N3C): Let’S Get Involved !

The National COVID Cohort Collaborative (N3C): Let’s Get Involved !

Warren A. Kibbe, PhD, FACMI June 15, 2021 Purdue Big Data in Cancer Workshop

@data2health covid.cd2h.org @wakibbe @ncats_nih_gov ncats.nih.gov/n3c Speaker Objectives A program of NIH’s National Center for Advancing Translational Sciences

● Real World Data ● Open Science ● Overview of N3C Warren Kibbe ● N3C Data Enclave statistics Duke Biostatistics & Bioinformatics ● How common data models and variables CTSA Informatics Duke Cancer Institute are harmonized Member N3C ● The scope of answerable questions ● Data access and security ● How common data models and variables are harmonized ● Oncology research in N3C Special thanks to:

● Chris Chute, N3C, Johns Hopkins

● Melissa Haendel, N3C, Colorado University

● Umit Topaloglu, N3C, Wake Forest

● Frank Rockhold, Duke

● Noha Sharafeldin, N3C, UAB Take homes

• N3C represents a unique resource to examine effects of COVID-19 on cancer outcomes • Largest COVID-19 and cancer cohort within the US • Consistent with previous literature, older age, male gender, increasing comorbidities, and hematological malignancies were associated with higher mortality in patients with cancer and COVID-19 • The N3C dataset confirmed that cancer patients with COVID-19 who received recent immuno-, or targeted therapies were not at higher risks of overall mortality

4 What is Real World Data?

Collected in the context of patient care. Real World Data was called out as part of the 21st Century Cures Act

21st Century Cures Act: https://www.fda.gov/regulatory-information/selected-amendments-fdc-act/21st-century-cures-act Graphic from HealthCatalyst: https://www.healthcatalyst.com/insights/real-world-data-chief-driver-drug-development Current sources of data

molecular genome pathology imaging labs notes sensors

Our ability to generate biomedical data continues to grow in terms of variety and volume

icons by the Noun Project AI is changing our ability to go both deep and broad

Trustworthy AI Reusable Provenance Reproducible Having a health equity lens

● Digital Health, precision medicine, and real world data all have the power to transform healthcare. However, we must pay attention to structural racism and implicit bias if we want to achieve equity. 21st Century Cures Act

Last year I discussed the NCI Cancer Moonshot and Precision Medicine activities funded under the 21st Century Cures Act FDA was directed by congress to focus on the use of RWD and RWE in drug design, development and outcomes assessment https://www.fda.gov/regulatory-information/selected- amendments-fdc-act/21st-century-cures-act Is it just about Real World Data?

What about Open Science? Data transparency? Data Access? The importance of Open Science

Calls for greater transparency and ‘open data access’ in clinical research continue actively. ● “Open science is the movement to make scientific research, data and dissemination accessible to all levels of an inquiring society”* ● Open Science Project**: “If we want open science to flourish, we should raise our expectations to: Work. Finish. Publish. Release.” ● FAIR Principles: Findability, Accessibility, Interoperability, and Reusability*** ● TRUST Principles: Transparency, Responsibility, User focus, Sustainability and Technology * https://www.fosteropenscience.eu/resources ** http://openscience.org/ *** https://www.nature.com/articles/sdata201618 ****https://www.nature.com/articles/s41597-020-0486-7 Open Science and Patient Data Access

Some of the challenges are: ● Patient privacy ● Academic credit ● Commercial sensitivity and intellectual property ● Data standards ● Resources (money and people)

There should be room for researchers and patients alike to gain from this effort.

Informatics experts and data scientists are essential elements of this discussion. One problem with Clinical Trials Data Sharing

● “The tendency for researchers to ‘‘sit’’ on their data for an unduly long period of time is neither desirable from a scientific point of view nor acceptable from an ethical perspective. ‘

● ‘After all, the data belong to the patients who agreed to participate in the research, not to the investigators who coordinated it, as the new European General Data Protection Regulation emphasizes.”*

*Rockhold, F, et al. Open science: The open clinical trials data journey, Clinical Trials, Vol 16 (5) 1-8, 2019 Access to patient-level data is important for research

There are certainly challenges, but question is not whether data should be shared, but rather how and when access should be granted. Responsible open access enables secondary analyses that:

● Enhance reproducibility of clinical research ● Honor the contributions of trial participants, ● Improve the design of future trials ● Generate new research findings

This journey of making patient data available is part of an evolution in transparency and not a sudden awakening. What about N3C?

