Epidemiology, time course, and risk factors for hospital-acquired bloodstream infections in a cohort of 14,884 patients before and during the COVID-19 pandemic

Abstract

Objective

COVID-19 pandemic has changed in-hospital care and was linked to superimposed infections. Here, we described epidemiology and risk factors for hospital-acquired bloodstream infections (HA-BSIs), before and during COVID-19 pandemic.

Methods

This retrospective, observational, single-center real-life study included 14,884 patients admitted to hospital wards and intensive care units (ICUs) with at least one blood culture, drawn 48 h after admission, either before (pre-COVID, N = 7382) or during pandemic (N = 7502, 1203 COVID-19+ and 6299 COVID-19–).

Results

Two thousand two hundred and forty-five HA-BSI were microbiologically confirmed in 14,884 patients (15.1%), significantly higher among COVID-19+ (22.9%; p_trend < .001). COVID-19+ disclosed a significantly higher mortality rate (33.8%; p < .001) and more ICU admissions (29.7%; p < .001). Independent HAI-BSI predictors were: COVID-19 (OR: 1.43, 95%CI: 1.21–1.69; p < .001), hospitalization length (OR: 1.04, 95%CI: 1.03–1.04; p < .001), ICU admission (OR: 1.38, 95%CI: 1.19–1.60; p < .001), neoplasms (OR:1.48, 95%CI: 1.34–1.65; p < .001) and kidney failure (OR: 1.81, 95%CI: 1.61–2.04; p < .001). Of note, HA-BSI IRs for Acinetobacter spp. (0.16 × 100 patient-days) and Staphylococcus aureus (0.24 × 100 patient-days) peaked during the interval between first and second pandemic waves in our National context.

Conclusions

Patients with HA-BSI admitted before and during pandemic substantially differed. COVID-19 represented a risk factor for HA-BSI, though not confirmed in the sole pandemic period. Some etiologies emerged between pandemic waves, suggesting potential COVID-19 long-term effect on HA-BSIs.

Keywords:

• COVID-19
• hospital-acquired bloodstream infections
• healthcare associated infections
• Data Mart
• pandemic

Disclosure statement

None to declare.

Additional information

Funding

This study received partial funding from the Italian Ministry for University and Research (MUR), under the Program, PON ‘Research and Innovation’, which supports the development of the artificial intelligence platform, Gemelli Generator, at the Policlinico Universitario Agostino Gemelli IRCCS.