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3. 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

5. 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

6. Shared genetic basis between genetic generalized epilepsy and background electroencephalographic oscillations

Author

Stevelink, Remi, Luykx, Jurjen J, Lin, Bochao D, Leu, Costin, Lal, Dennis, Smith, Alexander W, Schijven, Dick, Carpay, Johannes A, Rademaker, Koen, Baldez, Roiza A Rodrigues, Devinsky, Orrin, Braun, Kees PJ, Jansen, Floor E, Smit, Dirk JA, Koeleman, Bobby PC, Abou‐Khalil, Bassel, Auce, Pauls, Avbersek, Andreja, Bahlo, Melanie, Balding, David J, Bast, Thomas, Baum, Larry, Becker, Albert J, Becker, Felicitas, Berghuis, Bianca, Berkovic, Samuel F, Boysen, Katja E, Bradfield, Jonathan P, Brody, Lawrence C, Buono, Russell J, Campbell, Ellen, Cascino, Gregory D, Catarino, Claudia B, Cavalleri, Gianpiero L, Cherny, Stacey S, Chinthapalli, Krishna, Coffey, Alison J, Compston, Alastair, Coppola, Antonietta, Cossette, Patrick, Craig, John J, de Haan, Gerrit‐Jan, De Jonghe, Peter, de Kovel, Carolien GF, Delanty, Norman, Depondt, Chantal, Dlugos, Dennis J, Doherty, Colin P, Elger, Christian E, Eriksson, Johan G, Ferraro, Thomas N, Feucht, Martha, Francis, Ben, Franke, Andre, French, Jacqueline A, Freytag, Saskia, Gaus, Verena, Geller, Eric B, Gieger, Christian, Glauser, Tracy, Glynn, Simon, Goldstein, David B, Gui, Hongsheng, Guo, Youling, Haas, Kevin F, Hakonarson, Hakon, Hallmann, Kerstin, Haut, Sheryl, Heinzen, Erin L, Helbig, Ingo, Hengsbach, Christian, Hjalgrim, Helle, Iacomino, Michele, Ingason, Andrés, Jamnadas‐Khoda, Jennifer, Johnson, Michael R, Kälviäinen, Reetta, Kantanen, Anne‐Mari, Kasperavičiūte, Dalia, Trenite, Dorothee Kasteleijn‐Nolst, Kirsch, Heidi E, Knowlton, Robert C, Krause, Roland, Krenn, Martin, Kunz, Wolfram S, Kuzniecky, Ruben, Kwan, Patrick, Lau, Yu‐Lung, Lehesjoki, Anna‐Elina, Lerche, Holger, Lieb, Wolfgang, Lindhout, Dick, Lo, Warren D, Lopes‐Cendes, Iscia, Lowenstein, Daniel H, Malovini, Alberto, Marson, Anthony G, Mayer, Thomas, McCormack, Mark, and Mills, James L

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Genetics, Brain Disorders, Clinical Research, Human Genome, Neurodegenerative, Epilepsy, 2.1 Biological and endogenous factors, Aetiology, Neurological, Adult, Algorithms, Beta Rhythm, Cohort Studies, Databases, Factual, Electroencephalography, Epilepsy, Generalized, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Mendelian Randomization Analysis, Risk Assessment, Theta Rhythm, beta power, EEG, generalized epilepsy, GGE, oscillations, PRS, International League Against Epilepsy Consortium on Complex Epilepsies, Epi25 Collaborative, Neurology & Neurosurgery, Clinical sciences
Abstract