It is an open science, controlled access environment Clinical and Translational Science Awards (CTSA) Program A program of NIH’s National Center The pandemic highlights urgent needs for Advancing Translational Sciences

● Algorithms (diagnosis, triage, predictive, etc.) ● Drug discovery & pharmacogenetics ● Multimodal analytics (EHR, imaging, genomics) ● Interventions that reduce disease severity ● Best practices for resource allocation ● Coordinated research efforts to maximize efficiency and reproducibility

These all require the creation of a comprehensive clinical data set What Kinds of Questions Can N3C Address? A program of NIH’s National Center for Advancing Translational Sciences

The scope and scale of the information in the platform will support probing questions such as: ● What social determinants of health are risk factors for mortality? ● Do some therapies work better than others? By region? By demographics? ● Can we compare local rare clinical observations with national occurrences? ● Can we predict who might have severe outcomes if they have COVID-19? ● What factors will predict the effectiveness of vaccines? ● Can we predict acute kidney injury in COVID-19 patients? ● Who might need a ventilator because of lung failure? Cohort characterization objectives A program of NIH’s National Center for Advancing Translational Sciences

To clinically characterize the N3C cohort ● Largest U.S. COVID-19 cohort to date (+ representative controls) + ● Racially, ethnically, and geographically diverse

To develop and share validated, versioned OMOP representations of common variables (labs, vital signs, medications, treatments)

To generate hypotheses to be tested within N3C and elsewhere ● Clinical phenotypes and trajectories ? ● Treatment patterns and response ● … and many others Benefits for Participation A program of NIH’s National Center for Advancing Translational Sciences

●Access to large scale COVID-19 data from across the nation

●Pilot data for grant proposals

●Opportunities for KL2 and TL1 and other scholars

●Team science opportunities for new questions and access to Teams, statistics, machine learning (ML), informatics expertise

●Learn ML analytics, NLP methods & access to tools, software, additional datasets Step 4. FederatedWho is in Analytics the N3C? with HPC A program of NIH’s National Center for Advancing Translational Sciences The N3C Computable Phenotype ● At a high level, our phenotype looks for patients: ○ With a positive COVID-19 test (PCR or antibody) OR ○ With an ICD-10-CM code of U07.1 OR ○ Two or more COVID-like diagnosis codes (ARDS, pneumonia, etc.) during the same encounter, but only on or prior to 5/1/2020 ● Each one of these patients is then demographically matched to two patients with negative or equivocal COVID-19 tests.

Age 47 Age 49 Age 46

Gender M Gender M Gender M

Race Black Matching algorithm Race Black Race Black Ethnicit Unknow Ethnicit Hispanic/ Ethnicit Not y n y Latino y Hispanic

COVID Positive COVID Negative COVID Negative

● Each site securely sends this set of patients, along with their longitudinal EHR data from 1/1/2018 to the present, to the N3C on a regular basis. A program of NIH’s National Center N3C Timeline for Advancing Translational Sciences N3C Dashboard A program of NIH’s National Center for Advancing Translational Sciences covid.cd2h.org/dashboard

55 sites with data released (purple) and 37 sites with data pending (open circle). OCHIN is a national network of 131 sites (diamond).

covid.cd2h.org/teams

31 Domain teams! As of June 14, 2021 Data Transfer Agreement Signatories

6/14/2021 88 DTA Signatories

Northwestern University at Chicago ᛫ Tufts Medical Center ᛫ Advocate Health Care Network ᛫ University of Alabama at Birmingham ᛫ Oregon Health & Science University ᛫ University of Washington ᛫ Stanford University ᛫ The University of Michigan at Ann Arbor ᛫ Children's Hospital Colorado ᛫ Duke University ᛫ Medical College of Wisconsin ᛫ The Ohio State University ᛫ University of Nebraska Medical Center ᛫ University of Arkansas for Medical Sciences ᛫ George Washington University ᛫ Johns Hopkins University ᛫ West Virginia University ᛫ Medical University of South Carolina ᛫ University of North Carolina at Chapel Hill ᛫ University of Virginia ᛫ The University of Texas Medical Branch at Galveston ᛫ University of Minnesota ᛫ University of Cincinnati ᛫ Columbia University Irving Medical Center ᛫ Cincinnati Children's Hospital Medical Center ᛫ Rush University Medical Center ᛫ Nemours ᛫ University of Wisconsin-Madison ᛫ The State University of New York at Buffalo ᛫ Washington University in St. Louis ᛫ University of Rochester ᛫ The University of Chicago ᛫ University of Miami ᛫ The Scripps Research Institute ᛫ University of Texas Health Science Center at San Antonio ᛫ University of Kentucky ᛫ University of Illinois at Chicago ᛫ Virginia Commonwealth University ᛫ Weill Medical College of Cornell University ᛫ Carilion Clinic ᛫ University Medical Center New Orleans ᛫ The University of Iowa ᛫ Emory University ᛫ Maine Medical Center ᛫ The University of Texas Health Science Center at Houston ᛫ Boston University Medical Campus ᛫ The University of Utah ᛫ University of Southern California ᛫ George Washington Children's Research Institute ᛫ University of Colorado Denver I Anschutz Medical Campus ᛫ Mayo Clinic Rochester ᛫ The Rockefeller University ᛫ Montefiore Medical Center ᛫ University of Mississippi Medical Center ᛫ University of Oklahoma Health Sciences Center, Board of Regents ᛫ University of Massachusetts Medical School Worcester ᛫ Aurora Health Care ᛫ Penn State ᛫ University of New Mexico Health Sciences Center ᛫ NorthShore University HealthSystem ᛫ Wake Forest University Health Sciences ᛫ Vanderbilt University Medical Center ᛫ Regenstrief Institute ᛫ Brown University ᛫ Stony Brook University ᛫ University of California, Davis ᛫ Yale New Haven Hospital ᛫ Rutgers, The State University of New Jersey ᛫ MedStar Health Research Institute ᛫ Loyola University Chicago ᛫ Loyola University Medical Center ᛫ University of Delaware ᛫ Children's Hospital of Philadelphia

