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Your search keyword '"Pfaff, Emily"' showing total 152 results
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152 results on '"Pfaff, Emily"'

1. Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative.

Author

Bergquist, Timothy, Loomba, Johanna, Pfaff, Emily, Xia, Fangfang, Zhao, Zixuan, Zhu, Yitan, Mitchell, Elliot, Bhattacharya, Biplab, Shetty, Gaurav, Munia, Tamanna, Delong, Grant, Tariq, Adbul, Butzin-Dozier, Zachary, Ji, Yunwen, Li, Haodong, Coyle, Jeremy, Shi, Seraphina, Philips, Rachael, Mertens, Andrew, Pirracchio, Romain, van der Laan, Mark, Colford, John, Hubbard, Alan, Gao, Jifan, Chen, Guanhua, Velingker, Neelay, Li, Ziyang, Wu, Yinjun, Stein, Adam, Huang, Jiani, Dai, Zongyu, Long, Qi, Naik, Mayur, Holmes, John, Mowery, Danielle, Wong, Eric, Parekh, Ravi, Getzen, Emily, Hightower, Jake, and Blase, Jennifer

Subjects
COVID-19, Community challenge, Evaluation, Long COVID, Machine learning, PASC, Humans, COVID-19, Machine Learning, SARS-CoV-2, United States, Algorithms, Post-Acute COVID-19 Syndrome, Cohort Studies, Crowdsourcing
Abstract

BACKGROUND: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID. METHODS: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.). FINDINGS: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events. INTERPRETATION: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions. FUNDING: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438.

Published
2024

2. Clinical Annotation Research Kit (CLARK): Computable Phenotyping Using Machine Learning

Author

Pfaff, Emily R, Crosskey, Miles, Morton, Kenneth, and Krishnamurthy, Ashok

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Computable phenotypes are algorithms that translate clinical features into code that can be run against electronic health record (EHR) data to define patient cohorts. However, computable phenotypes that only make use of structured EHR data do not capture the full richness of a patient’s medical record. While natural language processing (NLP) methods have shown success in extracting clinical features from text, the use of such tools has generally been limited to research groups with substantial NLP expertise. Our goal was to develop an open-source phenotyping software, Clinical Annotation Research Kit (CLARK), that would enable clinical and translational researchers to use machine learning–based NLP for computable phenotyping without requiring deep informatics expertise. CLARK enables nonexpert users to mine text using machine learning classifiers by specifying features for the software to match in clinical notes. Once the features are defined, the user-friendly CLARK interface allows the user to choose from a variety of standard machine learning algorithms (linear support vector machine, Gaussian Naïve Bayes, decision tree, and random forest), cross-validation methods, and the number of folds (cross-validation splits) to be used in evaluation of the classifier. Example phenotypes where CLARK has been applied include pediatric diabetes (sensitivity=0.91; specificity=0.98), symptomatic uterine fibroids (positive predictive value=0.81; negative predictive value=0.54), nonalcoholic fatty liver disease (sensitivity=0.90; specificity=0.94), and primary ciliary dyskinesia (sensitivity=0.88; specificity=1.0). In each of these use cases, CLARK allowed investigators to incorporate variables into their phenotype algorithm that would not be available as structured data. Moreover, the fact that nonexpert users can get started with machine learning–based NLP with limited informatics involvement is a significant improvement over the status quo. We hope to disseminate CLARK to other organizations that may not have NLP or machine learning specialists available, enabling wider use of these methods.

Published
2020
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3. Fast Healthcare Interoperability Resources (FHIR) as a Meta Model to Integrate Common Data Models: Development of a Tool and Quantitative Validation Study

Author

Pfaff, Emily Rose, Champion, James, Bradford, Robert Louis, Clark, Marshall, Xu, Hao, Fecho, Karamarie, Krishnamurthy, Ashok, Cox, Steven, Chute, Christopher G, Overby Taylor, Casey, and Ahalt, Stan

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundIn a multisite clinical research collaboration, institutions may or may not use the same common data model (CDM) to store clinical data. To overcome this challenge, we proposed to use Health Level 7’s Fast Healthcare Interoperability Resources (FHIR) as a meta-CDM—a single standard to represent clinical data. ObjectiveIn this study, we aimed to create an open-source application termed the Clinical Asset Mapping Program for FHIR (CAMP FHIR) to efficiently transform clinical data to FHIR for supporting source-agnostic CDM-to-FHIR mapping. MethodsMapping with CAMP FHIR involves (1) mapping each source variable to its corresponding FHIR element and (2) mapping each item in the source data’s value sets to the corresponding FHIR value set item for variables with strict value sets. To date, CAMP FHIR has been used to transform 108 variables from the Informatics for Integrating Biology & the Bedside (i2b2) and Patient-Centered Outcomes Research Network data models to fields across 7 FHIR resources. It is designed to allow input from any source data model and will support additional FHIR resources in the future. ResultsWe have used CAMP FHIR to transform data on approximately 23,000 patients with asthma from our institution’s i2b2 database. Data quality and integrity were validated against the origin point of the data, our enterprise clinical data warehouse. ConclusionsWe believe that CAMP FHIR can serve as an alternative to implementing new CDMs on a project-by-project basis. Moreover, the use of FHIR as a CDM could support rare data sharing opportunities, such as collaborations between academic medical centers and community hospitals. We anticipate adoption and use of CAMP FHIR to foster sharing of clinical data across institutions for downstream applications in translational research.

Published
2019
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4. Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs

Author

O’Neil, Shawn T., Madlock-Brown, Charisse, Wilkins, Kenneth J., McGrath, Brenda M., Davis, Hannah E., Assaf, Gina S., Wei, Hannah, Zareie, Parya, French, Evan T., Loomba, Johanna, McMurry, Julie A., Zhou, Andrea, Chute, Christopher G., Moffitt, Richard A., Pfaff, Emily R., Yoo, Yun Jae, Leese, Peter, Chew, Robert F., Lieberman, Michael, and Haendel, Melissa A.

Published
2024
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6. NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study

Author

Reese, Justin T, Coleman, Ben, Chan, Lauren, Blau, Hannah, Callahan, Tiffany J, Cappelletti, Luca, Fontana, Tommaso, Bradwell, Katie R, Harris, Nomi L, Casiraghi, Elena, Valentini, Giorgio, Karlebach, Guy, Deer, Rachel, McMurry, Julie A, Haendel, Melissa A, Chute, Christopher G, Pfaff, Emily, Moffitt, Richard, Spratt, Heidi, Singh, Jasvinder A, Mungall, Christopher J, Williams, Andrew E, and Robinson, Peter N

Subjects
Microbiology, Biological Sciences, Emerging Infectious Diseases, Coronaviruses, Lung, Infectious Diseases, Kidney Disease, Good Health and Well Being, Acute Kidney Injury, Anti-Inflammatory Agents, Non-Steroidal, COVID-19, COVID-19 Testing, Cohort Studies, Humans, Pandemics, Retrospective Studies, NSAIDs, Cyclooxygenase inhibitors, Observational study, Medical Microbiology, Virology
Abstract

BackgroundNon-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use.MethodsA 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis.ResultsLogistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations.ConclusionsStudy interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