ObjectiveParoxysmal epileptiform abnormalities on electroencephalography (EEG) are the hallmark of epilepsies, but it is uncertain to what extent epilepsy and background EEG oscillations share neurobiological underpinnings. Here, we aimed to assess the genetic correlation between epilepsy and background EEG oscillations.MethodsConfounding factors, including the heterogeneous etiology of epilepsies and medication effects, hamper studies on background brain activity in people with epilepsy. To overcome this limitation, we compared genetic data from a genome-wide association study (GWAS) on epilepsy (n = 12 803 people with epilepsy and 24 218 controls) with that from a GWAS on background EEG (n = 8425 subjects without epilepsy), in which background EEG oscillation power was quantified in four different frequency bands: alpha, beta, delta, and theta. We replicated our findings in an independent epilepsy replication dataset (n = 4851 people with epilepsy and 20 428 controls). To assess the genetic overlap between these phenotypes, we performed genetic correlation analyses using linkage disequilibrium score regression, polygenic risk scores, and Mendelian randomization analyses.ResultsOur analyses show strong genetic correlations of genetic generalized epilepsy (GGE) with background EEG oscillations, primarily in the beta frequency band. Furthermore, we show that subjects with higher beta and theta polygenic risk scores have a significantly higher risk of having generalized epilepsy. Mendelian randomization analyses suggest a causal effect of GGE genetic liability on beta oscillations.SignificanceOur results point to shared biological mechanisms underlying background EEG oscillations and the susceptibility for GGE, opening avenues to investigate the clinical utility of background EEG oscillations in the diagnostic workup of epilepsy.

Published
2021

7. 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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8. 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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9. BPIFB4 and its longevity-associated haplotype protect from cardiac ischemia in humans and mice

Author

Cattaneo, Monica, Aleksova, Aneta, Malovini, Alberto, Avolio, Elisa, Thomas, Anita, Alvino, Valeria Vincenza, Kilcooley, Michael, Pieronne-Deperrois, Marie, Ouvrard-Pascaud, Antoine, Maciag, Anna, Spinetti, Gaia, Kussauer, Sophie, Lemcke, Heiko, Skorska, Anna, Vasudevan, Praveen, Castiglione, Stefania, Raucci, Angela, David, Robert, Richard, Vincent, Beltrami, Antonio Paolo, Madeddu, Paolo, and Puca, Annibale Alessandro

Published
2023
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10. 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

12. SurvMaximin: Robust federated approach to transporting survival risk prediction models

Author

Wang, Xuan, Zhang, Harrison G., Xiong, Xin, Hong, Chuan, Weber, Griffin M., Brat, Gabriel A., Bonzel, Clara-Lea, Luo, Yuan, Duan, Rui, Palmer, Nathan P., Hutch, Meghan R., Gutiérrez-Sacristán, Alba, Bellazzi, Riccardo, Chiovato, Luca, Cho, Kelly, Dagliati, Arianna, Estiri, Hossein, García-Barrio, Noelia, Griffier, Romain, Hanauer, David A., Ho, Yuk-Lam, Holmes, John H., Keller, Mark S., Klann MEng, Jeffrey G., L'Yi, Sehi, Lozano-Zahonero, Sara, Maidlow, Sarah E., Makoudjou, Adeline, Malovini, Alberto, Moal, Bertrand, Moore, Jason H., Morris, Michele, Mowery, Danielle L., Murphy, Shawn N, Neuraz, Antoine, Yuan Ngiam, Kee, Omenn, Gilbert S., Patel, Lav P., Pedrera-Jiménez, Miguel, Prunotto, Andrea, Jebathilagam Samayamuthu, Malarkodi, Sanz Vidorreta, Fernando J, Schriver, Emily R., Schubert, Petra, Serrano-Balazote, Pablo, South, Andrew M., Tan, Amelia L.M., Tan, Byorn W.L., Tibollo, Valentina, Tippmann, Patric, Visweswaran, Shyam, Xia, Zongqi, Yuan, William, Zöller, Daniela, Kohane, Isaac S., Avillach, Paul, Guo, Zijian, and Cai, Tianxi

Published
2022
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14. International electronic health record-derived post-acute sequelae profiles of COVID-19 patients

