Volume 52 | June 30, 2022
A program of NIH’s National Center for Advancing Translational Sciences
No Meeting Week
Enclave Holiday Support Schedule

July 4th - July 8th

To help our community with a positive and productive workload, CD2H-N3C has scheduled several “No Meeting Weeks” throughout the year. The Next N3C No Meeting Week will beJuly 4th to July 8th.

Most meetings will be canceled. Impromptu meetings can still occur to push through action items as needed during no meeting week. 

Workgroups and Domain Teams should check with their Leads to determine meeting schedules for that week. 

N3C support will continue with regular operations. If you have any trouble logging on to the enclave, please contact NCATSAuthSupport@mail.nih.gov.  
For all other issues please use the Support Desk

Enclave support for the holiday weekend
Friday, July 1st: 
  • Level 1 support (e.g. basic platform and product-related questions) will operate at full capacity.  
  • Level 2 support (i.e. issues that require more hands-on help or knowledge of N3C contextual information will be off).

Friday July 4th:
  • Enclave support will be off, but we will have an on-call rotation for platform outages and critical issues.

Future No Meeting Weeks:
4th Quarter: November 7-11
The Community Forum will not take place during these weeks 
What the Research Community is Saying about N3C
Johanna Loomba
iTHRIV CTSA Director of Informatics 
N3C Logic Liaison Lead
N3C Neurological Domain Team Lead
University of Virginia
"N3C has revolutionized public health research in the United States by providing data, technology, and organizational infrastructure to support agile and organic team science during a national crisis. Cross-institutional, interdisciplinary research teams have self-organized around important areas of investigation, proving that the harmonization and co-location of health record data not only supports local data cleaning and quality enhancements, but also results in cross-pollination of best practices.  The Logic Liaison team emerged through cross-domain dialogue and resulted in a set of community code templates designed to streamline data synthesis and cleaning. The templates have resulted in greater analytic alignment across domain teams and supported rapid onboarding and training of students and experts alike.  I look forward to watching the tools, environment and community continue to evolve."
N3C's NLP (Natural Language Processing) Data Enhancement Status

810,000,000+ NLP Facts

617,000+ Patients

Across 4 Sites
Currently, four sites have successfully submitted NLP (Natural Language Processing) facts into the N3C Enclave. NLP facts are derived from processing clinical notes and only contain coded information. Sites process the text locally and only submit coded data to the N3C enclave. In total there are 810 million NLP facts from 617,000 patients that have been ingested. These numbers will continue to grow as additional sites successfully submit their NLP facts to the N3C Enclave.
N3C on NPR's Marketplace Tech Interview
Marketplace’s Meghan McCarty Carino speaks with Emily Pfaff, an assistant professor of medicine at the University of North Carolina at Chapel Hill who uses artificial intelligence to analyze electronic health records, looking for patterns that might better identify the syndrome and treat patients. Pfaff said some of the most common markers the algorithm detects are fatigue, shortness of breath, and frequent doctor visits.

To listen or to read the interview, visit here.
Rare Diseases with Bryan Laraway, MS

With over 10,000 known rare diseases collectively affecting nearly 10% of the population, rare disease patient populations represent unique and varied cohorts for the study of COVID. Bryan Laraway and Melissa Haendel presented initial exploratory findings from their work to analyze the impact of SARS-CoV-2 infection on rare disease patients, including the prevalence of rare disease patients in the N3C enclave with and without SARS-CoV-2 infection, the COVID severity in SARS-CoV-2-positive rare disease patients, as well as the predicted incidence of long-COVID in those patients. Work is currently underway to identify and characterize additional rare disease populations within the enclave as well as more in-depth analyses examining the effect of pre-existing rare disease phenotypes on COVID severity and outcomes. Future analyses will leverage the diverse expertise of the research team and will utilize semantic technologies including ontologies, semantic similarity phenotype matching, and rare disease clustering.

While a regular meeting schedule is still being determined, those interested in joining the research team may contact Bryan Laraway at bryan@tislab.org for further details on joining the rare disease and COVID DUR.
National Center for Data to Health (CD2H)
CD2H Face-to-face Meeting
The CD2H Face-to-face meeting on June 13 and 14th in Denver, University of Colorado Anschutz Medical Campus
The CD2H Steering Committee, NCATS, and project members met to discuss numerous topics such as data harmonization, tenant model, maturity model, research discovery transition, and more. The group developed sustainability plans, lessons learned and what is next for N3C. Members of the Colorado Clinical and Translational Institute (CCTSI) also attended. After 3 years, it was great to see the group in person. Some who met each other for the very first time! 
CD2H No Cost Extension
The National Center for Data to Health was approved for a 12-month No-Cost Extension (NCE).
Details about the efforts covered during the NCE will be communicated in the coming weeks.

