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

Author

Hong C, Zhang HG, L'Yi S, Weber G, Avillach P, Tan BWQ, Gutiérrez-Sacristán A, Bonzel CL, Palmer NP, Malovini A, Tibollo V, Luo Y, Hutch MR, Liu M, Bourgeois F, Bellazzi R, Chiovato L, Sanz Vidorreta FJ, Le TT, Wang X, Yuan W, Neuraz A, Benoit V, Moal B, Morris M, Hanauer DA, Maidlow S, Wagholikar K, Murphy S, Estiri H, Makoudjou A, Tippmann P, Klann J, Follett RW, Gehlenborg N, Omenn GS, Xia Z, Dagliati A, Visweswaran S, Patel LP, Mowery DL, Schriver ER, Samayamuthu MJ, Kavuluru R, Lozano-Zahonero S, Zöller D, Tan ALM, Tan BWL, Ngiam KY, Holmes JH, Schubert P, Cho K, Ho YL, Beaulieu-Jones BK, Pedrera-Jiménez M, García-Barrio N, Serrano-Balazote P, Kohane I, South A, Brat GA, and Cai T

Subjects
Hospitalization, Humans, Retrospective Studies, SARS-CoV-2, COVID-19, Pandemics
Abstract

Objective: To assess changes in international mortality rates and laboratory recovery rates during hospitalisation for patients hospitalised with SARS-CoV-2 between the first wave (1 March to 30 June 2020) and the second wave (1 July 2020 to 31 January 2021) of the COVID-19 pandemic., Design, Setting and Participants: This is a retrospective cohort study of 83 178 hospitalised patients admitted between 7 days before or 14 days after PCR-confirmed SARS-CoV-2 infection within the Consortium for Clinical Characterization of COVID-19 by Electronic Health Record, an international multihealthcare system collaborative of 288 hospitals in the USA and Europe. The laboratory recovery rates and mortality rates over time were compared between the two waves of the pandemic., Primary and Secondary Outcome Measures: The primary outcome was all-cause mortality rate within 28 days after hospitalisation stratified by predicted low, medium and high mortality risk at baseline. The secondary outcome was the average rate of change in laboratory values during the first week of hospitalisation., Results: Baseline Charlson Comorbidity Index and laboratory values at admission were not significantly different between the first and second waves. The improvement in laboratory values over time was faster in the second wave compared with the first. The average C reactive protein rate of change was -4.72 mg/dL vs -4.14 mg/dL per day (p=0.05). The mortality rates within each risk category significantly decreased over time, with the most substantial decrease in the high-risk group (42.3% in March-April 2020 vs 30.8% in November 2020 to January 2021, p<0.001) and a moderate decrease in the intermediate-risk group (21.5% in March-April 2020 vs 14.3% in November 2020 to January 2021, p<0.001)., Conclusions: Admission profiles of patients hospitalised with SARS-CoV-2 infection did not differ greatly between the first and second waves of the pandemic, but there were notable differences in laboratory improvement rates during hospitalisation. Mortality risks among patients with similar risk profiles decreased over the course of the pandemic. The improvement in laboratory values and mortality risk was consistent across multiple countries., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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

Author

Weber GM, Zhang HG, L'Yi S, Bonzel CL, Hong C, Avillach P, Gutiérrez-Sacristán A, Palmer NP, Tan ALM, Wang X, Yuan W, Gehlenborg N, Alloni A, Amendola DF, Bellasi A, Bellazzi R, Beraghi M, Bucalo M, Chiovato L, Cho K, Dagliati A, Estiri H, Follett RW, García Barrio N, Hanauer DA, Henderson DW, Ho YL, Holmes JH, Hutch MR, Kavuluru R, Kirchoff K, Klann JG, Krishnamurthy AK, Le TT, Liu M, Loh NHW, Lozano-Zahonero S, Luo Y, Maidlow S, Makoudjou A, Malovini A, Martins MR, Moal B, Morris M, Mowery DL, Murphy SN, Neuraz A, Ngiam KY, Okoshi MP, Omenn GS, Patel LP, Pedrera Jiménez M, Prudente RA, Samayamuthu MJ, Sanz Vidorreta FJ, Schriver ER, Schubert P, Serrano Balazote P, Tan BW, Tanni SE, Tibollo V, Visweswaran S, Wagholikar KB, Xia Z, Zöller D, Kohane IS, Cai T, South AM, and Brat GA

Subjects
Adult, Aged, Female, Hospitalization, Hospitals, Humans, Male, Middle Aged, Retrospective Studies, SARS-CoV-2, COVID-19, Pandemics
Abstract

