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

Justin T Reese; Hannah Blau; Elena Casiraghi; Timothy Bergquist; Johanna J Loomba; Tiffany J Callahan; Bryan Laraway; Corneliu Antonescu; Ben Coleman; Michael Gargano; Kenneth J Wilkins; Luca Cappelletti; Tommaso Fontana; Nariman Ammar; Blessy Antony; T M Murali; J Harry Caufield; Guy Karlebach; 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; N3C Consortium; RECOVER Consortium

doi:10.1016/j.ebiom.2022.104413

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

EBioMedicine. 2023 Jan:87:104413. doi: 10.1016/j.ebiom.2022.104413. Epub 2022 Dec 21.

Authors

Justin T Reese¹, Hannah Blau², Elena Casiraghi³, Timothy Bergquist⁴, Johanna J Loomba⁵, Tiffany J Callahan⁶, Bryan Laraway⁷, Corneliu Antonescu⁸, Ben Coleman², Michael Gargano², Kenneth J Wilkins⁹, Luca Cappelletti¹⁰, Tommaso Fontana¹⁰, Nariman Ammar¹¹, Blessy Antony¹², T M Murali¹², J Harry Caufield¹, Guy Karlebach², Julie A McMurry⁷, Andrew Williams¹³, Richard Moffitt¹⁴, Jineta Banerjee⁴, Anthony E Solomonides¹⁵, Hannah Davis¹⁶, Kristin Kostka¹⁷, Giorgio Valentini¹⁰, David Sahner¹⁸, Christopher G Chute¹⁹, Charisse Madlock-Brown¹¹, Melissa A Haendel⁷, Peter N Robinson²⁰; N3C Consortium; RECOVER Consortium

Collaborators

Heidi Spratt, Shyam Visweswaran, Joseph Eugene Flack 4th, Yun Jae Yoo, Davera Gabriel, G Caleb Alexander, Hemalkumar B Mehta, Feifan Liu, Robert T Miller, Rachel Wong, Elaine L Hill, Lorna E Thorpe, Jasmin Divers

Affiliations

¹ Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
² The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA.
³ Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy.
⁴ Sage Bionetworks, Seattle, WA, USA.
⁵ The Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA.
⁶ Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
⁷ Departments of Biomedical Informatics and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
⁸ University of Arizona - Banner Health, Phoenix, AZ, USA.
⁹ Biostatistics Program, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA.
¹⁰ AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy.
¹¹ Health Science Center, University of Tennessee, Memphis, TN, USA.
¹² Department of Computer Science, Virginia Tech, Blacksburg, VA, USA.
¹³ Tufts Medical Center Clinical and Translational Science Institute, Tufts Medical Center, Boston, MA, USA; Tufts University School of Medicine, Institute for Clinical Research and Health Policy Studies, Boston, MA, USA; Northeastern University, OHDSI Center at the Roux Institute, Boston, MA, USA.
¹⁴ Department of Biomedical Informatics and Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY, USA.
¹⁵ HealthSystem Research Institute, NorthShore University, Evanston, IL, USA.
¹⁶ Patient-Led Research Collaborative, NY, USA.
¹⁷ Northeastern University, OHDSI Center at the Roux Institute, Boston, MA, USA.
¹⁸ Axle Informatics, Rockville, MD, USA.
¹⁹ Schools of Medicine, Public Health and Nursing, Johns Hopkins University, Baltimore, MD, USA.
²⁰ The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA. Electronic address: [email protected].

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.

Keywords: COVID-19; Human Phenotype Ontology; Long COVID; Machine learning; Precision medicine; Semantic similarity.

MeSH terms

COVID-19*
Disease Progression
Humans
Post-Acute COVID-19 Syndrome*
SARS-CoV-2

Abstract

MeSH terms

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