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COVID-19 patient profiles over four waves in Barcelona metropolitan area: A clustering approach.
Fernández, Daniel; Perez-Alvarez, Nuria; Molist, Gemma.
Afiliação
  • Fernández D; Department of Statistics and Operations Research (DEIO), Universitat Politècnica de Catalunya BarcelonaTech (UPC), Barcelona, Spain.
  • Perez-Alvarez N; Institute of Mathematics of UPC - BarcelonaTech (IMTech), Barcelona, Spain.
  • Molist G; Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III (CIBERSAM), Madrid, Spain.
PLoS One ; 19(5): e0302461, 2024.
Article em En | MEDLINE | ID: mdl-38713649
ABSTRACT

OBJECTIVES:

Identifying profiles of hospitalized COVID-19 patients and explore their association with different degrees of severity of COVID-19 outcomes (i.e. in-hospital mortality, ICU assistance, and invasive mechanical ventilation). The findings of this study could inform the development of multiple care intervention strategies to improve patient outcomes.

METHODS:

Prospective multicentre cohort study during four different waves of COVID-19 from March 1st, 2020 to August 31st, 2021 in four health consortiums within the southern Barcelona metropolitan region. From a starting point of over 292 demographic characteristics, comorbidities, vital signs, severity scores, and clinical analytics at hospital admission, we used both clinical judgment and supervised statistical methods to reduce to the 36 most informative completed covariates according to the disease outcomes for each wave. Patients were then grouped using an unsupervised semiparametric method (KAMILA). Results were interpreted by clinical and statistician team consensus to identify clinically-meaningful patient profiles.

RESULTS:

The analysis included nw1 = 1657, nw2 = 697, nw3 = 677, and nw4 = 787 hospitalized-COVID-19 patients for each of the four waves. Clustering analysis identified 2 patient profiles for waves 1 and 3, while 3 profiles were determined for waves 2 and 4. Patients allocated in those groups showed a different percentage of disease outcomes (e.g., wave 1 15.9% (Cluster 1) vs. 31.8% (Cluster 2) for in-hospital mortality rate). The main factors to determine groups were the patient's age and number of obese patients, number of comorbidities, oxygen support requirement, and various severity scores. The last wave is also influenced by the massive incorporation of COVID-19 vaccines.

CONCLUSION:

Our study suggests that a single care model at hospital admission may not meet the needs of hospitalized-COVID-19 adults. A clustering approach appears to be appropriate for helping physicians to differentiate patients and, thus, apply multiple care intervention strategies, as another way of responding to new outbreaks of this or future diseases.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Mortalidade Hospitalar / COVID-19 / Hospitalização Limite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged País/Região como assunto: Europa Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Mortalidade Hospitalar / COVID-19 / Hospitalização Limite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged País/Região como assunto: Europa Idioma: En Ano de publicação: 2024 Tipo de documento: Article