Published
2022

7. Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative

Author

Aggarwal, Ataes, Agor, Joseph, Al-Amery, Amera, Aminu, Oluwatobiloba, Anand, Adit, Antonescu, Corneliu, Arora, Mehak, Asaduzzaman, Sayed, Asmussen, Tanner, Baghbanzadeh, Mahdi, Baker, Frazier, Bangert, Bridget, Bekhet, Laila, Bhattacharya, Biplab, Blase, Jenny, Butzin-Dozier, Zachary, Caffo, Brian, Chang, Hao, Chen, Zeyuan, Chen, Jiandong, Chiang, Jeffrey, Cho, Peter, Cockrell, Robert, Combs, Parker, Coyle, Jeremy, Crosby, Ciara, Dai, Zongyu, Dai, Ran, Danesharasteh, Anseh, Yildirim, Elif, Delong, Grant, Demilt, Ryan, Deng, Kaiwen, Dey, Sanjoy, Dhamdhere, Rohan, Dickson, Andrew, Dijour, Phoebe, Dinh, Dong, Dixon, Richard, Domi, Albi, Dutta, Souradeep, Elizondo, Mirna, Ertem, Zeynep, Feuerwerker, Solomon, Fliss, Danica, Fowler, Jennifer, Fu, Sunyang, Gardner, Kelly, Getty, Neil, Ghalwash, Mohamed, Gloster, Logan, Greer, Phil, Guan, Yuanfang, Ham, Colby, Hanoudi, Samer, Harper, Jeremy, Hendrix, Nathaniel, Hershkovich, Leeor, Hightower, Jake, Hu, Junjie, Huang, Jiani, Huang, Yu, Huang, Tongtong, Hur, Junguk, Isgut, Monica, Ismail, Hamid, Izmirlian, Grant, Jang, Kuk, Jemiyo, Christianah, Jeong, Hayoung, Ji, Yunwen, Ji, Xiayan, Jiang, Ming, Jiang, Sihang, Jiang, Xiaoqian, Jiang, Yuye, Johnson, Akin, Analyst, Zach, Kapse, Saarthak, Kartoun, Uri, KC, Dukka, Fard, Zahra, Kosfeld, Tim, Krichevsky, Spencer, Kuo, Mike, Larie, Dale, Lederer, Lauren, Leng, Shan, Li, Ziyang, Li, Hongyang, Li, Haodong, Li, Jianfu, Li, Tiantian, Liang, Xinwen, Liang, Hengyue, Liu, Feifan, Liu, Daniel, Luo, Gang, Munia, Tamanna, Madduri, Ravi, Madhira, Vithal, Mani, Shivali, Mansourifard, Farzaneh, Matson, Robert, Mertens, Andrew, Metsis, Vangelis, Meyer, Pablo, Mikhailova, Catherine, Miller, Dante, Milo, Christopher, Mitchell, Elliot, Modanwal, Gourav, Moore, Ronald, Morgenthaler, David, Musal, Rasim, Naik, Mayur, Nalawade, Vinit, Narain, Rohan, Narendrula, Saideep, Obiri, Alena, Okawa, Satoshi, Okechukwu, Chima, Olorunnisola, Toluwanimi, Ossowski, Tim, Parekh, Harsh, Park, Jean, Patel, Saaya, Patterson, Jason, Paul, Chetan, Peng, Le, Perkins, Diana, Pokharel, Suresh, Poplavskiy, Dmytro, Pryor, Zach, Pungitore, Sarah, Qin, Hong, Rababa, Salahaldeen, Rahman, Mahbubur, Rahmani, Elior, Rahnavard, Gholamali, Raihan, Md, Rajendran, Suraj, Ravichandran, Sarangan, Reddy, Chandan, Reyes, Abel, Roghanizad, Ali, Rouffa, Sean, Ruan, Xiaoyang, Saha, Arpita, Sawant, Sahil, Schiaffino, Melody, Seira, Diego, Sengupta, Saurav, Shalaev, Ruslan, Shetty, Gaurav, Shi, Seraphina, Shinguyen, Linh, Singh, Karnika, Sinha, Soumya, Socia, Damien, Stalians, Halen, Stavropoulos, Charalambos, Strube, Jan, Subramanian, Devika, Sun, Jiehuan, Sun, Ju, Sun, Chengkun, Sundararajan, Prathic, Talebi, Salmonn, Tawiah, Edward, Tesic, Jelena, Thiess, Mikaela, Tian, Raymond, Torre-Healy; Ming-Tse Tsai, Luke, Tyus, David, Vardhan, Madhurima, Velingker, Neelay, Walzer, Benjamin, Walzer, Jacob, Wang, Junda, Wang, Lu, Wang, Will, Wang, Jonathan, Wang, Yisen, Weatherly, Chad, Wu, Fanyou, Wu, Yifeng, Wu, Yinjun, Xia, Fangfang, Yan, Hao, Yang, Zhichao, Ye, Biao, Yin, Rui, Yin, Changyu, Yoo, Yun, You, Albert, Yu, June, Zanaj, Martin, Zaiman, Zachary, Zhang, Kai, Zhang, Xiaoyi, Zhang, Tianmai, Zhao, Zixuan, Zhi, Degui, Zhong, Yishan, Zhou, Huixue, Zhou, Andrea, Zhu, Yuanda, Zhu, Yitan, Zhu, Sophie, Adams, Meredith, Alexander, Caleb, Amor, Benjamin, Anzalone, Alfred, Bates, Benjamin, Beasley, Will, Bennett, Tellen, Bissell, Mark, Boudreau, Eilis, Bozzette, Samuel, Bradwell, Katie, Bramante, Carolyn, Brown, Don, Burgoon, Penny, Buse, John, Callahan, Tiffany, Cato, Kenrick, Chapman, Scott, Chute, Christopher, Clark, Jaylyn, Clark, Marshall, Cooper, Will, Cottrell, Lesley, Crowley, Karen, Deacy, Mariam, Dillon, Christopher, Eichmann, David, Emmett, Mary, Erwin-Cohen, Rebecca, Francis, Patricia, French, Evan, Fuentes, Rafael, Gabriel, Davera, Gagnier, Joel, Garbarini, Nicole, Ge, Jin, Gersing, Kenneth, Girvin, Andrew, Gordon, Valery, Graves, Alexis, Guinney, Justin, Haendel, Melissa, Hayanga, J.W., Hendricks, Brian, Hernandez, Wenndy, Hill, Elaine, Hillegass, William, Hong, Stephanie, Housman, Dan, Hurley, Robert, Islam, Jessica, Jawa, Randeep, Johnson, Steve, Kamaleswaran, Rishi, Kibbe, Warren, Koraishy, Farrukh, Kostka, Kristin, Kurilla, Michael, Lee, Adam, Lehmann, Harold, Liu, Hongfang, Loomba, Johanna, Madlock-Brown; Sandeep Mallipattu, Charisse, Manna, Amin, Mariona, Federico, Marti, Emily, Martin, Greg, Mathew, Jomol, Mazzotti, Diego, McMurry, Julie, Mehta, Hemalkumar, Michael, Sam, Miller, Robert, Misquitta, Leonie, Moffitt, Richard, Morris, Michele, Murray, Kimberly, Northington, Lavance, O’Neil, Shawn, Olex, Amy, Palchuk, Matvey, Patel, Brijesh, Patel, Rena, Payne, Philip, Pfaff, Emily, Pincavitch, Jami, Portilla, Lili, Prior, Fred, Pyarajan, Saiju, Pyles, Lee, Qureshi, Nabeel, Robinson, Peter, Rutter, Joni, Sadan, Ofer, Safdar, Nasia, Saha, Amit, Saltz, Joel, Saltz, Mary, Schmitt, Clare, Setoguchi, Soko, Sharafeldin, Noha, Sharathkumar, Anjali, Sheikh, Usman, Sidky, Hythem, Sokos, George, Southerland, Andrew, Spratt, Heidi, Starren, Justin, Subbian, Vignesh, Suver, Christine, Takemoto, Cliff, Temple-O'Connor, Meredith, Topaloglu, Umit, Vedula, Satyanarayana, Walden, Anita, Walters, Kellie, Ward-Caviness, Cavin, Wilcox, Adam, Wilkins, Ken, Williams, Andrew, Wu, Chunlei, Zampino, Elizabeth, Zhang, Xiaohan, Zhu, Richard, Bergquist, Timothy, Tariq, Adbul, Philips, Rachael V., Pirracchio, Romain, van der Laan, Mark, Colford, John M., Jr., Hubbard, Alan, Gao, Jifan, Chen, Guanhua, Stein, Adam, Long, Qi, Holmes, John, Mowery, Danielle, Wong, Eric, Parekh, Ravi, Getzen, Emily, and Blase, Jennifer

Published
2024
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8. Progress toward a universal biomedical data translator

