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178 results on '"Beaulieu-Jones, Brett"'

1. The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)

Author

Miao, Brenda Y., Chen, Irene Y., Williams, Christopher YK, Davidson, Jaysón, Garcia-Agundez, Augusto, Sun, Shenghuan, Zack, Travis, Saria, Suchi, Arnaout, Rima, Quer, Giorgio, Sadaei, Hossein J., Torkamani, Ali, Beaulieu-Jones, Brett, Yu, Bin, Gianfrancesco, Milena, Butte, Atul J., Norgeot, Beau, and Sushil, Madhumita

Subjects
Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition
Abstract

Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language and image processing in medicine and marked a significant paradigm shift in how biomedical models can be developed and deployed. While these models are highly adaptable to new tasks, scaling and evaluating their usage presents new challenges not addressed in previous frameworks. In particular, the ability of these models to produce useful outputs with little to no specialized training data ("zero-" or "few-shot" approaches), as well as the open-ended nature of their outputs, necessitate the development of new guidelines for robust reporting of clinical generative model research. In response to gaps in standards and best practices for the development of clinical AI tools identified by US Executive Order 141103 and several emerging national networks for clinical AI evaluation, we begin to formalize some of these guidelines by building on the original MI-CLAIM checklist. The new checklist, MI-CLAIM-GEN (Table 1), aims to address differences in training, evaluation, interpretability, and reproducibility of new generative models compared to non-generative ("predictive") AI models. This MI-CLAIM-GEN checklist also seeks to clarify cohort selection reporting with unstructured clinical data and adds additional items on alignment with ethical standards for clinical AI research.

Published
2024

2. Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium

Author

Jeong, Hyewon, Jabbour, Sarah, Yang, Yuzhe, Thapta, Rahul, Mozannar, Hussein, Han, William Jongwon, Mehandru, Nikita, Wornow, Michael, Lialin, Vladislav, Liu, Xin, Lozano, Alejandro, Zhu, Jiacheng, Kocielnik, Rafal Dariusz, Harrigian, Keith, Zhang, Haoran, Lee, Edward, Vukadinovic, Milos, Balagopalan, Aparna, Jeanselme, Vincent, Matton, Katherine, Demirel, Ilker, Fries, Jason, Rashidi, Parisa, Beaulieu-Jones, Brett, Xu, Xuhai Orson, McDermott, Matthew, Naumann, Tristan, Agrawal, Monica, Zitnik, Marinka, Ustun, Berk, Choi, Edward, Yeom, Kristen, Gursoy, Gamze, Ghassemi, Marzyeh, Pierson, Emma, Chen, George, Kanjilal, Sanjat, Oberst, Michael, Zhang, Linying, Singh, Harvineet, Hartvigsen, Tom, Zhou, Helen, and Okolo, Chinasa T.

Subjects
Computer Science - Machine Learning
Abstract

The third ML4H symposium was held in person on December 10, 2023, in New Orleans, Louisiana, USA. The symposium included research roundtable sessions to foster discussions between participants and senior researchers on timely and relevant topics for the \ac{ML4H} community. Encouraged by the successful virtual roundtables in the previous year, we organized eleven in-person roundtables and four virtual roundtables at ML4H 2022. The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables. Each roundtable session included invited senior chairs (with substantial experience in the field), junior chairs (responsible for facilitating the discussion), and attendees from diverse backgrounds with interest in the session's topic. Herein we detail the organization process and compile takeaways from these roundtable discussions, including recent advances, applications, and open challenges for each topic. We conclude with a summary and lessons learned across all roundtables. This document serves as a comprehensive review paper, summarizing the recent advancements in machine learning for healthcare as contributed by foremost researchers in the field., Comment: ML4H 2023, Research Roundtables

Published
2024

3. What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask

Author

Kohane, Isaac S, Aronow, Bruce J, Avillach, Paul, Beaulieu-Jones, Brett K, Bellazzi, Riccardo, Bradford, Robert L, Brat, Gabriel A, Cannataro, Mario, Cimino, James J, García-Barrio, Noelia, Gehlenborg, Nils, Ghassemi, Marzyeh, Gutiérrez-Sacristán, Alba, Hanauer, David A, Holmes, John H, Hong, Chuan, Klann, Jeffrey G, Loh, Ne Hooi Will, Luo, Yuan, Mandl, Kenneth D, Daniar, Mohamad, Moore, Jason H, Murphy, Shawn N, Neuraz, Antoine, Ngiam, Kee Yuan, Omenn, Gilbert S, Palmer, Nathan, Patel, Lav P, Pedrera-Jiménez, Miguel, Sliz, Piotr, South, Andrew M, Tan, Amelia Li Min, Taylor, Deanne M, Taylor, Bradley W, Torti, Carlo, Vallejos, Andrew K, Wagholikar, Kavishwar B, Weber, Griffin M, and Cai, Tianxi

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

Coincident with the tsunami of COVID-19–related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

Published
2021
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5. Harnessing electronic health records for real-world evidence

Author

Hou, Jue, Zhao, Rachel, Gronsbell, Jessica, Beaulieu-Jones, Brett K., Webber, Griffin, Jemielita, Thomas, Wan, Shuyan, Hong, Chuan, Lin, Yucong, Cai, Tianrun, Wen, Jun, Panickan, Vidul A., Bonzel, Clara-Lea, Liaw, Kai-Li, Liao, Katherine P., and Cai, Tianxi

Subjects
Statistics - Applications
Abstract

While randomized controlled trials (RCTs) are the gold-standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data (RWD) has been vital in post-approval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of RWD is electronic health records (EHRs), which contain detailed information on patient care in both structured (e. g., diagnosis codes) and unstructured (e. g., clinical notes, images) form. Despite the granularity of the data available in EHRs, critical variables required to reliably assess the relationship between a treatment and clinical outcome can be challenging to extract. We provide an integrated data curation and modeling pipeline leveraging recent advances in natural language processing, computational phenotyping, modeling techniques with noisy data to address this fundamental challenge and accelerate the reliable use of EHRs for RWE, as well as the creation of digital twins. The proposed pipeline is highly automated for the task and includes guidance for deployment. Examples are also drawn from existing literature on EHR emulation of RCT and accompanied by our own studies with Mass General Brigham (MGB) EHR., Comment: 39 pages, 1 figure, 1 table

Published
2022

8. International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

Author

Weber, Griffin M, Hong, Chuan, Xia, Zongqi, Palmer, Nathan P, Avillach, Paul, L’Yi, Sehi, Keller, Mark S, Murphy, Shawn N, Gutiérrez-Sacristán, Alba, Bonzel, Clara-Lea, Serret-Larmande, Arnaud, Neuraz, Antoine, Omenn, Gilbert S, Visweswaran, Shyam, Klann, Jeffrey G, South, Andrew M, Loh, Ne Hooi Will, Cannataro, Mario, Beaulieu-Jones, Brett K, Bellazzi, Riccardo, Agapito, Giuseppe, Alessiani, Mario, Aronow, Bruce J, Bell, Douglas S, Benoit, Vincent, Bourgeois, Florence T, Chiovato, Luca, Cho, Kelly, Dagliati, Arianna, DuVall, Scott L, Barrio, Noelia García, Hanauer, David A, Ho, Yuk-Lam, Holmes, John H, Issitt, Richard W, Liu, Molei, Luo, Yuan, Lynch, Kristine E, Maidlow, Sarah E, Malovini, Alberto, Mandl, Kenneth D, Mao, Chengsheng, Matheny, Michael E, Moore, Jason H, Morris, Jeffrey S, Morris, Michele, Mowery, Danielle L, Ngiam, Kee Yuan, Patel, Lav P, Pedrera-Jimenez, Miguel, Ramoni, Rachel B, Schriver, Emily R, Schubert, Petra, Balazote, Pablo Serrano, Spiridou, Anastasia, Tan, Amelia LM, Tan, Byorn WL, Tibollo, Valentina, Torti, Carlo, Trecarichi, Enrico M, Wang, Xuan, Kohane, Isaac S, Cai, Tianxi, and Brat, Gabriel A

Subjects
Health Services and Systems, Health Sciences, Basic Behavioral and Social Science, Behavioral and Social Science, Good Health and Well Being, Consortium for Clinical Characterization of COVID-19 by EHR, Health services and systems
Abstract

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

Published
2022

9. ML4H Abstract Track 2019

Author

McDermott, Matthew B. A., Alsentzer, Emily, Finlayson, Sam, Oberst, Michael, Falck, Fabian, Naumann, Tristan, Beaulieu-Jones, Brett K., and Dalca, Adrian V.

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2019. This index is not complete, as some accepted abstracts chose to opt-out of inclusion.

Published
2020

10. Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data

Author

Klann, Jeffrey G, Estiri, Hossein, Weber, Griffin M, Moal, Bertrand, Avillach, Paul, Hong, Chuan, Tan, Amelia LM, Beaulieu-Jones, Brett K, Castro, Victor, Maulhardt, Thomas, Geva, Alon, Malovini, Alberto, South, Andrew M, Visweswaran, Shyam, Morris, Michele, Samayamuthu, Malarkodi J, Omenn, Gilbert S, Ngiam, Kee Yuan, Mandl, Kenneth D, Boeker, Martin, Olson, Karen L, Mowery, Danielle L, Follett, Robert W, Hanauer, David A, Bellazzi, Riccardo, Moore, Jason H, Loh, Ne-Hooi Will, Bell, Douglas S, Wagholikar, Kavishwar B, Chiovato, Luca, Tibollo, Valentina, Rieg, Siegbert, Li, Anthony LLJ, Jouhet, Vianney, Schriver, Emily, Xia, Zongqi, Hutch, Meghan, Luo, Yuan, Kohane, Isaac S, EHR, The Consortium for Clinical Characterization of COVID-19 by, Brat, Gabriel A, and Murphy, Shawn N

Subjects
Health Services and Systems, Health Sciences, Patient Safety, HIV/AIDS, Good Health and Well Being, COVID-19, Electronic Health Records, Hospitalization, Humans, Machine Learning, Prognosis, ROC Curve, Sensitivity and Specificity, Severity of Illness Index, novel coronavirus, disease severity, computable phenotype, medical informatics, data networking, data interoperability, Consortium for Clinical Characterization of COVID-19 by EHR (4CE), Information and Computing Sciences, Engineering, Medical and Health Sciences, Medical Informatics, Biomedical and clinical sciences, Health sciences, Information and computing sciences
Abstract

ObjectiveThe Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity.Materials and methodsTwelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site.ResultsThe full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review.DiscussionWe developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions.ConclusionsWe developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

Published
2021

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

Author

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

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

Author

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

Published
2023
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13. International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium

