The impact of the secondary infections in ICU patients affected by COVID-19 during three different phases of the SARS-CoV-2 pandemic

Abstract

Microbial secondary infections can contribute to an increase in the risk of mortality in COVID-19 patients, particularly in case of severe diseases. In this study, we collected and evaluated the clinical, laboratory and microbiological data of COVID-19 critical ill patients requiring intensive care (ICU) to evaluate the significance and the prognostic value of these parameters. One hundred seventy-eight ICU patients with severe COVID-19, hospitalized at the S. Francesco Hospital of Nuoro (Italy) in the period from March 2020 to May 2021, were enrolled in this study. Clinical data and microbiological results were collected. Blood chemistry parameters, relative to three different time points, were analyzed through multivariate and univariate statistical approaches. Seventy-four percent of the ICU COVID-19 patients had a negative outcome, while 26% had a favorable prognosis. A correlation between the laboratory parameters and days of hospitalization of the patients was observed with significant differences between the two groups. Moreover, Staphylococcus aureus, Enterococcus faecalis, Candida spp, Pseudomonas aeruginosa and Klebsiella pneumoniae were the most frequently isolated microorganisms from all clinical specimens. Secondary infections play an important role in the clinical outcome. The analysis of the blood chemistry tests was found useful in monitoring the progression of COVID-19.

Keywords: Blood chemistry parameters; COVID-19; Clinical outcome; Microbiological data; SARS-COV-2 infection; Secondary infections.

Fig. 1

A PLS model built considering the blood chemistry parameters of the deceased patients. This statistical approach allows to correlate the number of the days of hospitalization with numerical variables, such as the laboratory parameters, with the aim to quickly identify which of them change its concentration in line with the clinical evolution of the patients. White circles represent samples at the moment of the hospital admission (T0); gray circles represent samples belonging to the same patients but collected at an intermediate time of hospitalization; black circles represent the samples collected from the same patients before death. B The trend of the discriminant parameters resulted from the multivariate statistical analysis which changed their concentration significantly during the hospitalization of the deceased patients. Wilcoxon test was employed. ** = p value < 0.01, *** = p value < 0.001, **** = p value < 0.0001

Fig. 2

A PLS model built considering the blood chemistry parameters of the transferred patients. This statistical approach allows to correlate the number of the days of hospitalization with numerical variables, such as the laboratory parameters, with the aim to quickly identify which of them change its concentration in line with the clinical evolution of the patients. White circles represent samples at the moment of admission to the hospital (T0); gray circles represent samples belonging to the same patients but collected at an intermediate time of hospitalization; blue circles represent the samples collected from the same patients before the transfer from the ICU to other hospital wards. B The trend of the discriminant parameters resulted from the multivariate statistical analysis which significantly changed their concentration during the hospitalization of the transferred patients. Wilcoxon test was employed. ** = p value < 0.01, *** = p value < 0.001, **** = p value < 0.0001

Fig. 3

PLS-DA model of the T0 samples of the patients which had different outcomes (gray circles are transferred patients while light blue circles represent deceased patients)

Fig. 4

Comparisons of the concentrations of the laboratory parameters considering the three different time points of the deceased and transferred patients (red and blue bars, respectively). Mann–Whitney U test was used (* = p < 0.05, ** = p value < 0.01, *** = p value < 0.001, **** = p value < 0.0001)

Fig. 5

A Percentage of COVID-19 patients with secondary infections after 7 days of hospitalization and mortality relative to the positive patients. B Summary of the positive microbiological specimens in the deceased groups and transferred patients (black and white bars, respectively)

Fig. 6

A Microbiological results of the different specimens relative to the deceased patients. Blood cultures were positive mainly for CoNS (16%) and S. aureus (12%). Bronchoaspirates were positive mainly for Candida spp. (24%), P. aeruginosa (13%), Klebsiella pneumoniae (8%). Urine cultures were positive for E. faecalis (31%), Candida spp. (18%) and Klebsiella pneumoniae (18%); Finally, CVC cultures, were positive for CoNS (37%), while Candida spp. were found in 27% of the cases. B Microbiological results of the different specimens relative to the transferred patients. Blood cultures were positive for CoNS (28%), E. faecalis, E. aerogenes, and K. pneumoniae (12%). Bronchoaspirates were mainly positive for Candida spp. (23%), P. aeruginosa (18%), CoNS and Escherichia Coli (12%); Urine cultures were positive for Candida spp (26%). Finally, in CVC cultures, CoNS were the prevailing bacteria (32%), while P. aeruginosa and K. pneumoniae were found in 14%