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A Systematic Review and Meta-analysis to Identify Risk Factors for Developing Long COVID-19.
Muley, Arti; Mitra, Sona; Bhaliya, Bhargav; Soni, Simran; Joshi, Ankit.
Afiliação
  • Muley A; Professor, Department of Medicine, Parul Institute of Medical Sciences and Research, Parul University, Vadodara, Gujarat, India, Corresponding Author.
  • Mitra S; Clinical Research Scholar, Department of Medicine, Parul Institute of Medical Sciences and Research, Parul University, Vadodara, Gujarat, India.
  • Bhaliya B; Associate Professor, Department of Medicine, Pacific Institute of Medical Sciences, Udaipur, Rajasthan, India.
  • Soni S; Intern, Department of Medicine, Parul Institute of Medical Sciences and Research, Parul University, Vadodara, Gujarat, India.
  • Joshi A; Intern, Department of Medicine, Parul Institute of Medical Sciences and Research, Parul University, Vadodara, Gujarat, India.
J Assoc Physicians India ; 72(5): 68-74, 2024 May.
Article em En | MEDLINE | ID: mdl-38881113
ABSTRACT

AIM:

This systematic review and meta-analysis was undertaken to identify the risk factors of long coronavirus disease 2019 (COVID-19) to provide insight for selecting cases for more aggressive monitoring and treatment after COVID-19 infection and reduce morbidity due to long COVID-19. MATERIALS AND

METHODS:

All relevant studies published till July 2022 were searched for in PubMed, Trip database, and the Cochrane Central Register of Controlled Trials (CENTRAL; The Cochrane Library). Reference lists of the studies selected for appraisal were also considered. The National Institute of Health Clinical Database and Google Scholar were searched for unpublished studies. All cohort studies which studied risk factors for long COVID-19 in adults (>18 years age-group) were included. Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines were used for data extraction and bias assessment were. The outcomes were risk factors identified as being related with persistent symptoms 3 months after recovery from COVID-19. Random-effects model (RevMan 5.3) was used to pool the data.

RESULTS:

Total nine studies were included with overall quality scores ranging from 16 to 19 out of the maximum 22. Pooled results demonstrated statistically significant association of long COVID-19 with female gender [odds ratio (OR) -1.67; 95% confidence interval (CI) 1.33-2.09], need of hospitalization (OR -1.80; 95% CI 1.22-2.64), and hospital stay (OR 2.41; 95% CI 0.75-4.07).

CONCLUSION:

Female gender, need for hospitalization and duration of hospitalization during acute COVID-19 infection are the risk factors for later development of long COVID-19. There should be specific guidelines for monitoring and treatment of this population after acute COVID-19 infection.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: COVID-19 Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: COVID-19 Idioma: En Ano de publicação: 2024 Tipo de documento: Article