Background: 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., Objective: 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., Methods: 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., Results: 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., Conclusions: 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., (©Griffin M Weber, Harrison G Zhang, Sehi L'Yi, Clara-Lea Bonzel, Chuan Hong, Paul Avillach, Alba Gutiérrez-Sacristán, Nathan P Palmer, Amelia Li Min Tan, Xuan Wang, William Yuan, Nils Gehlenborg, Anna Alloni, Danilo F Amendola, Antonio Bellasi, Riccardo Bellazzi, Michele Beraghi, Mauro Bucalo, Luca Chiovato, Kelly Cho, Arianna Dagliati, Hossein Estiri, Robert W Follett, Noelia García Barrio, David A Hanauer, Darren W Henderson, Yuk-Lam Ho, John H Holmes, Meghan R Hutch, Ramakanth Kavuluru, Katie Kirchoff, Jeffrey G Klann, Ashok K Krishnamurthy, Trang T Le, Molei Liu, Ne Hooi Will Loh, Sara Lozano-Zahonero, Yuan Luo, Sarah Maidlow, Adeline Makoudjou, Alberto Malovini, Marcelo Roberto Martins, Bertrand Moal, Michele Morris, Danielle L Mowery, Shawn N Murphy, Antoine Neuraz, Kee Yuan Ngiam, Marina P Okoshi, Gilbert S Omenn, Lav P Patel, Miguel Pedrera Jiménez, Robson A Prudente, Malarkodi Jebathilagam Samayamuthu, Fernando J Sanz Vidorreta, Emily R Schriver, Petra Schubert, Pablo Serrano Balazote, Byorn WL Tan, Suzana E Tanni, Valentina Tibollo, Shyam Visweswaran, Kavishwar B Wagholikar, Zongqi Xia, Daniela Zöller, The Consortium For Clinical Characterization Of COVID-19 By EHR (4CE), Isaac S Kohane, Tianxi Cai, Andrew M South, Gabriel A Brat. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.10.2021.)

Published
2021
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3. Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data.

Author

Klann JG, Estiri H, Weber GM, Moal B, Avillach P, Hong C, Tan ALM, Beaulieu-Jones BK, Castro V, Maulhardt T, Geva A, Malovini A, South AM, Visweswaran S, Morris M, Samayamuthu MJ, Omenn GS, Ngiam KY, Mandl KD, Boeker M, Olson KL, Mowery DL, Follett RW, Hanauer DA, Bellazzi R, Moore JH, Loh NW, Bell DS, Wagholikar KB, Chiovato L, Tibollo V, Rieg S, Li ALLJ, Jouhet V, Schriver E, Xia Z, Hutch M, Luo Y, Kohane IS, Brat GA, and Murphy SN

Subjects
Hospitalization, Humans, Machine Learning, Prognosis, ROC Curve, Sensitivity and Specificity, COVID-19 classification, Electronic Health Records, Severity of Illness Index
Abstract

Objective: The 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 Methods: Twelve 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., Results: The 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., Discussion: We 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., Conclusions: We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Published
2021
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4. Blockchain-enabled immutable, distributed, and highly available clinical research activity logging system for federated COVID-19 data analysis from multiple institutions

Author

Kuo, Tsung-Ting, Pham, Anh, Edelson, Maxim E, Kim, Jihoon, Chan, Jason, Gupta, Yash, Ohno-Machado, Lucila, Anderson, David M, Balacha, Chandrasekar, Bath, Tyler, Baxter, Sally L, Becker-Pennrich, Andrea, Bell, Douglas S, Bernstam, Elmer V, Ngan, Chau, Day, Michele E, Doctor, Jason N, DuVall, Scott, El-Kareh, Robert, Florian, Renato, Follett, Robert W, Geisler, Benjamin P, Ghigi, Alessandro, Gottlieb, Assaf, Hinske, Ludwig C, Hu, Zhaoxian, Ir, Diana, Jiang, Xiaoqian, Kim, Katherine K, Knight, Tara K, Koola, Jejo D, Lee, Nelson, Mansmann, Ulrich, Matheny, Michael E, Meeker, Daniella, Mou, Zongyang, Neumann, Larissa, Nguyen, Nghia H, Nick, Anderson, Park, Eunice, Paul, Paulina, Pletcher, Mark J, Post, Kai W, Rieder, Clemens, Scherer, Clemens, Schilling, Lisa M, Soares, Andrey, SooHoo, Spencer, Soysal, Ekin, Steven, Covington, Tep, Brian, Toy, Brian, Wang, Baocheng, Wu, Zhen R, Xu, Hua, Yong, Choi, Zheng, Kai, Zhou, Yujia, and Zucker, Rachel A

