft Rejection Female Myocardium Aged Heart Failure Humans Young Adult Heart Transplantation Pathology, Molecular
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Predictive Abilities of Machine Learning Techniques May Be Limited by Dataset Characteristics: Insights From the UNOS Database.

Journal of cardiac failure Feb 6, 2019
BACKGROUND: Traditional statistical approaches to prediction of outcomes have drawbacks when applied to large clinical databases. It is hypothesized that machine learning methodologies might overcome these limitations by considering higher-dimensiona...
Survival Rate United States Tissue and Organ Procurement Heart Transplantation Humans Neural Networks, Computer Machine Learning Forecasting Databases, Factual
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Prediction of mortality following pediatric heart transplant using machine learning algorithms.

Pediatric transplantation Jan 29, 2019
BACKGROUND: Optimizing transplant candidates' priority for donor organs depends on the accurate assessment of post-transplant outcomes. Due to the complexity of transplantation and the wide range of possible serious complications, recipient outcomes ...
Heart Transplantation Multivariate Analysis Mortality Machine Learning Algorithms Databases, Factual Female Male Humans Neural Networks, Computer Infant Adolescent Child, Preschool Child Regression Analysis Area Under Curve Registries Heart Failure Pediatrics Calibration
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The Role of GDF-15 in Heart Failure Patients With Chronic Kidney Disease.

The Canadian journal of cardiology Dec 26, 2018
BACKGROUND: Growth differentiation factor-15 (GDF-15) is a stress-inducible cytokine and member of the transforming growth factor-β cytokine superfamily that refines prognostic assessment in subgroups of patients with heart failure (HF). We evaluated...
Cholesterol Proportional Hazards Models Angiotensin Receptor Antagonists Sodium Humans Middle Aged Systole Sex Factors Angiotensin-Converting Enzyme Inhibitors Biomarkers Renal Insufficiency, Chronic Heart-Assist Devices Growth Differentiation Factor 15 Female Glomerular Filtration Rate Heart Failure, Systolic Natriuretic Peptide, Brain Male Follow-Up Studies Heart Transplantation Blood Pressure
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