Author

Brat, Gabriel A, Weber, Griffin M, Gehlenborg, Nils, Avillach, Paul, Palmer, Nathan P, Chiovato, Luca, Cimino, James, Waitman, Lemuel R, Omenn, Gilbert S, Malovini, Alberto, Moore, Jason H, Beaulieu-Jones, Brett K, Tibollo, Valentina, Murphy, Shawn N, Yi, Sehi L’, Keller, Mark S, Bellazzi, Riccardo, Hanauer, David A, Serret-Larmande, Arnaud, Gutierrez-Sacristan, Alba, Holmes, John J, Bell, Douglas S, Mandl, Kenneth D, Follett, Robert W, Klann, Jeffrey G, Murad, Douglas A, Scudeller, Luigia, Bucalo, Mauro, Kirchoff, Katie, Craig, Jean, Obeid, Jihad, Jouhet, Vianney, Griffier, Romain, Cossin, Sebastien, Moal, Bertrand, Patel, Lav P, Bellasi, Antonio, Prokosch, Hans U, Kraska, Detlef, Sliz, Piotr, Tan, Amelia LM, Ngiam, Kee Yuan, Zambelli, Alberto, Mowery, Danielle L, Schiver, Emily, Devkota, Batsal, Bradford, Robert L, Daniar, Mohamad, Daniel, Christel, Benoit, Vincent, Bey, Romain, Paris, Nicolas, Serre, Patricia, Orlova, Nina, Dubiel, Julien, Hilka, Martin, Jannot, Anne Sophie, Breant, Stephane, Leblanc, Judith, Griffon, Nicolas, Burgun, Anita, Bernaux, Melodie, Sandrin, Arnaud, Salamanca, Elisa, Cormont, Sylvie, Ganslandt, Thomas, Gradinger, Tobias, Champ, Julien, Boeker, Martin, Martel, Patricia, Esteve, Loic, Gramfort, Alexandre, Grisel, Olivier, Leprovost, Damien, Moreau, Thomas, Varoquaux, Gael, Vie, Jill-Jênn, Wassermann, Demian, Mensch, Arthur, Caucheteux, Charlotte, Haverkamp, Christian, Lemaitre, Guillaume, Bosari, Silvano, Krantz, Ian D, South, Andrew, Cai, Tianxi, and Kohane, Isaac S

Subjects
Health Services and Systems, Health Sciences, Good Health and Well Being, Databases, Outcomes research, Viral infection, Health services and systems
Abstract

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

Published
2020

14. Examining the Use of Real‐World Evidence in the Regulatory Process

Author

Beaulieu‐Jones, Brett K, Finlayson, Samuel G, Yuan, William, Altman, Russ B, Kohane, Isaac S, Prasad, Vinay, and Yu, Kun‐Hsing

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Trials and Supportive Activities, Clinical Research, Clinical Trials as Topic, Decision Making, Evidence-Based Medicine, Humans, United States, United States Food and Drug Administration, Pharmacology and Pharmaceutical Sciences, Pharmacology & Pharmacy, Pharmacology and pharmaceutical sciences
Abstract

The 21st Century Cures Act passed by the United States Congress mandates the US Food and Drug Administration to develop guidance to evaluate the use of real-world evidence (RWE) to support the regulatory process. RWE has generated important medical discoveries, especially in areas where traditional clinical trials would be unethical or infeasible. However, RWE suffers from several issues that hinder its ability to provide proof of treatment efficacy at a level comparable to randomized controlled trials. In this review article, we summarized the advantages and limitations of RWE, identified the key opportunities for RWE, and pointed the way forward to maximize the potential of RWE for regulatory purposes.

Published
2020

15. Privacy-Preserving Distributed Deep Learning for Clinical Data

Author

Beaulieu-Jones, Brett K., Yuan, William, Finlayson, Samuel G., and Wu, Zhiwei Steven

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Deep learning with medical data often requires larger samples sizes than are available at single providers. While data sharing among institutions is desirable to train more accurate and sophisticated models, it can lead to severe privacy concerns due the sensitive nature of the data. This problem has motivated a number of studies on distributed training of neural networks that do not require direct sharing of the training data. However, simple distributed training does not offer provable privacy guarantees to satisfy technical safe standards and may reveal information about the underlying patients. We present a method to train neural networks for clinical data in a distributed fashion under differential privacy. We demonstrate these methods on two datasets that include information from multiple independent sites, the eICU collaborative Research Database and The Cancer Genome Atlas., Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216

Published
2018

16. Machine Learning for Health (ML4H) Workshop at NeurIPS 2018

Author

Antropova, Natalia, Beam, Andrew L., Beaulieu-Jones, Brett K., Chen, Irene, Chivers, Corey, Dalca, Adrian, Finlayson, Sam, Fiterau, Madalina, Fries, Jason Alan, Ghassemi, Marzyeh, Hughes, Mike, Jedynak, Bruno, Kandola, Jasvinder S., McDermott, Matthew, Naumann, Tristan, Schulam, Peter, Shamout, Farah, and Yahi, Alexandre

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held on December 8, 2018 in Montreal, Canada., Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216

Published
2018

17. Learning Contextual Hierarchical Structure of Medical Concepts with Poincair\'e Embeddings to Clarify Phenotypes

Author

Beaulieu-Jones, Brett K., Kohane, Isaac S., and Beam, Andrew L.

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Computation and Language
Abstract

Biomedical association studies are increasingly done using clinical concepts, and in particular diagnostic codes from clinical data repositories as phenotypes. Clinical concepts can be represented in a meaningful, vector space using word embedding models. These embeddings allow for comparison between clinical concepts or for straightforward input to machine learning models. Using traditional approaches, good representations require high dimensionality, making downstream tasks such as visualization more difficult. We applied Poincar\'e embeddings in a 2-dimensional hyperbolic space to a large-scale administrative claims database and show performance comparable to 100-dimensional embeddings in a euclidean space. We then examine disease relationships under different disease contexts to better understand potential phenotypes., Comment: To appear in 2019 Pacific Symposium on Biocomputing

Published
2018

18. An Unsupervised Homogenization Pipeline for Clustering Similar Patients using Electronic Health Record Data

Author

Ulloa, Alvaro, Basile, Anna, Wehner, Gregory J., Jing, Linyuan, Ritchie, Marylyn D., Beaulieu-Jones, Brett, Haggerty, Christopher M., and Fornwalt, Brandon K.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Electronic health records (EHR) contain a large variety of information on the clinical history of patients such as vital signs, demographics, diagnostic codes and imaging data. The enormous potential for discovery in this rich dataset is hampered by its complexity and heterogeneity. We present the first study to assess unsupervised homogenization pipelines designed for EHR clustering. To identify the optimal pipeline, we tested accuracy on simulated data with varying amounts of redundancy, heterogeneity, and missingness. We identified two optimal pipelines: 1) Multiple Imputation by Chained Equations (MICE) combined with Local Linear Embedding; and 2) MICE, Z-scoring, and Deep Autoencoders., Comment: conference

Published
2017

19. Machine Learning for Structured Clinical Data

Author

Beaulieu-Jones, Brett K.

Subjects
Computer Science - Learning
Abstract

Research is a tertiary priority in the EHR, where the priorities are patient care and billing. Because of this, the data is not standardized or formatted in a manner easily adapted to machine learning approaches. Data may be missing for a large variety of reasons ranging from individual input styles to differences in clinical decision making, for example, which lab tests to issue. Few patients are annotated at a research quality, limiting sample size and presenting a moving gold standard. Patient progression over time is key to understanding many diseases but many machine learning algorithms require a snapshot, at a single time point, to create a usable vector form. Furthermore, algorithms that produce black box results do not provide the interpretability required for clinical adoption. This chapter discusses these challenges and others in applying machine learning techniques to the structured EHR (i.e. Patient Demographics, Family History, Medication Information, Vital Signs, Laboratory Tests, Genetic Testing). It does not cover feature extraction from additional sources such as imaging data or free text patient notes but the approaches discussed can include features extracted from these sources.

Published
2017

22. Disease progression strikingly differs in research and real-world Parkinson's populations

Author

Beaulieu-Jones, Brett, primary, Frau, Francesca, additional, Bozzi, Sylvie, additional, Chandross, Karen J, additional, Peterschmitt, M Judith, additional, Cohen, Caroline, additional, Coulovrat, Catherine, additional, Kumar, Dinesh, additional, Kruger, Mark J, additional, Lipnick, Scott L, additional, Fitzsimmons, Lane, additional, Kohane, Isaac, additional, and Scherzer, Clemens, additional

Published
2024
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23. Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: a retrospective cohort study

Author

Beaulieu-Jones, Brett K, primary, Villamar, Mauricio F, additional, Scordis, Phil, additional, Bartmann, Ana Paula, additional, Ali, Waqar, additional, Wissel, Benjamin D, additional, Alsentzer, Emily, additional, de Jong, Johann, additional, Patra, Arijit, additional, and Kohane, Isaac, additional

Published
2023
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25. Accelerating diagnosis of Parkinson’s disease through risk prediction

Author

Yuan, William, Beaulieu-Jones, Brett, Krolewski, Richard, Palmer, Nathan, Veyrat-Follet, Christine, Frau, Francesca, Cohen, Caroline, Bozzi, Sylvie, Cogswell, Meaghan, Kumar, Dinesh, Coulouvrat, Catherine, Leroy, Bruno, Fischer, Tanya Z., Sardi, S. Pablo, Chandross, Karen J., Rubin, Lee L., Wills, Anne-Marie, Kohane, Isaac, and Lipnick, Scott L.

Published
2021
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26. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist

Author

Norgeot, Beau, Quer, Giorgio, Beaulieu-Jones, Brett K., Torkamani, Ali, Dias, Raquel, Gianfrancesco, Milena, Arnaout, Rima, Kohane, Isaac S., Saria, Suchi, Topol, Eric, Obermeyer, Ziad, Yu, Bin, and Butte, Atul J.

Subjects
Medical research -- Standards -- Ethical aspects, Medicine, Experimental -- Standards -- Ethical aspects, Artificial intelligence -- Models -- Usage, Disclosure of information -- Standards, Clinical trials -- Standards -- Ethical aspects, Machine learning -- Models -- Usage, Artificial intelligence, Biological sciences, Health
Abstract

Here we present the MI-CLAIM checklist, a tool intended to improve transparent reporting of AI algorithms in medicine., Author(s): Beau Norgeot [sup.1] , Giorgio Quer [sup.2] , Brett K. Beaulieu-Jones [sup.3] , Ali Torkamani [sup.2] , Raquel Dias [sup.2] , Milena Gianfrancesco [sup.4] , Rima Arnaout [sup.1] , [...]

Published
2020
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28. 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
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30. Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies

Author

Hou, Jue, primary, Zhao, Rachel, additional, Gronsbell, Jessica, additional, Lin, Yucong, additional, Bonzel, Clara-Lea, additional, Zeng, Qingyi, additional, Zhang, Sinian, additional, Beaulieu-Jones, Brett K, additional, Weber, Griffin M, additional, Jemielita, Thomas, additional, Wan, Shuyan Sabrina, additional, Hong, Chuan, additional, Cai, Tianrun, additional, Wen, Jun, additional, Ayakulangara Panickan, Vidul, additional, Liaw, Kai-Li, additional, Liao, Katherine, additional, and Cai, Tianxi, additional

Published
2023
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31. Developing better digital health measures of Parkinson's disease using free living data and a crowdsourced data analysis challenge.

