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.

BackgroundWorldwide, healthcare-associated SARS-CoV-2 infections are a major problem: they are associated with increased morbidity, mortality, and hospitalization costs. In-depth studies across the pandemic are crucial to understand and prevent transmission in hospital settings. The principal aims of this study were to characterise patients and validate ECDC definitions of healthcare-associated COVID-19 infections.MethodsWe set up a retrospective observational study spanning the first three waves of the COVID-19 pandemic in a Belgian university hospital: it describes the characteristics of COVID-19 patients admitted, with either healthcare- or community-associated infections. We performed a cluster analysis through epidemiological and viral genome analyses of the healthcare-associated infections, in order to validate the ECDC definitions of healthcare-associated COVID-19 infections.ResultsBetween week 10 of 2020 and week 22 of 2021, 168 patients were hospitalized with healthcare-associated COVID-19. The following factors were found more often in symptomatic healthcare- than in community-associated hospitalized patients: older age, increased frailty, smoking habits, and comorbidities. The genome-based cluster analyses showed that different viral lineages predominated in different timeframes. We observed a good correlation of epidemiological data with genome sequencing results in at least 12 different outbreaks in our hospital, thus validating the ECDC definitions. ConclusionsThis in-depth characterization sheds new light on the problem of healthcare-associated COVID-19 infections, in particular on patients’ characteristics, epidemiology, and cluster dynamics. Even though epidemiological evaluation of nosocomial infections is vital, management of nosocomial outbreaks can undoubtedly benefit from genome sequencing analyses to reinforce their strategy.