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Evolving phenotypes of non-hospitalized patients that indicate long COVID

Authors :
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)
Université Paris Cité (UPCité)
Source :
BMC Medicine, BMC Medicine, BioMed Central, 2021, 19 (1), pp.249. ⟨10.1186/s12916-021-02115-0⟩, medRxiv, BMC Medicine, 2021, 19 (1), pp.249. ⟨10.1186/s12916-021-02115-0⟩, BMC Medicine, Vol 19, Iss 1, Pp 1-10 (2021)
Publication Year :
2021
Publisher :
HAL CCSD, 2021.

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.

Details

Language :
English
ISSN :
17417015
Database :
OpenAIRE
Journal :
BMC Medicine, BMC Medicine, BioMed Central, 2021, 19 (1), pp.249. ⟨10.1186/s12916-021-02115-0⟩, medRxiv, BMC Medicine, 2021, 19 (1), pp.249. ⟨10.1186/s12916-021-02115-0⟩, BMC Medicine, Vol 19, Iss 1, Pp 1-10 (2021)
Accession number :
edsair.doi.dedup.....331acf24d2effa6d3a620111ac1d5cda
Full Text :
https://doi.org/10.1186/s12916-021-02115-0⟩