Evolving phenotypes of non-hospitalized patients that indicate long COVID.

Electronic health records Machine learning Phenotypes Post-acute sequelae of SARS-CoV-2

Journal

BMC medicine
ISSN: 1741-7015
Titre abrégé: BMC Med
Pays: England
ID NLM: 101190723

Informations de publication

Date de publication:
27 09 2021
Historique:
received: 28 04 2021
accepted: 01 09 2021
entrez: 27 9 2021
pubmed: 28 9 2021
medline: 16 10 2021
Statut: epublish

Résumé

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. 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. 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. 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.

Sections du résumé

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.

Identifiants

pubmed: 34565368
doi: 10.1186/s12916-021-02115-0
pii: 10.1186/s12916-021-02115-0
pmc: PMC8474909
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

249

Subventions

Organisme : British Heart Foundation
ID : FS/19/52/34563
Pays : United Kingdom
Organisme : NINDS NIH HHS
ID : R01 NS098023
Pays : United States
Organisme : NHGRI NIH HHS
ID : 3U01HG008685-05S2
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001420
Pays : United States
Organisme : NHLBI NIH HHS
ID : K23 HL148394
Pays : United States
Organisme : NHLBI NIH HHS
ID : L40 HL148910
Pays : United States
Organisme : NHGRI NIH HHS
ID : U01 HG008685
Pays : United States
Organisme : Medical Research Council
ID : MC_PC_18034
Pays : United Kingdom
Organisme : NLM NIH HHS
ID : T15 LM007092
Pays : United States
Organisme : NIA NIH HHS
ID : RF1 AG074372
Pays : United States
Organisme : NLM NIH HHS
ID : T32 LM012203
Pays : United States

