IBI Faculty and staff are leading the infrastructure development for electronic consenting of Penn Medicine patients into the COMPACTS protocol. The COMPACTS project is a component of the Penn Medicine BioBank. The Penn Medicine BioBank is an institutional resource whereby Penn Medicine patients are consented for participation which includes biospecimens, electronic health record data, and recontact for addition data collection and dissemination. Prior to COVID-19, all consenting into the biobank was done in person by research coordinators. The IBI faculty and staff, in collaboration with Penn Corporate Information Systems, have developed the capability to recruit and consent electronically.
In the COMPACTS project, we are collecting and storing biological specimens as well as information about people who may or may not have been exposed to the SARS-CoV-2 virus as well as those who have developed COVID-19 disease. This information will be used to help learn more about the risk of developing COVID-19 after exposure; to better understand why different people have different reactions to the virus; to learn why some people get very sick from the virus while others don’t; and to better understand the how other diseases and conditions influence the severity of COVID-19. The overall purpose of the biobank is to collect and store biological specimens, such as blood, urine, respiratory specimens, other bodily fluids, and tissue where available, as well as health information. Storing these samples and this information allows researchers to use them for research studies, including but not limited to COVID-19.
More information about the COMPACTS project can be found here: https://clinicalresearch.itmat.upenn.edu/clinicaltrial/6427/covid19-universal-covid-19-biobank-a-subset-of-the-penn-medicine-biobank/?qd=1527209
IBI faculty participate in this international consortium for electronic health record (EHR) data-driven studies of the COVID-19 pandemic. The goal of this effort is to inform doctors, epidemiologists and the public about COVID-19 patients with data acquired through the healthcare process. The consortium rapidly responded to the pandemic by sharing and integrating research results through a federated model whereby patient-level data stay at each participating institution with mapping to a biomedical ontology to ensure transferability of results.
- Brat GA, Weber GM, Gehlenborg N, Avillach P, Palmer NP, Chiovato L, Cimino J, Waitman LR, Omenn GS, Malovini A, Moore JH, Beaulieu-Jones BK, Tibollo V, Murphy SN, Yi SL, Keller MS, Bellazzi R, Hanauer DA, Serret-Larmande A, Gutierrez-Sacristan A, Holmes JJ, Bell DS, Mandl KD, Follett RW, Klann JG, Murad DA, Scudeller L, Bucalo M, Kirchoff K, Craig J, Obeid J, Jouhet V, Griffier R, Cossin S, Moal B, Patel LP, Bellasi A, Prokosch HU, Kraska D, Sliz P, Tan ALM, Ngiam KY, Zambelli A, Mowery DL, Schiver E, Devkota B, Bradford RL, Daniar M, Daniel C, Benoit V, Bey R, Paris N, Serre P, Orlova N, Dubiel J, Hilka M, Jannot AS, Breant S, Leblanc J, Griffon N, Burgun A, Bernaux M, Sandrin A, Salamanca E, Cormont S, Ganslandt T, Gradinger T, Champ J, Boeker M, Martel P, Esteve L, Gramfort A, Grisel O, Leprovost D, Moreau T, Varoquaux G, Vie JJ, Wassermann D, Mensch A, Caucheteux C, Haverkamp C, Lemaitre G, Bosari S, Krantz ID, South A, Cai T, Kohane IS. International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium. NPJ Digit Med. 2020 Aug 19;3:109. doi: 10.1038/s41746-020-00308-0. https://www.nature.com/articles/s41746-020-00308-0
- Weber GM, Hong C, Palmer NP, Avillach P, Murphy SN, Gutiérrez-Sacristán, A, Xia Z, Serret-Larmande A, Neuraz A, Omenn GS, Visweswaran S, Klann JG, South AM, Loh NHW, Cannataro M, Beaulieu-Jones B, Bellazzi R, Giuseppe A, Alessiani M, Aronow BJ, Bell DS, Bellasi A, Benoit V, Beraghi M, Boeker M, Booth J, Bosari S, Bourgeois FT, Brown NW, Chiovato L, Chiudinelli L, Dagliati A, Devkota B, Follett RW, Ganslandt T, Barrio NG, Gradinger T, Griffier R, Hanauer DA, Holmes JH, Horki P, Huling KM, Issitt RW, Jouhet V, Keller MS, Kraska D, Liu M, Luo Y, Malovini A, Mandl KD, Mao C, Maram A, Maulhardt T, Mauro B, Milano M, Moore JH, Morris JS, Morris M, Mowery DL, Naughton TP, Ngiam KY, Norman JB, Patel LP, Jimenez MP, Schriver ER, Scudeller L, Sebire NJ, Balazote PS, Spiridou A, Tan ALM, Tan ByornLL, Tibollo V, Torti C, Trecarichi EM, Trecarichi M, Vitacca M, Zambelli A, Zucco C, Consortium for Clinical Characterization of COVID-19 by EHR, Kohane IS, Cai T, Brat GA. International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries. medRxiv 12/2020. https://www.medrxiv.org/content/10.1101/2020.12.16.20247684v1
- Klann JG, Weber GM, Estiri H, Moal B, Avillach P, Hong C, Castro VM, Maulhardt T, Tan ALM, Geva A, Beaulieu-Jones BK, Malovini A, South AM, Visweswaran S, Omenn GS, Ngiam KY, Mandl KD, Boeker M, Olson KL, Mowery DL, Morris M, Follett RW, Hanauer DA, Bellazzi R, Moore JH, Loh WNH, Bell DS, Wagholikar K, Chiovato L, Tibollo V, Rieg S, Li ALLJ, Jouhet V, Schriver E, Samayamuthu MJ, Xia Z, The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), Kohane IS, Brat GA, Murphy SN. Validation of Patient Severity Phenotype Score to Support COVID-19 Analytics from the Electronic Health Record. medRxiv. 10/2020. https://www.medrxiv.org/content/10.1101/2020.10.13.20201855v1.full
- Parikh S, Davoudi A, Yu S, Giraldo C, Schriver E, Mowery DL. An Intrinsic and Extrinsic Evaluation of Learned COVID-19 Concepts using Open-Source Word Embedding Sources. (under review).
