Vern Kerchberger

Early Career Tenure-track Researcher, Vanderbilt University Medical Center

7 active projects

PPI, surveys, and genetic predictors of new post-COVID phenotypes.Tier v6

The coronavirus disease 2019 (COVID-19) pandemic continues to evolve, with more than 400 million confirmed cases worldwide over numerous waves. Although most COVID-19 patients ultimately recover, many survivors have persistent symptoms or develop new medical problems after recovery. With hundreds…

Scientific Questions Being Studied

The coronavirus disease 2019 (COVID-19) pandemic continues to evolve, with more than 400 million confirmed cases worldwide over numerous waves. Although most COVID-19 patients ultimately recover, many survivors have persistent symptoms or develop new medical problems after recovery. With hundreds of millions potentially at risk for long-term adverse health effects, there is a pressing need to efficiently identify new medical problems occurring among COVID-19 survivors and to understand their biological underpinnings. This study will identify patients in the All of Us Research Program who have been tested for the SARS-CoV-2 virus to identify medical conditions (phenotypes) occurring in patients after clinical COVID-19. Then, using genetic information available for these patients, we will identify genetic variants associated with the new post-COVID-19 medical phenotypes.

Project Purpose(s)

  • Disease Focused Research (Long COVID-19)

Scientific Approaches

This project will use a phenome-wide association study (PheWAS) approach to identify new post-acute COVID-19 diagnoses. PheWAS is high-throughput informatics framework initially developed to examine the effects of genetic variation on a wide range of physiological and clinical outcomes using electronic health records (EHR) data. PheWAS is simple and has a well-documented R package, facilitating easy dissemination of study design and harmonization of analytical methods across institutions. PheWAS also has previously been used to identify clinical risk factors for hospitalization among patients acutely infected with COVID-19.

Anticipated Findings

This study will assess how genetic differences contribute to development of medical problems after recovery from COVID-19, and ultimately improve our understanding of the "Long-COVID" syndrome.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Vern Kerchberger - Early Career Tenure-track Researcher, Vanderbilt University Medical Center

Collaborators:

  • WeiQi Wei - Other, All of Us Program Operational Use
  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center
  • Christopher Guardo - Project Personnel, Vanderbilt University Medical Center

AOU_Recover_Long_Covid_v6

Identify potential long-COVID patients with high accuracy, achieving areas under the receiver operator characteristic curve using the National COVID Cohort Collaborative’s (N3C) EHR repository, we developed XGBoost machine learning (ML) models.

Scientific Questions Being Studied

Identify potential long-COVID patients with high accuracy, achieving areas under the receiver operator characteristic curve using the National COVID Cohort Collaborative’s (N3C) EHR repository, we developed XGBoost machine learning (ML) models.

Project Purpose(s)

  • Disease Focused Research (Long COVID)

Scientific Approaches

Identify potential long-COVID patients with high accuracy, achieving areas under the receiver operator characteristic curve using the National COVID Cohort Collaborative’s (N3C) EHR repository, we developed XGBoost machine learning (ML) models.

Anticipated Findings

Identify potential long-COVID patients with high accuracy, achieving areas under the receiver operator characteristic curve using the National COVID Cohort Collaborative’s (N3C) EHR repository, we developed XGBoost machine learning (ML) models.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • WeiQi Wei - Other, All of Us Program Operational Use
  • Vern Kerchberger - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center
  • Mark Weiner - Mid-career Tenured Researcher, Cornell University
  • Hiral Master - Project Personnel, All of Us Program Operational Use
  • Gabriel Anaya - Administrator, National Heart, Lung, and Blood Institute (NIH - NHLBI)

Collaborators:

  • Chris Lunt - Other, All of Us Program Operational Use

Implementing Recover Algorithm on AOU Data

Identify potential long COVID patients among three groups in the database: All COVID-19 patients, patients hospitalized with COVID-19, and patients who had COVID-19 but were not hospitalized. The models proved to be accurate, as people identified as at risk for…

Scientific Questions Being Studied

Identify potential long COVID patients among three groups in the database: All COVID-19 patients, patients hospitalized with COVID-19, and patients who had COVID-19 but were not hospitalized. The models proved to be accurate, as people identified as at risk for long COVID were similar to patients seen at long COVID clinics.

