ArticleInfection2023
Clustering analysis reveals different profiles associating long-term post-COVID symptoms, COVID-19 symptoms at hospital admission and previous medical co-morbidities in previously hospitalized COVID-19 survivors.
Article in Infection, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it, 57 citations in OpenAlex.
- Global prevalence of post-COVID-19 condition: a systematic review and meta-analysis of prospective evidence.Health promotion and chronic disease prevention in Canada : research, policy and practice · 2025Pooled it
- The global prevalence of depression, anxiety, and sleep disorder among patients coping with Post COVID-19 syndrome (long COVID): a systematic review and meta-analysis.BMC psychiatry · 2024Pooled it
- Predicting low oxygen in patients with acute COVID-19 infection isolating at home: a clinical prediction model.BMJ open respiratory research · 2026Trial
- Trial
- Post-Acute COVID-19 Symptoms Clusters and Work Status.Occupational Health · 2026Article
- SYNERGY-VAE: An explainable generative deep learning framework for discovering depression subgroups from multimodal population health data.PLOS digital health · 2026Article
- Biopsychosocial factors associated with health-related quality of life in long COVID: a matched case-control study in primary care.BMC primary care · 2026Article
- Presentation and temporal nature of postacute sequelae of SARS-CoV-2 infection in a US national cohort.Brain, behavior, & immunity - health · 2026Article
- Post-COVID-19 physical and mental quality of life: a latent profile analysis and predictive factors.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Article
- Algorithm dependence of patient phenotypes in Long COVID: a patient-led, multi-method clustering of 6031 patients using 162 self-reported symptoms.Oxford open immunology · 2026Article
- Long COVID's Hidden Complexity: Machine Learning Reveals Why Personalized Care Remains Essential.Journal of clinical medicine · 2025Article
- Predictive Ability of Previous Pain and Disease Conditions on the Presentation of Post-COVID Pain in a Danish Cohort of Adult COVID-19 Survivors.European journal of pain (London, England) · 2025Article
- Article
- Six-month follow-up of multidomain cognitive impairment in non-hospitalized individuals with post-COVID-19 syndrome.European archives of psychiatry and clinical neuroscience · 2024Article
- A pilot cross-sectional investigation of symptom clusters and associations with patient-reported outcomes in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Post COVID-19 Condition.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024Article
- Long COVID Clusters of Symptoms Persist beyond Two Years after Infection: Insights from the CARDIO COVID 20-21 Registry.Viruses · 2024Article
- Symptom Clusters in Acute SARS-CoV-2 Infection and Long COVID Fatigue in Male and Female Outpatients.Journal of personalized medicine · 2024Article
- Device-assessed physical activity and sleep quality of post-COVID patients undergoing a rehabilitation program.BMC sports science, medicine & rehabilitation · 2024Article
- Long COVID exhibits clinically distinct phenotypes at 3-6 months post-SARS-CoV-2 infection: results from the P4O2 consortium.BMJ open respiratory research · 2024Article
- Long COVID Clinical Severity Types Based on Symptoms and Functional Disability: A Longitudinal Evaluation.Journal of clinical medicine · 2024Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
purposeTo identify subgroups of COVID-19 survivors exhibiting long-term post-COVID symptoms according to clinical/hospitalization data by using cluster analysis in order to foresee the illness progress and facilitate subsequent prognosis.
methodsAge, gender, height, weight, pre-existing medical comorbidities, Internal Care Unit (ICU) admission, days at hospital, and presence of COVID-19 symptoms at hospital admission were collected from hospital records in a sample of patients recovered from COVID-19 at five hospitals in Madrid (Spain). A predefined list of post-COVID symptoms was systematically assessed a mean of 8.4 months (SD 15.5) after hospital discharge. Anxiety/depressive levels and sleep quality were assessed with the Hospital Anxiety and Depression Scale and Pittsburgh Sleep Quality Index, respectively. Cluster analysis was used to identify groupings of COVID-19 patients without introducing any previous assumptions, yielding three different clusters associating post-COVID symptoms with acute COVID-19 symptoms at hospital admission.
resultsCluster 2 grouped subjects with lower prevalence of medical co-morbidities, lower number of COVID-19 symptoms at hospital admission, lower number of post-COVID symptoms, and almost no limitations with daily living activities when compared to the others. In contrast, individuals in cluster 0 and 1 exhibited higher number of pre-existing medical co-morbidities, higher number of COVID-19 symptoms at hospital admission, higher number of long-term post-COVID symptoms (particularly fatigue, dyspnea and pain), more limitations on daily living activities, higher anxiety and depressive levels, and worse sleep quality than those in cluster 2.
conclusionsThe identified subgrouping may reflect different mechanisms which should be considered in therapeutic interventions.
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Registered trials
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.