Author

Zhang, Harrison G., Dagliati, Arianna, Shakeri Hossein Abad, Zahra, Xiong, Xin, Bonzel, Clara-Lea, Xia, Zongqi, Tan, Bryce W. Q., Avillach, Paul, Brat, Gabriel A., Hong, Chuan, Morris, Michele, Visweswaran, Shyam, Patel, Lav P., Gutiérrez-Sacristán, Alba, Hanauer, David A., Holmes, John H., Samayamuthu, Malarkodi Jebathilagam, Bourgeois, Florence T., L’Yi, Sehi, Maidlow, Sarah E., Moal, Bertrand, Murphy, Shawn N., Strasser, Zachary H., Neuraz, Antoine, Ngiam, Kee Yuan, Loh, Ne Hooi Will, Omenn, Gilbert S., Prunotto, Andrea, Dalvin, Lauren A., Klann, Jeffrey G., Schubert, Petra, Vidorreta, Fernando J. Sanz, Benoit, Vincent, Verdy, Guillaume, Kavuluru, Ramakanth, Estiri, Hossein, Luo, Yuan, Malovini, Alberto, Tibollo, Valentina, Bellazzi, Riccardo, Cho, Kelly, Ho, Yuk-Lam, Tan, Amelia L. M., Tan, Byorn W. L., Gehlenborg, Nils, Lozano-Zahonero, Sara, Jouhet, Vianney, Chiovato, Luca, Aronow, Bruce J., Toh, Emma M. S., Wong, Wei Gen Scott, Pizzimenti, Sara, Wagholikar, Kavishwar B., Bucalo, Mauro, Cai, Tianxi, South, Andrew M., Kohane, Isaac S., and Weber, Griffin M.

Published
2022
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20. Transfer Learning for Urban Landscape Clustering and Correlation with Health Indexes

Author

Bellazzi, Riccardo, Caldarone, Alessandro Aldo, Pala, Daniele, Franzini, Marica, Malovini, Alberto, Larizza, Cristiana, Casella, Vittorio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pagán, José, editor, Mokhtari, Mounir, editor, Aloulou, Hamdi, editor, Abdulrazak, Bessam, editor, and Cabrera, María Fernanda, editor

Published
2019
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23. Assessing the Potential of GPT-4 to Annotate Medical Notes Across Different Languages and Countries: A Model Evaluation Study

Author

Saad Menezes, Maria Clara, primary, Hoffmann, Alexander F., additional, Tan, Amelia LM, additional, Nalbandyan, Marine, additional, Omenn, Gilbert S., additional, Mazzotti, Diego, additional, Hernández-Arango, Alejandro, additional, Visweswaran, Shyam, additional, Venkatesh, Shruthi, additional, Mandl, Kenneth D., additional, Bourgeois, Florence T., additional, Lee, James WK, additional, Makmur, Andrew, additional, Hanauer, David A., additional, Semanik, Michael G., additional, Kerivan, Lauren T., additional, Hill, Terra, additional, Forero, Julian, additional, Restrepo Castro, Carlos Andrés CR, additional, Vigna, Matteo, additional, Ceriana, Piero, additional, Abu-el-rub, Noor, additional, Avillach, Paul, additional, Bellazzi, Riccardo, additional, Callaci, Thomas, additional, Gutiérrez-Sacristán, Alba, additional, Malovini, Alberto, additional, Mathew, Jomol P., additional, Morris, Michele, additional, Murthy, Venkatesh, additional, Parimbelli, Enea, additional, Patel, Lav P., additional, Sáez, Carlos, additional, Samayamuthu, Malarkodi Jebathilagam, additional, Thompson, Jeffrey, additional, Tibollo, Valentina, additional, Xia, Zongqi, additional, Kohane, Isaac, additional, and anon, The Consortium for Clinical, additional

Published
2024
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24. 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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25. High-Flow Oxygen Therapy During Exercise Training in Patients With Chronic Obstructive Pulmonary Disease and Chronic Hypoxemia: A Multicenter Randomized Controlled Trial

Author

Vitacca, Michele, Paneroni, Mara, Zampogna, Elisabetta, Visca, Dina, Carlucci, Annalisa, Cirio, Serena, Banf, Paolo, Pappacoda, Gabriele, Trianni, Ludovico, Brogneri, Antonio, Belli, Stefano, Paracchini, Elena, Aliani, Maria, Spinelli, Vito, Gigliotti, Francesco, Lanini, Barbara, Lazzeri, Marta, Clini, Enrico M., Malovini, Alberto, and Ambrosino, Nicolino

Subjects
Anoxemia -- Analysis, Chronic obstructive lung disease -- Analysis, Patient satisfaction -- Analysis, Health
Abstract

