1. Generalisable long COVID subtypes: Findings from the NIH N3C and RECOVER programmes
- Author
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Reese, Justin T, Blau, Hannah, Casiraghi, Elena, Bergquist, Timothy, Loomba, Johanna J, Callahan, Tiffany J, Laraway, Bryan, Antonescu, Corneliu, Coleman, Ben, Gargano, Michael, Wilkins, Kenneth J, Cappelletti, Luca, Fontana, Tommaso, Ammar, Nariman, Antony, Blessy, Murali, TM, Caufield, J Harry, Karlebach, Guy, McMurry, Julie A, Williams, Andrew, Moffitt, Richard, Banerjee, Jineta, Solomonides, Anthony E, Davis, Hannah, Kostka, Kristin, Valentini, Giorgio, Sahner, David, Chute, Christopher G, Madlock-Brown, Charisse, Haendel, Melissa A, Robinson, Peter N, Consortium, N3C, Spratt, Heidi, Visweswaran, Shyam, Flack, Joseph Eugene, Yoo, Yun Jae, Gabriel, Davera, Alexander, G Caleb, Mehta, Hemalkumar B, Liu, Feifan, Miller, Robert T, Wong, Rachel, Hill, Elaine L, Consortium, RECOVER, Thorpe, Lorna E, and Divers, Jasmin
- Subjects
Biomedical and Clinical Sciences ,Clinical Sciences ,Emerging Infectious Diseases ,Infectious Diseases ,Machine Learning and Artificial Intelligence ,Networking and Information Technology R&D (NITRD) ,Precision Medicine ,Coronaviruses ,Good Health and Well Being ,Humans ,COVID-19 ,Disease Progression ,Post-Acute COVID-19 Syndrome ,SARS-CoV-2 ,N3C Consortium ,RECOVER Consortium ,Human Phenotype Ontology ,Long COVID ,Machine learning ,Precision medicine ,Semantic similarity ,Public Health and Health Services ,Clinical sciences ,Epidemiology - Abstract
BackgroundStratification 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.MethodsWe 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.FindingsWe 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.InterpretationSemantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.FundingNIH (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.
- Published
- 2023