Author

Fecho, Karamarie, Thessen, Anne E, Baranzini, Sergio E, Bizon, Chris, Hadlock, Jennifer J, Huang, Sui, Roper, Ryan T, Southall, Noel, Ta, Casey, Watkins, Paul B, Williams, Mark D, Xu, Hao, Byrd, William, Dančík, Vlado, Duby, Marc P, Dumontier, Michel, Glusman, Gustavo, Harris, Nomi L, Hinderer, Eugene W, Hyde, Greg, Johs, Adam, Su, Andrew I, Qin, Guangrong, Zhu, Qian, Dougherty, Jennifer, Huang, Conrad, Magis, Andrew, Smith, Brett, Celebi, Remzi, Chen, Zhehuan, Azevedo, Ricardo De Miranda, Emonet, Vincent, Lee, Jay, Weng, Chunhua, Yilmaz, Arif, Kim, Keum Joo, Santos, Eugene, Tonstad, Lucas, Veenhuis, Luke, Yakaboski, Chase, Acevedo, Liliana, Carrell, Steven, Deutsch, Eric, Glen, Amy, Hoffman, Andrew, Koslicki, David, Kvarfordt, Lindsey, Liu, Zheng, Liu, Shaopeng, Ma, Chunyu, Mendoza, Luis, Muluka, Arun Teja, Womack, Finn, Wood, Erica, Roach, Jared, Goel, Prateek, Weber, Rosina, Williams, Andrew, Gormley, Joseph, Zisk, Tom, Hanspers, Kristina, Hoatlin, Maureen, Pico, Alexander, Riutta, Anders, Callaghan, Jackson, Xu, Colleen, Ahalt, Stanley C, Balhoff, Jim, Edwards, Stephen, Haaland, Perry, Knowles, Michael, Krishnamurthy, Ashok, Mandal, Meisha, Peden, David B, Pfaff, Emily, Schurman, Shepherd, Shrivastava, Shalki, Yi, Hong, Reilly, Jason, Kanwar, Richa, Cox, Steven, Vaidya, Gaurav, Wang, Max, Alkanaq, Ahmed, Costanzo, Maria, Koesterer, Ryan, Flannick, Jason, Burtt, Noel, Kluge, Alexandria, Rubin, Irit, Strasser, Michael Michi, Chung, Lawrence, Kang, Jimin, Mantilla, Michelle, Muller, Sandrine, Persaud, Bria, Wei, Qi, Baumgartner, Andrew, Dai, Cheng, and Duvvuri, Venkata

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Biomedical Data Translator Consortium, Cardiorespiratory Medicine and Haematology, Oncology and Carcinogenesis, Other Medical and Health Sciences, General Clinical Medicine, Cardiovascular medicine and haematology, Pharmacology and pharmaceutical sciences
Abstract

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system's architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

Published
2022

9. From Alpha to Omicron and Beyond: Associations Between SARS-CoV-2 Variants and Surgical Outcomes

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred, Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O'Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O'Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Verhagen, Nathaniel B., Geissler, Thomas, SenthilKumar, Gopika, Gehl, Carson, Shaik, Tahseen, Flitcroft, Madelyn A., Yang, Xin, Taylor, Bradley W., Ghaferi, Amir A., Gould, Jon C., and Kothari, Anai N.

Published
2024
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10. The prevalence of postacute sequelae of coronavirus disease 2019 in solid organ transplant recipients: Evaluation of risk in the National COVID Cohort Collaborative

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred Jerrod, Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O'Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O'Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Vinson, Amanda J., Schissel, Makayla, Anzalone, Alfred J., Dai, Ran, French, Evan T., Olex, Amy L., Lee, Stephen B., Ison, Michael, and Mannon, Roslyn B.

Published
2024
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11. NSAID use and clinical outcomes in COVID-19 patients: A 38-center retrospective cohort study

Author

Reese, Justin T, Coleman, Ben, Chan, Lauren, Blau, Hannah, Callahan, Tiffany J, Cappelletti, Luca, Fontana, Tommaso, Bradwell, Katie Rebecca, Harris, Nomi L, Casiraghi, Elena, Valentini, Giorgio, Karlebach, Guy, Deer, Rachel, McMurry, Julie A, Haendel, Melissa A, Chute, Christopher G, Pfaff, Emily, Moffitt, Richard, Spratt, Heidi, Singh, Jasvinder, Mungall, Christopher J, Williams, Andrew E, and Robinson, Peter N

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Infectious Diseases, Emerging Infectious Diseases, Coronaviruses, Patient Safety, Good Health and Well Being, COVID-19, NSAIDs, cyclooxygenase inhibitors, observational study
Abstract

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of COVID-19 inpatients was constructed by matching cases (treated with NSAIDs) and controls (not treated) from 857,061 patients with COVID-19. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS: Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS: Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our findings are the largest EHR-based analysis of the effect of NSAIDs on outcome in COVID-19 patients to date. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

Published
2021

12. Decreasing Case Fatality Rates for Patients With Cirrhosis Infected With SARS-CoV-2: A National COVID Cohort Collaborative Study

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O'Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O'Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Far, Aryana T., Digitale, Jean C., Pletcher, Mark J., and Lai, Jennifer C.

Published
2024
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13. Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study

Author

Hill, Elaine L., Mehta, Hemalkumar B., Sharma, Suchetha, Mane, Klint, Singh, Sharad Kumar, Xie, Catherine, Cathey, Emily, Loomba, Johanna, Russell, Seth, Spratt, Heidi, DeWitt, Peter E., Ammar, Nariman, Madlock-Brown, Charisse, Brown, Donald, McMurry, Julie A., Chute, Christopher G., Haendel, Melissa A., Moffitt, Richard, Pfaff, Emily R., and Bennett, Tellen D.

Published
2023
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16. Nonelective coronary artery bypass graft outcomes are adversely impacted by Coronavirus disease 2019 infection, but not altered processes of care: A National COVID Cohort Collaborative and National Surgery Quality Improvement Program analysis

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O'Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O'Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Grimsley, Emily A., Torikashvili, Johnathan V., Janjua, Haroon M., Read, Meagan D., Kothari, Anai N., Verhagen, Nate B., Pietrobon, Ricardo, Kuo, Paul C., and Rogers, Michael P.

Published
2023
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17. Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortium

Author

Aaron, James R., Adam, Atif, Agapito, Giuseppe, Albayrak, Adem, Albi, Giuseppe, Alessiani, Mario, Alloni, Anna, Amendola, Danilo F., Angoulvant, François, Anthony, Li LLJ., Aronow, Bruce J., Ashraf, Fatima, Atz, Andrew, Avillach, Paul, Panickan, Vidul Ayakulangara, Azevedo, Paula S., Badenes, Rafael, Balshi, James, Batugo, Ashley, Beaulieu-Jones, Brendin R., Beaulieu-Jones, Brett K., Bell, Douglas S., Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal-Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Bhatnagar, Surbhi, Blanco-Martínez, Alvar, Boeker, Martin, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence T., Bradford, Robert L., Brat, Gabriel A., Bréant, Stéphane, Brown, Nicholas W., Bruno, Raffaele, Bryant, William A., Bucalo, Mauro, Bucholz, Emily, Burgun, Anita, Cai, Tianxi, Cannataro, Mario, Carmona, Aldo, Cattelan, Anna Maria, Caucheteux, Charlotte, Champ, Julien, Chen, Jin, Chen, Krista Y., Chiovato, Luca, Chiudinelli, Lorenzo, Cho, Kelly, Cimino, James J., Colicchio, Tiago K., Cormont, Sylvie, Cossin, Sébastien, Craig, Jean B., Cruz-Bermúdez, Juan Luis, Cruz-Rojo, Jaime, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Das, Priyam, Devkota, Batsal, Dionne, Audrey, Duan, Rui, Dubiel, Julien, DuVall, Scott L., Esteve, Loic, Estiri, Hossein, Fan, Shirley, Follett, Robert W., Ganslandt, Thomas, García-Barrio, Noelia, Garmire, Lana X., Gehlenborg, Nils, Getzen, Emily J., Geva, Alon, Goh, Rachel SJ., González, Tomás González, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Gutiérrez-Sacristán, Alba, Guzzi, Pietro H., Han, Larry, Hanauer, David A., Haverkamp, Christian, Hazard, Derek Y., He, Bing, Henderson, Darren W., Hilka, Martin, Ho, Yuk-Lam, Holmes, John H., Honerlaw, Jacqueline P., Hong, Chuan, Huling, Kenneth M., Hutch, Meghan R., Issitt, Richard W., Jannot, Anne Sophie, Jouhet, Vianney, Kainth, Mundeep K., Kate, Kernan F., Kavuluru, Ramakanth, Keller, Mark S., Kennedy, Chris J., Kernan, Kate F., Key, Daniel A., Kirchoff, Katie, Klann, Jeffrey G., Kohane, Isaac S., Krantz, Ian D., Kraska, Detlef, Krishnamurthy, Ashok K., L'Yi, Sehi, Leblanc, Judith, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, Will Loh, Ne Hooi, Long, Qi, Lozano-Zahonero, Sara, Luo, Yuan, Lynch, Kristine E., Mahmood, Sadiqa, Maidlow, Sarah E., Makoudjou, Adeline, Makwana, Simran, Malovini, Alberto, Mandl, Kenneth D., Mao, Chengsheng, Maram, Anupama, Maripuri, Monika, Martel, Patricia, Martins, Marcelo R., Marwaha, Jayson S., Masino, Aaron J., Mazzitelli, Maria, Mazzotti, Diego R., Mensch, Arthur, Milano, Marianna, Minicucci, Marcos F., Moal, Bertrand, Ahooyi, Taha Mohseni, Moore, Jason H., Moraleda, Cinta, Morris, Jeffrey S., Morris, Michele, Moshal, Karyn L., Mousavi, Sajad, Mowery, Danielle L., Murad, Douglas A., Murphy, Shawn N., Naughton, Thomas P., Breda Neto, Carlos Tadeu, Neuraz, Antoine, Newburger, Jane, Ngiam, Kee Yuan, Njoroge, Wanjiku FM., Norman, James B., Obeid, Jihad, Okoshi, Marina P., Olson, Karen L., Omenn, Gilbert S., Orlova, Nina, Ostasiewski, Brian D., Palmer, Nathan P., Paris, Nicolas, Patel, Lav P., Pedrera-Jiménez, Miguel, Pfaff, Ashley C., Pfaff, Emily R., Pillion, Danielle, Pizzimenti, Sara, Priya, Tanu, Prokosch, Hans U., Prudente, Robson A., Prunotto, Andrea, Quirós-González, Víctor, Ramoni, Rachel B., Raskin, Maryna, Rieg, Siegbert, Roig-Domínguez, Gustavo, Rojo, Pablo, Romero-Garcia, Nekane, Rubio-Mayo, Paula, Sacchi, Paolo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi Jebathilagam, Sanchez-Pinto, L. Nelson, Sandrin, Arnaud, Santhanam, Nandhini, Santos, Janaina C.C., Sanz Vidorreta, Fernando J., Savino, Maria, Schriver, Emily R., Schubert, Petra, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil J., Serrano-Balazote, Pablo, Serre, Patricia, Serret-Larmande, Arnaud, Shah, Mohsin A., Hossein Abad, Zahra Shakeri, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, South, Andrew M., Sperotto, Francesca, Spiridou, Anastasia, Strasser, Zachary H., Tan, Amelia LM., Tan, Bryce W.Q., Tan, Byorn W.L., Tanni, Suzana E., Taylor, Deanne M., Terriza-Torres, Ana I., Tibollo, Valentina, Tippmann, Patric, Toh, Emma MS., Torti, Carlo, Trecarichi, Enrico M., Vallejos, Andrew K., Varoquaux, Gael, Vella, Margaret E., Verdy, Guillaume, Vie, Jill-Jênn, Visweswaran, Shyam, Vitacca, Michele, Wagholikar, Kavishwar B., Waitman, Lemuel R., Wang, Xuan, Wassermann, Demian, Weber, Griffin M., Wolkewitz, Martin, Wong, Scott, Xia, Zongqi, Xiong, Xin, Ye, Ye, Yehya, Nadir, Yuan, William, Zachariasse, Joany M., Zahner, Janet J., Zambelli, Alberto, Zhang, Harrison G., Zöller, Daniela, Zuccaro, Valentina, Zucco, Chiara, Li, Xiudi, Rofeberg, Valerie N., Elias, Matthew D., Laird-Gion, Jessica, and Newburger, Jane W.

Published
2023
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18. Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study

Author

Aaron, James R., Agapito, Giuseppe, Albayrak, Adem, Albi, Giuseppe, Alessiani, Mario, Alloni, Anna, Amendola, Danilo F., François Angoulvant, Anthony, Li L.L.J., Aronow, Bruce J., Ashraf, Fatima, Atz, Andrew, Avillach, Paul, Azevedo, Paula S., Balshi, James, Beaulieu-Jones, Brett K., Bell, Douglas S., Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal-Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Bhatnagar, Surbhi, Blanco-Martínez, Alvar, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence T., Bradford, Robert L., Brat, Gabriel A., Bréant, Stéphane, Brown, Nicholas W., Bruno, Raffaele, Bryant, William A., Bucalo, Mauro, Bucholz, Emily, Burgun, Anita, Cai, Tianxi, Cannataro, Mario, Carmona, Aldo, Caucheteux, Charlotte, Champ, Julien, Chen, Jin, Chen, Krista Y., Chiovato, Luca, Chiudinelli, Lorenzo, Cho, Kelly, Cimino, James J., Colicchio, Tiago K., Cormont, Sylvie, Cossin, Sébastien, Craig, Jean B., Cruz-Bermúdez, Juan Luis, Cruz-Rojo, Jaime, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Das, Priyam, Devkota, Batsal, Dionne, Audrey, Duan, Rui, Dubiel, Julien, DuVall, Scott L., Esteve, Loic, Estiri, Hossein, Fan, Shirley, Follett, Robert W., Ganslandt, Thomas, Barrio, Noelia García, Garmire, Lana X., Gehlenborg, Nils, Getzen, Emily J., Geva, Alon, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Gutiérrez-Sacristán, Alba, Han, Larry, Hanauer, David A., Haverkamp, Christian, Hazard, Derek Y., He, Bing, Henderson, Darren W., Hilka, Martin, Ho, Yuk-Lam, Holmes, John H., Hong, Chuan, Huling, Kenneth M., Hutch, Meghan R., Issitt, Richard W., Jannot, Anne Sophie, Jouhet, Vianney, Kavuluru, Ramakanth, Keller, Mark S., Kennedy, Chris J., Key, Daniel A., Kirchoff, Katie, Klann, Jeffrey G., Kohane, Isaac S., Krantz, Ian D., Kraska, Detlef, Krishnamurthy, Ashok K., L'Yi, Sehi, Le, Trang T., Leblanc, Judith, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, Will Loh, Ne Hooi, Long, Qi, Lozano-Zahonero, Sara, Luo, Yuan, Lynch, Kristine E., Mahmood, Sadiqa, Maidlow, Sarah E., Makoudjou, Adeline, Malovini, Alberto, Mandl, Kenneth D., Mao, Chengsheng, Maram, Anupama, Martel, Patricia, Martins, Marcelo R., Marwaha, Jayson S., Masino, Aaron J., Mazzitelli, Maria, Mensch, Arthur, Milano, Marianna, Minicucci, Marcos F., Moal, Bertrand, Ahooyi, Taha Mohseni, Moore, Jason H., Moraleda, Cinta, Morris, Jeffrey S., Morris, Michele, Moshal, Karyn L., Mousavi, Sajad, Mowery, Danielle L., Murad, Douglas A., Murphy, Shawn N., Naughton, Thomas P., Breda Neto, Carlos Tadeu, Neuraz, Antoine, Newburger, Jane, Ngiam, Kee Yuan, Njoroge, Wanjiku F.M., Norman, James B., Obeid, Jihad, Okoshi, Marina P., Olson, Karen L., Omenn, Gilbert S., Orlova, Nina, Ostasiewski, Brian D., Palmer, Nathan P., Paris, Nicolas, Patel, Lav P., Pedrera-Jiménez, Miguel, Pfaff, Emily R., Pfaff, Ashley C., Pillion, Danielle, Pizzimenti, Sara, Prokosch, Hans U., Prudente, Robson A., Prunotto, Andrea, Quirós-González, Víctor, Ramoni, Rachel B., Raskin, Maryna, Rieg, Siegbert, Roig-Domínguez, Gustavo, Rojo, Pablo, Rubio-Mayo, Paula, Sacchi, Paolo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi Jebathilagam, Sanchez-Pinto, L. Nelson, Sandrin, Arnaud, Santhanam, Nandhini, Santos, Janaina C.C., Sanz Vidorreta, Fernando J., Savino, Maria, Schriver, Emily R., Schubert, Petra, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil J., Serrano-Balazote, Pablo, Serre, Patricia, Serret-Larmande, Arnaud, Shah, Mohsin, Hossein Abad, Zahra Shakeri, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, South, Andrew M., Spiridou, Anastasia, Strasser, Zachary H., Tan, Amelia L.M., Tan, Bryce W.Q., Tan, Byorn W.L., Tanni, Suzana E., Taylor, Deanne M., Terriza-Torres, Ana I., Tibollo, Valentina, Tippmann, Patric, Toh, Emma M.S., Torti, Carlo, Trecarichi, Enrico M., Tseng, Yi-Ju, Vallejos, Andrew K., Varoquaux, Gael, Vella, Margaret E., Verdy, Guillaume, Vie, Jill-Jênn, Visweswaran, Shyam, Vitacca, Michele, Wagholikar, Kavishwar B., Waitman, Lemuel R., Wang, Xuan, Wassermann, Demian, Weber, Griffin M., Wolkewitz, Martin, Wong, Scott, Xia, Zongqi, Xiong, Xin, Ye, Ye, Yehya, Nadir, Yuan, William, Zambelli, Alberto, Zhang, Harrison G., Zo¨ller, Daniela, Zuccaro, Valentina, Zucco, Chiara, Mesa, Rebecca, and Verdy, Guillame

