Search

Your search keyword '"Pfaff, Emily"' showing total 262 results
Start Over Author "Pfaff, Emily"
262 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
Full Text
View/download PDF

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
Full Text
View/download PDF

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
Full Text
View/download PDF

6. A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative

Author

Casiraghi, Elena, Wong, Rachel, Hall, Margaret, Coleman, Ben, Notaro, Marco, Evans, Michael D., Tronieri, Jena S., Blau, Hannah, Laraway, Bryan, Callahan, Tiffany J., Chan, Lauren E., Bramante, Carolyn T., Buse, John B., Moffitt, Richard A., Sturmer, Til, Johnson, Steven G., Shao, Yu Raymond, Reese, Justin, Robinson, Peter N., Paccanaro, Alberto, Valentini, Giorgio, Huling, Jared D., Wilkins, Kenneth, Bennet, Tell, Chute, Christopher, DeWitt, Peter, Gersing, Kenneth, Girvin, Andrew, Haendel, Melissa, Harper, Jeremy, Hajagos, Janos, Hong, Stephanie, Pfaff, Emily, Reusch, Jane, Antoniescu, Corneliu, and Robaski, Kimberly

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Statistics - Applications
Abstract

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful to assess associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases and the simple removal of these cases may introduce severe bias. For these reasons, several multiple imputation algorithms have been proposed to attempt to recover the missing information. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithms works best in a given scenario. Furthermore, the selection of each algorithm parameters and data-related modelling choices are also both crucial and challenging. In this paper, we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. The experiments presented here show that our approach could effectively highlight the most valid and performant missing-data handling strategy for our case study. Moreover, our methodology allowed us to gain an understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

Published
2022

7. 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

8. 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
Full Text
View/download PDF

9. 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

10. An Open Natural Language Processing Development Framework for EHR-based Clinical Research: A case demonstration using the National COVID Cohort Collaborative (N3C)

Author

Liu, Sijia, Wen, Andrew, Wang, Liwei, He, Huan, Fu, Sunyang, Miller, Robert, Williams, Andrew, Harris, Daniel, Kavuluru, Ramakanth, Liu, Mei, Abu-el-rub, Noor, Schutte, Dalton, Zhang, Rui, Rouhizadeh, Masoud, Osborne, John D., He, Yongqun, Topaloglu, Umit, Hong, Stephanie S, Saltz, Joel H, Schaffter, Thomas, Pfaff, Emily, Chute, Christopher G., Duong, Tim, Haendel, Melissa A., Fuentes, Rafael, Szolovits, Peter, Xu, Hua, Liu, Hongfang, Collaborative, National COVID Cohort, Processing, Natural Language, and Subgroup

Subjects
Computer Science - Computation and Language, Computer Science - Information Retrieval
Abstract

While we pay attention to the latest advances in clinical natural language processing (NLP), we can notice some resistance in the clinical and translational research community to adopt NLP models due to limited transparency, interpretability, and usability. In this study, we proposed an open natural language processing development framework. We evaluated it through the implementation of NLP algorithms for the National COVID Cohort Collaborative (N3C). Based on the interests in information extraction from COVID-19 related clinical notes, our work includes 1) an open data annotation process using COVID-19 signs and symptoms as the use case, 2) a community-driven ruleset composing platform, and 3) a synthetic text data generation workflow to generate texts for information extraction tasks without involving human subjects. The corpora were derived from texts from three different institutions (Mayo Clinic, University of Kentucky, University of Minnesota). The gold standard annotations were tested with a single institution's (Mayo) ruleset. This resulted in performances of 0.876, 0.706, and 0.694 in F-scores for Mayo, Minnesota, and Kentucky test datasets, respectively. The study as a consortium effort of the N3C NLP subgroup demonstrates the feasibility of creating a federated NLP algorithm development and benchmarking platform to enhance multi-institution clinical NLP study and adoption. Although we use COVID-19 as a use case in this effort, our framework is general enough to be applied to other domains of interest in clinical NLP., Comment: update on contents

Published
2021

11. 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
Full Text
View/download PDF

12. 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
Full Text
View/download PDF

13. Enabling Longitudinal Exploratory Analysis of Clinical COVID Data

Author

Borland, David, Brain, Irena, Fecho, Karamarie, Pfaff, Emily, Xu, Hao, Champion, James, Bizon, Chris, and Gotz, David