https://ncats.nih.gov/n3c/resources/data-contribution/data-transfer-agreement-signatories N3C Enclave Data Stats A program of NIH’s National Center for Advancing Translational Sciences

Pediatric cases N3C Enclave Data Stats A program of NIH’s National Center for Advancing Translational Sciences

Pediatric cases N3C Enclave Data Stats A program of NIH’s National Center for Advancing Translational Sciences The National COVID Cohort Collaborative: Clinical Characterization and Early Severity Prediction Predicting Clinical Severity using machine learning (64 input variables)

The most powerful predictors are patient age and widely available https://pubmed.ncbi.nlm.nih.gov/33469592/ vital sign and laboratory values. Step 4.How Federated does data Analytics get into N3C? with HPC A program of NIH’s National Center for Advancing Translational Sciences ● We have gone through the high-level purpose – EHR data about COVID-19 patients ● Identified the contributing sites ● Know what the inclusion criteria for N3C is – documented COVID-19 testing ● Seen the dashboard overview of N3C and the overall cohort characteristics

● What are the data ingestion, harmonization, query, and publication processes?

● Data governance and security?

● And finally, what about cancer and COVID-19? Leveraging Common Data Models A program of NIH’s National Center for Advancing Translational Sciences

● These four data models are commonly used by academic medical centers throughout the US. ● CDMs are used to store EHR data in a consistent way. ● Sites participating in N3C may send data in one of these four formats—the idea is to make it as convenient as possible for sites to submit. ● Common data models also allow us to write a consistent computable phenotype that can be run with few local changes at sites with one or more of these data models. A program of NIH’s National Center for Advancing Translational Sciences

Harmonization of N3C Data Data Availability vs Utility A program of NIH’s National Center for Advancing Translational Sciences

● Collections of data are not always useful ● Even if they are available

● Consistently classified data is alway more useful FAIR: Findable, Accessible,

A program of NIH’s National Center for Advancing Translational Sciences Interoperable, Reusable What does Interoperable mean with respect to data? Harmonized! Syntactic Interoperability (harmonization) ● One can make sense of the structure ● Metaphor: sentence has good grammar ● Domain of the data standards and data model communities Semantic interoperability (harmonization) ● One can make sense of the meaning ● Metaphor: the words are understandable ● Domain of the vocabulary, ontology, classification communities N3C Data Ingestion & Harmonization Pipeline A program of NIH’s National Center for Advancing Translational Sciences

Span manual curation of mapping resources to industrial scale (future) production transformation Harmonized, not Homogenous A program of NIH’s National Center for Advancing Translational Sciences

CDMs are built for purpose. Different CDMs emphasize and prioritize different things. Collaborative Analytics - N3C Secure Data Enclave

Secure, reproducible, transparent, versioned, provenanced, attributed, and shareable analytics on patient-level EHR data Federated versus Centralized DQ A program of NIH’s National Center for Advancing Translational Sciences

Many clinical data research networks are federated; N3C is centralized. Centralized datasets have some advantages where data quality assessment is concerned.

Federated Network Centralized Data

Questions asked directly against all sites’ data combined Federated versus Centralized DQ A program of NIH’s National Center for Advancing Translational Sciences

With federated data, sites are benchmarked against With centralized data, sites can be benchmarked themselves. against each other.

Site Patient Visit Type Adm. Date Disc. Date We have 43 1 123 IP 7/4/2020 7/8/2020 qualifying We have 806 inpatient We have 27 qualifying 1 456 IP 5/6/2020 5/20/2020 visits. inpatient qualifying 2 987 IP 8/2/2019 8/7/2019 inpatient visits. visits. 2 654 IP 9/3/2019 9/14/2019

3 234 IP 1/26/2021 1/26/2021

3 234 IP 1/26/2021 1/29/2021

3 234 IP 1/26/2021 1/30/2021 Site 1 Site 2 Site 3 3 234 IP 1/26/2021 1/27/2021 Clearly, sites differ in how they define “a visit.” N3C’s DQ Process A program of NIH’s National Center for Advancing Translational Sciences

How Would N3C Deal with This Finding? Site Patient Visit Type Adm. Date Disc. Date ● Discover and discuss at weekly DQ meetings. ● Determine: Is this an issue… 1 123 IP 7/4/2020 7/8/2020 ○ For the site to fix? ○ For us to handle on our end? 1 456 IP 5/6/2020 5/20/2020 ● Reach out to the site to get more information. 2 987 IP 8/2/2019 8/7/2019 ○ What if they can’t fix it? 2 654 IP 9/3/2019 9/14/2019