Author

BEAT-PD, DREAM Challenge Consortium [collaborator], Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center], Fonds National de la Recherche - FnR [sponsor], Sieberts, Solveig K., Borzymowski, Henryk, Guan, Yuanfang, Huang, Yidi, Matzner, Ayala, Page, Alex, Bar-Gad, Izhar, Beaulieu-Jones, Brett, El-Hanani, Yuval, Goschenhofer, Jann, Javidnia, Monica, Keller, Mark S., Li, Yan-Chak, Saqib, Mohammed, Smith, Greta, Stanescu, Ana, Venuto, Charles S., Zielinski, Robert, Glaab, Enrico, Jayaraman, Arun, Evers, Luc J. W., Foschini, Luca, Mariakakis, Alex, Pandey, Gaurav, Shawen, Nicholas, Synder, Phil, Omberg, Larsson, BEAT-PD, DREAM Challenge Consortium [collaborator], Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center], Fonds National de la Recherche - FnR [sponsor], Sieberts, Solveig K., Borzymowski, Henryk, Guan, Yuanfang, Huang, Yidi, Matzner, Ayala, Page, Alex, Bar-Gad, Izhar, Beaulieu-Jones, Brett, El-Hanani, Yuval, Goschenhofer, Jann, Javidnia, Monica, Keller, Mark S., Li, Yan-Chak, Saqib, Mohammed, Smith, Greta, Stanescu, Ana, Venuto, Charles S., Zielinski, Robert, Glaab, Enrico, Jayaraman, Arun, Evers, Luc J. W., Foschini, Luca, Mariakakis, Alex, Pandey, Gaurav, Shawen, Nicholas, Synder, Phil, and Omberg, Larsson

Abstract

One of the promising opportunities of digital health is its potential to lead to more holistic understandings of diseases by interacting with the daily life of patients and through the collection of large amounts of real-world data. Validating and benchmarking indicators of disease severity in the home setting is difficult, however, given the large number of confounders present in the real world and the challenges in collecting ground truth data in the home. Here we leverage two datasets collected from patients with Parkinson's disease, which couples continuous wrist-worn accelerometer data with frequent symptom reports in the home setting, to develop digital biomarkers of symptom severity. Using these data, we performed a public benchmarking challenge in which participants were asked to build measures of severity across 3 symptoms (on/off medication, dyskinesia, and tremor). 42 teams participated and performance was improved over baseline models for each subchallenge. Additional ensemble modeling across submissions further improved performance, and the top models validated in a subset of patients whose symptoms were observed and rated by trained clinicians.

Published
2023

33. Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies (Preprint)

Author

Hou, Jue, primary, Zhao, Rachel, additional, Gronsbell, Jessica, additional, Lin, Yucong, additional, Bonzel, Clara-Lea, additional, Zeng, Qingyi, additional, Zhang, Sinian, additional, Beaulieu-Jones, Brett K, additional, Weber, Griffin M, additional, Jemielita, Thomas, additional, Wan, Shuyan Sabrina, additional, Hong, Chuan, additional, Cai, Tianrun, additional, Wen, Jun, additional, Ayakulangara Panickan, Vidul, additional, Liaw, Kai-Li, additional, Liao, Katherine, additional, and Cai, Tianxi, additional

Published
2023
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34. Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: an international multi-centre observational cohort study

Author

Tan, Byorn W.L., primary, Tan, Bryce W.Q., additional, Tan, Amelia L.M., additional, Schriver, Emily R., additional, Gutiérrez-Sacristán, Alba, additional, Das, Priyam, additional, Yuan, William, additional, Hutch, Meghan R., additional, García Barrio, Noelia, additional, Pedrera Jimenez, Miguel, additional, Abu-el-rub, Noor, additional, Morris, Michele, additional, Moal, Bertrand, additional, Verdy, Guillaume, additional, Cho, Kelly, additional, Ho, Yuk-Lam, additional, Patel, Lav P., additional, Dagliati, Arianna, additional, Neuraz, Antoine, additional, Klann, Jeffrey G., additional, South, Andrew M., additional, Visweswaran, Shyam, additional, Hanauer, David A., additional, Maidlow, Sarah E., additional, Liu, Mei, additional, Mowery, Danielle L., additional, Batugo, Ashley, additional, Makoudjou, Adeline, additional, Tippmann, Patric, additional, Zöller, Daniela, additional, Brat, Gabriel A., additional, Luo, Yuan, additional, Avillach, Paul, additional, Bellazzi, Riccardo, additional, Chiovato, Luca, additional, Malovini, Alberto, additional, Tibollo, Valentina, additional, Samayamuthu, Malarkodi Jebathilagam, additional, Serrano Balazote, Pablo, additional, Xia, Zongqi, additional, Loh, Ne Hooi Will, additional, Chiudinelli, Lorenzo, additional, Bonzel, Clara-Lea, additional, Hong, Chuan, additional, Zhang, Harrison G., additional, Weber, Griffin M., additional, Kohane, Isaac S., additional, Cai, Tianxi, additional, Omenn, Gilbert S., additional, Holmes, John H., additional, Ngiam, Kee Yuan, additional, Aaron, James R., 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 L.L.J., additional, Aronow, Bruce J., additional, Ashraf, Fatima, additional, Atz, Andrew, additional, Panickan, Vidul Ayakulangara, additional, Azevedo, Paula S., additional, Balshi, James, additional, Beaulieu-Jones, Brett K., additional, Beaulieu-Jones, Brendin R., additional, Bell, Douglas S., additional, Bellasi, Antonio, 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, Bourgeois, Florence T., additional, Bradford, Robert L., additional, Bréant, Stéphane, additional, Brown, Nicholas W., additional, Bruno, Raffaele, additional, Bryant, William A., additional, Bucalo, Mauro, additional, Bucholz, Emily, 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, 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, Daniar, Mohamad, additional, Daniel, Christel, additional, Devkota, Batsal, additional, Dionne, Audrey, 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, García-Barrio, Noelia, additional, Garmire, Lana X., additional, Gehlenborg, Nils, additional, Getzen, Emily J., additional, Geva, Alon, additional, González, Tomás González, 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, Honerlaw, Jacqueline P., additional, Huling, Kenneth M., additional, Issitt, Richard W., additional, Jannot, Anne Sophie, additional, Jouhet, Vianney, additional, Kavuluru, Ramakanth, additional, Keller, Mark S., additional, Kennedy, Chris J., additional, Kernan, Kate F., additional, Key, Daniel A., additional, Kirchoff, Katie, additional, Krantz, Ian D., additional, Kraska, Detlef, additional, Krishnamurthy, Ashok K., additional, L'Yi, Sehi, additional, Le, Trang T., 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, Lynch, Kristine E., additional, Mahmood, Sadiqa, additional, Makwana, Simran, additional, Mandl, Kenneth D., 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, Ahooyi, Taha Mohseni, additional, Moore, Jason H., additional, Moraleda, Cinta, additional, Morris, Jeffrey S., additional, Moshal, Karyn L., additional, Mousavi, Sajad, additional, Murad, Douglas A., additional, Murphy, Shawn N., additional, Naughton, Thomas P., additional, Breda Neto, Carlos Tadeu, additional, Newburger, Jane, additional, Njoroge, Wanjiku F.M., 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, Pedrera-Jiménez, Miguel, 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, Rubio-Mayo, Paula, additional, Sacchi, Paolo, additional, Sáez, Carlos, additional, Salamanca, Elisa, 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, Schubert, Petra, additional, Schuettler, Juergen, additional, Scudeller, Luigia, additional, Sebire, Neil J., additional, Serrano-Balazote, Pablo, additional, Serre, Patricia, additional, Serret-Larmande, Arnaud, additional, Shah, Mohsin, additional, Hossein Abad, Zahra Shakeri, additional, Silvio, Domenick, additional, Sliz, Piotr, additional, Son, Jiyeon, additional, Sonday, Charles, additional, Sperotto, Francesca, additional, Spiridou, Anastasia, additional, Strasser, Zachary H., additional, Tan, Byorn W.L., additional, Tanni, Suzana E., additional, Taylor, Deanne M., additional, Terriza-Torres, Ana I., additional, Toh, Emma M.S., 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, Wang, Xuan, additional, Wassermann, Demian, additional, Wolkewitz, Martin, additional, Wong, Scott, additional, Xiong, Xin, additional, Ye, Ye, additional, Yehya, Nadir, additional, Zachariasse, Joany M., additional, Zahner, Janet J., additional, Zambelli, Alberto, additional, Zuccaro, Valentina, additional, and Zucco, Chiara, additional

Published
2023
Full Text
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35. Developing better digital health measures of Parkinson's disease using free living data and a crowdsourced data analysis challenge

Author

Sieberts, Solveig K., Borzymowski, Henryk, Guan, Yuanfang, Huang, Yidi, Matzner, Ayala, Page, Alex, Bar-Gad, Izhar, Beaulieu-Jones, Brett, El-Hanani, Yuval, Goschenhofer, Jann, Javidnia, Monica, Keller, Mark S., Li, Yan-Chak, Saqib, Mohammed, Smith, Greta, Stanescu, Ana, Venuto, Charles S., Zielinski, Robert, Glaab, Enrico, Jayaraman, Arun, Evers, Luc J. W., Foschini, Luca, Mariakakis, Alex, Pandey, Gaurav, Shawen, Nicholas, Synder, Phil, Omberg, Larsson, BEAT-PD, DREAM Challenge Consortium [collaborator], Fonds National de la Recherche - FnR [sponsor], and Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center]

Subjects
Multidisciplinaire, généralités & autres [F99] [Sciences du vivant], Neurologie [D14] [Sciences de la santé humaine], Multidisciplinaire, généralités & autres [D99] [Sciences de la santé humaine], Biotechnologie [F06] [Sciences du vivant], Neurology [D14] [Human health sciences], Biotechnology [F06] [Life sciences], Multidisciplinary, general & others [F99] [Life sciences], Multidisciplinary, general & others [D99] [Human health sciences]
Abstract

One of the promising opportunities of digital health is its potential to lead to more holistic understandings of diseases by interacting with the daily life of patients and through the collection of large amounts of real-world data. Validating and benchmarking indicators of disease severity in the home setting is difficult, however, given the large number of confounders present in the real world and the challenges in collecting ground truth data in the home. Here we leverage two datasets collected from patients with Parkinson's disease, which couples continuous wrist-worn accelerometer data with frequent symptom reports in the home setting, to develop digital biomarkers of symptom severity. Using these data, we performed a public benchmarking challenge in which participants were asked to build measures of severity across 3 symptoms (on/off medication, dyskinesia, and tremor). 42 teams participated and performance was improved over baseline models for each subchallenge. Additional ensemble modeling across submissions further improved performance, and the top models validated in a subset of patients whose symptoms were observed and rated by trained clinicians.