Objective. The study aimed to evaluate whether high-flow oxygen therapy (HFOT) during training was more effective than oxygen in improving exercise capacity in hypoxemic chronic obstructive pulmonary disease (COPD). Methods. A total of 171 patients with COPD and chronic hypoxemia were consecutively recruited in 8 rehabilitation hospitals in a randomized controlled trial. Cycle-ergometer exercise training was used in 20 supervised sessions at iso inspiratory oxygen fraction in both groups. Pre- and post-training endurance time (Tlim), 6-minute walking distance (6MWD), respiratory and limb muscle strength, arterial blood gases, Barthel Index, Barthel Dyspnea Index, COPD Assessment Test, Maugeri Respiratory Failure questionnaire, and patient satisfaction were evaluated. Results. Due to 15.4% and 24.1% dropout rates, 71 and 66 patients were analyzed in HFOT and Venturi mask (V-mask) groups, respectively. Exercise capacity significantly improved after training in both groups with similar patient satisfaction. Between-group difference in post-training improvement in 6MWD (mean: 17.14 m; 95% CI = 0.87 to 33.43 m) but not in Tlim (mean: 141.85 seconds; 95% CI = -18.72 to 302.42 seconds) was significantly higher in HFOT. The minimal clinically important difference of Tlim was reached by 47% of patients in the V-mask group and 56% of patients in the HFOT group, whereas the minimal clinically important difference of 6MWD was reached by 51% of patients in the V-mask group and 69% of patients in the HFOT group, respectively. Conclusion. In patients with hypoxemic COPD, exercise training is effective in improving exercise capacity. Impact Statement. The addition of HFOT during exercise training is not more effective than oxygen through V-mask in improving endurance time, the primary outcome, whereas it is more effective in improving walking distance., Pulmonary rehabilitation, including aerobic exercise training, has stronger evidence of effectiveness to improve exercise capacity, dyspnea, and health-related quality of life (HRQL) than almost all other therapies in patients with [...]

Published
2020
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27. Interplay Between Genetic Substrate, QTc Duration, and Arrhythmia Risk in Patients With Long QT Syndrome

Author

Mazzanti, Andrea, Maragna, Riccardo, Vacanti, Gaetano, Monteforte, Nicola, Bloise, Raffaella, Marino, Maira, Braghieri, Lorenzo, Gambelli, Patrick, Memmi, Mirella, Pagan, Eleonora, Morini, Massimo, Malovini, Alberto, Ortiz, Martin, Sacilotto, Luciana, Bellazzi, Riccardo, Monserrat, Lorenzo, Napolitano, Carlo, Bagnardi, Vincenzo, and Priori, Silvia G.

Published
2018
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28. Clinical characteristics of acute lacosamide poisoning: Pavia Poison Control Centre experience.

Author

Bernasconi, Lucia, Schicchi, Azzurra, Petrolini, Valeria M., Malovini, Alberto, Lonati, Davide, Fassio, Federico, Buscaglia, Eleonora, Scaravaggi, Giulia, Crema, Francesca, and Locatelli, Carlo A.

Subjects
POISONS, VIMPAT, POISONING, VALPROIC acid, SYMPTOMS, DROWSINESS, DEEP brain stimulation
Abstract

Aims: Lacosamide is a third‐generation antiepileptic drug used as adjunctive therapy for partial seizures. Since its approval in 2008 very few cases of lacosamide overdose have been described in literature. The aim of our study was to evaluate clinical characteristics of acute lacosamide poisoning. Methods: A retrospective observational study was performed including all cases of acute lacosamide poisoning referred to Pavia Poison Control Centre from January 2012 to December 2021. For each patient age, sex, ingested dose, coingestants, clinical manifestations, treatment and outcome were collected. Results: A total of 31 subjects (median age 39 years, [interquartile range: 26.5–46.5]; females 22/31) were included. The median lacosamide ingested dose was 1500 mg [650–2800]. In 35.5% of cases lacosamide was the single ingested substance, while in 64.5% coingestants were also present. Coingestants varied from a minimum of 1 to a maximum of 3, with the more common being benzodiazepines and valproic acid. Clinical manifestations were present in 87% patients the most common were: vomiting (29%); seizures (29%), coma (25.8%), drowsiness (25.8%), confusion (12.9%), agitation (12.9%), tachycardia (12.9%), tremors (9.7%), bradycardia (9.7%), headache (6.5%) and hypertension (3.2%). The median lacosamide ingested dose was significantly higher in patients that experienced coma compared to patient who did not (2800 vs. 800 mg; P =.0082). Orotracheal intubation was necessary in 32.3% of patients. All patients fully recovered. Conclusion: Lacosamide acute overdose may lead to a severe clinical picture. Dentral nervous system symptoms predominated, particularly seizures and coma occurred in a high percentage of cases. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Health-related quality of life and clinical complexity of a real-life cohort of patients with advanced HR+/HER2– breast cancer treated with CDK4/6 inhibitors and endocrine therapy

