ArticleEBioMedicine2023
Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.
Article in EBioMedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 121 papers, 2 of them syntheses that pooled it.
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Who cites it
121 citing papers in PubMed, 2 syntheses or guidelines pooled it, 174 citations in OpenAlex.
- Interventions for the management of long covid (post-covid condition): living systematic review.BMJ (Clinical research ed.) · 2024Pooled it
- Definition and measurement of post-COVID-19 conditions in real-world practice: a global systematic literature review.BMJ open · 2024Pooled it
- Post-Acute COVID-19 Symptoms Clusters and Work Status.Occupational Health · 2026Article
- Subjective and objective working memory deficits in the post-acute phase of COVID-19 in a clinical trial population.Brain, behavior, & immunity - health · 2026Article
- Posterior Circulation Ischemic Stroke Occurring After ChAdOx1 nCoV-19/AZD1222 Vaccination Without Evidence of Vaccine-Induced Immune Thrombotic Thrombocytopenia: A Case Report and Focused Narrative Review.Journal of clinical medicine · 2026Review
- SSRI prescription during acute COVID-19 and risk of Long COVID symptoms and conditions among patients with depression.medRxiv : the preprint server for health sciences · 2026Article
- Metformin and Severe Post-COVID-19 Outcomes Among Individuals with Diabetes Mellitus.medRxiv : the preprint server for health sciences · 2026Article
- GLP Medications and Severe Post-COVID-19 Outcomes Among Individuals with Type 2 Diabetes Mellitus.medRxiv : the preprint server for health sciences · 2026Article
- Article
- T-cell repertoire response in individuals with post-acute sequelae of COVID-19.bioRxiv : the preprint server for biology · 2026Article
- Factors associated with long COVID in sub-Saharan Africa: a scoping review.BMC infectious diseases · 2026Article
- IL-6 Receptor Antagonists and Severe Post-COVID-19 Outcomes: An Emulated Target Trial.medRxiv : the preprint server for health sciences · 2026Article
- A perfect storm: the immunological and pathophysiological landscape of pediatric post-COVID-19 condition.Frontiers in immunology · 2026Review
- Factors associated with severe neurological sequelae of COVID-19: findings from the multicenter COVID-BRAIN imaging cohort.Frontiers in human neuroscience · 2026Article
- Article
- Identifying subtypes of Long COVID: a systematic review.EClinicalMedicine · 2026Article
- Assessment of long COVID symptom burden in patients testing positive for SARS-CoV-2 at a nationwide retail pharmacy.PloS one · 2026Article
- Biological Plausibility Between Long-COVID and Periodontal Disease Development or Progression.Biomedicines · 2025Review
- Scoping review of the emerging definition of long COVID: implications for future research and clinical practice.Revista de salud publica (Bogota, Colombia) · 2025Article
- Pre-pandemic lifestyle patterns and mental health outcomes among people reporting post-acute sequelae of COVID-19: evidence from a Slovenian population-based sample.BMC public health · 2025Article
61 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
33 authors at 19 institutions in 2 countries.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.