Published
2023

36. International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

Author

Weber, Griffin, Hong, Chuan, Xia, Zongqi, Palmer, Nathan, Avillach, Paul, L’yi, Sehi, Keller, Mark, Murphy, Shawn, Gutiérrez-Sacristán, Alba, Bonzel, Clara-Lea, Serret-Larmande, Arnaud, Neuraz, Antoine, Omenn, Gilbert, Visweswaran, Shyam, Klann, Jeffrey, South, Andrew, Loh, Ne Hooi Will, Cannataro, Mario, Beaulieu-Jones, Brett, Bellazzi, Riccardo, Agapito, Giuseppe, Alessiani, Mario, Aronow, Bruce, Bell, Douglas, Benoit, Vincent, Bourgeois, Florence, Chiovato, Luca, Cho, Kelly, Dagliati, Arianna, Duvall, Scott, Barrio, Noelia García, Hanauer, David, Ho, Yuk-Lam, Holmes, John, Issitt, Richard, Liu, Molei, Luo, Yuan, Lynch, Kristine, Maidlow, Sarah, Malovini, Alberto, Mandl, Kenneth, Mao, Chengsheng, Matheny, Michael, Moore, Jason, Morris, Jeffrey, Morris, Michele, Mowery, Danielle, Ngiam, Kee Yuan, Patel, Lav, Pedrera Jiménez, Miguel, Ramoni, Rachel, Schriver, Emily, Schubert, Petra, Balazote, Pablo Serrano, Spiridou, Anastasia, Tan, Amelia, Tan, Byorn, Tibollo, Valentina, Torti, Carlo, Trecarichi, Enrico, Wang, Xuan, Aaron, James, Albayrak, Adem, Albi, Giuseppe, Balshi, James, Alloni, Anna, Amendola, Danilo, Angoulvant, François, Anthony, Li, Ashraf, Fatima, Atz, Andrew, Azevedo, Paula, Bellasi, Antonio, Beraghi, Michele, Bernal-Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Bhatnagar, Surbhi, Blanco-Martínez, Alvar, Boeker, Martin, Booth, John, Bosari, Silvano, Bradford, Robert, Brat, Gabriel, Bréant, Stéphane, Brown, Nicholas, Bruno, Raffaele, Bryant, William, Bucalo, Mauro, Bucholz, Emily, Burgun, Anita, Cai, Tianxi, Carmona, Aldo, Caucheteux, Charlotte, Champ, Julien, Chen, Krista, Chen, Jin, Chiudinelli, Lorenzo, Cimino, James, Colicchio, Tiago, Cormont, Sylvie, Cossin, Sébastien, Craig, Jean, Cruz-Bermúdez, Juan Luis, Cruz-Rojo, Jaime, Daniar, Mohamad, Daniel, Christel, Das, Priyam, Devkota, Batsal, Garmire, Lana, Dionne, Audrey, Duan, Rui, Dubiel, Julien, Esteve, Loic, Estiri, Hossein, Fan, Shirley, Follett, Robert, Ganslandt, Thomas, García-Barrio, Noelia, Gehlenborg, Nils, Getzen, Emily, Geva, Alon, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Han, Larry, Haverkamp, Christian, Key, Daniel, Hazard, Derek, He, Bing, Henderson, Darren, Hilka, Martin, Huling, Kenneth, Hutch, Meghan, Jannot, Anne Sophie, Jouhet, Vianney, Kavuluru, Ramakanth, Kennedy, Chris, Kernan, Kate, Kirchoff, Katie, Kohane, Isaac, Krantz, Ian, Kraska, Detlef, Krishnamurthy, Ashok, Le, Trang, Leblanc, Judith, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Long, Qi, Lozano-Zahonero, Sara, Mahmood, Sadiqa, Makoudjou, Adeline, Maram, Anupama, Martel, Patricia, Martins, Marcelo, Marwaha, Jayson, Masino, Aaron, Mazzitelli, Maria, Mensch, Arthur, Milano, Marianna, Minicucci, Marcos, Moal, Bertrand, Ahooyi, Taha Mohseni, Moraleda, Cinta, Moshal, Karyn, Mousavi, Sajad, Murad, Douglas, Naughton, Thomas, Neto, Carlos Tadeu Breda, Newburger, Jane, Njoroge, Wanjiku, Norman, James, Obeid, Jihad, Okoshi, Marina, Olson, Karen, Orlova, Nina, Ostasiewski, Brian, Paris, Nicolas, Pedrera-Jiménez, Miguel, Pfaff, Ashley, Pfaff, Emily, Pillion, Danielle, Pizzimenti, Sara, Prokosch, Hans, Prudente, Robson, Prunotto, Andrea, Quirós-González, Víctor, 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, Sanz Vidorreta, Fernando, Savino, Maria, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil, Serrano-Balazote, Pablo, Serre, Patricia, Shah, Mohsin, Abad, Zahra Shakeri Hossein, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, Sperotto, Francesca, Strasser, Zachary, Tan, Bryce, Tanni, Suzana, Taylor, Deanne, Terriza-Torres, Ana, Tippmann, Patric, Toh, Emma, Tseng, Yi-Ju, Vallejos, Andrew, Varoquaux, Gael, Vella, Margaret, Verdy, Guillaume, Vie, Jill-Jênn, Vitacca, Michele, Wagholikar, Kavishwar, Waitman, Lemuel, Wassermann, Demian, Wolkewitz, Martin, Wong, Scott, Xiong, Xin, Ye, Ye, Yehya, Nadir, Yuan, William, Zambelli, Alberto, Zhang, Harrison, Zöller, Daniela, Zuccaro, Valentina, Zucco, Chiara, Harvard Medical School [Boston] (HMS), University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), Massachusetts General Hospital [Boston], Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Service d'informatique médicale et biostatistiques [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), University of Michigan [Ann Arbor], University of Michigan System, Wake Forest School of Medicine [Winston-Salem], Wake Forest Baptist Medical Center, National University Health System [Singapore] (NUHS), Università degli Studi 'Magna Graecia' di Catanzaro = University of Catanzaro (UMG), Università degli Studi di Pavia = University of Pavia (UNIPV), Istituti Clinici Scientifici Maugeri [Pavia] (IRCCS Pavia - ICS Maugeri), ASST Pavia, University of Cincinnati (UC), University of California [Los Angeles] (UCLA), University of California (UC), VA Boston Healthcare System, Hospital Universitario 12 de Octubre [Madrid], University of Pennsylvania, Great Ormond Street Hospital for Children [London] (GOSH), Harvard School of Public Health, Northwestern University [Chicago, Ill. USA], VA Salt Lake City Health Care System, Boston Children's Hospital, University of Kansas [Kansas City], and National University Hospital [Singapore] (NUH)

Subjects
Health Information Management, Medicine (miscellaneous), Health Informatics, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Computer Science Applications
Abstract

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

Published
2022

38. Temporal Trends in Clinical Evidence of 5-Year Survival Within Electronic Health Records Among Patients With Early-Stage Colon Cancer Managed With Laparoscopy-Assisted Colectomy vs Open Colectomy

Author

Hou, Jue, primary, Zhao, Rachel, additional, Cai, Tianrun, additional, Beaulieu-Jones, Brett, additional, Seyok, Thany, additional, Dahal, Kumar, additional, Yuan, Qianyu, additional, Xiong, Xin, additional, Bonzel, Clara-Lea, additional, Fox, Claire, additional, Christiani, David C., additional, Jemielita, Thomas, additional, Liao, Katherine P., additional, Liaw, Kai-Li, additional, and Cai, Tianxi, additional

Published
2022
Full Text
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39. Examining the Use of Real-World Evidence in the Regulatory Process.

Author

Beaulieu-Jones, Brett K, Beaulieu-Jones, Brett K, Finlayson, Samuel G, Yuan, William, Altman, Russ B, Kohane, Isaac S, Prasad, Vinay, Yu, Kun-Hsing, Beaulieu-Jones, Brett K, Beaulieu-Jones, Brett K, Finlayson, Samuel G, Yuan, William, Altman, Russ B, Kohane, Isaac S, Prasad, Vinay, and Yu, Kun-Hsing

Abstract

The 21st Century Cures Act passed by the United States Congress mandates the US Food and Drug Administration to develop guidance to evaluate the use of real-world evidence (RWE) to support the regulatory process. RWE has generated important medical discoveries, especially in areas where traditional clinical trials would be unethical or infeasible. However, RWE suffers from several issues that hinder its ability to provide proof of treatment efficacy at a level comparable to randomized controlled trials. In this review article, we summarized the advantages and limitations of RWE, identified the key opportunities for RWE, and pointed the way forward to maximize the potential of RWE for regulatory purposes.

Published
2020

40. New Developments in Synthetic Data Generation

Author

Beaulieu-Jones, Brett, Jingchen (Monika) Hu, and Williams, Aaron R.

Abstract

These talks were presented for the Privacy Day Webinar 2022sponsored by the American Statistical Association's Committee on Privacy and Confidentiality. Link to recording. Talk 1: "The potential of privacy-preserving generative deep neural networks to support clinical data sharing" Brett Beaulieu-Jones, Harvard Medical School Abstract: Data sharing accelerates scientific progress but sharing individual-level data while preserving patient privacy presents a barrier. Deep generative adversarial networks have the potential to produce synthetic data while maintaining privacy. In some cases, the synthetic data has been shown to maintain statistical properties of source data and to be indistinguishable to human experts. This raises two important questions: 1.) How can we do this? And 2.) What is the privacy-preserving synthetic data good for? Brett Beaulieu-Jones is an Instructor of Biomedical Informatics at Harvard Medical School. He obtained his PhD from the Perelman School of Medicine at the University of Pennsylvania under the supervision of Dr. Jason Moore and Dr. Casey Greene. Beaulieu-Jones’ doctoral researchfocused on using machine learning-based methods to more precisely define phenotypes from large-scale biomedical data repositories, e.g., those contained in clinical records. He joined Dr.Isaac Kohane’s lab to do his postdoc, wherehe has been focused on using machine learning to better understand the heterogeneity of neurological diseases and conditions, specificallyParkinson’s disease and Epilepsy.He is a former general chair and on the advisory board for the Machine Learning for Health Workshop at NeurIPS and is a founding board member forthe Association for Health Learning and Inference (parentorganization of ML4H and CHIL). Talk 2: "Incorporating disclosure risk in designing data synthesis models" Jingchen (Monika) Hu, Vassar College The generation and release of synthetic data can facilitate microdata dissemination by statistical agencies. Often times, agencies would need to strike a desirable balance of the utility-risk trade-off of the synthetic data. We propose a novel approach that can incorporate the disclosure risk of each record in designing any Bayesian synthesis model. In this way, records with a higher risk can receive a higher level of protection in the resulting synthetic data. We illustrate our methods with an application to the Consumer Expenditure Surveys of the U.S. Bureau of Labor Statistics. Jingchen (Monika) Hu is an assistant professor of statistics at Vassar College. Her research focuses on statistical data privacy methods, mainly synthetic data and differential privacy. She teaches a senior seminar on statistical data privacy at Vassar and engages undergraduate students in learning cutting-edge methods and conducting applied research in this area. Talk 3: "Fully synthetic microdata for public policy analysis" Aaron R. Williams, Urban Institute Government agencies possess data that could be invaluable for evaluating public policy but often do not publicly release the data due to disclosure concerns. For instance, the IRS has rich administrative data about Americans with incomes below the income tax filing threshold and tax filers and is restricted to a select few with IRS clearance. This talk will overview the generation of the fully synthetic 2012 IRS Statistics of Income Division supplemental public use file, ongoing work in generating the fully synthetic 2012 IRS SOI PUF, and a formally-private validation server for analysis with tax data. We use sequentialClassification and Regression Trees (CART) and kernel density smoothing to create useful microlevel data with disclosure protection. We test and evaluate the tradeoffs between data utility and disclosure risks of different parameterizations using a variety of validation metrics. The resulting synthetic data sets have high utility, particularly for summary statistics and microsimulation, and low disclosure risk. Aaron R. Williamsis a senior data scientist in the Income and Benefits Policy Center at the Urban Institute, where he works on retirement policy, microsimulation models, data privacy, and dataimputation methods. He has worked on Urban’s Dynamic Simulation of Income (DYNASIM)microsimulation model, the Social Security Administration’s Modeling Income in the Near Term (MINT) microsimulation model, and the Tax Policy Center’s synthesis of individual tax records.Williams is an adjunct professor in the McCourt School of Public Policy at Georgetown University.

