Journal article
Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes
EBioMedicine, Vol.87, pp.104413-104413
01/01/2023
DOI: 10.1016/j.ebiom.2022.104413
PMCID: PMC9769411
PMID: 36563487
Abstract
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.
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.
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.
Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.
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.
Details
- Title: Subtitle
- Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes
- Creators
- Justin T Reese - Lawrence Berkeley National LaboratoryHannah Blau - Jackson LaboratoryElena Casiraghi - Lawrence Berkeley National LaboratoryTimothy Bergquist - Sage BionetworksJohanna J Loomba - University of VirginiaTiffany J Callahan - Columbia University Irving Medical CenterBryan Laraway - University of Colorado Anschutz Medical CampusCorneliu Antonescu - Banner HealthBen Coleman - Jackson LaboratoryMichael Gargano - Jackson LaboratoryKenneth J Wilkins - National Institute of Diabetes and Digestive and Kidney DiseasesLuca Cappelletti - University of MilanTommaso Fontana - University of MilanNariman Ammar - University of Tennessee Health Science CenterBlessy Antony - Virginia TechT M Murali - Virginia TechJ Harry Caufield - Lawrence Berkeley National LaboratoryGuy Karlebach - Jackson LaboratoryJulie A McMurry - University of Colorado Anschutz Medical CampusAndrew Williams - Tufts Medical CenterRichard Moffitt - Stony Brook UniversityJineta Banerjee - Sage BionetworksAnthony E Solomonides - NorthShore University HealthSystemHannah Davis - Patient-Led Research CollaborativeKristin Kostka - Northeastern UniversityGiorgio Valentini - University of MilanDavid Sahner - American Axle & Manufacturing (United States)Christopher G Chute - Johns Hopkins UniversityCharisse Madlock-Brown - University of Tennessee Health Science CenterMelissa A Haendel - University of Colorado Anschutz Medical CampusPeter N Robinson - Jackson LaboratoryRECOVER Consortium
- Resource Type
- Journal article
- Publication Details
- EBioMedicine, Vol.87, pp.104413-104413
- DOI
- 10.1016/j.ebiom.2022.104413
- PMID
- 36563487
- PMCID
- PMC9769411
- NLM abbreviation
- EBioMedicine
- ISSN
- 2352-3964
- eISSN
- 2352-3964
- Grant note
- DOI: 10.13039/100000002, name: NIH; DOI: 10.13039/100000015, name: Department of Energy
- Language
- English
- Date published
- 01/01/2023
- Academic Unit
- Nursing
- Record Identifier
- 9984446984202771
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