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CME/MOC

Awards

HomeMembersAssemblies and SectionsAssembliesCritical CareAwards ▶ 2019 ATS Abstract Scholarship Award Recipients
2019 ATS Abstract Scholarship Award Recipients

These Abstract Scholarship Awards have been funded by the American Thoracic Society

Lindsay Boole, MD, MPH
Duke University
Abstract Title: Sepsis Management in a Resource-Limited Setting: A Clinical Trial of Quantitative Resuscitation in a National Referral Hospital in Kenya

Nicholas Bosch, MD
Boston University School of Medicine, Department of Medicine
Abstract Title: New-Onset Atrial Fibrillation as a Sepsis Defining Organ Failure

Ryan Brown, MD
Vanderbilt University Medical Center
Abstract Title: Effects of Balanced Crystalloids Vs Saline on Vasopressor Dose in Septic Shock

Angela Bustamante, PhD
University of Michigan
Abstract Title: Transcriptomic Profiles of Sepsis in the Human Brain

Danielle Callaway, MD, PhD
Baylor College of Medicine
Abstract Title: Decreased Survival and Increased Oxygen-Mediated Lung Injury in Mice Lacking Nrf2: Protection by Beta-Naphthoflavone

Lu Chen, MD
Interdepartmental Division of Critical Care Medicine, University of Toronto
Abstract Title: A Simple Method to Assess Lung Recruitability at the Bedside for Patients with Acute Respiratory Distress Syndrome

Bronwen Connolly, BSc(Hons), MSc, PhD
St.Thomas' Hospital
Abstract Title: Physical Rehabilitation Core Outcomes in Critical Illness: A Modified Delphi Consensus Study to Establish a Core Outcome Set

Scott Denstaedt, MD
University of Michigan Medical School
Abstract Title: Survivors of Murine Pneumosepsis Exhibit Long-Term Behavior Change and Primed Brain IL-1β Expression

Beatriz Guillen-Guio, MSc
Hospital Universitario N.S. de Candelaria, Universidad de La Laguna
Abstract Title: Genome-Wide Association Study Implicates a Vascular Endothelial Grown Factor Receptor in Sepsis-Induced Acute Respiratory Distress Syndrome

Matthew Hensley, MD, MPH
University of Michigan
Abstract Title: Epidemiology and Outcomes of Maternal Sepsis in the US

Joseph Hippensteel, MD, MS
University of Colorado Denver
Abstract Title: Highly NS/2S-Sulfated Heparan Sulfate Fragments Circulating During Sepsis Cause Persistent Cognitive Dysfunction After Sepsis

Nicholas Ingraham, MD
University of Minnesota
Abstract Title: Dysfunction Junction: 8-Year Trends in Deteriorating Intensive Care Functional Status

Vern Kerchberger, MD
Vanderbilt University Medical Center
Abstract Title: Haptoglobin Genotype Modifies Risk of ARDS During Clinical and Experimental Sepsis

Yasin Khan, MD
University of Toronto
Abstract Title: Women, Visible Minorities and Residents of Lower-Middle Income Countries Are Underrepresented in Leading Respirology and Critical Care Journals

Jacqueline Kruser, MD
Northwestern University
Abstract Title: Quality of End-of-Life Care in the Intensive Care Unit: An Observational Study from the ICU Liberation Collaborative

Aleksandra Leligdowicz, MD, DPhil
University of Toronto
Abstract Title: Novel Approach to Identifying Leukocyte Functional Heterogeneity in Patients with Early Sepsis Based on Endotoxin Tolerance

Kantha Medepalli, MD
Jackson Memorial Hospital/University of Miami Miller School of Medicine
Abstract Title: Variable Access to Girl Idols May Negatively Affect (VAGINA) Academic Pursuits: The Association of Faculty and Trainee Sex in Academic Internal Medicine Specialties

Nasim Motayar, MD
University of Rochester Medical Center
Abstract Title: Early Red Cell Transfusion Is Associated with Lower Mortality in Critically Ill Patients with Moderate Anemia

Tai Pham, MD, PhD, MPH
St Michael's Hospital
Abstract Title: Reverse Triggering During Invasive Mechanical Ventilation: Validation of an Automatic Detection Software

Stefanie Purdon, MD, MBA
Jackson Memorial Hospital/University of Miami Miller School of Medicine
Abstract Title: Variable Access to Girl Idols May Negatively Affect (VAGINA) Academic Pursuits: Women Are Under-Represented in Internal Medicine, PCCM, and Cardiology Academic Leadership

Xian Qiao, MD
Virginia Commonwealth University
Abstract Title: A Multicenter Parsimonious Biomarker Mortality Prediction Model for Sepsis-Induced ARDS

Juan Rojas, MD
University of Chicago
Abstract Title: Man vs. Machine: Comparison of a Machine Learning Algorithm to Clinician Intuition for Predicting Intensive Care Unit Readmission

Brian Rosenberg, MD, PhD
Columbia University Vagelos College of Physicians and Surgeons
Abstract Title: Serum Biomarkers of Mitochondrial Myopathy, Strength, and Recovery in Older Survivors of Acute Respiratory Failure

Toshihiro Sakakibara, MD
Nagoya University Graduate School of Medicine
Abstract Title: A New Scoring System for Predicting Adverse Events in Hospitalized Patients with Community-Onset Pneumonia

Michael Sklar, MD, FRCPC
University of Toronto
Abstract Title: Decreased Baseline Diaphragm Thickness Independently Predicts Increased Risk of Morbidity and Mortality in Mechanically Ventilated Patients

Xing Song, PhD
University of Kansas Medical Center
Abstract Title: Discovering Factors Inducing Rapid Treatment of Sepsis

Mark Weinreich, MD
Univ of Washington Harborview Med Ctr
Abstract Title: Understanding Variability in Mobility Practices: Do Higher Performing Hospitals Mobilize More Complex Patients?

Blair Wendlandt, MD
University of North Carolina at Chapel Hill
Abstract Title: The Association of Provider Support and Communication with Post-Traumatic Stress Disorder Symptoms for Family Caregivers of Patients with Chronic Critical Illness

Yi Xin, MSc
University of Pennsylvania
Abstract Title: Visualizing Regional Changes Between Lateral, Supine and Prone Position in Pigs with Lung Injury

These Abstract Scholarship has been funded by a generous donation from ATS Public Advisory Roundtable member - ARDS Foundation

Beatriz Guillen-Guio, MSc
Hospital Universitario N.S. de Candelaria Universidad de La Laguna
Abstract Title: Genome-Wide Association Study Implicates a Vascular Endothelial Grown Factor Receptor in Sepsis-Induced Acute Respiratory Distress Syndrome

Pratik Sinha, BSc(Hons), MB BCh, PhD
Pulmonary Critical Care
Abstract Title: Machine Learning Classifier Models Can Identify ARDS Phenotypes Using Readily Available Clinical Data