Search

Your search keyword '"Malarkodi Jebathilagam Samayamuthu"' showing total 14 results
Start Over Author "Malarkodi Jebathilagam Samayamuthu" Search Limiters Available in Library Collection
14 results on '"Malarkodi Jebathilagam Samayamuthu"'

1. Characterization of Post–COVID-19 Definitions and Clinical Coding Practices: Longitudinal Study

Author

Monika Maripuri, Andrew Dey, Jacqueline Honerlaw, Chuan Hong, Yuk-Lam Ho, Vidisha Tanukonda, Alicia W Chen, Vidul Ayakulangara Panickan, Xuan Wang, Harrison G Zhang, Doris Yang, Malarkodi Jebathilagam Samayamuthu, Michele Morris, Shyam Visweswaran, Brendin Beaulieu-Jones, Rachel Ramoni, Sumitra Muralidhar, J Michael Gaziano, Katherine Liao, Zongqi Xia, Gabriel A Brat, Tianxi Cai, and Kelly Cho

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

BackgroundPost–COVID-19 condition (colloquially known as “long COVID-19”) characterized as postacute sequelae of SARS-CoV-2 has no universal clinical case definition. Recent efforts have focused on understanding long COVID-19 symptoms, and electronic health record (EHR) data provide a unique resource for understanding this condition. The introduction of the International Classification of Diseases, Tenth Revision (ICD-10) code U09.9 for “Post COVID-19 condition, unspecified” to identify patients with long COVID-19 has provided a method of evaluating this condition in EHRs; however, the accuracy of this code is unclear. ObjectiveThis study aimed to characterize the utility and accuracy of the U09.9 code across 3 health care systems—the Veterans Health Administration, the Beth Israel Deaconess Medical Center, and the University of Pittsburgh Medical Center—against patients identified with long COVID-19 via a chart review by operationalizing the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) definitions. MethodsPatients who were COVID-19 positive with either a U07.1 ICD-10 code or positive polymerase chain reaction test within these health care systems were identified for chart review. Among this cohort, we sampled patients based on two approaches: (1) with a U09.9 code and (2) without a U09.9 code but with a new onset long COVID-19–related ICD-10 code, which allows us to assess the sensitivity of the U09.9 code. To operationalize the long COVID-19 definition based on health agency guidelines, symptoms were grouped into a “core” cluster of 11 commonly reported symptoms among patients with long COVID-19 and an extended cluster that captured all other symptoms by disease domain. Patients having ≥2 symptoms persisting for ≥60 days that were new onset after their COVID-19 infection, with ≥1 symptom in the core cluster, were labeled as having long COVID-19 per chart review. The code’s performance was compared across 3 health care systems and across different time periods of the pandemic. ResultsOverall, 900 patient charts were reviewed across 3 health care systems. The prevalence of long COVID-19 among the cohort with the U09.9 ICD-10 code based on the operationalized WHO definition was between 23.2% and 62.4% across these health care systems. We also evaluated a less stringent version of the WHO definition and the CDC definition and observed an increase in the prevalence of long COVID-19 at all 3 health care systems. ConclusionsThis is one of the first studies to evaluate the U09.9 code against a clinical case definition for long COVID-19, as well as the first to apply this definition to EHR data using a chart review approach on a nationwide cohort across multiple health care systems. This chart review approach can be implemented at other EHR systems to further evaluate the utility and performance of the U09.9 code.

Published
2024
Full Text
View/download PDF

2. Neurological diagnoses in hospitalized COVID-19 patients associated with adverse outcomes: A multinational cohort study

Author

Meghan R. Hutch, Jiyeon Son, Trang T. Le, Chuan Hong, Xuan Wang, Zahra Shakeri Hossein Abad, Michele Morris, Alba Gutiérrez-Sacristán, Jeffrey G. Klann, Anastasia Spiridou, Ashley Batugo, Riccardo Bellazzi, Vincent Benoit, Clara-Lea Bonzel, William A. Bryant, Lorenzo Chiudinelli, Kelly Cho, Priyam Das, Tomás González González, David A. Hanauer, Darren W. Henderson, Yuk-Lam Ho, Ne Hooi Will Loh, Adeline Makoudjou, Simran Makwana, Alberto Malovini, Bertrand Moal, Danielle L. Mowery, Antoine Neuraz, Malarkodi Jebathilagam Samayamuthu, Fernando J. Sanz Vidorreta, Emily R. Schriver, Petra Schubert, Jeffery Talbert, Amelia L. M. Tan, Byorn W. L. Tan, Bryce W. Q. Tan, Valentina Tibollo, Patric Tippman, Guillaume Verdy, William Yuan, Paul Avillach, Nils Gehlenborg, Gilbert S. Omenn, The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), Shyam Visweswaran, Tianxi Cai, Yuan Luo, and Zongqi Xia

