Search

Your search keyword '"Vallejos, Andrew"' showing total 11 results
Start Over Author "Vallejos, Andrew" Language english
11 results on '"Vallejos, Andrew"'

1. 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
Full Text
View/download PDF

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

Author

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

Published
2023
Full Text
View/download PDF

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

Author

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

Published
2023
Full Text
View/download PDF

5. 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
Full Text
View/download PDF

6. 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
Full Text
View/download PDF

7. 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
Full Text
View/download PDF

8. Influence of a Hyperglycemic Microenvironment on a Diabetic Versus Healthy Rat Vascular Endothelium Reveals Distinguishable Mechanistic and Phenotypic Responses.

Author

Haspula, Dhanush, Vallejos, Andrew K., Moore, Timothy M., Tomar, Namrata, Dash, Ranjan K., and Hoffmann, Brian R.

Subjects
HYPERGLYCEMIA treatment, LABORATORY rats, VASCULAR endothelium, PHENOTYPES, TYPE 2 diabetes, ENDOTHELIAL cells
Abstract

Hyperglycemia is a critical factor in the development of endothelial dysfunction in type 2 diabetes mellitus (T2DM). Whether hyperglycemic states result in a disruption of similar molecular mechanisms in endothelial cells under both diabetic and non-diabetic states, remains largely unknown. This study aimed to address this gap in knowledge through molecular and functional characterization of primary rat cardiac microvascular endothelial cells (RCMVECs) derived from the T2DM Goto-Kakizaki (GK) rat model in comparison to control Wistar-Kyoto (WKY) in response to a normal (NG) and hyperglycemic (HG) microenvironment. GK and WKY RCMVECs were cultured under NG (4.5 mM) and HG (25 mM) conditions for 3 weeks, followed by tandem mass spectrometry (MS/MS), qPCR, tube formation assay, microplate based fluorimetry, and mitochondrial respiration analyses. Following database matching and filtering (false discovery rate ≤ 5%, scan count ≥ 10), we identified a greater percentage of significantly altered proteins in GK (7.1%, HG versus NG), when compared to WKY (3.5%, HG versus NG) RCMVECs. Further stringent filters (log2ratio of > 2 or < –2, p < 0.05) followed by enrichment and pathway analyses of the MS/MS and quantitative PCR datasets (84 total genes screened), resulted in the identification of several molecular targets involved in angiogenic, redox and metabolic functions that were distinctively altered in GK as compared to WKY RCMVECs following HG exposure. While the expression of thirteen inflammatory and apoptotic genes were significantly increased in GK RCMVECs under HG conditions (p < 0.05), only 2 were significantly elevated in WKY RCMVECs under HG conditions. Several glycolytic enzymes were markedly reduced and pyruvate kinase activity was elevated in GK HG RCMVECs, while in mitochondrial respiratory chain activity was altered. Supporting this, TNFα and phorbol ester (PMA)-induced Reactive Oxygen Species (ROS) production were significantly enhanced in GK HG RCMVECs when compared to baseline levels (p < 0.05). Additionally, PMA mediated increase was the greatest in GK HG RCMVECs (p < 0.05). While HG caused reduction in tube formation assay parameters for WKY RCMVECs, GK RCMVECs exhibited impaired phenotypes under baseline conditions regardless of the glycemic microenvironment. We conclude that hyperglycemic microenvironment caused distinctive changes in the bioenergetics and REDOX pathways in the diabetic endothelium as compared to those observed in a healthy endothelium. [ABSTRACT FROM AUTHOR]

Published
2019
Full Text
View/download PDF

9. Genome-wide map of proximity linkage to renin proximal promoter in rat.

Author

Stodola, Timothy J., Pengyuan Liu, Yong Liu, Vallejos, Andrew K., Geurts, Aron M., Greene, Andrew S., and Mingyu Liang

Subjects
GENE mapping, PROMOTERS (Genetics), RENIN, ENDOTHELIAL cells, X chromosome
Abstract

A challenge to understanding enhancer-gene relationships is that enhancers are not always sequentially close to the gene they regulate. Physical proximity mapping through sequencing can provide an unbiased view of the chromatin close to the proximal promoter of the renin gene (Ren). Our objective was to determine genomic regions that physically interact with the renin proximal promoter, using two different genetic backgrounds, the Dahl salt sensitive and normotensive SS-13BN, which have been shown to have different regulation of plasma renin in vivo. The chromatin conformation capture method with sequencing focused at the Ren proximal promoter in rat-derived cardiac endothelial cells was used. Cells were fixed, chromatin close to the Ren promoter was captured, and fragments were sequenced. The clustering of mapped reads produced a genome-wide map of chromatin in contact with the Ren promoter. The largest number of contacts was found on chromosome 13, the chromosome with Ren, and contacts were found on all other chromosomes except chromosome X. These contacts were significantly enriched with genes positively correlated with Ren expression and with mapped quantitative trait loci associated with blood pressure, cardiovascular, and renal phenotypes. The results were reproducible in an independent biological replicate. The findings reported here represent the first map between a critical cardiovascular gene and physical interacting loci throughout the genome and will provide the basis for several new directions of research. [ABSTRACT FROM AUTHOR]

Published
2018
Full Text
View/download PDF

10. InterMOD: integrated data and tools for the unification of model organism research.

Author

Sullivan J, Karra K, Moxon SA, Vallejos A, Motenko H, Wong JD, Aleksic J, Balakrishnan R, Binkley G, Harris T, Hitz B, Jayaraman P, Lyne R, Neuhauser S, Pich C, Smith RN, Trinh Q, Cherry JM, Richardson J, Stein L, Twigger S, Westerfield M, Worthey E, and Micklem G

Subjects
Animals, Databases, Factual, Databases, Genetic, Genomics methods, Genome, Models, Genetic
Abstract

Model organisms are widely used for understanding basic biology, and have significantly contributed to the study of human disease. In recent years, genomic analysis has provided extensive evidence of widespread conservation of gene sequence and function amongst eukaryotes, allowing insights from model organisms to help decipher gene function in a wider range of species. The InterMOD consortium is developing an infrastructure based around the InterMine data warehouse system to integrate genomic and functional data from a number of key model organisms, leading the way to improved cross-species research. So far including budding yeast, nematode worm, fruit fly, zebrafish, rat and mouse, the project has set up data warehouses, synchronized data models, and created analysis tools and links between data from different species. The project unites a number of major model organism databases, improving both the consistency and accessibility of comparative research, to the benefit of the wider scientific community.

Published
2013
Full Text
View/download PDF

11. Low cost, scalable proteomics data analysis using Amazon's cloud computing services and open source search algorithms.

Author

Halligan BD, Geiger JF, Vallejos AK, Greene AS, and Twigger SN

Subjects
Cluster Analysis, Databases, Protein, Internet, Algorithms, Proteomics methods, Software
Abstract

One of the major difficulties for many laboratories setting up proteomics programs has been obtaining and maintaining the computational infrastructure required for the analysis of the large flow of proteomics data. We describe a system that combines distributed cloud computing and open source software to allow laboratories to set up scalable virtual proteomics analysis clusters without the investment in computational hardware or software licensing fees. Additionally, the pricing structure of distributed computing providers, such as Amazon Web Services, allows laboratories or even individuals to have large-scale computational resources at their disposal at a very low cost per run. We provide detailed step-by-step instructions on how to implement the virtual proteomics analysis clusters as well as a list of current available preconfigured Amazon machine images containing the OMSSA and X!Tandem search algorithms and sequence databases on the Medical College of Wisconsin Proteomics Center Web site ( http://proteomics.mcw.edu/vipdac ).

Published
2009
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results