Project Purpose(s)

  • Disease Focused Research (Long COVID)

Scientific Approaches

XGBoost machine learning model is developed to identify potential patients with long COVID.
Base population is defined as any non-deceased adult patient (age ≥18 years) with either an International Classification of Diseases-10-Clinical Modification COVID-19 diagnosis code (U07.1) from an inpatient or emergency visit, or a positive SARS-CoV-2 PCR or antigen test, and for whom at least 90 days have passed since COVID-19 index date.
The model examines demographics, health-care utilization, diagnoses, and medications for adults with COVID-19.

Anticipated Findings

Identify with high accuracy, patients who potentially have long COVID. Find the important features.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • WeiQi Wei - Other, All of Us Program Operational Use
  • Vern Kerchberger - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center
  • Hiral Master - Project Personnel, All of Us Program Operational Use
  • Gabriel Anaya - Administrator, National Heart, Lung, and Blood Institute (NIH - NHLBI)

Collaborators:

  • Chris Lunt - Other, All of Us Program Operational Use

RECOVER+AoU

The goal of this initial cross-platform testing effort is focused on expanding the analytical capability of available data sources that have collected data on SARS-CoV-2. As we gather data across the US, we can use independent data sources to better…

Scientific Questions Being Studied

The goal of this initial cross-platform testing effort is focused on expanding the analytical capability of available data sources that have collected data on SARS-CoV-2. As we gather data across the US, we can use independent data sources to better understand PASC in our population and identify possible interventions. As a first step, we hope to leverage available RECOVER data tools and apply within the All of Us Researcher Workbench to assess cross-platform interoperability and analytical equivalence. This would provide a path to engage our research community and guide research towards our understanding of PASC.

Project Purpose(s)

  • Population Health
  • Methods Development
  • Control Set
  • Other Purpose (Testing PASC ML Algorithm from N3C-RECOVER in AoU Platform)

Scientific Approaches

Bring existing data query code and data analytics code from the RECOVER researcher team into the All of Us Researcher Workbench. Use “equivalent” code sets to explore and expand our understanding of PASC and its effects on the US population. Share reproducible findings through programming “notebook” and analysis of standardized datasets (OMOP).

Anticipated Findings

This research activity will be developed in conjunction with an awareness campaign of the collaborative efforts undertaken by both RECOVER and AoU. We intend to highlight the available datasets with SARS-CoV-2 data, as well as the cloud-based researcher workspaces (RECOVER, AoU). With the awareness campaign and cross-platform testing, we intent to create an on-ramp for experienced and young researchers within two large and diverse datasets.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • WeiQi Wei - Other, All of Us Program Operational Use
  • Vern Kerchberger - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center
  • Hiral Master - Project Personnel, All of Us Program Operational Use
  • Gabriel Anaya - Administrator, National Heart, Lung, and Blood Institute (NIH - NHLBI)
  • Chris Lunt - Other, All of Us Program Operational Use

PPI, survey information, and genetic predictors of new post-COVID phenotypes

The coronavirus disease 2019 (COVID-19) pandemic continues to evolve, with more than 400 million confirmed cases worldwide over numerous waves. Although most COVID-19 patients ultimately recover, many survivors have persistent symptoms or develop new medical problems after recovery. With hundreds…

Scientific Questions Being Studied

The coronavirus disease 2019 (COVID-19) pandemic continues to evolve, with more than 400 million confirmed cases worldwide over numerous waves. Although most COVID-19 patients ultimately recover, many survivors have persistent symptoms or develop new medical problems after recovery. With hundreds of millions potentially at risk for long-term adverse health effects, there is a pressing need to efficiently identify new medical problems occurring among COVID-19 survivors and to understand their biological underpinnings. This study will identify patients in the All of Us Research Program who have been tested for the SARS-CoV-2 virus to identify medical conditions (phenotypes) occurring in patients after clinical COVID-19. Then, using genetic information available for these patients, we will identify genetic variants associated with the new post-COVID-19 medical phenotypes.