Published
2022
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41. Evolving phenotypes of non-hospitalized patients that indicate long COVID

Author

ESTIRI, Hossein, Strasser, Zachary, Brat, Gabriel, Semenov, Yevgeniy, Patel, Chirag, Murphy, Shawn, Aaron, James, Agapito, Giuseppe, Albayrak, Adem, Alessiani, Mario, Amendola, Danilo, Anthony, Li, Aronow, Bruce, Ashraf, Fatima, Atz, Andrew, Avillach, Paul, Balshi, James, Beaulieu-Jones, Brett, Bell, Douglas, Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Sobrino, José Luis Bernal, Bernaux, Mélodie, Bey, Romain, Martínez, Alvar Blanco, Boeker, Martin, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence, Bradford, Robert, Bréant, Stéphane, Brown, Nicholas, Bryant, William, Bucalo, Mauro, Burgun, Anita, Cai, Tianxi, Cannataro, Mario, Carmona, Aldo, Caucheteux, Charlotte, Champ, Julien, Chen, Jin, Chen, Krista, Chiovato, Luca, Chiudinelli, Lorenzo, Cho, Kelly, Cimino, James, Colicchio, Tiago, Cormont, Sylvie, COSSIN, Sébastien, Craig, Jean, Bermúdez, Juan Luis Cruz, Rojo, Jaime Cruz, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Davoudi, Anahita, Devkota, Batsal, Dubiel, Julien, Esteve, Loic, Fan, Shirley, Follett, Robert, Gaiolla, Paula, Ganslandt, Thomas, Barrio, Noelia García, Garmire, Lana, Gehlenborg, Nils, GEVA, Alon, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Gutiérrez-Sacristán, Alba, Hanauer, David, Haverkamp, Christian, He, Bing, Henderson, Darren, Hilka, Martin, Holmes, John, Hong, Chuan, Horki, Petar, Huling, Kenneth, HUTCH, Meghan, Issitt, Richard, Jannot, Anne Sophie, Jouhet, Vianney, Keller, Mark, Kirchoff, Katie, Klann, Jeffrey, Kohane, Isaac, Krantz, Ian, Kraska, Detlef, Krishnamurthy, Ashok, L’Yi, Sehi, Le, Trang, Leblanc, Judith, Leite, Andressa, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, LOH, Ne Hooi Will, Lozano-Zahonero, Sara, Luo, Yuan, Lynch, Kristine, Mahmood, Sadiqa, Maidlow, Sarah, Malovini, Alberto, Mandl, Kenneth, Mao, Chengsheng, Maram, Anupama, Martel, Patricia, Masino, Aaron, Mazzitelli, Maria, Mensch, Arthur, Milano, Marianna, Minicucci, Marcos, Moal, Bertrand, Moore, Jason, Moraleda, Cinta, Morris, Jeffrey, MORRIS, Michele, Moshal, Karyn, Mousavi, Sajad, Mowery, Danielle, Murad, Douglas, Naughton, Thomas, Neuraz, Antoine, Ngiam, Kee Yuan, Norman, James, Obeid, Jihad, Okoshi, Marina, Olson, Karen, Omenn, Gilbert, Orlova, Nina, Ostasiewski, Brian, Palmer, Nathan, Paris, Nicolas, Patel, Lav, Jimenez, Miguel Pedrera, Pfaff, Emily, Pillion, Danielle, Prokosch, Hans, Prudente, Robson, González, Víctor Quirós, Ramoni, Rachel, Raskin, Maryna, RIEG, Siegbert, Domínguez, Gustavo Roig, Rojo, Pablo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi, Sandrin, Arnaud, Santos, Janaina, Savino, Maria, SCHRIVER, Emily, Schubert, Petra, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil, Balazote, Pablo Serrano, Serre, Patricia, Serret-Larmande, Arnaud, Shakeri, Zahra, Silvio, Domenick, Sliz, Piotr, SON, Jiyeon, Sonday, Charles, South, Andrew, Spiridou, Anastasia, Tan, Amelia, Tan, Bryce, Tan, Byorn, Tanni, Suzana, Taylor, Deanne, Terriza Torres, Ana, Tibollo, Valentina, Tippmann, Patric, Torti, Carlo, Trecarichi, Enrico, Tseng, Yi-Ju, Vallejos, Andrew, Varoquaux, Gael, Vella, Margaret, Verdy, Guillaume, Vie, Jill-Jênn, Visweswaran, Shyam, Vitacca, Michele, Wagholikar, Kavishwar, Waitman, Lemuel, Wang, Xuan, Wassermann, Demian, Weber, Griffin, XIA, Zongqi, Yehya, Nadir, Yuan, William, Zambelli, Alberto, Zhang, Harrison, Zoeller, Daniel, Zucco, Chiara, Massachusetts General Hospital [Boston], Harvard Medical School [Boston] (HMS), Service d'informatique médicale et biostatistiques [CHU Necker], CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Université de Paris - UFR Médecine Paris Centre [Santé] (UP Médecine Paris Centre), Université de Paris (UP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPC)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPC), Université Paris Cité - UFR Médecine Paris Centre [Santé] (UPC Médecine Paris Centre), Université Paris Cité (UPC), This work was supported by the National Human Genome Research Institute grant 3U01HG008685-05S2 and the National Library of Medicine grant T15LM007092., École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), UFR Médecine [Santé] - Université Paris Cité (UFR Médecine UPCité), and Université Paris Cité (UPCité)

Subjects
medicine.medical_specialty, Neurological disorder, Chest pain, MESH: Phenotype, Article, 03 medical and health sciences, 0302 clinical medicine, Post-Acute COVID-19 Syndrome, Diabetes mellitus, Internal medicine, Machine learning, medicine, Chronic fatigue syndrome, Humans, Electronic health records, Post-acute sequelae of SARS-CoV-2, MESH: COVID-19, 030304 developmental biology, Retrospective Studies, 0303 health sciences, MESH: Humans, business.industry, Medical record, Type 2 Diabetes Mellitus, COVID-19, Retrospective cohort study, MESH: Retrospective Studies, General Medicine, medicine.disease, 3. Good health, Dysgeusia, Phenotypes, Phenotype, Medicine, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, medicine.symptom, business, 030217 neurology & neurosurgery, Research Article, Cohort study
Abstract

Background For some SARS-CoV-2 survivors, recovery from the acute phase of the infection has been grueling with lingering effects. Many of the symptoms characterized as the post-acute sequelae of COVID-19 (PASC) could have multiple causes or are similarly seen in non-COVID patients. Accurate identification of PASC phenotypes will be important to guide future research and help the healthcare system focus its efforts and resources on adequately controlled age- and gender-specific sequelae of a COVID-19 infection. Methods In this retrospective electronic health record (EHR) cohort study, we applied a computational framework for knowledge discovery from clinical data, MLHO, to identify phenotypes that positively associate with a past positive reverse transcription-polymerase chain reaction (RT-PCR) test for COVID-19. We evaluated the post-test phenotypes in two temporal windows at 3–6 and 6–9 months after the test and by age and gender. Data from longitudinal diagnosis records stored in EHRs from Mass General Brigham in the Boston Metropolitan Area was used for the analyses. Statistical analyses were performed on data from March 2020 to June 2021. Study participants included over 96 thousand patients who had tested positive or negative for COVID-19 and were not hospitalized. Results We identified 33 phenotypes among different age/gender cohorts or time windows that were positively associated with past SARS-CoV-2 infection. All identified phenotypes were newly recorded in patients’ medical records 2 months or longer after a COVID-19 RT-PCR test in non-hospitalized patients regardless of the test result. Among these phenotypes, a new diagnosis record for anosmia and dysgeusia (OR 2.60, 95% CI [1.94–3.46]), alopecia (OR 3.09, 95% CI [2.53–3.76]), chest pain (OR 1.27, 95% CI [1.09–1.48]), chronic fatigue syndrome (OR 2.60, 95% CI [1.22–2.10]), shortness of breath (OR 1.41, 95% CI [1.22–1.64]), pneumonia (OR 1.66, 95% CI [1.28–2.16]), and type 2 diabetes mellitus (OR 1.41, 95% CI [1.22–1.64]) is one of the most significant indicators of a past COVID-19 infection. Additionally, more new phenotypes were found with increased confidence among the cohorts who were younger than 65. Conclusions The findings of this study confirm many of the post-COVID-19 symptoms and suggest that a variety of new diagnoses, including new diabetes mellitus and neurological disorder diagnoses, are more common among those with a history of COVID-19 than those without the infection. Additionally, more than 63% of PASC phenotypes were observed in patients under 65 years of age, pointing out the importance of vaccination to minimize the risk of debilitating post-acute sequelae of COVID-19 among younger adults.