Published
2023
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19. The National COVID Cohort Collaborative (N3C): Rationale, Design, Infrastructure, and Deployment

Author

Haendel, Melissa A, Chute, Christopher G, Bennett, Tellen D, Eichmann, David A, Guinney, Justin, Kibbe, Warren A, Payne, Philip RO, Pfaff, Emily R, Robinson, Peter N, Saltz, Joel H, Spratt, Heidi, Suver, Christine, Wilbanks, John, Wilcox, Adam B, Williams, Andrew E, Wu, Chunlei, Blacketer, Clair, Bradford, Robert L, Cimino, James J, Clark, Marshall, Colmenares, Evan W, Francis, Patricia A, Gabriel, Davera, Graves, Alexis, Hemadri, Raju, Hong, Stephanie S, Hripscak, George, Jiao, Dazhi, Klann, Jeffrey G, Kostka, Kristin, Lee, Adam M, Lehmann, Harold P, Lingrey, Lora, Miller, Robert T, Morris, Michele, Murphy, Shawn N, Natarajan, Karthik, Palchuk, Matvey B, Sheikh, Usman, Solbrig, Harold, Visweswaran, Shyam, Walden, Anita, Walters, Kellie M, Weber, Griffin M, Zhang, Xiaohan Tanner, Zhu, Richard L, Amor, Benjamin, Girvin, Andrew T, Manna, Amin, Qureshi, Nabeel, Kurilla, Michael G, Michael, Sam G, Portilla, Lili M, Rutter, Joni L, Austin, Christopher P, and Gersing, Ken R

Subjects
COVID-19, Computer Security, Data Analysis, Data Science, Ethics Committees, Research, Government Regulation, Humans, Information Dissemination, Intersectoral Collaboration, National Institutes of Health (U.S.), United States, N3C Consortium, EHR data, SARS-CoV-2, clinical data model harmonization, collaborative analytics, open science, Information and Computing Sciences, Engineering, Medical and Health Sciences, Medical Informatics
Abstract

ObjectiveCoronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers.Materials and methodsThe Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics.ResultsOrganized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access.ConclusionsThe N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.

Published
2021

20. Hormone replacement therapy and COVID-19 outcomes in solid organ transplant recipients compared with the general population

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O'Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O'Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Vinson, Amanda J., Anzalone, Alfred, Schissel, Makayla, Dai, Ran, French, Evan T., Olex, Amy L., and Mannon, Roslyn B.

Published
2023
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21. Electronic Medical Record Search Engine (EMERSE): An Information Retrieval Tool for Supporting Cancer Research

Author

Hanauer, David A, Barnholtz-Sloan, Jill S, Beno, Mark F, Del Fiol, Guilherme, Durbin, Eric B, Gologorskaya, Oksana, Harris, Daniel, Harnett, Brett, Kawamoto, Kensaku, May, Benjamin, Meeks, Eric, Pfaff, Emily, Weiss, Janie, and Zheng, Kai

Subjects
Information and Computing Sciences, Health Services and Systems, Information Systems, Health Sciences, Clinical Research, Cancer, Electronic Health Records, Humans, Information Storage and Retrieval, Natural Language Processing, Neoplasms, Search Engine, Software
Abstract

PurposeThe Electronic Medical Record Search Engine (EMERSE) is a software tool built to aid research spanning cohort discovery, population health, and data abstraction for clinical trials. EMERSE is now live at three academic medical centers, with additional sites currently working on implementation. In this report, we describe how EMERSE has been used to support cancer research based on a variety of metrics.MethodsWe identified peer-reviewed publications that used EMERSE through online searches as well as through direct e-mails to users based on audit logs. These logs were also used to summarize use at each of the three sites. Search terms for two of the sites were characterized using the natural language processing tool MetaMap to determine to which semantic types the terms could be mapped.ResultsWe identified a total of 326 peer-reviewed publications that used EMERSE through August 2019, although this is likely an underestimation of the true total based on the use log analysis. Oncology-related research comprised nearly one third (n = 105; 32.2%) of all research output. The use logs showed that EMERSE had been used by multiple people at each site (nearly 3,500 across all three) who had collectively logged into the system > 100,000 times. Many user-entered search queries could not be mapped to a semantic type, but the most common semantic type for terms that did match was "disease or syndrome," followed by "pharmacologic substance."ConclusionEMERSE has been shown to be a valuable tool for supporting cancer research. It has been successfully deployed at other sites, despite some implementation challenges unique to each deployment environment.

Published
2020

22. Early Empiric Antibiotic Use in Patients Hospitalized With COVID-19: A Retrospective Cohort Study*

Author

Widere, J. Christian, Davis, Claire Leilani, Loomba, Johanna Jean, Bell, Taison D., Enfield, Kyle B., Barros, Andrew Julio, Amor, Benjamin, Bissell, Mark M., Bradwell, Katie Rebecca, Girvin, Andrew T., Manna, Amin, Qureshi, Nabeel, Chute, Christopher G., Pfaff, Emily R., Gabriel, Davera, Hong, Stephanie S., Kostka, Kristin, Lehmann, Harold P., Moffitt, Richard A., Morris, Michele, Palchuk, Matvey B., Zhang, Xiaohan Tanner, Zhu, Richard L., Austin, Christopher P., Gersing, Kenneth R., Bozzette, Samuel, Deacy, Mariam, Garbarini, Nicole, Kurilla, Michael G., Michael, Sam G., Rutter, Joni L., Temple-O’Connor, Meredith, Haendel, Melissa A., Bennett, Tellen D., Chute, Christopher G., Eichmann, David A., Guinney, Justin, Kibbe, Warren A., Liu, Hongfang, Payne, Philip R.O., Pfaff, Emily R., Robinson, Peter N., Saltz, Joel H., Spratt, Heidi, Starren, Justin, Suver, Christine, Wilcox, Adam B., Williams, Andrew E., Wu, Chunlei, Pfaff, Emily R., Amor, Benjamin, Bissell, Mark M., Clark, Marshall, Girvin, Andrew T., Hong, Stephanie S., Kostka, Kristin, Lee, Adam M., Miller, Robert T., Morris, Michele, Palchuk, Matvey B., Walters, Kellie M., Haendel, Melissa A., Chute, Christopher G., Gersing, Kenneth R., Walden, Anita, Walden, Anita, Chae, Yooree, Cook, Connor, Dest, Alexandra, Dietz, Racquel R., Dillon, Thomas, Francis, Patricia A., Fuentes, Rafael, Graves, Alexis, McMurry, Julie A., Neumann, Andrew J., O’Neil, Shawn T., Sheikh, Usman, Volz, Andréa M., Zampino, Elizabeth, Saltz, Mary Morrison, Suver, Christine, Chute, Christopher G., Haendel, Melissa A., McMurry, Julie A., Volz, Andréa M., Walden, Anita, Bramante, Carolyn, Harper, Jeremy Richard, Hernandez, Wenndy, Koraishy, Farrukh M, Mariona, Federico, Saha, Amit, and Vedula, Satyanarayana