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Published
2024

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

Author

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

Subjects
Multisystem inflammatory syndrome, Paediatric inflammatory multisystem syndrome, COVID-19, SARS-CoV-2, Variants, Pediatrics, Medicine (General), R5-920
Abstract

Summary: Background: Multisystem inflammatory syndrome in children (MIS-C) is a severe complication of SARS-CoV-2 infection. It remains unclear how MIS-C phenotypes vary across SARS-CoV-2 variants. We aimed to investigate clinical characteristics and outcomes of MIS-C across SARS-CoV-2 eras. Methods: We performed a multicentre observational retrospective study including seven paediatric hospitals in four countries (France, Spain, U.K., and U.S.). All consecutive confirmed patients with MIS-C hospitalised between February 1st, 2020, and May 31st, 2022, were included. Electronic Health Records (EHR) data were used to calculate pooled risk differences (RD) and effect sizes (ES) at site level, using Alpha as reference. Meta-analysis was used to pool data across sites. Findings: Of 598 patients with MIS-C (61% male, 39% female; mean age 9.7 years [SD 4.5]), 383 (64%) were admitted in the Alpha era, 111 (19%) in the Delta era, and 104 (17%) in the Omicron era. Compared with patients admitted in the Alpha era, those admitted in the Delta era were younger (ES −1.18 years [95% CI −2.05, −0.32]), had fewer respiratory symptoms (RD −0.15 [95% CI −0.33, −0.04]), less frequent non-cardiogenic shock or systemic inflammatory response syndrome (SIRS) (RD −0.35 [95% CI −0.64, −0.07]), lower lymphocyte count (ES −0.16 × 109/uL [95% CI −0.30, −0.01]), lower C-reactive protein (ES −28.5 mg/L [95% CI −46.3, −10.7]), and lower troponin (ES −0.14 ng/mL [95% CI −0.26, −0.03]). Patients admitted in the Omicron versus Alpha eras were younger (ES −1.6 years [95% CI −2.5, −0.8]), had less frequent SIRS (RD −0.18 [95% CI −0.30, −0.05]), lower lymphocyte count (ES −0.39 × 109/uL [95% CI −0.52, −0.25]), lower troponin (ES −0.16 ng/mL [95% CI −0.30, −0.01]) and less frequently received anticoagulation therapy (RD −0.19 [95% CI −0.37, −0.04]). Length of hospitalization was shorter in the Delta versus Alpha eras (−1.3 days [95% CI −2.3, −0.4]). Interpretation: Our study suggested that MIS-C clinical phenotypes varied across SARS-CoV-2 eras, with patients in Delta and Omicron eras being younger and less sick. EHR data can be effectively leveraged to identify rare complications of pandemic diseases and their variation over time. Funding: None.

Published
2023
Full Text
View/download PDF

4. Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort studyResearch in Context

Author

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

Subjects
Post-acute sequelae of SARS-CoV-2, PASC, COVID-19, SARS-CoV-2, Electronic health records, Medicine (General), R5-920
Abstract

Summary: Background: Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes can enhance screening capacities, disease management, and treatment planning. Methods: We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PASC sub-phenotypes. Findings: Our framework characterized seven PASC sub-phenotypes. We estimated that on average 15.7% of the hospitalized COVID-19 patients were likely to suffer from at least one PASC symptom and almost 5.98%, on average, had multiple symptoms. Joint pain and dyspnea had the highest prevalence, with an average prevalence of 5.45% and 4.53%, respectively. Interpretation: We provided a scalable framework to every participating healthcare system for estimating PASC sub-phenotypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented sub-phenotypes across the different systems. Funding: Authors are supported by National Institute of Allergy and Infectious Diseases, National Institute on Aging, National Center for Advancing Translational Sciences, National Medical Research Council, National Institute of Neurological Disorders and Stroke, European Union, National Institutes of Health, National Center for Advancing Translational Sciences.