Subjects
Distributed Computing and Systems Software, Information and Computing Sciences, Emerging Infectious Diseases, Infectious Diseases, Coronaviruses, Humans, Blockchain, COVID-19, Research, electronic health record, blockchain distributed ledger technology, clinical information systems, decision support systems, R2D2 Consortium, Engineering, Medical and Health Sciences, Medical Informatics, Biomedical and clinical sciences, Health sciences, Information and computing sciences
Abstract

ObjectiveWe aimed to develop a distributed, immutable, and highly available cross-cloud blockchain system to facilitate federated data analysis activities among multiple institutions.Materials and methodsWe preprocessed 9166 COVID-19 Structured Query Language (SQL) code, summary statistics, and user activity logs, from the GitHub repository of the Reliable Response Data Discovery for COVID-19 (R2D2) Consortium. The repository collected local summary statistics from participating institutions and aggregated the global result to a COVID-19-related clinical query, previously posted by clinicians on a website. We developed both on-chain and off-chain components to store/query these activity logs and their associated queries/results on a blockchain for immutability, transparency, and high availability of research communication. We measured run-time efficiency of contract deployment, network transactions, and confirmed the accuracy of recorded logs compared to a centralized baseline solution.ResultsThe smart contract deployment took 4.5 s on an average. The time to record an activity log on blockchain was slightly over 2 s, versus 5-9 s for baseline. For querying, each query took on an average less than 0.4 s on blockchain, versus around 2.1 s for baseline.DiscussionThe low deployment, recording, and querying times confirm the feasibility of our cross-cloud, blockchain-based federated data analysis system. We have yet to evaluate the system on a larger network with multiple nodes per cloud, to consider how to accommodate a surge in activities, and to investigate methods to lower querying time as the blockchain grows.ConclusionBlockchain technology can be used to support federated data analysis among multiple institutions.

Published
2023

5. Privacy-protecting, reliable response data discovery using COVID-19 patient observations

Author

Kim, Jihoon, Neumann, Larissa, Paul, Paulina, Day, Michele E, Aratow, Michael, Bell, Douglas S, Doctor, Jason N, Hinske, Ludwig C, Jiang, Xiaoqian, Kim, Katherine K, Matheny, Michael E, Meeker, Daniella, Pletcher, Mark J, Schilling, Lisa M, SooHoo, Spencer, Xu, Hua, Zheng, Kai, Ohno-Machado, Lucila, Anderson, David M, Anderson, Nicholas R, Balacha, Chandrasekar, Bath, Tyler, Baxter, Sally L, Becker-Pennrich, Andrea, Bernstam, Elmer V, Carter, William A, Chau, Ngan, Choi, Yong, Covington, Steven, DuVall, Scott, El-Kareh, Robert, Florian, Renato, Follett, Robert W, Geisler, Benjamin P, Ghigi, Alessandro, Gottlieb, Assaf, Hu, Zhaoxian, Ir, Diana, Knight, Tara K, Koola, Jejo D, Kuo, Tsung-Ting, Lee, Nelson, Mansmann, Ulrich, Mou, Zongyang, Murphy, Robert E, Nguyen, Nghia H, Niedermayer, Sebastian, Park, Eunice, Perkins, Amy M, Post, Kai W, Rieder, Clemens, Scherer, Clemens, Soares, Andrey, Soysal, Ekin, Tep, Brian, Toy, Brian, Wang, Baocheng, Wu, Zhen R, Zhou, Yujia, and Zucker, Rachel A

Subjects
Information and Computing Sciences, Health Services and Systems, Health Sciences, Infectious Diseases, Emerging Infectious Diseases, Coronaviruses, Generic health relevance, Good Health and Well Being, Algorithms, COVID-19, Common Data Elements, Computer Communication Networks, Confidentiality, Electronic Health Records, Female, Humans, Information Storage and Retrieval, Logistic Models, Male, Natural Language Processing, Registries, observational study, common data elements, electronic health record, regression analysis, R2D2 Consortium, Engineering, Medical and Health Sciences, Medical Informatics, Biomedical and clinical sciences, Health sciences, Information and computing sciences
Abstract

ObjectiveTo utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online.Materials and methodsWe developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data.ResultsOur public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems.Discussion and conclusionsWe present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.

Published
2021

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
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7. 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

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

Author

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

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

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

Published
2021
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