Author

Tagliaferri, Barbara, primary, Mollica, Ludovica, additional, Palumbo, Raffella, additional, Leli, Claudia, additional, Malovini, Alberto, additional, Terzaghi, Matteo, additional, Quaquarini, Erica, additional, Teragni, Cristina, additional, Maccarone, Stefano, additional, Premoli, Andrea, additional, and Sottotetti, Federico, additional

Published
2023
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33. Author Correction: A rare genetic variant of BPIFB4 predisposes to high blood pressure via impairment of nitric oxide signaling

Author

Vecchione, Carmine, Villa, Francesco, Carrizzo, Albino, Spinelli, Chiara Carmela, Damato, Antonio, Ambrosio, Mariateresa, Ferrario, Anna, Madonna, Michele, Uccellatore, Annachiara, Lupini, Silvia, Maciag, Anna, Ryskalin, Larisa, Milanesi, Luciano, Frati, Giacomo, Sciarretta, Sebastiano, Bellazzi, Riccardo, Genovese, Stefano, Ceriello, Antonio, Auricchio, Alberto, Malovini, Alberto, and Puca, Annibale Alessandro

Published
2019
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35. Efficacy and activity of treatments after progression from palbociclib plus endocrine therapy in patients with HR+/HER2– metastatic breast cancer: a prospective, monocentric study

Author

Palumbo, Raffaella, primary, Quaquarini, Erica, additional, Saltalamacchia, Giuseppe, additional, Malovini, Alberto, additional, Lapidari, Pietro, additional, Tagliaferri, Barbara, additional, Mollica, Ludovica, additional, Teragni, Cristina Maria, additional, Barletta, Chiara, additional, Locati, Laura Deborah, additional, and Sottotetti, Federico, additional

Published
2023
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36. 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
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38. 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
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39. The prognostic role of variations in tumour markers (CEA, CA15.3) in patients with metastatic breast cancer treated with CDK4/6 inhibitors

Author

Sottotetti, Federico, primary, Ferraris, Elisa, additional, Tagliaferri, Barbara, additional, Palumbo, Raffaella, additional, Quaquarini, Erica, additional, Teragni, Cristina, additional, Balletti, Emanuela, additional, Leli, Claudia, additional, Premoli, Andrea, additional, Mollica, Ludovica, additional, Puglisi, Silvia, additional, Sardi, Silvia, additional, Malovini, Alberto, additional, Pedrazzoli, Paolo, additional, and Bernardo, Antonio, additional

Published
2022
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40. A process mining pipeline to characterise COVID-19 patients’ trajectories and identify relevant temporal phenotypes from EHR data.

Author

Dagliati, Arianna, Gatta, Roberto, Malovini, Alberto, Tibollo, Valentina, Sacchi, Lucia, Cascini, Fidelia, Chiovato, Luca, Bellazzi, Riccardo, Fidelia Cascini (ORCID:0000-0001-6499-0734), Dagliati, Arianna, Gatta, Roberto, Malovini, Alberto, Tibollo, Valentina, Sacchi, Lucia, Cascini, Fidelia, Chiovato, Luca, Bellazzi, Riccardo, and Fidelia Cascini (ORCID:0000-0001-6499-0734)