Published
2023
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23. Risk of severe acute respiratory syndrome coronavirus 2 infection among women with polycystic ovary syndrome

Author

Haendel, Melissa A., Chute, Christopher G., Walden, Anita, Gersing, Kenneth R., Misquitta, Leonie, Burgoon, Penny Wung, Bozzette, Samuel, Deacy, Mariam, Dillon, Christopher, Erwin-Cohen, Rebecca, Garbarini, Nicole, Gordon, Valery, Kurilla, Michael G., Marti, Emily Carlson, Michael, Sam G., Portilla, Lili, Schmitt, Clare, Temple-O'Connor, Meredith, Bennett, Tellen D., Eichmann, David A., Guinney, Justin, Kibbe, Warren A., Liu, Hongfang, Payne, Philip R.O., Pfaff, Emily R., Robinson, Peter N., Saltz, Joel H., Spratt, Heidi, Starren, Justin, Suver, Christine, Wilcox, Adam B., Williams, Andrew E., Wu, Chunlei, Gabriel, Davera, Hong, Stephanie S., Kostka, Kristin, Lehmann, Harold P., Moffitt, Richard A., Morris, Michele, Palchuk, Matvey B., Zhang, Xiaohan Tanner, Zhu, Richard L., Clark, Marshall, Girvin, Andrew T., Lee, Adam M., Miller, Robert T., Walters, Kellie M., Cooper, Will, Francis, Patricia A., Fuentes, Rafael, Graves, Alexis, McMurry, Julie A., Neumann, Andrew J., O'Neil, Shawn T., Sheikh, Usman, Zampino, Elizabeth, Bissell, Mark M., Bradwell, Katie Rebecca, Manna, Amin, Qureshi, Nabeel, Saltz, Mary Morrison, Volz, Andréa M., Bramante, Carolyn, Harper, Jeremy Richard, Hernandez, Wenndy, Koraishy, Farrukh M., Mariona, Federico, Saha, Amit, Vedula, Satyanarayana, Alur-Gupta, Snigdha, Boland, Mary Regina, and Dokras, Anuja

Published
2023
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24. COVID-19 outcomes in persons with hemophilia: results from a US-based national COVID-19 surveillance registry

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J. W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O’Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O’Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Sharathkumar, Anjali, Wendt, Linder, Ortman, Chris, Srinivasan, Ragha, Chute, Christopher G., Chrischilles, Elizabeth, and Takemoto, Clifford M.

Published
2023
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25. Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery

Author

Zhang, Xingmin Aaron, Yates, Amy, Vasilevsky, Nicole, Gourdine, JP, Callahan, Tiffany J, Carmody, Leigh C, Danis, Daniel, Joachimiak, Marcin P, Ravanmehr, Vida, Pfaff, Emily R, Champion, James, Robasky, Kimberly, Xu, Hao, Fecho, Karamarie, Walton, Nephi A, Zhu, Richard L, Ramsdill, Justin, Mungall, Christopher J, Köhler, Sebastian, Haendel, Melissa A, McDonald, Clement J, Vreeman, Daniel J, Peden, David B, Bennett, Tellen D, Feinstein, James A, Martin, Blake, Stefanski, Adrianne L, Hunter, Lawrence E, Chute, Christopher G, and Robinson, Peter N

Subjects
Health Services and Systems, Health Sciences, Networking and Information Technology R&D (NITRD), Lung, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Respiratory, Good Health and Well Being, Health services and systems
Abstract

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.

Published
2019

27. Thirty-Day Mortality and Complication Rates in Total Joint Arthroplasty After a Recent COVID-19 Diagnosis: A Retrospective Cohort in the National COVID Cohort Collaborative (N3C)

Author

Pincavitch, Jami D., Pisquiy, John J., Wen, Sijin, Bryan, Nicole, Ammons, Jeffrey, Makwana, Priyal, Dietz, Matthew J., Abel, Amber, Eicher, Jennifer, Danley, Suzanne, Gabriel, Davera, Kasicky, Kathryn, Levitt, Eli, Patrick, Sharon, Russell, Michael, Mozingo, Casey, Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O’Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O’Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, and Zhang, Xiaohan Tanner

Published
2023
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28. Identifying who has long COVID in the USA: a machine learning approach using N3C data

Author

Bramante, Carolyn, Dorr, David, Morris, Michele, Parker, Ann M, Sidky, Hythem, Gersing, Ken, Hong, Stephanie, Niehaus, Emily, Pfaff, Emily R, Girvin, Andrew T, Bennett, Tellen D, Bhatia, Abhishek, Brooks, Ian M, Deer, Rachel R, Dekermanjian, Jonathan P, Jolley, Sarah Elizabeth, Kahn, Michael G, Kostka, Kristin, McMurry, Julie A, Moffitt, Richard, Walden, Anita, Chute, Christopher G, and Haendel, Melissa A

Published
2022
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29. Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs.

Author

O'Neil, Shawn T., Madlock-Brown, Charisse, Wilkins, Kenneth J., McGrath, Brenda M., Davis, Hannah E., Assaf, Gina S., Wei, Hannah, Zareie, Parya, French, Evan T., Loomba, Johanna, McMurry, Julie A., Zhou, Andrea, Chute, Christopher G., Moffitt, Richard A., Pfaff, Emily R., Yoo, Yun Jae, Leese, Peter, Chew, Robert F., Lieberman, Michael, and Haendel, Melissa A.

Subjects
DATA analysis, POST-acute COVID-19 syndrome, FISHER exact test, LONGITUDINAL method, ELECTRONIC health records, STATISTICS
Abstract

Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Risk for stillbirth among pregnant individuals with SARS-CoV-2 infection varied by gestational age

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O’Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O’Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Lyu, Tianchu, Liang, Chen, Liu, Jihong, Hung, Peiyin, Zhang, Jiajia, Campbell, Berry, Ghumman, Nadia, Olatosi, Bankole, Hikmet, Neset, Zhang, Manting, Yi, Honggang, and Li, Xiaoming

Published
2023
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35. Preexisting Autoimmunity Is Associated With Increased Severity of Coronavirus Disease 2019: A Retrospective Cohort Study Using Data From the National COVID Cohort Collaborative (N3C).

Author

Yadaw, Arjun S, Sahner, David K, Sidky, Hythem, Afzali, Behdad, Hotaling, Nathan, Pfaff, Emily R, and Mathé, Ewy A

Subjects
COVID-19, CONFIDENCE intervals, ANTI-inflammatory agents, AUTOIMMUNE diseases, RETROSPECTIVE studies, ACQUISITION of data, SEVERITY of illness index, RISK assessment, CATASTROPHIC illness, MEDICAL records, DESCRIPTIVE statistics, HOSPITAL care, RESEARCH funding, LOGISTIC regression analysis, IMMUNOSUPPRESSIVE agents, ODDS ratio, LONGITUDINAL method, DISEASE risk factors, DISEASE complications
Abstract

Background Identifying individuals with a higher risk of developing severe coronavirus disease 2019 (COVID-19) outcomes will inform targeted and more intensive clinical monitoring and management. To date, there is mixed evidence regarding the impact of preexisting autoimmune disease (AID) diagnosis and/or immunosuppressant (IS) exposure on developing severe COVID-19 outcomes. Methods A retrospective cohort of adults diagnosed with COVID-19 was created in the National COVID Cohort Collaborative enclave. Two outcomes, life-threatening disease and hospitalization, were evaluated by using logistic regression models with and without adjustment for demographics and comorbidities. Results Of the 2 453 799 adults diagnosed with COVID-19, 191 520 (7.81%) had a preexisting AID diagnosis and 278 095 (11.33%) had a preexisting IS exposure. Logistic regression models adjusted for demographics and comorbidities demonstrated that individuals with a preexisting AID (odds ratio [OR], 1.13; 95% confidence interval [CI]: 1.09–1.17; P <.001), IS exposure (OR, 1.27; 95% CI: 1.24–1.30; P <.001), or both (OR, 1.35; 95% CI: 1.29–1.40; P <.001) were more likely to have a life-threatening disease. These results were consistent when hospitalization was evaluated. A sensitivity analysis evaluating specific IS revealed that tumor necrosis factor inhibitors were protective against life-threatening disease (OR, 0.80; 95% CI:.66–.96; P =.017) and hospitalization (OR, 0.80; 95% CI:.73–.89; P <.001). Conclusions Patients with preexisting AID, IS exposure, or both are more likely to have a life-threatening disease or hospitalization. These patients may thus require tailored monitoring and preventative measures to minimize negative consequences of COVID-19. [ABSTRACT FROM AUTHOR]

Published
2023
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36. De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository.