Published
2021

42. Multinational characterization of neurological phenotypes in patients hospitalized with COVID-19

Author

Le, Trang, Gutiérrez-Sacristán, Alba, Son, Jiyeon, Hong, Chuan, South, Andrew, Beaulieu-Jones, Brett, Loh, Ne Hooi Will, Luo, Yuan, Morris, Michele, Ngiam, Kee Yuan, Patel, Lav, Samayamuthu, Malarkodi, Schriver, Emily, Tan, Amelia, Moore, Jason, Cai, Tianxi, Omenn, Gilbert, Avillach, Paul, Kohane, Isaac, Visweswaran, Shyam, Mowery, Danielle, Xia, Zongqi, Aaron, James, Agapito, Giuseppe, Albayrak, Adem, Alessiani, Mario, Amendola, Danilo, Angoulvant, François, Anthony, Li, Aronow, Bruce, Atz, Andrew, Balshi, James, Bell, Douglas, Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Blanco Martínez, Alvar, Boeker, Martin, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence, Bradford, Robert, Brat, Gabriel, Bréant, Stéphane, Brown, Nicholas, Bryant, William, Bucalo, Mauro, Burgun, Anita, Cannataro, Mario, Carmona, Aldo, Caucheteux, Charlotte, Champ, Julien, Chen, Krista, Chen, Jin, Chiovato, Luca, Chiudinelli, Lorenzo, Cimino, James, Colicchio, Tiago, Cormont, Sylvie, Cossin, Sébastien, Craig, Jean, Cruz Bermúdez, Juan Luis, Cruz Rojo, Jaime, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Davoudi, Anahita, Devkota, Batsal, Dubiel, Julien, Esteve, Loic, Fan, Shirley, Follett, Robert, Gaiolla, Paula, Ganslandt, Thomas, García Barrio, Noelia, Garmire, Lana, Gehlenborg, Nils, Geva, Alon, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Hanauer, David, Haverkamp, Christian, He, Bing, Henderson, Darren, Hilka, Martin, Holmes, John, Horki, Petar, Huling, Kenneth, Hutch, Meghan, Issitt, Richard, Jannot, Anne Sophie, Jouhet, Vianney, Kavuluru, Ramakanth, Keller, Mark, Kirchoff, Katie, Klann, Jeffrey, Krantz, Ian, Kraska, Detlef, Krishnamurthy, Ashok, L’yi, Sehi, Leblanc, Judith, Leite, Andressa, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, Lozano-Zahonero, Sarah, Lynch, Kristine, Mahmood, Sadiqa, Maidlow, Sarah, Makoudjou Tchendjou, Adeline, Malovini, Alberto, Mandl, Kenneth, Mao, Chengsheng, Maram, Anupama, Martel, Patricia, Masino, Aaron, Matheny, Michael, Maulhardt, Thomas, Mazzitelli, Maria, Mcduffie, Michael, Mensch, Arthur, Ashraf, Fatima, Milano, Marianna, Minicucci, Marcos, Moal, Bertrand, Moraleda, Cinta, Morris, Jeffrey, Moshal, Karyn, Mousavi, Sajad, Murad, Douglas, Murphy, Shawn, Naughton, Thomas, Neuraz, Antoine, Norman, James, Obeid, Jihad, Okoshi, Marina, Olson, Karen, Orlova, Nina, Ostasiewski, Brian, Palmer, Nathan, Paris, Nicolas, Pedrera Jimenez, Miguel, Pfaff, Emily, Pillion, Danielle, Prokosch, Hans, Prudente, Robson, Quirós González, Víctor, Ramoni, Rachel, Raskin, Maryna, Rieg, Siegbert, Roig Domínguez, Gustavo, Rojo, Pablo, Sáez, Carlos, Salamanca, Elisa, Sandrin, Arnaud, Santos, Janaina, Savino, Maria, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil, Balazote, Pablo Serrano, Serre, Patricia, Serret-Larmande, Arnaud, Shakeri, Zahra, Silvio, Domenick, Sliz, Piotr, Sonday, Charles, Spiridou, Anastasia, Tan, Bryce, Tan, Byorn, Tanni, Suzana, Taylor, Deanne, Terriza-Torres, Ana, Tibollo, Valentina, Tippmann, Patric, Torti, Carlo, Trecarichi, Enrico, Tseng, Yi-Ju, Vallejos, Andrew, Varoquaux, Gael, Vella, Margaret, Vie, Jill-Jênn, Vitacca, Michele, Wagholikar, Kavishwar, Waitman, Lemuel, Wassermann, Demian, Weber, Griffin, William, Yuan, Yehya, Nadir, Zambelli, Alberto, Zhang, Harrison, Zoeller, Daniela, Zucco, Chiara, Unité d'informatique médicale, CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), UFR Médecine [Santé] - Université Paris Cité (UFR Médecine UPCité), Université Paris Cité (UPCité), AS is funded by National Institutes of Health (NIH) National Heart Lung, and Blood Institute (NHLBI) K23HL148394 and L40HL148910, and NIH-National Center for Advancing Translational Sciences (NCATS) UL1TR001420. JM is funded by NIH-National Institute of Allergy and Infectious Disease (NIAD) AI11679. LP is funded by NCATS Clinical and Translational Science Award (CTSA) Number UL1TR002366. GO is funded by NIH National Institute of Environmental Health Sciences (NIEHS) P30ES017885 and National Cancer Institute (NCI) U24CA210967. SV is funded by NIH-National Library of Medicine (NLM) R01LM012095 and NCATS UL1TR001857. DM is funded by NCATS CTSA Number UL1-TR001878. ZX is funded by NIH National Institute of Neurological Disorders and Stroke (NINDS) R01NS098023., Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), National Cancer Institute, École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Université de Paris - UFR Médecine Paris Centre [Santé] (UP Médecine Paris Centre), Université de Paris (UP), University of Pennsylvania Perelman School of Medicine, Harvard Medical School, University of Pittsburgh, Wake Forest School of Medicine, National University Health Systems, Northwestern University, University of Kansas Medical Center, University of Pennsylvania Health System, University of Michigan, University of Kentucky, University Magna Graecia of Catanzaro, INC., Lombardia Region Health System, Universidade Estadual Paulista (UNESP), Assistance Publique-Hôpitaux de Paris, Tan Tock Seng Hospital, University of Cincinnati, Medical University of South Carolina, St. Luke’s University Health Network, David Geffen School of Medicine at UCLA, ASST Papa Giovanni XXIII, University of Pavia, APHP Greater Paris University Hospital, ASST Pavia, Hospital Universitario, University of Freiburg, Informatics and Virtual Environments (DRIVE), IRCCS Ca’ Granda Ospedale Maggiore Policlinico di Milano, University of North Carolina, BIOMERIS (BIOMedical Research Informatics Solutions), CEA, LIRMM, Boston Children’s Hospital, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, University of Alabama at Birmingham, Bordeaux University Hospital/ERIAS-Inserm U1219 BPH, Children’s Hospital of Philadelphia, Inria Centre de Paris, Heidelberg University, and Pain Medicine Boston Children’s Hospital, University of Michigan Medical School, MSHI Medical University of South Carolina, Massachusetts General Hospital, The Children’s Hospital of Philadelphia, University Hospital, Clevy.io, Harvard T.H. Chan School of Public Health, VA Salt Lake City Health Care System, Veterans Affairs Medical Center, PSL Université Paris, School of Biomedical Informatics, Great Ormond Street Hospital for Children, University of Erlangen-Nürnberg, Office of Research and Development, Universitat Politècnica de València, Nurse Department of FMB-Medicine School of Botucatu, FAU Erlangen-Nürnberg, National University Hospital, Chang Gung University, Medical College of Wisconsin, McGill University, Inria Lille, ICS S Maugeri IRCCS, University of Missouri, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPC)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPC), Université Paris Cité - UFR Médecine Paris Centre [Santé] (UPC Médecine Paris Centre), and Université Paris Cité (UPC)

Subjects
Male, Epidemiology, Cross-sectional study, Disease, Severity of Illness Index, MESH: Aged, 80 and over, 0302 clinical medicine, MESH: Child, Prevalence, MESH: COVID-19, 030212 general & internal medicine, Young adult, Child, Aged, 80 and over, MESH: Aged, MESH: Middle Aged, Multidisciplinary, MESH: Infant, Newborn, Middle Aged, MESH: Infant, 3. Good health, Neurology, MESH: Young Adult, Child, Preschool, Medicine, Female, Encephalitis, Adult, MESH: Pandemics, medicine.medical_specialty, Adolescent, Science, Myelitis, MESH: Nervous System Diseases, Article, Young Adult, 03 medical and health sciences, Medical research, MESH: Cross-Sectional Studies, MESH: Severity of Illness Index, Internal medicine, Severity of illness, medicine, Humans, Pandemics, MESH: Prevalence, Aged, MESH: Adolescent, MESH: Humans, business.industry, MESH: Child, Preschool, Infant, Newborn, COVID-19, Infant, MESH: Adult, medicine.disease, MESH: Male, Confidence interval, Cross-Sectional Studies, Relative risk, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Nervous System Diseases, business, MESH: Female, Neurological disorders, 030217 neurology & neurosurgery
Abstract

