Christopher Lord
Project Personnel, All of Us Program Operational Use
4 active projects
AFib epidemiology (AOU v4)
Scientific Questions Being Studied
The overall goal of this study, as a Demonstration project, is to evaluate the ability of the All of Us Research Program data to replicate epidemiologic patterns of atrial fibrillation (AF), a common arrhythmia, previously described in other setting. We will address this goal with these two aims:
• Specific Aim 1. To determine the association of race and ethnicity with the prevalence and incidence of atrial fibrillation (AF). We hypothesize than non-whites will have lower prevalence and incidence of AF than whites.
• Specific Aim 2. To estimate associations of established risk factors for AF with the prevalence and incidence of AF. We hypothesize that increased body mass index, higher blood pressure, diabetes, smoking and a prior history of cardiovascular diseases will be associated with increased prevalence and incidence of AF.
Project Purpose(s)
- Population Health
- Other Purpose (This work is a result of an All of Us Research Program Demonstration Project. The projects are efforts by the Program designed to meet the program's goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. This work was reviewed and overseen by the All of Us Research Program Science Committee and the Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use.)
Scientific Approaches
We will select all All of Us participants who self-reported sex at birth male or female, whose self-reported race was white, black or Asian, as well as those who self-reported being Hispanics.
Atrial fibrillation (AF) will be identified from self-reports in the medical survey or from electronic health records (EHR).
Clinical factors will be identified from EHR and study measurements (blood pressure, weight, height).
We will evaluate the association of demographic (age, sex, race/ethnicity) and clinical (body mass index, blood pressure, smoking, cardiovascular diseases) factors with prevalence of self-reported AF and prevalence of AF in the EHR, as well as incident AF ascertained from the EHR.
Anticipated Findings
The overall goal of this project is to evaluate the prevalence and incidence of atrial fibrillation (AF), overall and by race/ethnicity, as well as to confirm the association of established risk factors for AF in the All of Us Research participants. We expect to confirm associations between demographic and clinical variables previously reported in the literature, demonstrating the value of the All of Us Research Program data to address questions regarding this common cardiovascular disease.
Demographic Categories of Interest
- Race / Ethnicity
- Age
Data Set Used
Registered TierResearch Team
Owner:
- Peter Buto - Graduate Trainee, Emory University
- Christopher Lord - Project Personnel, All of Us Program Operational Use
- Alvaro Alonso - Late Career Tenured Researcher, Emory University
Collaborators:
- Vignesh Subbian - Early Career Tenure-track Researcher, University of Arizona
- Francis Ratsimbazafy - Other, All of Us Program Operational Use
- Aymone Kouame - Other, All of Us Program Operational Use
- Aniqa Alam
- Konstantinos Sidiropoulos - Other, Nova Southeastern University
Duplicate of How to Get Started with Registered Tier Data (v7)
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.
Project Purpose(s)
- Educational
- Methods Development
- 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.)
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.
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.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Maria Rosario Marin Marmol kilrain - Project Personnel, All of Us Program Operational Use
- Jun Qian - Other, All of Us Program Operational Use
- Jessica Hamblin - Graduate Trainee, Pennsylvania State University
- Christopher Lord - Project Personnel, All of Us Program Operational Use
- Aymone Kouame - Other, All of Us Program Operational Use
AOU_Recover_Long_Covid_v6
Scientific Questions Being Studied
The purpose of this workspace was to implement the published XGBoost machine learning (ML) model, which was developed using the National COVID Cohort Collaborative’s (N3C) EHR repository to identify potential patients with PASC/Long COVID in All of Us Research Program. N3C, All of Us, PCORnet and RECOVER teams collaborated to execute this purpose to enhance the overall PASC/Long COVID efforts.
Project Purpose(s)
- Disease Focused Research (Long COVID)
Scientific Approaches
To achieve this objective, data science workflows were used to apply ML algorithms on the Researcher Workbench. This effort allowed an expansion in the number of participants used to evaluate the ML models used to identify risk of PASC/Long COVID and also serve to validate the efforts of one team and providing insight to other teams. These models were implemented within the All of Us Controlled Tier data (C2022Q2R2), which was last refreshed on June 22, 2022. We intend to provide a step-by-step guide for the implementation of N3C's ML Model for identification of PASC/Long COVID Phenotype in the All of Us dataset. It also evaluated demographic characteristics for participants who were identified as possibly having PASC/Long COVID, and provides additional details on model performance, such as areas under the receiver operator characteristic curve and confusion matrix.
Anticipated Findings
We intend to provide a step-by-step guide for the implementation of N3C's ML Model for identification of PASC/Long COVID Phenotype in the All of Us dataset. The findings and code use to generate the demographic characteristics for participants who were identified as possibly having PASC/Long COVID, and provides additional details on model performance, such as areas under the receiver operator characteristic curve and confusion matrix.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierResearch 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)
- David Mohs - Other, All of Us Program Operational Use
- Christopher Lord - Project Personnel, All of Us Program Operational Use
- Chenchal Subraveti - Project Personnel, All of Us Program Operational Use
Collaborators:
- Jun Qian - Other, All of Us Program Operational Use
- Chris Lunt - Other, All of Us Program Operational Use
Wearables and The Human Phenome (Published Work)
Scientific Questions Being Studied
Our primary goal is to understand the relation between activity levels with the development and progression of human disease. Higher physical activity is associated with lower prevalence and better outcomes in virtually every human disease. These analyses will generate hypotheses guiding clinical and research interventions focused on activity to reduce morbidity and mortality in patients seeking care.
This workspace is replication workspace for Wearables and The Human Phenome project. We replicated the workspace to provide a clean and reduced version of code that was used to generate the findings, which were published in Nature Medicine (https://www.nature.com/articles/s41591-022-02012-w).
Project Purpose(s)
- Population Health
- Social / Behavioral
Scientific Approaches
We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, and survey results.
Anticipated Findings
We expect to find that lower levels of activity are associated with a higher prevalence and more rapid progression of chronic diseases. These data will provide the rationale to link wearables data with electronic health records nationwide as a window into behavioral activity choice as a modifiable risk factor for chronic diseases. We may find substantial variation in activity and disease prevalence/severity by socioeconomic status, which would motivate studies/interventions to reduce these health disparities.
Demographic Categories of Interest
- Race / Ethnicity
- Geography
- Access to Care
- Education Level
- Income Level
Data Set Used
Registered TierResearch Team
Owner:
- Hiral Master - Project Personnel, All of Us Program Operational Use
- Christopher Lord - Project Personnel, All of Us Program Operational Use
- Chenchal Subraveti - Project Personnel, All of Us Program Operational Use
- Jeffrey Annis - Other, Vanderbilt University Medical Center
Collaborators:
- Jun Qian - Other, All of Us Program Operational Use
Request a Review of this Research Project
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.
