Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.

Justin T Reese
Hannah Blau, The Jackson Laboratory
Elena Casiraghi
Timothy Bergquist
Johanna J Loomba
Tiffany J Callahan
Bryan Laraway
Corneliu Antonescu
Ben D Coleman, The Jackson Laboratory
Michael Gargano, The Jackson Laboratory
Kenneth J Wilkins
Luca Cappelletti
Tommaso Fontana
Nariman Ammar
Blessy Antony
T M Murali
J Harry Caufield
Guy Karlebach, The Jackson Laboratory
Julie A McMurry
Andrew Williams
Richard Moffitt
Jineta Banerjee
Anthony E Solomonides
Hannah Davis
Kristin Kostka
Giorgio Valentini
David Sahner
Christopher G Chute
Charisse Madlock-Brown
Melissa A Haendel
Peter N. Robinson, The Jackson Laboratory


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.