Viral respiratory diseases, including SARS-Co-V2 or COVID-19, can often entail complicated bacterial superinfections. This additional infection has been identified as having detrimental effects on patient outcomes. Data analyses related to hospital discharges and death rates may potentially influence the recorded impact of these superinfections due to the dynamic nature of patient cohorts over time. This factor necessitates a thorough understanding of this change in risk assessment for accurate statistical analyses of hospital data.
This study was based on a retrospective cohort data, comprising 268 critically ill patients. The study took into account discharge and death as competing risks during the statistical analysis. The primary focus was bacterial respiratory and bloodstream infections, and the data was processed using multi-state statistical models to represent varying patient states. These models allow healthcare professionals to better estimate the risks associated with superinfections, the probability of discharge or death over time and analysed the patient subpopulation based on age and gender.
The majority of the identified pathogen spectrum consisted of Enterobacterales, Nonfermenters, and Staphylococcus aureus. The results highlighted an increased mortality risk from bacterial infections of the respiratory tract or bloodstream, as well as a reduction in discharge rate. It was observed that female patients tended to have a lesser risk of acquiring superinfections, although they faced a higher mortality rate in case of an infection compared to male patients.
Through addressing the factors that could change the risk of superinfections over time, the study provides valuable insights into the risks associated with bacterial superinfections in critically ill COVID-19 patients. It validates the need for microbiological sampling in SARS-CoV-2-infected patients while also shedding light on the elevated risk of death in patients who develop a superinfection.
Taking into account patient-specific characteristics like age and sex and their effect on the risks can aid in optimizing hospital-acquired infection prevention strategies. It becomes evident how critical it is to acknowledge these individual factors and their influence over time during the treatment process of COVID-19 within hospital settings.
Source: https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-025-10983-7