3 234 IP 1/26/2021 1/26/2021

3 234 IP 1/26/2021 1/29/2021

3 234 IP 1/26/2021 1/30/2021

3 234 IP 1/26/2021 1/27/2021 N3C’s DQ Process A program of NIH’s National Center for Advancing Translational Sciences

3 234 IP 1/26/2021 1/29/2021 if: ● the visit type is inpatient 3 234 IP 1/26/2021 1/30/2021 ● and there are > 1 per patient per day 3 234 IP 1/26/2021 1/27/2021 then: ● merge into a single “macro” visit N3C’s DQ Process A program of NIH’s National Center for Advancing Translational Sciences

Original Table Ready for Analysis

Site Patient Visit Type Adm. Date Disc. Date Site Patient Visit Type Adm. Date Disc. Date

1 123 IP 7/4/2020 7/8/2020 1 123 IP 7/4/2020 7/8/2020

1 456 IP 5/6/2020 5/20/2020 1 456 IP 5/6/2020 5/20/2020

2 987 IP 8/2/2019 8/7/2019 2 987 IP 8/2/2019 8/7/2019 DQ fix 2 654 IP 9/3/2019 9/14/2019 2 654 IP 9/3/2019 9/14/2019

3 234 IP 1/26/2021 1/26/2021 3 234 IP 1/26/2021 1/30/2021

3 234 IP 1/26/2021 1/29/2021 Takeaways 3 234 IP 1/26/2021 1/30/2021 ● Centralized DQ processes allow us to fully

3 234 IP 1/26/2021 1/27/2021 realize the potential of N3C’s large sample size. ● All transformations are fully logged and always completely reversible if needed. N3C Data Ingestion & Harmonization Pipeline A program of NIH’s National Center for Advancing Translational Sciences

(future) Harmonizing numeric data A program of NIH’s National Center for Advancing Translational Sciences

● Problem: Different sites provide their data in different units

● Solution: Harmonize each to a standard unit Kilograms = Pounds / 2.20462 Kilograms = Ounces / 35.274 Kilograms = Grams / 1000 Harmonizing numeric data A program of NIH’s National Center for Advancing Translational Sciences

● Problem: Some units are missing

● Solution 1: Contact the source

● Solution 2: N3C inference engine Kilograms = x / 2.20462 ? Kilograms = x / 35.274 ? Kilograms = x / 1000 ? Harmonization progress A program of NIH’s National Center for Advancing Translational Sciences

Humans measured in grams do not ● Harmonized measurements look the same as humans measured ○ By original unit in kilograms! ○ Across many sites

Homogeneity after harmonization Unit harmonization progress A program of NIH’s National Center for Advancing Translational Sciences

● ~2x increase in usable data from our harmonization procedures

Canonical unit Uses a known conversion Unit not plausible Missing unit inferred Unit still missing

We can rescue a lot of data! N3C Data Ingestion & Harmonization Pipeline A program of NIH’s National Center for Advancing Translational Sciences

(future) Long-COVID phenotypes are myriad patient-reported and researcher-measured phenotypes are starkly different

40 141 7

Map literature and patient- reported terms to HPO

Pharyngalgia = Sore throat Plain-language medical vocabulary for precision diagnosis. Nat Genet. 2018 50:474-476. N3C Harmonization Takeaways A program of NIH’s National Center for Advancing Translational Sciences

What N3C has revealed most in terms of needs:

● Interoperability - we need syntactic and semantic! ○ FHIR ⇒ OMOP (syntactic) ○ Common vocabulary/codeset mapping provenance and management (semantic) ● Approach data harmonization from an end-to-end data life cycle perspective ● Leverage USCDI, but build for interoperable semantic modeling and extensions A program of NIH’s National Center for Advancing Translational Sciences

Governing N3C Data N3C: Unique Data Use and Privacy A program of NIH’s National Center for Advancing Translational Sciences

Goal of the Data Use Agreement is Privacy Protection to Promote broad access:

● COVID-Related research only ● NIH housed secure repository ● No re-identification of individuals or data source ● No download or capture of raw data ● Open platform to all researchers ● Investigator activities are recorded and can be audited for security and reproducibility N3C: Governance and Access Data Levels to Access Data Use and Privacy

Goal of the Data Use Agreement is Privacy Protection to Promote broad access: ● COVID-Related research only ● No re-identification of individuals or data source ● No download or capture of raw data ● Open platform to all researchers ● Security: Activities in the N3C Data Enclave are recorded and can be audited ● Disclosure of research results to the N3C Data Enclave for the public good ● Analytics provenance ● Contributor Attribution tracking N3C Provenance, Transparency,

A program of NIH’s National Center for Advancing Translational Sciences Attribution & Rapid Sharing