Subjects
Computer Science - Human-Computer Interaction
Abstract

As the COVID-19 pandemic continues to impact the world, data is being gathered and analyzed to better understand the disease. Recognizing the potential for visual analytics technologies to support exploratory analysis and hypothesis generation from longitudinal clinical data, a team of collaborators worked to apply existing event sequence visual analytics technologies to a longitudinal clinical data from a cohort of 998 patients with high rates of COVID-19 infection. This paper describes the initial steps toward this goal, including: (1) the data transformation and processing work required to prepare the data for visual analysis, (2) initial findings and observations, and (3) qualitative feedback and lessons learned which highlight key features as well as limitations to address in future work., Comment: To Appear in Proceedings of Visual Analytics in Healthcare 2021

Published
2021
Full Text
View/download PDF

14. 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

15. 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
Full Text
View/download PDF

16. 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
Full Text
View/download PDF

19. 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
Full Text
View/download PDF

20. 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
Full Text
View/download PDF

21. 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
Full Text
View/download PDF

22. 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

23. 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
Full Text
View/download PDF

24. 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

25. 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
Full Text
View/download PDF

26. 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
Full Text
View/download PDF

27. 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
Full Text
View/download PDF

28. 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

30. 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
Full Text
View/download PDF

31. 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
Full Text
View/download PDF

32. 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
Full Text
View/download PDF

35. The Prevalence of Post-Acute Sequelae of COVID-19 in Solid Organ Transplant Recipients: Evaluation of Risk in the National COVID Cohort Collaborative (N3C)

Author

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

Published
2024
Full Text
View/download PDF

36. Effect of Paxlovid Treatment on Long COVID Onset: An EHR-Based Target Trial Emulation from N3C

Author

Preiss, Alexander, primary, Bhatia, Abhishek, additional, Zang, Chengxi, additional, Aragon, Leyna V., additional, Baratta, John M., additional, Baskaran, Monika, additional, Blancero, Frank, additional, Brannock, M. Daniel, additional, Chew, Robert F., additional, Díaz, Iván, additional, Fitzgerald, Megan, additional, Kelly, Elizabeth P., additional, Zhou, Andrea, additional, Weiner, Mark G., additional, Carton, Thomas W., additional, Wang, Fei, additional, Kaushal, Rainu, additional, Chute, Christopher G., additional, Haendel, Melissa, additional, Moffitt, Richard, additional, and Pfaff, Emily, additional

Published
2024
Full Text
View/download PDF

37. 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
Full Text
View/download PDF

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

Author

Vinson, Amanda J., Schissel, Makayla, Anzalone, Alfred J., Dai, Ran, French, Evan T., Olex, Amy L., Lee, Stephen B., Ison, Michael, Mannon, Roslyn B., 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, and Zhang, Xiaohan Tanner

Abstract

Postacute sequelae after the coronavirus disease (COVID) of 2019 (PASC) is increasingly recognized, although data on solid organ transplant (SOT) recipients (SOTRs) are limited. Using the National COVID Cohort Collaborative, we performed 1:1 propensity score matching (PSM) of all adult SOTR and nonimmunosuppressed/immunocompromised (ISC) patients with acute COVID infection (August 1, 2021 to January 13, 2023) for a subsequent PASC diagnosis using International Classification of Diseases, 10th Revision, Clinical Modification codes. Multivariable logistic regression was used to examine not only the association of SOT status with PASC, but also other patient factors after stratifying by SOT status. Prior to PSM, there were 8769 SOT and 1 576 769 non-ISC patients with acute COVID infection. After PSM, 8756 SOTR and 8756 non-ISC patients were included; 2.2% of SOTR (n = 192) and 1.4% (n = 122) of non-ISC patients developed PASC (Pvalue < .001). In the overall matched cohort, SOT was independently associated with PASC (adjusted odds ratio [aOR], 1.48; 95% confidence interval [CI], 1.09-2.01). Among SOTR, COVID infection severity (aOR, 11.6; 95% CI, 3.93-30.0 for severe vs mild disease), older age (aOR, 1.02; 95% CI, 1.01-1.03 per year), and mycophenolate mofetil use (aOR, 2.04; 95% CI, 1.38-3.05) were each independently associated with PASC. In non-ISC patients, only depression (aOR, 1.96; 95% CI, 1.24-3.07) and COVID infection severity were. In conclusion, PASC occurs more commonly in SOTR than in non-ISC patients, with differences in risk profiles based on SOT status.