Published works using N3C data are listed on the N3C Cohort Exploration Dashboard. The Publications tab displays titles and links to fully published articles, articles online ahead of print, and published preprints, as well as a list of accepted conference presentations and posters.

Recent N3C Articles Accepted for Journal Publication

The Impact of Malnutrition on Clinical Outcomes in Patients Diagnosed with COVID-19. In the Journal of Parental and Enteral Nutrition (June 6, 2022)

Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19. In The Studies in Health Technology and Informatics (May 25, 2022)

Decreasing Incidence of Chemosensory Changes by COVID-19 Variant. In the Journal of Otolaryngology-Head and Neck Surgery (May 3, 2022)

Help Us Track N3C Publications!

When you have a research product that is ready for publication or accepted for presentation, please submit it via the N3C Publication Intent Form, which will notify the Publication Committee of N3C output to be registered. (Research products include: manuscripts, posters, conference papers, blogs, press releases, podium presentations, etc.)

Per the N3C Attribution and Publication Principles, all manuscripts using N3C community resources must be reviewed by the Publication Committee. Non-manuscript products do not require review but should be submitted after they have been accepted by the conference to allow for promotion and tracking of collaborator accomplishments.

View the Publication Review web page for more details.
N3C In the News

The Lancet: Big Data and Long COVID (June 22, 2022)
In this issue, Emily Pfaff and colleagues show that machine learning analysis of electronic health records could be crucial in diagnosing patients with long COVID.

TUESDAY, June 14, 2022 (HealthDay News) — Patients are at increased risk for new-onset psychiatric conditions in the early postacute phase of COVID-19 infection, according to a letter to the editor published in the June issue of World Psychiatry.

Scientists have urged patients and health care staff to be more proactive about addressing mental health concerns as a new study identifies a higher risk of psychiatric disorders among people suffering from COVID-19.

Covid-19 may increase the risk of developing a psychiatric disorders in the months after infection with the SARS-CoV-2 virus, according to a study conducted in the US.

The COVID-19 pandemic has demanded a huge effort to identify the risks associated with poor outcomes. The focus has been particularly relevant in patients with immune-mediated inflammatory diseases and those on therapies that suppress the immune system. Small early observational studies looked worrisome, but as data from larger studies became available a consistent picture became evident. Demographic risk factors such as age and comorbidity are really the salient factors, with some risk from underlying disease and a few specific therapeutic agents, such as rituximab.

COVID-19 patients were at a 25 percent greater risk of developing a psychiatric disorder in the months after infection than those who experienced other respiratory illnesses, according to a recent study. 

COVID-19 may increase the risk of developing a psychiatric disorder in the months after infection with the SARS-CoV-2 virus, according to a study conducted in the US. Researchers at the Oregon State University (OSU) in the US found that COVID-19 patients had about 25 per cent increased risk of developing a psychiatric disorder in the four months following their infection, compared with people who had other types of respiratory tract infections.

A recent Oregon State University study found that COVID-19 patients had a roughly 25% increased risk of developing a psychiatric disorder in the four months following their infection, compared with people who had other types of respiratory tract infections.

As the number of people reporting persistent, and sometimes debilitating, symptoms from COVID-19 increases, researchers have struggled to pinpoint exactly how common so-called "long COVID" is, as well as how to clearly define exactly who has it or who is likely to get it.

A research team supported by the National Institutes of Health has identified characteristics of people with long COVID and those likely to have it.

A research team supported by the National Institutes of Health has identified characteristics of people with long COVID and those likely to have it.

Clinical scientists have explored de-identified electronic health record data in the National COVID Cohort Collaborative(N3C), a National Institutes of Health-funded national clinical database, using machine learning models to help decipher characteristics of individuals with long COVID and attributes that may help identify such patients using information from medical records.

Machine learning models may be able to predict who will develop Covid-19, FDA authorizes an at-home test to detect the coronavirus, flu, and respiratory syncytial virus (RSV), and more in this week's roundup of Covid-19 news.
Sign Up for the Community Forum!