N3C Attribution and Publication Principles

● Transparent and collaborative environment where all contributions are acknowledged ● Provenance and reproducibility ● Promptly sharing research results with N3C users ● Publish in high-impact journals ● Attribution for all N3C artifacts

Researchers, projects, and artifacts are all linked together in the enclave using the Contributor Attribution Model (CAM). N3C Data Access: Process A program of NIH’s National Center for Advancing Translational Sciences

Data Use Agreement

Data Use Request

HSP / Security Training

https://ncats.nih.gov/n3c/about/applying-for-access A program of NIH’s National Center for Advancing Translational Sciences

Realizing Team Science N3C team Science within & across institutions

CTSAs N3C Domain Team Expertise: ● Enclave technology Key functions can ● Data model (OMOP) nucleate projects: ● Terminologies ● Data quality ● Education & training ● Codesets, variables, ● Biostatistics phenotype ● Study design ● Using/parsing N3C data ● Evaluation ● Workflows, methods, ● Informatics algorithms ● Clinical expertise ● Innovation & Roles commercialization Ingredients (Methods, datasets, instruments) ● Community & Scientific questions partnerships https://covid.cd2h.org/domain-teams OUTCOMES OF COVID-19 IN CANCER PATIENTS: REPORT FROM THE NATIONAL COVID COHORT COLLABORATIVE (N3C)

Noha Sharafeldin, Benjamin Bates, Qianqian Song, Vithal Madhira, Yao Yan, Sharlene Dong, Eileen Lee, Nathaniel Kuhrt, Yu Raymond Shao, Feifan Liu, Timothy Bergquist, Justin Guinney, Jing Su, Umit Topaloglu on behalf of the N3C Consortium

Given on June 4, 2021 https://covid.cd2h.org/ cd2h.slack.com @data2health 60 N3C Oncology Domain Team (ODT)

Leadership

Umit Topaloglu, PhD Noha Sharafeldin, MD, PhD Benjamin Bates, MD Wake Forest The University of Alabama at Rutgers University University Birmingham

https://covid.cd2h.org/oncology Slack channel: #n3c-tt-oncology

Noha Sharafeldin, MBBCh, PhD 61 N3C ODT Expertise

Informatics Biostatistics Clinical Epidemiology N3C data and Logic

Umit Topaloglu Jing Su Noha Sharafeldin Benjamin Bates Justin Guinney Vithal Madhira Tim Bergquist

Feifan Liu Qianqian Song Yu Raymond Shao Nate Kuhrt Sharlene Dong Yao Yan Eileen Lee

Noha Sharafeldin, MBBCh, PhD N3C Oncology A program of NIH’s National Center for Advancing Translational Sciences

http://ascopubs.org/doi/full/10.1200/JCO.21.01074 63 N3C Cancer Cohort Primary Diagnosis

Noha Sharafeldin, MBBCh, PhD 64 N3C Cancer Cohort

Primary Outcome • All- cause mortality

Secondary Outcomes (Clinical severity indicators requiring hospitalization) • Mechanical Ventilation

Noha Sharafeldin, MBBCh, PhD 659 Demographic, clinical, and tumor characteristics COVID-19 Positive

Age Sex 2% 13%

18-29 30-49 Female 49% 51% 54% 50-64 Male 31% 65+

Race Geographical Location 4% 11% 22% 13% 22% US-Northeast Hispanic US-Midwest Non-Hispanic Black 5% US-South Non-Hispanic White 34% US-West Other or Unknown Unknown

61% 28%

Insert Name Noha Sharafeldin, MBBCh, PhD (Insert > Header & Footer > Apply to All) 6610 Demographic, clinical, and tumor characteristics

COVID-19 Positive

ADJUSTED CCI Smoking status 18000 16000 41% 14000

12000 14% 28% Non-smoker 10000 8000 16% Current or 6000 Former smoker 4000 9% 6% 86% 2000 0 0 1 2 3 ≥4

Insert Name Noha Sharafeldin, MBBCh, PhD (Insert > Header & Footer > Apply to All) 6711 Demographic, clinical, and tumor characteristics

COVID-19 Positive

Type of primary malignancy

MULTI-SITE 11% 3% 3% 11% GASTROINTESTINAL CANCERS 9%

Solid HEMATOLOGICAL CANCERS 12% 12% Liquid Multi-Site Unknown PROSTATE CANCER 12% Undefined Primary BREAST CANCER 14% 71%

SKIN CANCERS 15%

0 1000 2000 3000 4000 5000 6000 7000

Insert Name Noha Sharafeldin, MBBCh, PhD (Insert > Header & Footer > Apply to All) 68 COVID-19 Treatment

COVID-19 Treatment (Yes) COVID positive (n=38,614) Systemic antibiotics 4032(15.75%) Systemic steroids 3514(13.73%) Azithromycin 1197(4.68%) Remdesivir 1047(4.09%) Dexamethasone 1029(4.02%) Hydroxychloroquine (HCQ) 364(1.42%)