Abstract

The impact of the COVID-19 pandemic involved the disruption of the processes of care and the need for immediately effective re-organizational procedures. In the context of digital health, it is of paramount importance to determine how a specific patients’ population reflects into the healthcare dynamics of the hospital, to investigate how patients’ sub-group/strata respond to the different care processes, in order to generate novel hypotheses regarding the most effective healthcare strategies. We present an analysis pipeline based on the heterogeneous collected data aimed at identifying the most frequent healthcare processes patterns, jointly analyzing them with demographic and physiological disease trajectories, and stratify the observed cohort on the basis of the mined patterns. This is a process-oriented pipeline which integrates process mining algorithms, and trajectory mining by topological data analyses and pseudo time approaches. Data was collected for 1,179 COVID-19 positive patients, hospitalized at the Italian Hospital “Istituti Clinici Salvatore Maugeri” in Lombardy, integrating different sources including text admission letters, EHR and hospital infrastructure data. We identified five temporal phenotypes, from laboratory values trajectories, which are characterized by statistically significant different death risk estimates. The process mining algorithms allowed splitting the data in sub-cohorts as function of the pandemic waves and of the temporal trajectories showing statistically significant differences in terms of events characteristics.

Published
2022

42. The Genetic Landscape of Renal Complications in Type 1 Diabetes

Author

Sandholm, Niina, Van Zuydam, Natalie, Ahlqvist, Emma, Juliusdottir, Thorhildur, Deshmukh, Harshal A., Rayner, N. William, Di Camillo, Barbara, Forsblom, Carol, Fadista, Joao, Ziemek, Daniel, Salem, Rany M., Hiraki, Linda T., Pezzolesi, Marcus, Trégouët, David, Dahlström, Emma, Valo, Erkka, Oskolkov, Nikolay, Ladenvall, Claes, Marcovecchio, M. Loredana, Cooper, Jason, Sambo, Francesco, Malovini, Alberto, Manfrini, Marco, McKnight, Amy Jayne, Lajer, Maria, Harjutsalo, Valma, Gordin, Daniel, Parkkonen, Maija, Lyssenko, Valeriya, McKeigue, Paul M., Rich, Stephen S., Brosnan, Mary Julia, Fauman, Eric, Bellazzi, Riccardo, Rossing, Peter, Hadjadj, Samy, Krolewski, Andrzej, Paterson, Andrew D., Hirschhorn, Joel N., Maxwell, Alexander P., Cobelli, Claudio, Colhoun, Helen M., Groop, Leif, McCarthy, Mark I., Groop, Per-Henrik, Sandholm, N., Van Zuydam, N., Ahlqvist, E., Juliusdottir, T., Deshmukh, H.A., Di Camillo, B., Forsblom, C., Fadista, J., Ziemek, D., Salem, R.M., Hiraki, L.T., Pezzolesi, M., Trégouët, D., Dahlström, E., Valo, E., Oskolkov, N., Ladenvall, C., Marcovecchio, M.L., Cooper, J., Sambo, F., Malovini, A., Manfrini, M., McKnight, A. J., Lajer, M., V, Gordin, D., Parkkonen, M., Tuomilehto, J., V, McKeigue, P.M., Rich, S.S., Brosnan, M.J., Fauman, E., Bellazzi, R., Rossing, P., Hadjadj, S., Krolewski, A., Paterson, A.D., Florez, J.C., Hirschhorn, J.N., Maxwell, A.P., Dunger, D., Cobelli, C., Colhoun, H.M., Groop, L., McCarthy, M.I., and Groop, P.-H.

Published
2017
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45. Clinical and Biological Variables Influencing Outcome in Patients with Advanced Non-Small Cell Lung Cancer (NSCLC) Treated with Anti-PD-1/PD-L1 Antibodies: A Prospective Multicentre Study

Author

Quaquarini, Erica, primary, Sottotetti, Federico, additional, Agustoni, Francesco, additional, Pozzi, Emma, additional, Malovini, Alberto, additional, Teragni, Cristina Maria, additional, Palumbo, Raffaella, additional, Saltalamacchia, Giuseppe, additional, Tagliaferri, Barbara, additional, Balletti, Emanuela, additional, Rinaldi, Pietro, additional, Canino, Costanza, additional, Pedrazzoli, Paolo, additional, and Bernardo, Antonio, additional