Published
2023
Full Text
View/download PDF

5. A retrospective cohort analysis leveraging augmented intelligence to characterize long COVID in the electronic health record: A precision medicine framework.

Author

Zachary H Strasser, Arianna Dagliati, Zahra Shakeri Hossein Abad, Jeffrey G Klann, Kavishwar B Wagholikar, Rebecca Mesa, Shyam Visweswaran, Michele Morris, Yuan Luo, Darren W Henderson, Malarkodi Jebathilagam Samayamuthu, Consortium for Clinical Characterization of COVID-19 by EHR (4CE), Gilbert S Omenn, Zongqi Xia, John H Holmes, Hossein Estiri, and Shawn N Murphy

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Physical and psychological symptoms lasting months following an acute COVID-19 infection are now recognized as post-acute sequelae of COVID-19 (PASC). Accurate tools for identifying such patients could enhance screening capabilities for the recruitment for clinical trials, improve the reliability of disease estimates, and allow for more accurate downstream cohort analysis. In this retrospective cohort study, we analyzed the EHR of hospitalized COVID-19 patients across three healthcare systems to develop a pipeline for better identifying patients with persistent PASC symptoms (dyspnea, fatigue, or joint pain) after their SARS-CoV-2 infection. We implemented distributed representation learning powered by the Machine Learning for modeling Health Outcomes (MLHO) to identify novel EHR features that could suggest PASC symptoms outside of typical diagnosis codes. MLHO applies an entropy-based feature selection and boosting algorithms for representation mining. These improved definitions were then used for estimating PASC among hospitalized patients. 30,422 hospitalized patients were diagnosed with COVID-19 across three healthcare systems between March 13, 2020 and February 28, 2021. The mean age of the population was 62.3 years (SD, 21.0 years) and 15,124 (49.7%) were female. We implemented the distributed representation learning technique to augment PASC definitions. These definitions were found to have positive predictive values of 0.73, 0.74, and 0.91 for dyspnea, fatigue, and joint pain, respectively. We estimated that 25 percent (CI 95%: 6-48), 11 percent (CI 95%: 6-15), and 13 percent (CI 95%: 8-17) of hospitalized COVID-19 patients will have dyspnea, fatigue, and joint pain, respectively, 3 months or longer after a COVID-19 diagnosis. We present a validated framework for screening and identifying patients with PASC in the EHR and then use the tool to estimate its prevalence among hospitalized COVID-19 patients.

Published
2023
Full Text
View/download PDF

6. International electronic health record-derived post-acute sequelae profiles of COVID-19 patients

Author

Harrison G. Zhang, Arianna Dagliati, Zahra Shakeri Hossein Abad, Xin Xiong, Clara-Lea Bonzel, Zongqi Xia, Bryce W. Q. Tan, Paul Avillach, Gabriel A. Brat, Chuan Hong, Michele Morris, Shyam Visweswaran, Lav P. Patel, Alba Gutiérrez-Sacristán, David A. Hanauer, John H. Holmes, Malarkodi Jebathilagam Samayamuthu, Florence T. Bourgeois, Sehi L’Yi, Sarah E. Maidlow, Bertrand Moal, Shawn N. Murphy, Zachary H. Strasser, Antoine Neuraz, Kee Yuan Ngiam, Ne Hooi Will Loh, Gilbert S. Omenn, Andrea Prunotto, Lauren A. Dalvin, Jeffrey G. Klann, Petra Schubert, Fernando J. Sanz Vidorreta, Vincent Benoit, Guillaume Verdy, Ramakanth Kavuluru, Hossein Estiri, Yuan Luo, Alberto Malovini, Valentina Tibollo, Riccardo Bellazzi, Kelly Cho, Yuk-Lam Ho, Amelia L. M. Tan, Byorn W. L. Tan, Nils Gehlenborg, Sara Lozano-Zahonero, Vianney Jouhet, Luca Chiovato, Bruce J. Aronow, Emma M. S. Toh, Wei Gen Scott Wong, Sara Pizzimenti, Kavishwar B. Wagholikar, Mauro Bucalo, The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), Tianxi Cai, Andrew M. South, Isaac S. Kohane, and Griffin M. Weber

