Healthcare-associated infections in cardiac surgery: epidemiological features

Abstract

Aim. To identify the epidemiological features of HAIs in all patients admitted for surgery from 2018 to 2022. in a cardiac surgery hospital for the implementation of a risk-based prevention strategy.Materials and Methods. A descriptive retrospective epidemiological study of the HAI epidemic process was performed from 2018 to 2022. in patients of a large cardiac surgery hospital (n = 6179). Stratified indicators were calculated. To display unknown relationships and make a forecast, Fourier spectral analysis was performed, followed by the use of artificial intelligence technology - neural networks. The STATISTICA Automated Neural Networks (SANN) tool was used, as well as the StatTech v. 3.0.5.Results. The average rate of HAIs incidence over a 5-year period was 4.22 per 1000 patient days. We revealed decreasing trend of HAIs. Incidence of HCAI cardiopulmonary bypass surgery (CBS) was 3 times higher than without CBS (4.68 and 1.51 per 1000 patient-days, respectively). Fourier analysis revealed 10, 20, 30 cyclicity due to the dominant Klebsiella pneumoniae without the same time-series for other pathogens. The technology of neural network modeling did not reveal neural networks suitable for describing the forecast. Klebsiella pneumoniae showed properties typical of the hospital population and caused 35.49% of all cases of HAIs, had multidrug resistance to antibiotics in 74.45% of cases, with more than half of the strains having extended resistance, and 10.21% were pan-resistant. Acinetobacter baumanii also showed high epidemic activity, causing almost a fifth of all cases of HAIs, although its antimicrobial resistance characteristics were less pronounced than those of Klebsiella pneumoniae.Conclusion. The epidemiological characteristics of the epidemic process of HCAI is one of the mandatory components of risk identification. The identified features of the dynamics of the epidemic process of HCAI in a cardiac surgery hospital, risk groups and time, the structure and characteristics of the microbiota should be taken into account in the HCAI risk management system.