Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.
Document Type
Article
Publication Date
1-1-2023
Original Citation
Reese J,
Blau H,
Casiraghi E,
Bergquist T,
Loomba J,
Callahan T,
Laraway B,
Antonescu C,
Coleman B,
Gargano M,
Wilkins K,
Cappelletti L,
Fontana T,
Ammar N,
Antony B,
Murali T,
Caufield J,
Karlebach G,
McMurry J,
Williams A,
Moffitt R,
Banerjee J,
Solomonides A,
Davis H,
Kostka K,
Valentini G,
Sahner D,
Chute C,
Madlock-Brown C,
Haendel M,
Robinson P.
Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes. EBioMedicine. 2022;87:104413.
Keywords
JGM, Humans, COVID-19, Disease Progression, Post-Acute COVID-19 Syndrome, SARS-CoV-2
JAX Source
EBioMedicine. 2022;87:104413.
ISSN
2352-3964
PMID
36563487
DOI
https://doi.org/10.1016/j.ebiom.2022.104413
Grant
The authors acknowledge the following funding sources: National In- stitutes of Health grant CD2H NCATS U24 TR002306 (J.T.R., C.C., H.B., N.A., B.L., K.K., M.A.H., P.N.R.). National Institutes of Health grant NHLBI RECOVER Agreement OT2HL161847-01 (J.T.R., K.K., B.L., M.A.H., P.N.R.). National Institutes of Health grant Office of the Director Monarch Initiative R24 OD011883 (M.A.H., P.N.R.). National Institutes of Health grant NHGRI Center of Excellence in Genome Sciences RM1 HG010860 (M.A.H., P.N.R.). National Institutes of Health grant NCATS UL1TR003015 (B.A., T.M.M.). National Institutes of Health grant NCATS KL2TR003016 (B.A., T.M.M.). Director, Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy Contract No. DE-AC02-05CH11231 (J.T.R.). Donald A. Roux Family Fund at the Jackson Laboratory (P.N.R.). Marsico Family at the University of Colorado Anschutz (M.A.H.).
Abstract
BACKGROUND: Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.
METHODS: We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning.
FINDINGS: We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems.
INTERPRETATION: Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.
FUNDING: NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.