ArticleBMJ open respiratory research2024
Long COVID exhibits clinically distinct phenotypes at 3-6 months post-SARS-CoV-2 infection: results from the P4O2 consortium.
Article in BMJ open respiratory research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Biopsychosocial factors associated with health-related quality of life in long COVID: a matched case-control study in primary care.BMC primary care · 2026Article
- Designing studies for post-treatment Lyme disease and other infection-associated chronic illnesses.Brain : a journal of neurology · 2026Review
- Long COVID longitudinal symptoms burden clusters within a national community-based cohort.BMC infectious diseases · 2026Article
- Trajectory, healthcare utilisation and recovery in 3590 individuals with long covid: a 4-year prospective cohort analysis.BMJ open · 2026Article
- Identifying subtypes of Long COVID: a systematic review.EClinicalMedicine · 2026Article
- Long COVID Longitudinal Symptoms Burden Clusters Within A National Community-Based Cohort.medRxiv : the preprint server for health sciences · 2025Article
- Long COVID's Hidden Complexity: Machine Learning Reveals Why Personalized Care Remains Essential.Journal of clinical medicine · 2025Article
- Contemporary positive signs of functional limb weakness in post-acute sequelae of SARS-CoV-2: an exploratory analysis of their utility in diagnosis and follow-up.BMJ neurology open · 2025Article
- Long COVID is not the same for everyone: a hierarchical cluster analysis of Long COVID symptoms 9 and 12 months after SARS-CoV-2 test.BMC infectious diseases · 2024Article
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29 authors.
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Abstract
backgroundFour months after SARS-CoV-2 infection, 22%-50% of COVID-19 patients still experience complaints. Long COVID is a heterogeneous disease and finding subtypes could aid in optimising and developing treatment for the individual patient.
methodsData were collected from 95 patients in the P4O2 COVID-19 cohort at 3-6 months after infection. Unsupervised hierarchical clustering was performed on patient characteristics, characteristics from acute SARS-CoV-2 infection, long COVID symptom data, lung function and questionnaires describing the impact and severity of long COVID. To assess robustness, partitioning around medoids was used as alternative clustering.
resultsThree distinct clusters of patients with long COVID were revealed. Cluster 1 (44%) represented predominantly female patients (93%) with pre-existing asthma and suffered from a median of four symptom categories, including fatigue and respiratory and neurological symptoms. They showed a milder SARS-CoV-2 infection. Cluster 2 (38%) consisted of predominantly male patients (83%) with cardiovascular disease (CVD) and suffered from a median of three symptom categories, most commonly respiratory and neurological symptoms. This cluster also showed a significantly lower forced expiratory volume within 1 s and diffusion capacity of the lung for carbon monoxide. Cluster 3 (18%) was predominantly male (88%) with pre-existing CVD and diabetes. This cluster showed the mildest long COVID, and suffered from symptoms in a median of one symptom category.
conclusionsLong COVID patients can be clustered into three distinct phenotypes based on their clinical presentation and easily obtainable information. These clusters show distinction in patient characteristics, lung function, long COVID severity and acute SARS-CoV-2 infection severity. This clustering can help in selecting the most beneficial monitoring and/or treatment strategies for patients suffering from long COVID. Follow-up research is needed to reveal the underlying molecular mechanisms implicated in the different phenotypes and determine the efficacy of treatment.
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