Investigateurs

James R Aaron (JR)
Giuseppe Agapito (G)
Adem Albayrak (A)
Mario Alessiani (M)
Danilo F Amendola (DF)
Li L L J Anthony (LLLJ)
Bruce J Aronow (BJ)
Fatima Ashraf (F)
Andrew Atz (A)
Paul Avillach (P)
James Balshi (J)
Brett K Beaulieu-Jones (BK)
Douglas S Bell (DS)
Antonio Bellasi (A)
Riccardo Bellazzi (R)
Vincent Benoit (V)
Michele Beraghi (M)
José Luis Bernal Sobrino (JLB)
Mélodie Bernaux (M)
Romain Bey (R)
Alvar Blanco Martínez (AB)
Martin Boeker (M)
Clara-Lea Bonzel (CL)
John Booth (J)
Silvano Bosari (S)
Florence T Bourgeois (FT)
Robert L Bradford (RL)
Gabriel A Brat (GA)
Stéphane Bréant (S)
Nicholas W Brown (NW)
William A Bryant (WA)
Mauro Bucalo (M)
Anita Burgun (A)
Tianxi Cai (T)
Mario Cannataro (M)
Aldo Carmona (A)
Charlotte Caucheteux (C)
Julien Champ (J)
Jin Chen (J)
Krista Chen (K)
Luca Chiovato (L)
Lorenzo Chiudinelli (L)
Kelly Cho (K)
James J Cimino (JJ)
Tiago K Colicchio (TK)
Sylvie Cormont (S)
Sébastien Cossin (S)
Jean B Craig (JB)
Juan Luis Cruz Bermúdez (JLC)
Jaime Cruz Rojo (JC)
Arianna Dagliati (A)
Mohamad Daniar (M)
Christel Daniel (C)
Anahita Davoudi (A)
Batsal Devkota (B)
Julien Dubiel (J)
Loic Esteve (L)
Hossein Estiri (H)
Shirley Fan (S)
Robert W Follett (RW)
Paula S A Gaiolla (PSA)
Thomas Ganslandt (T)
Noelia García Barrio (NG)
Lana X Garmire (LX)
Nils Gehlenborg (N)
Alon Geva (A)
Tobias Gradinger (T)
Alexandre Gramfort (A)
Romain Griffier (R)
Nicolas Griffon (N)
Olivier Grisel (O)
Alba Gutiérrez-Sacristán (A)
David A Hanauer (DA)
Christian Haverkamp (C)
Bing He (B)
Darren W Henderson (DW)
Martin Hilka (M)
John H Holmes (JH)
Chuan Hong (C)
Petar Horki (P)
Kenneth M Huling (KM)
Meghan R Hutch (MR)
Richard W Issitt (RW)
Anne Sophie Jannot (AS)
Vianney Jouhet (V)
Mark S Keller (MS)
Katie Kirchoff (K)
Jeffrey G Klann (JG)
Isaac S Kohane (IS)
Ian D Krantz (ID)
Detlef Kraska (D)
Ashok K Krishnamurthy (AK)
Sehi L'Yi (S)
Trang T Le (TT)
Judith Leblanc (J)
Andressa R R Leite (ARR)
Guillaume Lemaitre (G)
Leslie Lenert (L)
Damien Leprovost (D)
Molei Liu (M)
Ne Hooi Will Loh (NHW)
Sara Lozano-Zahonero (S)
Yuan Luo (Y)
Kristine E Lynch (KE)
Sadiqa Mahmood (S)
Sarah Maidlow (S)
Alberto Malovini (A)
Kenneth D Mandl (KD)
Chengsheng Mao (C)
Anupama Maram (A)
Patricia Martel (P)
Aaron J Masino (AJ)
Maria Mazzitelli (M)
Arthur Mensch (A)
Marianna Milano (M)
Marcos F Minicucci (MF)
Bertrand Moal (B)
Jason H Moore (JH)
Cinta Moraleda (C)
Jeffrey S Morris (JS)
Michele Morris (M)
Karyn L Moshal (KL)
Sajad Mousavi (S)
Danielle L Mowery (DL)
Douglas A Murad (DA)
Shawn N Murphy (SN)
Thomas P Naughton (TP)
Antoine Neuraz (A)
Kee Yuan Ngiam (KY)
James B Norman (JB)
Jihad Obeid (J)
Marina P Okoshi (MP)
Karen L Olson (KL)
Gilbert S Omenn (GS)
Nina Orlova (N)
Brian D Ostasiewski (BD)
Nathan P Palmer (NP)
Nicolas Paris (N)
Lav P Patel (LP)
Miguel Pedrera Jimenez (MP)
Emily R Pfaff (ER)
Danielle Pillion (D)
Hans U Prokosch (HU)
Robson A Prudente (RA)
Víctor Quirós González (VQ)
Rachel B Ramoni (RB)
Maryna Raskin (M)
Siegbert Rieg (S)
Gustavo Roig Domínguez (GR)
Pablo Rojo (P)
Carlos Sáez (C)
Elisa Salamanca (E)
Malarkodi J Samayamuthu (MJ)
Arnaud Sandrin (A)
Janaina C C Santos (JCC)
Maria Savino (M)
Emily R Schriver (ER)
Petra Schubert (P)
Juergen Schuettler (J)
Luigia Scudeller (L)
Neil J Sebire (NJ)
Pablo Serrano Balazote (PS)
Patricia Serre (P)
Arnaud Serret-Larmande (A)
Zahra Shakeri (Z)
Domenick Silvio (D)
Piotr Sliz (P)
Jiyeon Son (J)
Charles Sonday (C)
Andrew M South (AM)
Anastasia Spiridou (A)
Amelia L M Tan (ALM)
Bryce W Q Tan (BWQ)
Byorn W L Tan (BWL)
Suzana E Tanni (SE)
Deanne M Taylor (DM)
Ana I Terriza Torres (AI)
Valentina Tibollo (V)
Patric Tippmann (P)
Carlo Torti (C)
Enrico M Trecarichi (EM)
Yi-Ju Tseng (YJ)
Andrew K Vallejos (AK)
Gael Varoquaux (G)
Margaret E Vella (ME)
Guillaume Verdy (G)
Jill-Jênn Vie (JJ)
Shyam Visweswaran (S)
Michele Vitacca (M)
Kavishwar B Wagholikar (KB)
Lemuel R Waitman (LR)
Xuan Wang (X)
Demian Wassermann (D)
Griffin M Weber (GM)
Zongqi Xia (Z)
Nadir Yehya (N)
William Yuan (W)
Alberto Zambelli (A)
Harrison G Zhang (HG)
Daniel Zoeller (D)
Chiara Zucco (C)

Commentaires et corrections

Type : UpdateOf

Informations de copyright

© 2021. The Author(s).

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Auteurs

Hossein Estiri (H)

Laboratory of Computer Science, Massachusetts General Hospital, Boston, MA, 02114, USA. hestiri@mgh.harvard.edu.
Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA. hestiri@mgh.harvard.edu.

Zachary H Strasser (ZH)

Laboratory of Computer Science, Massachusetts General Hospital, Boston, MA, 02114, USA.
Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

Gabriel A Brat (GA)

Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

Yevgeniy R Semenov (YR)

Department of Dermatology, Massachusetts General Hospital, Boston, MA, 02114, USA.

Chirag J Patel (CJ)

Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

Shawn N Murphy (SN)

Laboratory of Computer Science, Massachusetts General Hospital, Boston, MA, 02114, USA.
Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA.
Research Information Science and Computing, Mass General Brigham, Boston, MA, USA.

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