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract The risk profiles of post-acute sequelae of COVID-19 (PASC) have not been well characterized in multi-national settings with appropriate controls. We leveraged electronic health record (EHR) data from 277 international hospitals representing 414,602 patients with COVID-19, 2.3 million control patients without COVID-19 in the inpatient and outpatient settings, and over 221 million diagnosis codes to systematically identify new-onset conditions enriched among patients with COVID-19 during the post-acute period. Compared to inpatient controls, inpatient COVID-19 cases were at significant risk for angina pectoris (RR 1.30, 95% CI 1.09–1.55), heart failure (RR 1.22, 95% CI 1.10–1.35), cognitive dysfunctions (RR 1.18, 95% CI 1.07–1.31), and fatigue (RR 1.18, 95% CI 1.07–1.30). Relative to outpatient controls, outpatient COVID-19 cases were at risk for pulmonary embolism (RR 2.10, 95% CI 1.58–2.76), venous embolism (RR 1.34, 95% CI 1.17–1.54), atrial fibrillation (RR 1.30, 95% CI 1.13–1.50), type 2 diabetes (RR 1.26, 95% CI 1.16–1.36) and vitamin D deficiency (RR 1.19, 95% CI 1.09–1.30). Outpatient COVID-19 cases were also at risk for loss of smell and taste (RR 2.42, 95% CI 1.90–3.06), inflammatory neuropathy (RR 1.66, 95% CI 1.21–2.27), and cognitive dysfunction (RR 1.18, 95% CI 1.04–1.33). The incidence of post-acute cardiovascular and pulmonary conditions decreased across time among inpatient cases while the incidence of cardiovascular, digestive, and metabolic conditions increased among outpatient cases. Our study, based on a federated international network, systematically identified robust conditions associated with PASC compared to control groups, underscoring the multifaceted cardiovascular and neurological phenotype profiles of PASC.

Published
2022
Full Text
View/download PDF

7. Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort studyResearch in context

Author

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

Subjects
COVID-19, Acute kidney injury, SARS-CoV-2, Chronic kidney disease, Electronic health records, Medicine (General), R5-920
Abstract

Summary: Background: While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking. Methods: A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1–365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021. Findings: Advanced age (HR 2.77, 95%CI 2.53–3.04, p

Published
2023
Full Text
View/download PDF

8. 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

Ramakanth Kavuluru, Xuan Wang, Paul Avillach, Florence Bourgeois, Kee Yuan Ngiam, Gabriel A Brat, Isaac Kohane, Tianxi Cai, Yuk-Lam Ho, Yuan Luo, Harrison G Zhang, Kelly Cho, Vincent Benoit, Antoine Neuraz, Chuan Hong, Sehi L'Yi, Griffin Weber, Bryce W Q Tan, Alba Gutiérrez-Sacristán, Clara-Lea Bonzel, Nathan P Palmer, Alberto Malovini, Valentina Tibollo, Meghan R Hutch, Molei Liu, Riccardo Bellazzi, Luca Chiovato, Fernando J Sanz Vidorreta, Trang T Le, William Yuan, Bertrand Moal, Michele Morris, David A Hanauer, Sarah Maidlow, Kavishwar Wagholikar, Shawn Murphy, Hossein Estiri, Adeline Makoudjou, Patric Tippmann, Jeffery Klann, Robert W Follett, Nils Gehlenborg, Gilbert S Omenn, Zongqi Xia, Arianna Dagliati, Shyam Visweswaran, Lav P Patel, Danielle L Mowery, Emily R Schriver, Malarkodi Jebathilagam Samayamuthu, Sara Lozano-Zahonero, Daniela Zöller, Amelia L M Tan, Byorn W L Tan, John H Holmes, Petra Schubert, Brett K. Beaulieu-Jones, Miguel Pedrera-Jiménez, Noelia García-Barrio, Pablo Serrano-Balazote, and Andrew South

Subjects
Medicine
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 (47.1% in March–April 2020 vs 30.8% in November 2020 to January 2021, p