Project Purpose(s)

  • Disease Focused Research (Long COVID-19)

Scientific Approaches

This project will use a phenome-wide association study (PheWAS) approach to identify new post-acute COVID-19 diagnoses. PheWAS is high-throughput informatics framework initially developed to examine the effects of genetic variation on a wide range of physiological and clinical outcomes using electronic health records (EHR) data. PheWAS is simple and has a well-documented R package, facilitating easy dissemination of study design and harmonization of analytical methods across institutions. PheWAS also has previously been used to identify clinical risk factors for hospitalization among patients acutely infected with COVID-19.

Anticipated Findings

This study will assess how genetic differences contribute to development of medical problems after recovery from COVID-19, and ultimately improve our understanding of the "Long-COVID" syndrome.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Vern Kerchberger - Early Career Tenure-track Researcher, Vanderbilt University Medical Center

Collaborators:

  • WeiQi Wei - Other, All of Us Program Operational Use
  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center

VEK -- Duplicate of How to Work with All of Us Physical Measurements Data

This is a testing workspace that is a copy of the example workspace "How to Work with All of Us Physical Measurements Data". This project focuses on learning how to navigate around physical measurements in the All of Us Researcher…

Scientific Questions Being Studied

This is a testing workspace that is a copy of the example workspace "How to Work with All of Us Physical Measurements Data". This project focuses on learning how to navigate around physical measurements in the All of Us Researcher Workbench.

I am using this workspace to get an overview of the data available in the Registered Tier of the AoU dataset.

Project Purpose(s)

  • Other Purpose (Improve the researcher's understanding of the All of Us Researcher Workbench. I am using this workspace to get an overview of the data available in the Registered Tier of the AoU dataset.)

Scientific Approaches

This workspace uses the data provided in the original All of Us "How to Work with All of Us Physical Measurements Data".

I am using this workspace to get an overview of the data available in the Registered Tier of the AoU dataset.

Anticipated Findings

The researcher will gain additional knowledge on how to use the All of Us Research Workbench.

I am using this workspace to get an overview of the data available in the Registered Tier of the AoU dataset.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Vern Kerchberger - Early Career Tenure-track Researcher, Vanderbilt University Medical Center

VEK -- Duplicate of How to Get Started with Registered Tier Data (tier 5)

We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data. What should you expect? This notebook will give you an overview of what data is available in the current…

Scientific Questions Being Studied

We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data.

What should you expect? This notebook will give you an overview of what data is available in the current Curated Data Repository (CDR). It will also teach you how to retrieve information about Electronic Health Record (EHR), Physical Measurements (PM), and Survey data.

I am using this to get an overview of the data available in the Registered Tier of the AoU dataset.

Project Purpose(s)

  • Educational
  • Other Purpose (This is an All of Us Tutorial Workspace. It is meant to provide instruction for key Researcher Workbench components and All of Us data representation. I am using this workspace to develop familiarity with the AoU workspace.)

Scientific Approaches

This Tutorial Workspace contains two Jupyter Notebooks (one written in Python, the other in R). Each notebook is divided into the following sections:

1. Setup: How to set up this notebook, install and import software packages, and select the correct version of the CDR.
2. Data Availability Part 1: How to summarize the number of unique participants with major data types: Physical Measurements, Survey, and EHR;
3. Data Availability Part 2: How to delve a little deeper into data availability within each major data type;
4. Data Organization: An explanation of how data is organized according to our common data model.
5. Example Queries: How to directly query the CDR, using two examples of SQL queries to extract demographic data.
6. Expert Tip: How to access the base version of the CDR, for users that want to do their own cleaning.

I am using this workspace to get an overview of the data available in the Registered Tier of the AoU dataset.

Anticipated Findings

By reading and running the notebooks in this Tutorial Workspace, you will understand the following:

All of Us data are made available in a Curated Data Repository. Participants may contribute any combination of survey, physical measurement, and electronic health record data. Not all participants contribute all possible data types. Each unique piece of health information is given a unique identifier called a concept_id and organized into specific tables according to our common data model. You can use these concept_ids to query the CDR and pull data on specific health information relevant to your analysis. See our support article Learning the Basics of the All of Us Dataset for more info.

I am using this workspace to get an overview of the data available in the Registered Tier of the AoU dataset.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Vern Kerchberger - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
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You can request that the All of Us Resource Access Board (RAB) review a research purpose description if you have concerns that this research project may stigmatize All of Us participants or violate the Data User Code of Conduct in some other way. To request a review, you must fill in a form, which you can access by selecting ‘request a review’ below.