Noha Sharafeldin, MBBCh, PhD 69 Death and invasive ventilation in hospitalized patients

Outcome COVID positive COVID negative (n=19,515) (n=184,988) Death 2,894 (14.8%) 23,207 (12.5%) Invasive Ventilation 1,606 (8.2%) 9,576 (5.2%)

Noha Sharafeldin, MBBCh, PhD 70

Survival Probability – by COVID status

HR = 1.20 (95%CI: 1.15 – 1.24, p<0.001)

Noha Sharafeldin, MBBCh, PhD 71

Survival Probability by cancer type among COVID positive patients

Noha Sharafeldin, MBBCh, PhD Hazard ratios associated with 1-year all-cause 72 mortality among COVID-positive patients

Noha Sharafeldin, MBBCh, PhD Hazard ratios associated with 1-year all-cause 73 mortality among COVID-positive patients

Noha Sharafeldin, MBBCh, PhD Hazard ratios associated with 1-year all-cause 74 mortality among COVID-positive patients

Noha Sharafeldin, MBBCh, PhD Hazard ratios associated with 1-year all-cause 75 mortality among COVID-positive patients

Noha Sharafeldin, MBBCh, PhD 76 Limitations

• RWD Challenges (e.g. data missingness) • Limited capture of recent cancer therapy • Potential misclassification of cancer patients • Challenges in primary cancer diagnosis mapping and limited historical data • Method for construction of COVID-19 negative control

Noha Sharafeldin, MBBCh, PhD 77 Conclusions

Noha Sharafeldin, MBBCh, PhD 78 Acknowledgements

The Patients NCATS U24 TR002306 US Data Partners NIGMS 5U54GM104942-04 N3C Consortial Authors NCI P30CA012197 [UT, QS] Christopher Chute LLS 3386-19 [NS] Melissa Haendel Indiana University Precision Health Amit Mitra Initiative [JS] Ramakanth Kavuluru

N3C Core Teams

Noha Sharafeldin, MBBCh, PhD 79 Acknowledgements

We gratefully acknowledge contributions from the following N3C core teams: • Principal Investigators: Melissa A. Haendel*, Christopher G. Chute*, Kenneth R. Gersing, Anita Walden • Workstream, subgroup and administrative leaders: Melissa A. Haendel*, Tellen D. Bennett, Christopher G. Chute, David A. Eichmann, Justin Guinney, Warren A. Kibbe, Hongfang Liu, Philip R.O. Payne, Emily R. Pfaff, Peter N. Robinson, Joel H. Saltz, Heidi Spratt, Justin Starren, Christine Suver, Adam B. Wilcox, Andrew E. Williams, Chunlei Wu • Key liaisons at data partner sites • Regulatory staff at data partner sites • Individuals at the sites who are responsible for creating the datasets and submitting data to N3C • Data Ingest and Harmonization Team: Christopher G. Chute*, Emily R. Pfaff*, Davera Gabriel, Stephanie S. Hong, Kristin Kostka, Harold P. Lehmann, Richard A. Moffitt, Michele Morris, Matvey B. Palchuk, Xiaohan Tanner Zhang, Richard L. Zhu • Phenotype Team (Individuals who create the scripts that the sites use to submit their data, based on the COVID and Long COVID definitions): Emily R. Pfaff*, Benjamin Amor, Mark M. Bissell, Marshall Clark, Andrew T. Girvin, Stephanie S. Hong, Kristin Kostka, Adam M. Lee, Robert T. Miller, Michele Morris, Matvey B. Palchuk, Kellie M. Walters • Project Management and Operations Team: Anita Walden*, Yooree Chae, Connor Cook, Alexandra Dest, Racquel R. Dietz, Thomas Dillon, Patricia A. Francis, Rafael Fuentes, Alexis Graves, Julie A. McMurry, Andrew J. Neumann, Shawn T. O'Neil, Andréa M. Volz, Elizabeth Zampino • Partners from NIH and other federal agencies: Christopher P. Austin*, Kenneth R. Gersing*, Samuel Bozzette, Mariam Deacy, Nicole Garbarini, Michael G. Kurilla, Sam G. Michael, Joni L. Rutter, Meredith Temple-O'Connor • Analytics Team (Individuals who build the Enclave infrastructure, help create codesets, variables, and help Domain Teams and project teams with their datasets): Benjamin Amor*, Mark M. Bissell, Katie Rebecca Bradwell, Andrew T. Girvin, Amin Manna, Nabeel Qureshi • Publication Committee Management Team: Mary Morrison Saltz*, Christine Suver*, Christopher G. Chute, Melissa A. Haendel, Julie A. McMurry, Andréa M. Volz, Anita Walden • Publication Committee Review Team: Carolyn Bramante, Jeremy Richard Harper, Wenndy Hernandez, Farrukh M Koraishy, Federico Mariona, Saidulu Mattapally, Amit Saha, Satyanarayana Vedula