Published
2022
Full Text
View/download PDF

9. Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study

Author

Jeffrey G Klann, Zachary H Strasser, Meghan R Hutch, Chris J Kennedy, Jayson S Marwaha, Michele Morris, Malarkodi Jebathilagam Samayamuthu, Ashley C Pfaff, Hossein Estiri, Andrew M South, Griffin M Weber, William Yuan, Paul Avillach, Kavishwar B Wagholikar, Yuan Luo, Gilbert S Omenn, Shyam Visweswaran, John H Holmes, Zongqi Xia, Gabriel A Brat, and Shawn N Murphy

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

BackgroundAdmissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. Electronic health record (EHR)–based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. Although the need to improve classification of COVID-19 versus incidental SARS-CoV-2 is well understood, the magnitude of the problems has only been characterized in small, single-center studies. Furthermore, there have been no peer-reviewed studies evaluating methods for improving classification. ObjectiveThe aims of this study are to, first, quantify the frequency of incidental hospitalizations over the first 15 months of the pandemic in multiple hospital systems in the United States and, second, to apply electronic phenotyping techniques to automatically improve COVID-19 hospitalization classification. MethodsFrom a retrospective EHR-based cohort in 4 US health care systems in Massachusetts, Pennsylvania, and Illinois, a random sample of 1123 SARS-CoV-2 PCR-positive patients hospitalized from March 2020 to August 2021 was manually chart-reviewed and classified as “admitted with COVID-19” (incidental) versus specifically admitted for COVID-19 (“for COVID-19”). EHR-based phenotyping was used to find feature sets to filter out incidental admissions. ResultsEHR-based phenotyped feature sets filtered out incidental admissions, which occurred in an average of 26% of hospitalizations (although this varied widely over time, from 0% to 75%). The top site-specific feature sets had 79%-99% specificity with 62%-75% sensitivity, while the best-performing across-site feature sets had 71%-94% specificity with 69%-81% sensitivity. ConclusionsA large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research.

Published
2022
Full Text
View/download PDF

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

Author

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, Isaac S Kohane, Tianxi Cai, Andrew M South, and Gabriel A Brat

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Published
2021
Full Text
View/download PDF

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

Author

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, Isaac S Kohane, Tianxi Cai, Andrew M South, and Gabriel A Brat

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

BackgroundMany 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. ObjectiveIn 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. MethodsUsing 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. ResultsData 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. ConclusionsPatients 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
Full Text
View/download PDF

12. Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study

Author

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

Subjects
General Medicine
Abstract

While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking.A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021.Advanced age (HR 2.77, 95%CI 2.53-3.04, p 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03-4.17, p 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55-5.00, p 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14-1.39, p 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37-0.46, p 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17-1.54, p 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20-1.58, p 0.0001), male sex (HR 1.67, 95%CI 1.45-1.93, p 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80-13.91, p 0.0001), and hypertension (HR 1.22, 95%CI 1.10-1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32-1.67) and 365 days (RR 1.54, 95%CI 1.21-1.96) compared to COVID-19 patients with no AKI.COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery.Authors are supported by various funders, with full details stated in the acknowledgement section.

Published
2022

13. Distinguishing Admissions Specifically for COVID-19 from Incidental SARS-CoV-2 Admissions: A National EHR Research Consortium Study

Author

Jeffrey G, Klann, Zachary H, Strasser, Meghan R, Hutch, Chris J, Kennedy, Jayson S, Marwaha, Michele, Morris, Malarkodi Jebathilagam, Samayamuthu, Ashley C, Pfaff, Hossein, Estiri, Andrew M, South, Griffin M, Weber, William, Yuan, Paul, Avillach, Kavishwar B, Wagholikar, Yuan, Luo, Gilbert S, Omenn, Shyam, Visweswaran, John H, Holmes, Zongqi, Xia, Gabriel A, Brat, and Shawn N, Murphy

Subjects
Hospitalization, SARS-CoV-2, COVID-19, Electronic Health Records, Humans, Article, Retrospective Studies
Abstract

Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. Electronic health record (EHR)-based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. Although the need to improve classification of COVID-19 versus incidental SARS-CoV-2 is well understood, the magnitude of the problems has only been characterized in small, single-center studies. Furthermore, there have been no peer-reviewed studies evaluating methods for improving classification.The aims of this study are to, first, quantify the frequency of incidental hospitalizations over the first 15 months of the pandemic in multiple hospital systems in the United States and, second, to apply electronic phenotyping techniques to automatically improve COVID-19 hospitalization classification.From a retrospective EHR-based cohort in 4 US health care systems in Massachusetts, Pennsylvania, and Illinois, a random sample of 1123 SARS-CoV-2 PCR-positive patients hospitalized from March 2020 to August 2021 was manually chart-reviewed and classified as "admitted with COVID-19" (incidental) versus specifically admitted for COVID-19 ("for COVID-19"). EHR-based phenotyping was used to find feature sets to filter out incidental admissions.EHR-based phenotyped feature sets filtered out incidental admissions, which occurred in an average of 26% of hospitalizations (although this varied widely over time, from 0% to 75%). The top site-specific feature sets had 79%-99% specificity with 62%-75% sensitivity, while the best-performing across-site feature sets had 71%-94% specificity with 69%-81% sensitivity.A large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research.

Published
2022

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

Author

Xuan Wang, Harrison G. Zhang, Xin Xiong, Chuan Hong, Griffin M. Weber, Gabriel A. Brat, Clara-Lea Bonzel, Yuan Luo, Rui Duan, Nathan P. Palmer, Meghan R. Hutch, Alba Gutiérrez-Sacristán, Riccardo Bellazzi, Luca Chiovato, Kelly Cho, Arianna Dagliati, Hossein Estiri, Noelia García-Barrio, Romain Griffier, David A. Hanauer, Yuk-Lam Ho, John H. Holmes, Mark S. Keller, Jeffrey G. Klann MEng, Sehi L'Yi, Sara Lozano-Zahonero, Sarah E. Maidlow, Adeline Makoudjou, Alberto Malovini, Bertrand Moal, Jason H. Moore, Michele Morris, Danielle L. Mowery, Shawn N Murphy, Antoine Neuraz, Kee Yuan Ngiam, Gilbert S. Omenn, Lav P. Patel, Miguel Pedrera-Jiménez, Andrea Prunotto, Malarkodi Jebathilagam Samayamuthu, Fernando J Sanz Vidorreta, Emily R. Schriver, Petra Schubert, Pablo Serrano-Balazote, Andrew M. South, Amelia L.M. Tan, Byorn W.L. Tan, Valentina Tibollo, Patric Tippmann, Shyam Visweswaran, Zongqi Xia, William Yuan, Daniela Zöller, Isaac S. Kohane, Paul Avillach, Zijian Guo, Tianxi Cai, Neuraz, Antoine, Harvard T.H. Chan School of Public Health, Harvard Medical School [Boston] (HMS), Northwestern University [Chicago, Ill. USA], Harvard University [Cambridge], Università degli Studi di Pavia = University of Pavia (UNIPV), Istituti Clinici Scientifici Maugeri [Pavia] (IRCCS Pavia - ICS Maugeri), VA Boston Healthcare System, Massachusetts General Hospital [Boston], Hospital Universitario 12 de Octubre [Madrid], 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), CHU Bordeaux [Bordeaux], University of Michigan [Ann Arbor], University of Michigan System, University of Pennsylvania, University of Freiburg [Freiburg], Cedars-Sinai Medical Center, University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), 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), National University Health System [Singapore] (NUHS), University of Kansas Medical Center [Kansas City, KS, USA], David Geffen School of Medicine [Los Angeles], University of California [Los Angeles] (UCLA), University of California (UC)-University of California (UC), Wake Forest School of Medicine [Winston-Salem], Wake Forest Baptist Medical Center, National University Hospital [Singapore] (NUH), Rutgers, The State University of New Jersey [New Brunswick] (RU), and Rutgers University System (Rutgers)

Subjects
[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Privacy, Electronic Health Records, Humans, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Health Informatics, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], Survival Analysis, Article, Algorithms, Proportional Hazards Models, Computer Science Applications
Abstract

International audience; ObjectiveFor multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information.Materials and Methods For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or can be a single center, corresponding to transfer learning.Results Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations.Conclusions The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results