Healthcare-Associated COVID-19 across Five Pandemic Waves: Prediction Models and Genomic Analyses.
Viruses
; 14(10)2022 10 18.
Article
em En
| MEDLINE
| ID: mdl-36298847
ABSTRACT
BACKGROUND:
Healthcare-associated SARS-CoV-2 infections need to be explored further. Our study is an analysis of hospital-acquired infections (HAIs) and ambulatory healthcare workers (aHCWs) with SARS-CoV-2 across the pandemic in a Belgian university hospital.METHODS:
We compared HAIs with community-associated infections (CAIs) to identify the factors associated with having an HAI. We then performed a genomic cluster analysis of HAIs and aHCWs. We used this alongside the European Centre for Disease Control (ECDC) case source classifications of an HAI.RESULTS:
Between March 2020 and March 2022, 269 patients had an HAI. A lower BMI, a worse frailty index, lower C-reactive protein (CRP), and a higher thrombocyte count as well as death and length of stay were significantly associated with having an HAI. Using those variables to predict HAIs versus CAIs, we obtained a positive predictive value (PPV) of 83.6% and a negative predictive value (NPV) of 82.2%; the area under the ROC was 0.89. Genomic cluster analyses and representations on epicurves and minimal spanning trees delivered further insights into HAI dynamics across different pandemic waves. The genomic data were also compared with the clinical ECDC definitions for HAIs; we found that 90.0% of the 'definite', 87.8% of the 'probable', and 70.3% of the 'indeterminate' HAIs belonged to one of the twenty-two COVID-19 genomic clusters we identified.CONCLUSIONS:
We propose a novel prediction model for HAIs. In addition, we show that the management of nosocomial outbreaks will benefit from genome sequencing analyses.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Contexto em Saúde:
4_TD
/
6_ODS3_enfermedades_notrasmisibles
Base de dados:
MEDLINE
Assunto principal:
Infecção Hospitalar
/
COVID-19
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Viruses
Ano de publicação:
2022
Tipo de documento:
Article