Published
2022
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47. Neurological Diagnoses in Hospitalized COVID-19 Patients Associated With Adverse Outcomes: A Multinational Cohort Study

Author

Hutch, Meghan R., primary, Son, Jiyeon, additional, Le, Trang T., additional, Hong, Chuan, additional, Wang, Xuan, additional, Shakeri Hossein Abad, Zahra, additional, Morris, Michele, additional, Gutiérrez-Sacristán, Alba, additional, Klann, Jeffrey G., additional, Spiridou, Anastasia, additional, Bellazzi, Riccardo, additional, Benoit, Vincent, additional, Bonzel, Clara-Lea, additional, Bryant, William A., additional, Cho, Kelly, additional, Das, Priyam, additional, Hanauer, David A., additional, Henderson, Darren W., additional, Ho, Yuk-Lam, additional, Loh, Ne Hooi Will, additional, Makoudjou, Adeline, additional, Malovini, Alberto, additional, Moal, Bertrand, additional, Mowery, Danielle L., additional, Samayamuthu, Malarkodi Jebathilagam, additional, Sanz Vidorreta, Fernando J., additional, Schriver, Emily R., additional, Schubert, Petra, additional, Talbert, Jeffrey, additional, Tan, Amelia LM, additional, Tan, Byorn WL, additional, Tan, Bryce WQ, additional, Tibollo, Valentina, additional, Yuan, William, additional, Avillach, Paul, additional, Gehlenborg, Nils, additional, Omenn, Gilbert S., additional, Visweswaran, Shyam, additional, Cai, Tianxi, additional, Luo, Yuan, additional, and Xia, Zongqi, additional

Published
2022
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49. 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
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50. Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study

Author

Weber, Griffin M, Zhang, Harrison G, L'Yi, Sehi, Bonzel, Clara-Lea, Hong, Chuan, Avillach, Paul, Gutiérrez-Sacristán, Alba, Palmer, Nathan P, Tan, Amelia Li Min, Wang, Xuan, Yuan, William, Gehlenborg, Nils, Alloni, Anna, Amendola, Danilo F, Bellasi, Antonio, Bellazzi, Riccardo, Beraghi, Michele, Bucalo, Mauro, Chiovato, Luca, Cho, Kelly, Dagliati, Arianna, Estiri, Hossein, Follett, Robert W, García Barrio, Noelia, Hanauer, David A, Henderson, Darren W, Ho, Yuk-Lam, Holmes, John H, Hutch, Meghan R, Kavuluru, Ramakanth, Kirchoff, Katie, Klann, Jeffrey G, Krishnamurthy, Ashok K, Le, Trang T, Liu, Molei, Loh, Ne Hooi Will, Lozano-Zahonero, Sara, Luo, Yuan, Maidlow, Sarah, Makoudjou, Adeline, Malovini, Alberto, Martins, Marcelo Roberto, Moal, Bertrand, Morris, Michele, Mowery, Danielle L, Murphy, Shawn N, Neuraz, Antoine, Ngiam, Kee Yuan, Okoshi, Marina P, Omenn, Gilbert S, Patel, Lav P, Pedrera Jiménez, Miguel, Prudente, Robson A, Samayamuthu, Malarkodi Jebathilagam, Sanz Vidorreta, Fernando J, Schriver, Emily R, Schubert, Petra, Serrano Balazote, Pablo, Tan, Byorn WL, Tanni, Suzana E, Tibollo, Valentina, Visweswaran, Shyam, Wagholikar, Kavishwar B, Xia, Zongqi, Zöller, Daniela, Kohane, Isaac S, Cai, Tianxi, South, Andrew M, and Brat, Gabriel A

Subjects
Adult, Male, medicine.medical_specialty, Coronavirus disease 2019 (COVID-19), business.industry, SARS-CoV-2, COVID-19, Health Informatics, Retrospective cohort study, Middle Aged, Corrigenda and Addenda, Hospitals, Hospitalization, Family medicine, medicine, Humans, Female, business, Pandemics, Aged, Retrospective Studies
Abstract

Many countries have experienced 2 predominant waves of COVID-19-related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic.In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic.Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19.Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain.Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.

Published
2021

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