Author

Pfaff, Emily R, Girvin, Andrew T, Crosskey, Miles, Gangireddy, Srushti, Master, Hiral, Wei, Wei-Qi, Kerchberger, V Eric, Weiner, Mark, Harris, Paul A, Basford, Melissa, Lunt, Chris, Chute, Christopher G, Moffitt, Richard A, Haendel, Melissa, and Consortia, N3C and RECOVER

Abstract

Machine learning (ML)-driven computable phenotypes are among the most challenging to share and reproduce. Despite this difficulty, the urgent public health considerations around Long COVID make it especially important to ensure the rigor and reproducibility of Long COVID phenotyping algorithms such that they can be made available to a broad audience of researchers. As part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, researchers with the National COVID Cohort Collaborative (N3C) devised and trained an ML-based phenotype to identify patients highly probable to have Long COVID. Supported by RECOVER, N3C and NIH's All of Us study partnered to reproduce the output of N3C's trained model in the All of Us data enclave, demonstrating model extensibility in multiple environments. This case study in ML-based phenotype reuse illustrates how open-source software best practices and cross-site collaboration can de-black-box phenotyping algorithms, prevent unnecessary rework, and promote open science in informatics. [ABSTRACT FROM AUTHOR]

Published
2023
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38. Clinical encounter heterogeneity and methods for resolving in networked EHR data: a study from N3C and RECOVER programs.

Author

Leese, Peter, Anand, Adit, Girvin, Andrew, Manna, Amin, Patel, Saaya, Yoo, Yun Jae, Wong, Rachel, Haendel, Melissa, Chute, Christopher G, Bennett, Tellen, Hajagos, Janos, Pfaff, Emily, and Moffitt, Richard

Abstract

Objective Clinical encounter data are heterogeneous and vary greatly from institution to institution. These problems of variance affect interpretability and usability of clinical encounter data for analysis. These problems are magnified when multisite electronic health record (EHR) data are networked together. This article presents a novel, generalizable method for resolving encounter heterogeneity for analysis by combining related atomic encounters into composite "macrovisits." Materials and Methods Encounters were composed of data from 75 partner sites harmonized to a common data model as part of the NIH Researching COVID to Enhance Recovery Initiative, a project of the National Covid Cohort Collaborative. Summary statistics were computed for overall and site-level data to assess issues and identify modifications. Two algorithms were developed to refine atomic encounters into cleaner, analyzable longitudinal clinical visits. Results Atomic inpatient encounters data were found to be widely disparate between sites in terms of length-of-stay (LOS) and numbers of OMOP CDM measurements per encounter. After aggregating encounters to macrovisits, LOS and measurement variance decreased. A subsequent algorithm to identify hospitalized macrovisits further reduced data variability. Discussion Encounters are a complex and heterogeneous component of EHR data and native data issues are not addressed by existing methods. These types of complex and poorly studied issues contribute to the difficulty of deriving value from EHR data, and these types of foundational, large-scale explorations, and developments are necessary to realize the full potential of modern real-world data. Conclusion This article presents method developments to manipulate and resolve EHR encounter data issues in a generalizable way as a foundation for future research and analysis. [ABSTRACT FROM AUTHOR]

Published
2023
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42. Use of administrative and electronic health record data for development of automated algorithms for childhood diabetes case ascertainment and type classification: the SEARCH for Diabetes in Youth Study†

Author

Zhong, Victor W., Pfaff, Emily R., Beavers, Daniel P., Thomas, Joan, Jaacks, Lindsay M., Bowlby, Deborah A., Carey, Timothy S., Lawrence, Jean M., Dabelea, Dana, Hamman, Richard F., Pihoker, Catherine, Saydah, Sharon H., and Mayer-Davis, Elizabeth J.

Published
2014
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43. Harmonizing units and values of quantitative data elements in a very large nationally pooled electronic health record (EHR) dataset.

Author

Bradwell, Katie R, Wooldridge, Jacob T, Amor, Benjamin, Bennett, Tellen D, Anand, Adit, Bremer, Carolyn, Yoo, Yun Jae, Qian, Zhenglong, Johnson, Steven G, Pfaff, Emily R, Girvin, Andrew T, Manna, Amin, Niehaus, Emily A, Hong, Stephanie S, Zhang, Xiaohan Tanner, Zhu, Richard L, Bissell, Mark, Qureshi, Nabeel, Saltz, Joel, and Haendel, Melissa A

Abstract

Objective The goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing. Materials and Methods The National COVID Cohort Collaborative (N3C) table of laboratory measurement data—over 3.1 billion patient records and over 19 000 unique measurement concepts in the Observational Medical Outcomes Partnership (OMOP) common-data-model format from 55 data partners. We grouped ontologically similar OMOP concepts together for 52 variables relevant to COVID-19 research, and developed a unit-harmonization pipeline comprised of (1) selecting a canonical unit for each measurement variable, (2) arriving at a formula for conversion, (3) obtaining clinical review of each formula, (4) applying the formula to convert data values in each unit into the target canonical unit, and (5) removing any harmonized value that fell outside of accepted value ranges for the variable. For data with missing units for all the results within a lab test for a data partner, we compared values with pooled values of all data partners, using the Kolmogorov-Smirnov test. Results Of the concepts without missing values, we harmonized 88.1% of the values, and imputed units for 78.2% of records where units were absent (41% of contributors' records lacked units). Discussion The harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections. Unique properties of centralized data are harnessed to enable unit inference. Conclusion The pipeline we developed for the pooled N3C data enables use of measurements that would otherwise be unavailable for analysis. [ABSTRACT FROM AUTHOR]

Published
2022
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44. Development and Application of an Open Tool for Sharing and Analyzing Integrated Clinical and Environmental Exposures Data: Asthma Use Case.

Author

Fecho, Karamarie, Ahalt, Stanley C., Appold, Stephen, Arunachalam, Saravanan, Pfaff, Emily, Stillwell, Lisa, Valencia, Alejandro, Hao Xu, and Peden, David B.

Subjects
ASTHMA, ELECTRONIC health records, AIR pollutants, SOCIOECONOMICS, CLINICAL trials
Abstract

Background: The Integrated Clinical and Environmental Exposures Service (ICEES) serves as an open-source, disease-agnostic, regulatory-compliant framework and approach for openly exposing and exploring clinical data that have been integrated at the patient level with a variety of environmental exposures data. ICEES is equipped with tools to support basic statistical exploration of the integrated data in a completely open manner. Objective: This study aims to further develop and apply ICEES as a novel tool for openly exposing and exploring integrated clinical and environmental data. We focus on an asthma use case. Methods: We queried the ICEES open application programming interface (OpenAPI) using a functionality that supports chi-square tests between feature variables and a primary outcome measure, with a Bonferroni correction for multiple comparisons (α=.001). We focused on 2 primary outcomes that are indicative of asthma exacerbations: annual emergency department (ED) or inpatient visits for respiratory issues; and annual prescriptions for prednisone. Results: Of the 157,410 patients within the asthma cohort, 26,332 (16.73%) had 1 or more annual ED or inpatient visits for respiratory issues, and 17,056 (10.84%) had 1 or more annual prescriptions for prednisone. We found that close proximity to a major roadway or highway, exposure to high levels of particulate matter ≤2.5 μm (PM2.5) or ozone, female sex, Caucasian race, low residential density, lack of health insurance, and low household income were significantly associated with asthma exacerbations (P<.001). Asthma exacerbations did not vary by rural versus urban residence. Moreover, the results were largely consistent across outcome measures. Conclusions: Our results demonstrate that the open-source ICEES can be used to replicate and extend published findings on factors that influence asthma exacerbations. As a disease-agnostic, open-source approach for integrating, exposing, and exploring patient-level clinical and environmental exposures data, we believe that ICEES will have broad adoption by other institutions and application in environmental health and other biomedical fields. [ABSTRACT FROM AUTHOR]

Published
2022
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45. Supporting research, protecting data: one institution's approach to clinical data warehouse governance.