The COVID-19 host genetics initiative brings together the human genetics community to generate, share, and analyze data to learn more about the potential genetic determinants of COVID-19 disease susceptibility, disease severity, and disease outcomes. Such discoveries could help to generate hypotheses for drug repurposing, identify individuals at unusually high or low risk, and contribute to global knowledge of the biology of SARS-CoV-2 infection and disease. The Penn Medicine BioBank (PMBB) has information about COVID-19 status through both the electronic health record, as well as the participation in the COVID-19 patient survey. IBI Faculty and staff are performing the genetic analyses of the COVID-19 disease susceptibility and severity analyses in the PMBB to contribute the results to the consortium.
More information about the consortium can be found here: https://www.covid19hg.org/
Understanding the clinical risk factors for COVID-19 disease severity and patient outcomes requires a combination of data from electronic health records and patient reported information. To facilitate the collection of patient-reported data, as well as accelerate and standardize the collection of data about host factors, investigators in the IBI at Penn Medicine have constructed a COVID-19 survey in collaboration with investigators at Columbia University. This survey is implemented in REDCap and freely available to the scientific community to send electronically for patients to complete online. This patient survey is designed to be comprehensive, yet not overly burdensome, to gather data useful for a range of clinical investigations, and to accommodate a wide variety of implementation settings including at a COVID-19 testing site, at home during infection or after recovery, and/or for individuals while they are hospitalized. A widely adopted standardized survey that can be implemented online with minimal resources can serve as a critical tool for combining and comparing data across studies to improve our understanding of COVID-19 disease.
More information about the COVID-19 patient survey can be found at: http://covidhealthquest.com/
- A publication about the survey has been published in the Journal of Clinical and Translation Science: https://www.cambridge.org/core/journals/journal-of-clinical-and-translational-science/article/research-on-covid19-through-patientreported-data-a-survey-for-observational-studies-in-the-covid19-pandemic/5B517DAC543A550C6DCE627F7D2974E3
Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies.
- S Madhavan, L Bastarache, J S Brown, A Butte, D Dorr, P J Embi, C P Friedman, K B Johnson, J H Moore, I S Kohane, P R O Payne, J D Tenenbaum, M W Weiner, A Wilcox, L Ohno-Machado, Use of Electronic Health Records to Support a Public Health Response to the COVID-19 Pandemic in the United States: A Perspective from Fifteen Academic Medical Centers, Journal of the American Medical Informatics Association, , ocaa287, https://doi.org/10.1093/jamia/ocaa287. Published: 03 November 2020
The Integrating Biology and the Bedside (i2b2) platform is a clinical research database widely-adopted throughout the Clinical and Translational Science Awards (CTSA) network. i2b2 supports federated queries of aggregate patient data across partnered institutions within the NCATS-funded CTSA Accrual of Clinical Trials (ACT) network. The Penn COVID-19 i2b2 Database contains all COVID-19-tested Penn Medicine patients and their resulting clinical data from the electronic health record (EHR). To date, the database contains over 100,000 patients and growing. All EHR data elements are mapped to clinical terminologies and organized according to the i2b2 COVID-19 ontology developed at the University of Pittsburgh (https://github.com/shyamvis/covid-phenotyping).
The IBI Clinical Research Informatics Core (CIC) has partnered with the Penn Data Analytics Center and Office of Clinical Research to provide COVID-19 EHR data support. The CIC is available to execute Penn Medicine investigator-generated queries from the Penn COVID-19 i2b2 Database.