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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.
Fernández-de-Las-Peñas, César; Martín-Guerrero, José D; Florencio, Lidiane L; Navarro-Pardo, Esperanza; Rodríguez-Jiménez, Jorge; Torres-Macho, Juan; Pellicer-Valero, Oscar J.
Afiliação
  • Fernández-de-Las-Peñas C; Department of Physical Therapy, Occupational Therapy, Physical Medicine and Rehabilitation, Facultad de Ciencias de la Salud, Universidad Rey Juan Carlos (URJC), Avenida de Atenas s/n, Alcorcón, 28922, Madrid, Spain. cesar.fernandez@urjc.es.
  • Martín-Guerrero JD; Intelligent Data Analysis Laboratory, Department of Electronic Engineering, ETSE (Engineering School), Universitat de València (UV), Valencia, Spain.
  • Florencio LL; Department of Physical Therapy, Occupational Therapy, Physical Medicine and Rehabilitation, Facultad de Ciencias de la Salud, Universidad Rey Juan Carlos (URJC), Avenida de Atenas s/n, Alcorcón, 28922, Madrid, Spain.
  • Navarro-Pardo E; Department of Developmental and Educational Psychology, Universitat de València (UV), Valencia, Spain.
  • Rodríguez-Jiménez J; Department of Physical Therapy, Occupational Therapy, Physical Medicine and Rehabilitation, Facultad de Ciencias de la Salud, Universidad Rey Juan Carlos (URJC), Avenida de Atenas s/n, Alcorcón, 28922, Madrid, Spain.
  • Torres-Macho J; Department of Medicine, Universidad Complutense de Madrid (UCM), Madrid, Spain.
  • Pellicer-Valero OJ; Department of Internal Medicine, Hospital Universitario Infanta Leonor-Virgen de la Torre, Madrid, Spain.
Infection ; 51(1): 61-69, 2023 Feb.
Article em En | MEDLINE | ID: mdl-35451721
ABSTRACT

PURPOSE:

To 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.

METHODS:

Age, 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.

RESULTS:

Cluster 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.

CONCLUSIONS:

The identified subgrouping may reflect different mechanisms which should be considered in therapeutic interventions.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: COVID-19 Idioma: En Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Espanha

Texto completo: 1 Base de dados: MEDLINE Assunto principal: COVID-19 Idioma: En Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Espanha