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Clustering Analysis Identified Three Long COVID Phenotypes and Their Association with General Health Status and Working Ability.
Kisiel, Marta A; Lee, Seika; Malmquist, Sara; Rykatkin, Oliver; Holgert, Sebastian; Janols, Helena; Janson, Christer; Zhou, Xingwu.
Afiliação
  • Kisiel MA; Department of Medical Sciences, Occupational and Environmental Medicine, Uppsala University, 751 85 Uppsala, Sweden.
  • Lee S; Department of Neurobiology, Care Sciences and Society, Primary Care Medicine, Karolinska Institute, 171 77 Stockholm, Sweden.
  • Malmquist S; Department of Statistics, Uppsala University, 751 20 Uppsala, Sweden.
  • Rykatkin O; Department of Statistics, Uppsala University, 751 20 Uppsala, Sweden.
  • Holgert S; Department of Medical Sciences, Occupational and Environmental Medicine, Uppsala University, 751 85 Uppsala, Sweden.
  • Janols H; Department of Medical Sciences, Infection Disease, Uppsala University, 751 85 Uppsala, Sweden.
  • Janson C; Department of Medical Sciences: Respiratory, Allergy and Sleep Research, Uppsala University, 751 85 Uppsala, Sweden.
  • Zhou X; Department of Statistics, Uppsala University, 751 20 Uppsala, Sweden.
J Clin Med ; 12(11)2023 May 23.
Article em En | MEDLINE | ID: mdl-37297812
BACKGROUND/AIM: This study aimed to distinguish different phenotypes of long COVID through the post-COVID syndrome (PCS) score based on long-term persistent symptoms following COVID-19 and evaluate whether these symptoms affect general health and work ability. In addition, the study identified predictors for severe long COVID. METHOD: This cluster analysis included cross-sectional data from three cohorts of patients after COVID-19: non-hospitalized (n = 401), hospitalized (n = 98) and those enrolled at the post-COVID outpatient's clinic (n = 85). All the subjects responded to the survey on persistent long-term symptoms and sociodemographic and clinical factors. K-Means cluster analysis and ordinal logistic regression were used to create PCS scores that were used to distinguish patients' phenotypes. RESULTS: 506 patients with complete data on persistent symptoms were divided into three distinct phenotypes: none/mild (59%), moderate (22%) and severe (19%). The patients with severe phenotype, with the predominating symptoms were fatigue, cognitive impairment and depression, had the most reduced general health status and work ability. Smoking, snuff, body mass index (BMI), diabetes, chronic pain and symptom severity at COVID-19 onset were factors predicting severe phenotype. CONCLUSION: This study suggested three phenotypes of long COVID, where the most severe was associated with the highest impact on general health status and working ability. This knowledge on long COVID phenotypes could be used by clinicians to support their medical decisions regarding prioritizing and more detailed follow-up of some patient groups.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Idioma: En Revista: J Clin Med Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Idioma: En Revista: J Clin Med Ano de publicação: 2023 Tipo de documento: Article