Made available in DSpace on 2022-04-29T08:35:59Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-12-01 Division of Intramural Research, National Institute of Allergy and Infectious Diseases Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases National Institute of Allergy and Infectious Diseases National Center for Advancing Translational Sciences National Heart, Lung, and Blood Institute National Institute of Environmental Health Sciences U.S. National Library of Medicine National Institute of Neurological Disorders and Stroke Division of Cancer Prevention, National Cancer Institute Neurological complications worsen outcomes in COVID-19. To define the prevalence of neurological conditions among hospitalized patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test in geographically diverse multinational populations during early pandemic, we used electronic health records (EHR) from 338 participating hospitals across 6 countries and 3 continents (January–September 2020) for a cross-sectional analysis. We assessed the frequency of International Classification of Disease code of neurological conditions by countries, healthcare systems, time before and after admission for COVID-19 and COVID-19 severity. Among 35,177 hospitalized patients with SARS-CoV-2 infection, there was an increase in the proportion with disorders of consciousness (5.8%, 95% confidence interval [CI] 3.7–7.8%, pFDR < 0.001) and unspecified disorders of the brain (8.1%, 5.7–10.5%, pFDR < 0.001) when compared to the pre-admission proportion. During hospitalization, the relative risk of disorders of consciousness (22%, 19–25%), cerebrovascular diseases (24%, 13–35%), nontraumatic intracranial hemorrhage (34%, 20–50%), encephalitis and/or myelitis (37%, 17–60%) and myopathy (72%, 67–77%) were higher for patients with severe COVID-19 when compared to those who never experienced severe COVID-19. Leveraging a multinational network to capture standardized EHR data, we highlighted the increased prevalence of central and peripheral neurological phenotypes in patients hospitalized with COVID-19, particularly among those with severe disease. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Department of Biomedical Informatics Harvard Medical School Department of Neurology University of Pittsburgh, Biomedical Science Tower 3, Suite 7014, 3501 5th Avenue Department of Pediatrics Wake Forest School of Medicine Department of Critical Care National University Health Systems Department of Preventive Medicine Northwestern University Department of Biomedical Informatics University of Pittsburgh Department of Surgery National University Health Systems Department of Internal Medicine University of Kansas Medical Center Data Analytics Center University of Pennsylvania Health System Department of Computational Medicine and Bioinformatics University of Michigan Department of Biomedical Informatics University of Kentucky Department of Legal Economic and Social Sciences University Magna Graecia of Catanzaro Health Catalyst INC. Department of Surgery ASST Pavia Lombardia Region Health System Clinical Research Unit of Botucatu Medical School São Paulo State University Pediatric Emergency Department Hôpital Necker-Enfants Malades Assistance Publique-Hôpitaux de Paris National Center for Infectious Diseases Tan Tock Seng Hospital Departments of Biomedical Informatics Pediatrics Cincinnati Children’s Hospital Medical Center University of Cincinnati Department of Pediatrics Medical University of South Carolina Department of Surgery St. Luke’s University Health Network Department of Medicine David Geffen School of Medicine at UCLA UOC Ricerca Innovazione e Brand Reputation ASST Papa Giovanni XXIII Department of Electrical Computer and Biomedical Engineering University of Pavia IT Department Innovation & Data APHP Greater Paris University Hospital I.T. Department ASST Pavia Health Informatics Hospital Universitario, 12 de Octubre Strategy and Transformation Department APHP Greater Paris University Hospital Faculty of Medicine and Medical Center University of Freiburg Digital Research Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children Scientific Direction IRCCS Ca’ Granda Ospedale Maggiore Policlinico di Milano North Carolina Translational and Clinical Sciences (NC TraCS) Institute University of North Carolina BIOMERIS (BIOMedical Research Informatics Solutions) Department of Biomedical Informatics HEGP APHP Greater Paris University Hospital Department of Medical and Surgical Sciences Data Analytics Research Center University Magna Graecia of Catanzaro Department of Anesthesia St. Luke’s University Health Network Université Paris-Saclay Inria CEA INRIA Sophia-Antipolis–ZENITH Team LIRMM Computational Health Informatics Program Boston Children’s Hospital Department of Internal Medicine University of Kentucky Unit of Internal Medicine and Endocrinology Istituti Clinici Scientifici Maugeri SpA SB IRCCS Department of Internal Medicine and Therapeutics University of Pavia Informatics Institute University of Alabama at Birmingham IAM Unit Bordeaux University Hospital/ERIAS-Inserm U1219 BPH Biomedical Informatics Center Medical University of South Carolina Clinical Research Informatics Boston Children’s Hospital Department of Biomedical and Health Informatics Children’s Hospital of Philadelphia SED/SIERRA Inria Centre de Paris Health Information Technology & Services University of Michigan Internal Medicine Department Botucatu Medical School São Paulo State University Heinrich-Lanz-Center for Digital Health University Medicine Mannheim Heidelberg University Department of Anesthesiology Critical Care and Pain Medicine Boston Children’s Hospital Department of Learning Health Sciences University of Michigan Medical School MSHI Medical University of South Carolina Department of Medicine Massachusetts General Hospital Division of Human Genetics Department of Pediatrics The Children’s Hospital of Philadelphia Center for Medical Information and Communication Technology University Hospital Renaissance Computing Institute/Department of Computer Science University of North Carolina Clinical Research Unit Saint Antoine Hospital APHP Greater Paris University Hospital Clevy.io Department of Biostatistics Harvard T.H. Chan School of Public Health VA Informatics and Computing Infrastructure VA Salt Lake City Health Care System MICHR Informatics University of Michigan Laboratory of Informatics and Systems Engineering for Clinical Research Istituti Clinici Scientifici Maugeri SpA SB IRCCS Harvard Catalyst Harvard Medical School Clinical Research Unit Paris Saclay APHP Greater Paris University Hospital Department of Anesthesiology and Critical Care Children’s Hospital of Philadelphia VA Informatics and Computing Infrastructure Tennessee Valley Healthcare System Veterans Affairs Medical Center École Normale Supérieure PSL Université Paris BIG-ARC The University of Texas Health Science Center at Houston School of Biomedical Informatics Pediatric Infectious Disease Department Hospital Universitario, 12 de Octubre Department of Infectious Diseases Great Ormond Street Hospital for Children Department of Neurology Massachusetts General Hospital Internal Medicine Department of Botucatu Medical School São Paulo State University Department of Pediatrics Boston Children’s Hospital Center for Biomedical Informatics Wake Forest School of Medicine Department of Medical Informatics University of Erlangen-Nürnberg Department of Veterans Affairs Office of Research and Development Biomedical Data Science Lab ITACA Institute Universitat Politècnica de València Nurse Department of FMB-Medicine School of Botucatu Management Engineering ASST Pavia Lombardia Region Health System Department of Anesthesiology University Hospital Erlangen FAU Erlangen-Nürnberg Critical Care Medicine Department of Medicine St. Luke’s University Health Network Department of Medicine National University Hospital Department of Information Management Chang Gung University Clinical & Translational Science Institute Medical College of Wisconsin Montréal Neurological Institute McGill University SequeL Inria Lille Respiratory Department ICS S Maugeri IRCCS Department of Health Management and Informatics University of Missouri Department of Oncology ASST Papa Giovanni XXIII Clinical Research Unit of Botucatu Medical School São Paulo State University Internal Medicine Department Botucatu Medical School São Paulo State University Internal Medicine Department of Botucatu Medical School São Paulo State University Division of Intramural Research, National Institute of Allergy and Infectious Diseases: AI11679 Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases: AI11679 National Institute of Allergy and Infectious Diseases: AI11679 National Center for Advancing Translational Sciences: CTSA Award #UL1TR001878 National Center for Advancing Translational Sciences: CTSA Award #UL1TR002366 National Heart, Lung, and Blood Institute: K23HL148394 National Institute of Environmental Health Sciences: P30ES017885 U.S. National Library of Medicine: R01LM012095 National Institute of Neurological Disorders and Stroke: R01NS098023 Division of Cancer Prevention, National Cancer Institute: U24CA210967 National Center for Advancing Translational Sciences: UL1TR001420 National Center for Advancing Translational Sciences: UL1TR001857

Published
2021

43. International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries

Author

Bourgeois, Florence, Gutiérrez-Sacristán, Alba, Keller, Mark, Liu, Molei, Hong, Chuan, Bonzel, Clara-Lea, Tan, Amelia, Aronow, Bruce, Boeker, Martin, Booth, John, Cruz Rojo, Jaime, Devkota, Batsal, García Barrio, Noelia, Gehlenborg, Nils, Geva, Alon, Hanauer, David, Hutch, Meghan, Issitt, Richard, Klann, Jeffrey, Luo, Yuan, Mandl, Kenneth, Mao, Chengsheng, Moal, Bertrand, Moshal, Karyn, Murphy, Shawn, Neuraz, Antoine, Ngiam, Kee Yuan, Omenn, Gilbert, Patel, Lav, Jiménez, Miguel Pedrera, Sebire, Neil, Balazote, Pablo Serrano, Serret-Larmande, Arnaud, South, Andrew, Spiridou, Anastasia, Taylor, Deanne, Tippmann, Patric, Visweswaran, Shyam, Weber, Griffin, Kohane, Isaac, Cai, Tianxi, Avillach, Paul, Cruz-Rojo, Jaime, García-Barrio, Noelia, Pedrera-Jiménez, Miguel, Serrano-Balazote, Pablo, Aaron, James, Agapito, Giuseppe, Albayrak, Adem, Alessiani, Mario, Amendola, Danilo, Angoulvant, François, Anthony, Li Llj, Atz, Andrew, Balshi, James, Beaulieu-Jones, Brett, Bell, Douglas, Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Blanco Martínez, Alvar, Bosari, Silvano, Bradford, Robert, Brat, Gabriel, Bréant, Stéphane, Brown, Nicholas, Bryant, William, Bucalo, Mauro, Burgun, Anita, Cannataro, Mario, Carmona, Aldo, Caucheteux, Charlotte, Champ, Julien, Chen, Krista, Chen, Jin, Chiovato, Luca, Chiudinelli, Lorenzo, Cimino, James, Colicchio, Tiago, Cormont, Sylvie, Cossin, Sébastien, Craig, Jean, Cruz Bermúdez, Juan Luis, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Davoudi, Anahita, Dubiel, Julien, Duvall, Scott, Esteve, Loic, Fan, Shirley, Follett, Robert, Gaiolla, Paula Sa, Ganslandt, Thomas, Garmire, Lana, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Haverkamp, Christian, He, Bing, Henderson, Darren, Hilka, Martin, Holmes, John, Horki, Petar, Huling, Kenneth, Jannot, Anne Sophie, Jouhet, Vianney, Kavuluru, Ramakanth, Kirchoff, Katie, Krantz, Ian, Kraska, Detlef, Krishnamurthy, Ashok, L'Yi, Sehi, Le, Trang, Leblanc, Judith, Leite, Andressa Rr, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Loh, Ne Hooi Will, Lynch, Kristine, Mahmood, Sadiqa, Maidlow, Sarah, Malovini, Alberto, Maram, Anupama, Martel, Patricia, Masino, Aaron, Matheny, Michael, Maulhardt, Thomas, Mazzitelli, Maria, Mcduffie, Michael, Mensch, Arthur, Milano, Marianna, Minicucci, Marcos, Moore, Jason, Moraleda, Cinta, Morris, Jeffrey, Morris, Michele, Mousavi, Sajad, Mowery, Danielle, Murad, Douglas, Naughton, Thomas, Norman, James, Obeid, Jihad, Okoshi, Marina, Olson, Karen, Orlova, Nina, Ostasiewski, Brian, Palmer, Nathan, Paris, Nicolas, Pfaff, Emily, Pillion, Danielle, Prokosch, Hans, Prudente, Robson, Quirós González, Víctor, Ramoni, Rachel, Raskin, Maryna, Rieg, Siegbert, Roig Domínguez, Gustavo, Rojo, Pablo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi, Sandrin, Arnaud, Santos, Janaina Cc, Savino, Maria, Schriver, Emily, Schuettler, Juergen, Scudeller, Luigia, Serre, Patricia, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, Tan, Bryce Wq, Tan, Byorn Wl, Tanni, Suzana, Terriza Torres, Ana, Tibollo, Valentina, Torti, Carlo, Trecarichi, Enrico, Tseng, Yi-Ju, Vallejos, Andrew, Varoquaux, Gael, Vie, Jill-Jênn, Vitacca, Michele, Wagholikar, Kavishwar, Waitman, Lemuel, Wassermann, Demian, William, Yuan, Xia, Zongqi, Yehya, Nadir, Zambelli, Alberto, Zhang, Harrison, Zucco, Chiara, Service d'informatique médicale et biostatistiques [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), UFR Médecine [Santé] - Université Paris Cité (UFR Médecine UPCité), Université Paris Cité (UPCité), Dr Bourgeois was funded by a grant from the Burroughs Wellcome Fund and supported by the Harvard-MIT Center for Regulatory Science. Mr Keller was funded by grant 5T32HG002295-18 from the National Human Genome Research Institute (NHGRI). Dr Aronow was funded by grant U24 HL148865 from the National Heart, Lung, and Blood Institute (NHLBI). Ms García Barrio was supported by grant PI18/00981 from the Carlos III Health Institute. Dr Gehlenborg was funded by grant T15 LM007092 from the NIH National Library of Medicine. Dr Geva was funded by grant K12 HD047349 from the NIH and Eunice Kennedy Shriver National Institute of Child Health and Human Development. Dr Hanauer was funded by grant UL1TR002240 from the National Center for Advancing Translational Sciences (NCATS). Drs Klann and Murphy were funded by grant 5UL1TR001857-05 from the NCATS and grant 5R01HG009174-04 from the NHGRI. Dr Luo was funded by grant R01LM013337 from the NLM. Mr Patel was funded by grant UL1TR002366 from the NCATS. Dr Gutiérrez-Sacristán was funded by grants K23HL148394 and L40HL148910 from the NIH NHLBI and grant UL1TR001420 from the NIH NCATS. Dr Visweswaran was funded by grant R01LM012095 from the NLM and grant UL1TR001857 from the NCATS. Dr Weber was supported by grants UL1TR002541 and UL1TR000005 from the NIH-NCATS, and grant R01LM013345 from the NLM., CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Université de Paris - UFR Médecine Paris Centre [Santé] (UP Médecine Paris Centre), Université de Paris (UP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPC)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPC), Université Paris Cité - UFR Médecine Paris Centre [Santé] (UPC Médecine Paris Centre), and Université Paris Cité (UPC)

