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. 2022 Oct 18;14(10):2292.
doi: 10.3390/v14102292.

Healthcare-Associated COVID-19 across Five Pandemic Waves: Prediction Models and Genomic Analyses

Thomas Demuyser  1   2 , Lucie Seyler  3 , Rhea Buttiens  1 , Oriane Soetens  1 , Els Van Nedervelde  3 , Ben Caljon  4 , Jessy Praet  5 , Thomas Seyler  6 , Joost Boeckmans  7   8 , Jessy Meert  1   2 , Robin Vanstokstraeten  1 , Helena Martini  1 , Florence Crombé  1 , Denis Piérard  1 , Sabine D Allard  3 , Ingrid Wybo  1
Affiliations

Healthcare-Associated COVID-19 across Five Pandemic Waves: Prediction Models and Genomic Analyses

Thomas Demuyser et al. Viruses. .

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.

Keywords: genomic analysis; healthcare-associated COVID-19; prediction modelling.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Multiple logistic regression modelling of HAI versus CAI patients. (A) Multiple logistic regression without death and length of stay variables. (B) Multiple logistic regression with death and length of stay variables.
Figure 2
Figure 2
Epicurve and MST of healthcare-associated COVID-19 infections by WHO SARS-CoV-2 class and week of diagnosis. (A) Epicurve of our cases showing all healthcare-associated COVID-19 infections (HAI and aHCW) with SARS-CoV-2 WHO classes by color. Patients from whom no genomic data were available are marked in light blue. (B) MST (minimal spanning tree) of SARS-CoV-2 genomes of the patients whose samples were available for sequencing. The black genome represents the reference genome Wuhan-Hu-1 [18]. The size of the circles is proportional to the number of cases. Genomes that differed by ≤2 SNPs were clustered using the grey contours (HAI and aHCW together). Green asterisks are used to show the 22 clusters that contained at least 1 HAI case.
Figure 3
Figure 3
Epicurve and minimum spanning tree (MST) of hospital-acquired infections (HAIs) (n = 269) by ECDC case source definition and week of diagnosis (aHCWs and CAIs were included for reference). (A) Epicurve of our cases showing all HAIs by ECDC categories and by week. CAIs (pink) and aHCWs (yellow) are shown for reference. (B) MST (minimal spanning tree) of SARS-CoV-2 genomes of the patients whose samples were available for sequencing. The black genome represents the reference genome Wuhan-Hu-1 [18]. The size of the circles is proportional to the number of cases. Genomes that differed by ≤ 2 SNPs were clustered using the grey contours (HAI and aHCW together). Green asterisks are used to show the 22 clusters that contained at least 1 HAI case.
See this image and copyright information in PMC

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