Please register for your Community Forum meeting link. After registering, you will receive a confirmation email from cd2h@uw.edu containing information about joining the meeting and you will also have the ability to download the .ics file to create calendar reminders for future Community Forums.
Register for the Community Forum at https://covid.cd2h.org/forum

N3Community Forum

Presentations take place on select Mondays from 5–6 p.m. ET/2–3 p.m. PT. To attend these and future N3Community Forum presentations, please register here.

Missed an N3Community Forum or want to revisit a past Forum? You can find all the videos on our YouTube page.

July 18, 2022

Topic: Harmonizing Units and Values of Quantitative Data Elements in a Very Large Nationally-Pooled EHR Dataset (DOI: ocac054) in Journal of the American Medical Informatics Association
Presenters: Kate Bradwell, PhD and Richard Moffitt, PhD
Palantir and Stony Brook University

July 25, 2022

Topic: Vaccine Effectiveness Domain Team
Presenter: Nasia Safdar, MD, PhD
University of Wisconsin

N3Community Forum will take a break in August and resume on Monday, September 12, 2022
Congratulations to our Newest PhD!

Senior Scientist
C. Kenneth and Dianne Wright Center for Clinical and Translational Research
Virginia Commonwealth University, Computer Science

My dissertation research was in the field of Clinical Natural Language Processing and specifically focused on improving the classification of relative temporal expressions in clinical notes. 

My long-term research goals are to utilize NLP to extract a patient's complete medical timeline from unstructured EHR data for both visualization and use by the medical team, and for research involving the disease trajectories of Multiple Sclerosis patients. 

Role on N3C
  • Informatics Lead for the ImmunoSuppressed/Compromised Domain Team
  • Member of the Logic Liaison Team.
New Team Member
Chris Roeder
Research Informatics Specialist, MS
University of Colorado
Important SLACK Update!

Starting on July 6, any members that have not onboarded to CD2H/N3C will be removed from the CD2H Slack Channel.
N3Connect Bulletin Break

We are taking a mini break in August. We will see you in September!
We will continue to send out communication as needed to inform the community of important updates and information. Please see our CD2H Slack channel for important information and upcoming events.
N3C Domain Teams

N3C Domain Teams enable researchers with shared interests to analyze data within the N3C Data Enclave and collaborate more efficiently in a team science environment. They include multidisciplinary Clinical Domains composed of subject matter experts, statisticians, informaticists, and machine learning specialists who focus on clinical questions surrounding COVID-19's impact on health. Cross-Cutting Domains have a varied focus that applies to multiple domains. These teams provide an opportunity to collect pilot data for grant submissions, train algorithms on larger datasets, inform clinical trial design, learn how to use tools for large-scale COVID-19 data, and validate results. N3C encourages researchers of all levels to join a Domain Team that represents their interests, or to suggest new clinical areas to explore.


As we have recently crossed the 2-year mark for the COVID-19 pandemic, it is a good inflection point to identify colleagues who have completed their efforts with CD2H/N3C projects and have transitioned to other great opportunities. If this is you, or perhaps your colleague, we would like to ask that you complete this 2-minute form (Bit.ly/cd2h-offboarding-form) to offboard CD2H and or N3C projects. You can continue to just get the newsletter if you wish.

We thank you for your tireless efforts in CD2H/N3C projects and look forward to working with you on many other projects.
Reporting Concerns

In the event that you come across activities that pose misalignment with the principles outlined in the Community Guiding Principles for the National COVID Cohort Collaborative (N3C), you can privately notify us using the Report Conduct Concerns form located on the N3C website under the SUPPORT menu. Your feedback is important and we will take prompt and confidential action to address your concerns. All data management incidents should also be reported to NCATS. Thank you for your contribution!
The National COVID Cohort Collaborative (N3C) is a complementary and synergistic partnership among the Clinical and Translational Science Awards (CTSA) Program hubs, the National Center for Data to Health (CD2H), distributed clinical data networks (PCORnet, OHDSI, ACT, TriNetX), and other partner organizations, with overall stewardship by NIH’s National Center for Advancing Translational Sciences (NCATS). The N3C aims to improve the efficiency and accessibility of analyses using a very large row-level (patient-level) COVID-19 clinical dataset, demonstrate a novel approach for collaborative pandemic data sharing, and speed understanding of and treatments for COVID-19.
CD2H is supported by the National Center for Advancing Translational Sciences (NCATS) 
at the National Institutes of Health
(Grant U24TR002306).