Subjects
medicine.medical_specialty, MESH: Pandemics, education, Health Informatics, MESH: Global Health, MESH: Hospitalization, Procalcitonin, 03 medical and health sciences, 0302 clinical medicine, 030225 pediatrics, Internal medicine, MESH: Child, Epidemiology, medicine, Infection control, MESH: COVID-19, MESH: SARS-CoV-2, 030212 general & internal medicine, health care economics and organizations, MESH: Electronic Health Records, Original Investigation, MESH: Adolescent, Disease surveillance, MESH: Humans, business.industry, Research, MESH: Infant, Newborn, MESH: Child, Preschool, Retrospective cohort study, MESH: Retrospective Studies, General Medicine, medicine.disease, MESH: Infant, MESH: Male, 3. Good health, Online Only, Respiratory failure, Viral pneumonia, Cohort, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, MESH: Female
Abstract

This cohort study aims to describe international hospitalization trends and key epidemiological and clinical features of children and youth with COVID-19., Key Points Question What are international trends in hospitalizations for children and youth with SARS-CoV-2, and what are the epidemiological and clinical features of these patients? Findings This cohort study of 671 children and youth found discrete surges in hospitalizations with variable trends and timing across countries. Common complications included cardiac arrhythmias and viral pneumonia, and laboratory findings included elevations in markers of inflammation and abnormalities of coagulation; few children and youth were treated with medications directed specifically at SARS-CoV-2. Meaning These findings suggest large-scale informatics-based approaches used to incorporate electronic health record data across health care systems can provide an efficient source of information to monitor disease activity and define epidemiological and clinical features of pediatric patients hospitalized with SARS-CoV-2 infections., Importance Additional sources of pediatric epidemiological and clinical data are needed to efficiently study COVID-19 in children and youth and inform infection prevention and clinical treatment of pediatric patients. Objective To describe international hospitalization trends and key epidemiological and clinical features of children and youth with COVID-19. Design, Setting, and Participants This retrospective cohort study included pediatric patients hospitalized between February 2 and October 10, 2020. Patient-level electronic health record (EHR) data were collected across 27 hospitals in France, Germany, Spain, Singapore, the UK, and the US. Patients younger than 21 years who tested positive for COVID-19 and were hospitalized at an institution participating in the Consortium for Clinical Characterization of COVID-19 by EHR were included in the study. Main Outcomes and Measures Patient characteristics, clinical features, and medication use. Results There were 347 males (52%; 95% CI, 48.5-55.3) and 324 females (48%; 95% CI, 44.4-51.3) in this study’s cohort. There was a bimodal age distribution, with the greatest proportion of patients in the 0- to 2-year (199 patients [30%]) and 12- to 17-year (170 patients [25%]) age range. Trends in hospitalizations for 671 children and youth found discrete surges with variable timing across 6 countries. Data from this cohort mirrored national-level pediatric hospitalization trends for most countries with available data, with peaks in hospitalizations during the initial spring surge occurring within 23 days in the national-level and 4CE data. A total of 27 364 laboratory values for 16 laboratory tests were analyzed, with mean values indicating elevations in markers of inflammation (C-reactive protein, 83 mg/L; 95% CI, 53-112 mg/L; ferritin, 417 ng/mL; 95% CI, 228-607 ng/mL; and procalcitonin, 1.45 ng/mL; 95% CI, 0.13-2.77 ng/mL). Abnormalities in coagulation were also evident (D-dimer, 0.78 ug/mL; 95% CI, 0.35-1.21 ug/mL; and fibrinogen, 477 mg/dL; 95% CI, 385-569 mg/dL). Cardiac troponin, when checked (n = 59), was elevated (0.032 ng/mL; 95% CI, 0.000-0.080 ng/mL). Common complications included cardiac arrhythmias (15.0%; 95% CI, 8.1%-21.7%), viral pneumonia (13.3%; 95% CI, 6.5%-20.1%), and respiratory failure (10.5%; 95% CI, 5.8%-15.3%). Few children were treated with COVID-19–directed medications. Conclusions and Relevance This study of EHRs of children and youth hospitalized for COVID-19 in 6 countries demonstrated variability in hospitalization trends across countries and identified common complications and laboratory abnormalities in children and youth with COVID-19 infection. Large-scale informatics-based approaches to integrate and analyze data across health care systems complement methods of disease surveillance and advance understanding of epidemiological and clinical features associated with COVID-19 in children and youth.

Published
2021

45. Changes in laboratory value improvement and mortality rates over the course of the pandemic: an international retrospective cohort study of hospitalised patients infected with SARS-CoV-2

Author

Hong, Chuan, Zhang, Harrison, L'Yi, Sehi, Weber, Griffin, Avillach, Paul, Tan, Bryce, Gutiérrez-Sacristán, Alba, Bonzel, Clara-Lea, Palmer, Nathan, Malovini, Alberto, Tibollo, Valentina, Luo, Yuan, Hutch, Meghan, Liu, Molei, Bourgeois, Florence, Bellazzi, Riccardo, Chiovato, Luca, Sanz Vidorreta, Fernando, Le, Trang, Wang, Xuan, Yuan, William, Neuraz, Antoine, Benoit, Vincent, Moal, Bertrand, Morris, Michele, Hanauer, David, Maidlow, Sarah, Wagholikar, Kavishwar, Murphy, Shawn, Estiri, Hossein, Makoudjou, Adeline, Tippmann, Patric, Klann, Jeffery, Follett, Robert, Gehlenborg, Nils, Omenn, Gilbert, Xia, Zongqi, Dagliati, Arianna, Visweswaran, Shyam, Patel, Lav, Mowery, Danielle, Schriver, Emily, Samayamuthu, Malarkodi Jebathilagam, Kavuluru, Ramakanth, Lozano-Zahonero, Sara, Zöller, Daniela, Tan, Amelia, Tan, Byorn, Ngiam, Kee Yuan, Holmes, John, Schubert, Petra, Cho, Kelly, Ho, Yuk-Lam, Beaulieu-Jones, Brett, Pedrera-Jiménez, Miguel, García-Barrio, Noelia, Serrano-Balazote, Pablo, Kohane, Isaac, South, Andrew, Brat, Gabriel, Cai, T, Harvard Medical School [Boston] (HMS), Brigham & Women’s Hospital [Boston] (BWH), Columbia University [New York], National University Hospital [Singapore] (NUH), Istituti Clinici Scientifici Maugeri [Pavia] (IRCCS Pavia - ICS Maugeri), Northwestern University [Chicago, Ill. USA], Università degli Studi di Pavia = University of Pavia (UNIPV), Laboratoire Magmas et Volcans (LMV), Institut national des sciences de l'Univers (INSU - CNRS)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut de Recherche pour le Développement et la société-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS)-Observatoire de Physique du Globe de Clermont-Ferrand (OPGC), Institut national des sciences de l'Univers (INSU - CNRS)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), University of Science and Technology of China [Hefei] (USTC), Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Service d'informatique médicale et biostatistiques [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Northwestern University [Evanston], Harvard T.H. Chan School of Public Health, David Geffen School of Medicine [Los Angeles], University of California [Los Angeles] (UCLA), University of California (UC)-University of California (UC), University of Pennsylvania, École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université de Bordeaux (UB), University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), University of Michigan Medical School [Ann Arbor], University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, University of Michigan System, Massachusetts General Hospital [Boston], University of Freiburg [Freiburg], University of Kansas [Kansas City], Hospital of the University of Pennsylvania (HUP), Perelman School of Medicine, University of Pennsylvania-University of Pennsylvania, University of Kentucky (UK), VA Boston Healthcare System, Hospital Universitario 12 de Octubre [Madrid], and Wake Forest University

Subjects
Hospitalization, SARS-CoV-2, COVID-19, Humans, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, General Medicine, Pandemics, Retrospective Studies
Abstract

ObjectiveTo assess changes in international mortality rates and laboratory recovery rates during hospitalisation for patients hospitalised with SARS-CoV-2 between the first wave (1 March to 30 June 2020) and the second wave (1 July 2020 to 31 January 2021) of the COVID-19 pandemic.Design, setting and participantsThis is a retrospective cohort study of 83 178 hospitalised patients admitted between 7 days before or 14 days after PCR-confirmed SARS-CoV-2 infection within the Consortium for Clinical Characterization of COVID-19 by Electronic Health Record, an international multihealthcare system collaborative of 288 hospitals in the USA and Europe. The laboratory recovery rates and mortality rates over time were compared between the two waves of the pandemic.Primary and secondary outcome measuresThe primary outcome was all-cause mortality rate within 28 days after hospitalisation stratified by predicted low, medium and high mortality risk at baseline. The secondary outcome was the average rate of change in laboratory values during the first week of hospitalisation.ResultsBaseline Charlson Comorbidity Index and laboratory values at admission were not significantly different between the first and second waves. The improvement in laboratory values over time was faster in the second wave compared with the first. The average C reactive protein rate of change was –4.72 mg/dL vs –4.14 mg/dL per day (p=0.05). The mortality rates within each risk category significantly decreased over time, with the most substantial decrease in the high-risk group (47.1% in March–April 2020 vs 30.8% in November 2020 to January 2021, pConclusionsAdmission profiles of patients hospitalised with SARS-CoV-2 infection did not differ greatly between the first and second waves of the pandemic, but there were notable differences in laboratory improvement rates during hospitalisation. Mortality risks among patients with similar risk profiles decreased over the course of the pandemic. The improvement in laboratory values and mortality risk was consistent across multiple countries.

Published
2022

46. Additional file 1 of Accelerating diagnosis of Parkinson’s disease through risk prediction

Author

Yuan, William, Beaulieu-Jones, Brett, Krolewski, Richard, Palmer, Nathan, Veyrat-Follet, Christine, Frau, Francesca, Cohen, Caroline, Bozzi, Sylvie, Cogswell, Meaghan, Kumar, Dinesh, Coulouvrat, Catherine, Leroy, Bruno, Fischer, Tanya Z., S. Pablo Sardi, Chandross, Karen J., Rubin, Lee L., Wills, Anne-Marie, Kohane, Isaac, and Lipnick, Scott L.

Abstract

Additional file 1: Supplementary Methods. Table S1. Included and excluded codes for initial analysis. Table S2. Included and excluded codes for feature tracking and gait/tremor indexed analysis. Table S3. Tremor-only Cohort, PD associated Diagnoses. Table S4: Gait-only Cohort, PD associated Diagnoses.

Published
2021
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