Noha Sharafeldin, MBBCh, PhD N3C Registration/Training A program of NIH’s National Center https://covid.cd2h.org/tutorials for Advancing Translational Sciences

Registration for Documents, Meetings & the N3C Data Enclave

Requires Authentication

Enclave Checklist

Training Office Hours: Tuesdays & Thursdays at 10-11 am PT/1-2 pm ET Registration Required at this link

Orientation Video Coming Soon

Additional Training Tutorials available in the Enclave Step 4. FederatedTakeaways Analytics with HPC A program of NIH’s National Center for Advancing Translational Sciences ● N3C comprises the largest, most representative patient-level COVID-19 cohort in the US and continues to grow

● We CAN do transparent, reproducible, innovative science (including ML) on sensitive observational data at scale, together!

● N3C is an innovative partnership between clinical sites, CDM communities, NIH ICs, CD2H, and commercial partners

● Automation of data extraction and minimum requirements reduces burden and increases site participation

● Robust attribution of all contributors; also provides great venue for trainees

● N3C data is complicated, but there are many people and resources to help users do good science How to Get Involved with N3C A program of NIH’s National Center for Advancing Translational Sciences

Joining Workstreams: N3C Data Ingestion & Harmonization Workstream Slack Channel Harmonization Google Group Harmonization

N3C Phenotype & Data Acquisition Workstream Slack Channel Phenotype Google Group Phenotype

N3C Collaborative Analytics Workstream Slack Channel Analytics Google Group Analytics

N3C Data Partnership & Governance Workstream NCATS N3C Webpage N3C Website Slack Channel Governance Google Group Governance

N3C Synthetic Clinical Data Workstream Additional Information: Slack Channel Synthetic Onboarding N3C, Slack, Google | Finding and Joining a Google Group Google Group Synthetic

N3C Implementation Workstream- Coming soon https://academic.oup.com/jamia/advance- article/doi/10.1093/jamia/ocaa196/5893482