Published
2024
Full Text
View/download PDF

39. Genetic and Survey Data Improves Performance of Machine Learning Model for Long COVID

Author

Wei, Wei-Qi, primary, Guardo, Christopher, additional, Gandireddy, Srushti, additional, Yan, Chao, additional, Ong, Henry, additional, Kerchberger, Vern, additional, Dickson, Alyson, additional, Pfaff, Emily, additional, Master, Hiral, additional, Basford, Melissa, additional, Tran, Nguyen, additional, Mancuso, Salvatore, additional, Syed, Toufeeq, additional, Zhao, Zhongming, additional, Feng, QiPing, additional, Haendel, Melissa, additional, Lunt, Christopher, additional, Ginsburg, Geoffrey, additional, Chute, Christopher, additional, Denny, Joshua, additional, and Roden, Dan, additional

Published
2023
Full Text
View/download PDF

40. Reengineering a machine learning phenotype to adapt to the changing COVID-19 landscape: A study from the N3C and RECOVER consortia

Author

Crosskey, Miles, primary, McIntee, Tomas, additional, Preiss, Alexander J, additional, Brannock, M Daniel, additional, Yoo, Yun Jae, additional, Hadley, Emily C, additional, Blanceró, Frank, additional, Chew, Rob, additional, Loomba, Johanna, additional, Bhatia, Abhishek, additional, Chute, Christopher G, additional, Haendel, Melissa, additional, Moffitt, Richard, additional, and Pfaff, Emily, additional

Published
2023
Full Text
View/download PDF

41. The N3C governance ecosystem: a model socio-technical partnership for the future of collaborative analytics at scale

Author

Suver, Christine, primary, Harper, Jeremy, additional, Loomba, Johanna, additional, Saltz, Mary, additional, Solway, Julian, additional, Anzalone, Alfred Jerrod, additional, Walters, Kellie, additional, Pfaff, Emily, additional, Walden, Anita, additional, McMurry, Julie, additional, Chute, Christopher G, additional, and Haendel, Melissa, additional

Published
2023
Full Text
View/download PDF

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

Author

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

Published
2023
Full Text
View/download PDF

44. 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

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

Published
2023
Full Text
View/download PDF

45. An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C)

Author

Liu, Sijia, primary, Wen, Andrew, additional, Wang, Liwei, additional, He, Huan, additional, Fu, Sunyang, additional, Miller, Robert, additional, Williams, Andrew, additional, Harris, Daniel, additional, Kavuluru, Ramakanth, additional, Liu, Mei, additional, Abu-el-Rub, Noor, additional, Schutte, Dalton, additional, Zhang, Rui, additional, Rouhizadeh, Masoud, additional, Osborne, John D, additional, He, Yongqun, additional, Topaloglu, Umit, additional, Hong, Stephanie S, additional, Saltz, Joel H, additional, Schaffter, Thomas, additional, Pfaff, Emily, additional, Chute, Christopher G, additional, Duong, Tim, additional, Haendel, Melissa A, additional, Fuentes, Rafael, additional, Szolovits, Peter, additional, Xu, Hua, additional, and Liu, Hongfang, additional

Published
2023
Full Text
View/download PDF

49. Risk of post-acute sequelae of SARS-CoV-2 infection associated with pre-coronavirus disease obstructive sleep apnea diagnoses: an electronic health record-based analysis from the RECOVER initiative

Author

Mandel, Hannah L, primary, Colleen, Gunnar, additional, Abedian, Sajjad, additional, Ammar, Nariman, additional, Bailey, L Charles, additional, Bennett, Tellen D, additional, Brannock, M Daniel, additional, Brosnahan, Shari B, additional, Chen, Yu, additional, Chute, Christopher G, additional, Divers, Jasmin, additional, Evans, Michael D, additional, Haendel, Melissa, additional, Hall, Margaret A, additional, Hirabayashi, Kathryn, additional, Hornig, Mady, additional, Katz, Stuart D, additional, Krieger, Ana C, additional, Loomba, Johanna, additional, Lorman, Vitaly, additional, Mazzotti, Diego R, additional, McMurry, Julie, additional, Moffitt, Richard A, additional, Pajor, Nathan M, additional, Pfaff, Emily, additional, Radwell, Jeff, additional, Razzaghi, Hanieh, additional, Redline, Susan, additional, Seibert, Elle, additional, Sekar, Anisha, additional, Sharma, Suchetha, additional, Thaweethai, Tanayott, additional, Weiner, Mark G, additional, Yoo, Yun Jae, additional, Zhou, Andrea, additional, and Thorpe, Lorna E, additional

Published
2023
Full Text
View/download PDF

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

Author

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

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results