Christopher Haggerty

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Adjunct Assistant Professor

Columbia University

United States

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Christopher Haggerty serves as an Adjunct Assistant Professor at Columbia University in the United States. His research focuses on the application of machine learning and multimodal approaches to cardiovascular health, specifically in predicting mortality risk and arrhythmia. Recent articles highlight his work on genetic risk factors for atrial fibrillation and the use of deep learning techniques for echocardiography and myocardial strain analysis.

Recent Grants

Grant: Close

Using Electrocardiogram Genetics to Inform Arrhythmia Risk

Open Date: 2022-01-20

Close Date: 2025-12-31

Grant: Close

A Precision Medicine Approach to Arrhythmogenic Cardiomyopathy

Open Date: 2019-01-19

Close Date: 2023-12-31

Grant: Close

Scripps Translational Science Institute

Open Date: 2018-05-01

Close Date: 2023-04-30

Grant: Close

Aortic Wall Distensibility in the Growth and Rupture of Abdominal Aortic Aneurysm

Open Date: 2014-09-01

Close Date: 2015-12-15

Grant: Close

Relating Hemodynamics of the Total Cavopulmonary Connection to Single Ventricle Function in the Fontan Population

Open Date: 2010-07-01

Close Date: 2012-06-30

Articles (16)

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2022

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2021

Collaborators (14)

Laura Rasmussen-Torvik

Associate Professor and Chief of Epidemiology

Northwestern University Feinberg School of Medicine

UNITED STATES

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Changyu Sun

Assistant Professor

University of Missouri

UNITED STATES

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Elsayed Soliman

Director/Professor

Wake Forest School of Medicine

UNITED STATES

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Pedro Ferreira

Imperial College London

UNITED KINGDOM

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RuiJun Chen

Clinical Assistant Professor

Weill Cornell Medical College

UNITED STATES

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Patricia Munroe

Professor of Molecular Medicine

Queen Mary University of London

UNITED KINGDOM

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Kenneth Mangion

University of Glasgow

UNITED KINGDOM

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Megan Roy-Puckelwartz

Northwestern University

UNITED STATES

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Nicholas Marston

Harvard University

UNITED STATES

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Karin Hoth

Iowa State University

UNITED STATES

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Marios Pattichis

Professor

University of New Mexico

UNITED STATES

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Cynthia James

Assistant Professor

Johns Hopkins University

UNITED STATES

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James Pirruccello

University of California, San Francisco

UNITED STATES

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Magalie Viallon

Université Claude Bernard Lyon 1

FRANCE

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Christopher Haggerty

Research Interests

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Recent Grants

Articles (16)

Sequencing in over 50,000 cases identifies coding and structural variation underlying atrial fibrillation risk.

A Large-scale Multimodal Study for Predicting Mortality Risk Using Minimal and Low Parameter Models and Separable Risk Assessment

Predicted Risk of Ventricular Arrhythmias in a Genome-First Population With Genetic Risk for Arrhythmogenic Right Ventricular Cardiomyopathy

Promise and Peril of a Genotype‐First Approach to Mendelian Cardiovascular Disease

Deep Learning for Echo Analysis, Tracking, and Evaluation of Mitral Regurgitation (DELINEATE-MR)

StrainNet: Improved Myocardial Strain Analysis of Cine MRI by Deep Learning from DENSE

An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk

Moving From PQRST to AI

Genome-wide association and multi-trait analyses characterize the common genetic architecture of heart failure.

Endophenotype effect sizes support variant pathogenicity in monogenic disease susceptibility genes.

Loss-of-Function <i>FLNC</i> Variants Are Associated With Arrhythmogenic Cardiomyopathy Phenotypes When Identified Through Exome Sequencing of a General Clinical Population.

Monogenic and Polygenic Contributions to QTc Prolongation in the Population

rECHOmmend: An ECG-based Machine-learning Approach for Identifying Patients at High-risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography.

Analysis of rare genetic variation underlying cardiometabolic diseases and traits among 200,000 individuals in the UK Biobank.

Generalizability and quality control of deep learning-based 2D echocardiography segmentation models in a large clinical dataset.

3D-Encoded DENSE MRI with Zonal Excitation for Quantifying Biventricular Myocardial Strain During a Breath-Hold

Collaborators (14)

Laura Rasmussen-Torvik

Changyu Sun

Elsayed Soliman

Pedro Ferreira

RuiJun Chen

Patricia Munroe

Kenneth Mangion

Megan Roy-Puckelwartz

Nicholas Marston

Karin Hoth

Marios Pattichis

Cynthia James

James Pirruccello

Magalie Viallon

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