Melissa A. Haendel,1,4,7,8,10,13,14,52,78,101 Christopher G. Chute,1,4,8,10,13,14,52,78,100,101 Tellen D. Bennett,9,10,13,14,52,100,101 David A. Eichmann,4,9,10,13,78,101 Justin Guinney,4,9,10,14,78,101 Warren A. Kibbe,9,10,52,78,101 Philip R.O. Payne,4,9,10,78,101 Emily R. Pfaff,9,10,13,15,52,78 Peter N. Robinson,4,9,10,15,52,78,100 Joel H. Saltz,10,13,14,15,52,78,101 Heidi Spratt,9,10,100 Christine Suver,10,78,101 John Wilbanks,10,78,101 Adam B. Wilcox,10,101 Andrew E. Williams,10,13,78 Chunlei Wu,9,13,14,78 Clair Blacketer,15,52 Robert L. Bradford,9,52 James J. Cimino,10,14,101 Marshall Clark,9,15,52 Evan W. Colmenares,9,15,52 Patricia A. Francis,78 Davera Gabriel,9,10,13,14,15,52 Alexis Graves,7,9,78 Raju Hemadri,9,15,52 Stephanie S. Hong,9,15,52 George Hripscak,10,52 Dazhi Jiao,9,15,52 Jeffrey G. Klann,14,52,101 Kristin Kostka,9,15,52 Adam M. Lee,9,15,52 Harold P. Lehmann,9,15,52 Lora Lingrey,9,15,52 Robert T. Miller,9,15,52 Michele Morris,9,15,52 Shawn N. Murphy,9,15,52 Karthik Natarajan,9,15,52 Matvey B. Palchuk,9,15,52 Usman Sheikh,9,78 Harold Solbrig,9,15,52 Shyam Visweswaran,10,15,52,101 Anita Walden,7,10,13,14,52,101 Kellie M. Walters,10,14,101 Griffin M. Weber,10,101 Xiaohan Tanner Zhang,9,15,52 Richard L. Zhu,9,15,52 Benjamin Amor,78 Andrew T. Girvin,15,78 Amin Manna,78 Nabeel Qureshi,15,78 Michael G. Kurilla,10,78 Sam G. Michael,10,78 Lili M. Portilla,101 Joni L. Rutter,1,101 Christopher P. Austin,101 Ken R. Gersing,78,101 Shaymaa Al-Shukri,4,15 Adil Alaoui,101 Ahmad Baghal,15 Pamela D. Banning,15,100 Edward M. Barbour,8,15 Michael J. Becich,15,52,101 Afshin Beheshti,14 Gordon R. Bernard,8,15 Sharmodeep Bhattacharyya,100 Mark M. Bissell,9,15 L. Ebony Boulware,14,100 Samuel Bozzette,100,101 Donald E. Brown,101 John B. Buse,14 Brian J. Bush,8,101 Tiffany J. Callahan,14,52 Thomas R. Campion,8,15 Elena Casiraghi,9,15 Ammar A. Chaudhry,13,14 Guanhua Chen,9 Anjun Chen,13 Gari D. Clifford,8,15 Megan P. Coffee,14,100 Tom Conlin,14 Connor Cook,7,78 Keith A. Crandall,9,14,101 Mariam Deacy,78 Racquel R. Dietz,78 Nicholas J. Dobbins,8,9 Peter L. Elkin,15,52,100 Peter J. Embi,52,101 Julio C. Facelli,8,15 Karamarie Fecho,13 Xue Feng,9 Randi E. Foraker,8,13,15 Tamas S. Gal,8,15 Linqiang Ge,14 George Golovko,15,101 Ramkiran Gouripeddi,14,15 Casey S. Greene,13,14 Sangeeta Gupta,52,101 Ashish Gupta,13,101 Janos G. Hajagos,9,15 David A. Hanauer,15,52 Jeremy Richard Harper,9,14,52 Nomi L. Harris,14 Paul A. Harris,101 Mehadi R. Hassan,9 Yongqun He,15,52,100 Elaine L. Hill,9,14 Maureen E. Hoatlin,14 Kristi L. Holmes,4,101 LaRon Hughes,14 Randeep S. Jawa,14 Guoqian Jiang,14 Xia Jing,7,14 Marcin P. Joachimiak,8,15 Steven G. Johnson,9,14,101 Rishikesan Kamaleswaran,9,15,78 Thomas George Kannampallil,15,101 Andrew S. Kanter,15,52 Ramakanth Kavuluru,9,13,14 Kamil Khanipov,8,14 Hadi Kharrazi,9,14 Dongkyu Kim,15,52 Boyd M. Knosp,8,15 Arunkumar Krishnan,9 Tahsin Kurc,9,15 Albert M. Lai,101 Christophe G. Lambert,52,101 Michael Larionov,14 Stephen B. Lee,1,14 Michael D. Lesh,9 Olivier Lichtarge,14 John Liu,9 Sijia Liu,8,9,101 Hongfang Liu,9,15 Johanna J. Loomba,1,15,78,101 Sandeep K. Mallipattu,9,14,15 Chaitanya K. Mamillapalli,14 Christopher E. Mason,15 Jomol P. Mathew,8,15,52 James C. McClay,101 Julie A. McMurry,1,4,7,9,13,14,78 Paras P. Mehta,14 Ofer Mendelevitch,9 Stephane Meystre,8,14,15 Richard A. Moffitt,9,13,15 Jason H. Moore,8,9 Hiroki Morizono,13,14,15,52 Christopher J. Mungall,15,52 Monica C. Munoz-Torres,7,10,78 Andrew J. Neumann,78 Xia Ning,14 Jennifer E. Nyland,13,14 Lisa O'Keefe,78 Anna O'Malley,78 Shawn T. O'Neil,78 Jihad S. Obeid,10,14,15 Elizabeth L. Ogburn,13 Jimmy Phuong,9,15,52,100,101 Jose D Posada,8,15 Prateek Prasanna,14,52 Fred Prior,9,14,15 Justin Prosser,9,78 Amanda Lienau Purnell,101 Ali Rahnavard,9,52 Harish Ramadas,9,52,78 Justin T. Reese,9,10 Jennifer L. Robinson,14,100 Daniel L. Rubin,101 Cody D. Rutherford,9,101 Eugene M. Sadhu,8,15 Amit Saha,9 Mary Morrison Saltz,15,52,101 Thomas Schaffter,78 Titus KL Schleyer,14 Soko Setoguchi,8,14,15 Nigam H. Shah,8,14 Noha Sharafeldin,14 Evan Sholle,15,52 Jonathan C. Silverstein,15,52,101 Anthony Solomonides,101 Julian Solway,14,101 Jing Su,101 Vignesh Subbian,9,52,101 Hyo Jung Tak,15 Bradley W. Taylor,9,14 Anne E. Thessen,14,101 Jason A. Thomas,15 Umit Topaloglu,15,52 Deepak R. Unni,8,9,15,52 Joshua T. Vogelstein,14 Andréa M. Volz,7 David A. Williams,14,15 Kelli M. Wilson,9,78 Clark B. Xu,8,9,15 Hua Xu,9,10,14 Yao Yan,9,15,52 Elizabeth Zak,8,15 Lanjing Zhang,101 Chengda Zhang,14 Jingyi Zheng,14 1CREDIT_00000001 (Conceptualization) 4CREDIT_00000004 (Funding acquisition) 7CRO_0000007 (Marketing and Communications) 8CREDIT_00000008 (Resources) 9CREDIT_00000009 (Software role) 10CREDIT_00000010 (Supervision role) 13CREDIT_00000013 (Original draft) 14CREDIT_00000014 (Review and editing) 15CRO_0000015 (Data role) 52CRO_0000052 (Standards role) 78CRO_0000078 (Infrastructure role) 100Clinical Use Cases 101Governance Questions or Comments? A program of NIH’s National Center for Advancing Translational Sciences

Thank you! Thank you!