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

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...

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Detalles Bibliográficos
Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9028890/
https://www.ncbi.nlm.nih.gov/pubmed/35451721
http://dx.doi.org/10.1007/s15010-022-01822-x
Descripción
Sumario: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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