Author

Walters, Kellie M, Jojic, Anna, Pfaff, Emily R, Rape, Marie, Spencer, Donald C, Shaheen, Nicholas J, Lamm, Brent, and Carey, Timothy S

Abstract

Institutions must decide how to manage the use of clinical data to support research while ensuring appropriate protections are in place. Questions about data use and sharing often go beyond what the Health Insurance Portability and Accountability Act of 1996 (HIPAA) considers. In this article, we describe our institution's governance model and approach. Common questions we consider include (1) Is a request limited to the minimum data necessary to carry the research forward? (2) What plans are there for sharing data externally?, and (3) What impact will the proposed use of data have on patients and the institution? In 2020, 302 of the 319 requests reviewed were approved. The majority of requests were approved in less than 2 weeks, with few or no stipulations. For the remaining requests, the governance committee works with researchers to find solutions to meet their needs while also addressing our collective goal of protecting patients. [ABSTRACT FROM AUTHOR]

Published
2022
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46. Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative.

Author

Pfaff, Emily R, Girvin, Andrew T, Gabriel, Davera L, Kostka, Kristin, Morris, Michele, Palchuk, Matvey B, Lehmann, Harold P, Amor, Benjamin, Bissell, Mark, Bradwell, Katie R, Gold, Sigfried, Hong, Stephanie S, Loomba, Johanna, Manna, Amin, McMurry, Julie A, Niehaus, Emily, Qureshi, Nabeel, Walden, Anita, Zhang, Xiaohan Tanner, and Zhu, Richard L

Abstract

Objective: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations.Materials and Methods: We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements.Results: Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback.Discussion: We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate.Conclusion: By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require. [ABSTRACT FROM AUTHOR]

Published
2022
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47. Sex, obesity, diabetes, and exposure to particulate matter among patients with severe asthma: Scientific insights from a comparative analysis of open clinical data sources during a five-day hackathon

Author

Fecho, Karamarie, Ahalt, Stanley C., Arunachalam, Saravanan, Champion, James, Chute, Christopher G., Davis, Sarah, Gersing, Kenneth, Glusman, Gustavo, Hadlock, Jennifer, Lee, Jewel, Pfaff, Emily, Robinson, Max, Sid, Eric, Ta, Casey, Xu, Hao, Zhu, Richard, Zhu, Qian, and Peden, David B.

Published
2019
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48. Using a Patient Portal to Increase Enrollment in a Newborn Screening Research Study: Observational Study.

Author

Gehtland, Lisa M., Paquin, Ryan S., Andrews, Sara M., Lee, Adam M., Gwaltney, Angela, Duparc, Martin, Pfaff, Emily R., and Bailey Jr., Donald B.

Subjects
ELECTRONIC health records, NEWBORN infants, PATIENT selection, HEALTH of Black women, RACISM
Abstract

Background: Many research studies fail to enroll enough research participants. Patient-facing electronic health record applications, known as patient portals, may be used to send research invitations to eligible patients. Objective: The first aim was to determine if receipt of a patient portal research recruitment invitation was associated with enrollment in a large ongoing study of newborns (Early Check). The second aim was to determine if there were differences in opening the patient portal research recruitment invitation and study enrollment by race and ethnicity, age, or rural/urban home address. Methods: We used a computable phenotype and queried the health care system's clinical data warehouse to identify women whose newborns would likely be eligible. Research recruitment invitations were sent through the women's patient portals. We conducted logistic regressions to test whether women enrolled their newborns after receipt of a patient portal invitation and whether there were differences by race and ethnicity, age, and rural/urban home address. Results: Research recruitment invitations were sent to 4510 women not yet enrolled through their patient portals between November 22, 2019, through March 5, 2020. Among women who received a patient portal invitation, 3.6% (161/4510) enrolled their newborns within 27 days. The odds of enrolling among women who opened the invitation was nearly 9 times the odds of enrolling among women who did not open their invitation (SE 3.24, OR 8.86, 95% CI 4.33-18.13; P<.001). On average, it took 3.92 days for women to enroll their newborn in the study, with 64% (97/161) enrolling their newborn within 1 day of opening the invitation. There were disparities by race and urbanicity in enrollment in the study after receipt of a patient portal research invitation but not by age. Black women were less likely to enroll their newborns than White women (SE 0.09, OR 0.29, 95% CI 0.16-0.55; P<.001), and women in urban zip codes were more likely to enroll their newborns than women in rural zip codes (SE 0.97, OR 3.03, 95% CI 1.62-5.67; P=.001). Black women (SE 0.05, OR 0.67, 95% CI 0.57-0.78; P<.001) and Hispanic women (SE 0.07, OR 0.73, 95% CI 0.60-0.89; P=.002) were less likely to open the research invitation compared to White women. Conclusions: Patient portals are an effective way to recruit participants for research studies, but there are substantial racial and ethnic disparities and disparities by urban/rural status in the use of patient portals, the opening of a patient portal invitation, and enrollment in the study. [ABSTRACT FROM AUTHOR]

Published
2022
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49. Research informatics and the COVID-19 pandemic: Challenges, innovations, lessons learned, and recommendations.

Author

Bookman, Richard J., Cimino, James J., Harle, Christopher A., Kost, Rhonda G., Mooney, Sean, Pfaff, Emily, Rojevsky, Svetlana, Tobin, Jonathan N., Wilcox, Adam, and Tsinoremas, Nick F.

Abstract

The recipients of NIH's Clinical and Translational Science Awards (CTSA) have worked for over a decade to build informatics infrastructure in support of clinical and translational research. This infrastructure has proved invaluable for supporting responses to the current COVID-19 pandemic through direct patient care, clinical decision support, training researchers and practitioners, as well as public health surveillance and clinical research to levels that could not have been accomplished without the years of ground-laying work by the CTSAs. In this paper, we provide a perspective on our COVID-19 work and present relevant results of a survey of CTSA sites to broaden our understanding of the key features of their informatics programs, the informatics-related challenges they have experienced under COVID-19, and some of the innovations and solutions they developed in response to the pandemic. Responses demonstrated increased reliance by healthcare providers and researchers on access to electronic health record (EHR) data, both for local needs and for sharing with other institutions and national consortia. The initial work of the CTSAs on data capture, standards, interchange, and sharing policies all contributed to solutions, best illustrated by the creation, in record time, of a national clinical data repository in the National COVID-19 Cohort Collaborative (N3C). The survey data support seven recommendations for areas of informatics and public health investment and further study to support clinical and translational research in the post-COVID-19 era. [ABSTRACT FROM AUTHOR]

Published
2021
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50. Ensuring a safe(r) harbor: Excising personally identifiable information from structured electronic health record data.

Author

Pfaff, Emily R., Haendel, Melissa A., Kostka, Kristin, Lee, Adam, Niehaus, Emily, Palchuk, Matvey B., Walters, Kellie, and Chute, Christopher G.

Subjects
PERSONALLY identifiable information, ELECTRONIC health records, DATA recorders & recording, COMMUNITIES, TELEPHONE numbers
Abstract

Recent findings have shown that the continued expansion of the scope and scale of data collected in electronic health records are making the protection of personally identifiable information (PII) more challenging and may inadvertently put our institutions and patients at risk if not addressed. As clinical terminologies expand to include new terms that may capture PII (e.g., Patient First Name, Patient Phone Number), institutions may start using them in clinical data capture (and in some cases, they already have). Once in use, PII-containing values associated with these terms may find their way into laboratory or observation data tables via extract-transform-load jobs intended to process structured data, putting institutions at risk of unintended disclosure. Here we aim to inform the informatics community of these findings, as well as put out a call to action for remediation by the community. [ABSTRACT FROM AUTHOR]

Published
2022
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