Risk factors associated with bacteremia in COVID-19 patients admitted to intensive care unit: a retrospective multicenter cohort study

Corticosteroid Dosing Level, Incidence and Profile of Bacterial Blood Stream Infections in Hospitalized COVID-19 Patients

COVID-19 patients with severe or critical symptoms are often treated with corticosteroids, per contemporary guidelines. Due to their immunosuppressive and immunomodulatory properties, corticosteroids are associated with the development of superinfections. We aimed to retrospectively assess patterns of corticosteroid use and the profiles of bacterial blood stream infections associated with exposure to different dosing levels, in a cohort of 1558 real-life adult COVID-19 patients. A total of 1391 (89.3%) patients were treated with corticosteroids, with 710 (45.6%) patients receiving low, 539 (34.6%) high and 142 (9.1%) very high corticosteroid doses. Bacteremia developed in a total of 178 (11.4%) patients. The risk of bacteremia was of similar magnitude between the no and low-dose corticosteroid treatments (p = 0.352), whereas it progressively increased with high (OR 6.18, 95% CI (2.66-14.38), p < 0.001) and very high corticosteroid doses (OR 8.12, 95% CI (3.29-20.05), p < 0.001), compared to no corticosteroid treatment. These associations persisted after multivariate adjustments and were present independently of sex, comorbidity burden, and mechanical ventilation. The profiles of individual bacterial pathogens differed depending on the used corticosteroid doses. High and very high corticosteroid doses are frequently used for real-life COVID-19 patients with severe and critical clinical presentations and are associated with a higher risk of bacteremia independently of sex, comorbidity burden, and mechanical ventilation use.

Bloodstream Infections in Intensive Care Unit during Four Consecutive SARS-CoV-2 Pandemic Waves

Critically ill COVID-19 patients are at an increased risk of bloodstream infections (BSIs). We performed a retrospective observational single-center study on COVID-19 patients admitted to intensive care unit (ICU) to assess the incidence of BSIs in four consecutive periods: 21 February-31 July 2020 (W1), 1 August 2020-31 January 2021 (W2), 1 February-30 September 2021 (W3) and 1 October 2021 and 30 April 2022 (W4). BSIs that occurred 48 h after ICU admission were included. The crude incidence of BSIs was estimated by means of Poisson distribution normalized to 1000 patient-days. A total of 404 critically ill COVID-19 patients were admitted to ICU, of whom 284 (61%) developed at least one episode of BSI with an overall crude incidence of 87 events every 1000 patient-days (95% CI 77-98) without a significant difference in consecutive epidemic periods (p = 0.357). Gram-positive bacteria were the most frequent etiological agents of BSIs, contributing to 74.6% episodes. A progressive decrease in BSIs due to Enterococcus spp. was observed (W1 57.4%, W2 43.7%, W3 35.7% and W4 32.7%; p = 0.004). The incidence of BSIs remained stable during different epidemic periods. Enterococcus spp. prevalence was significantly reduced, although still accounted for one third of BSIs in more recent epidemic periods.

Identification of risk factors and development of a predictive model for bloodstream infection in intensive care unit COVID-19 patients

OBJECTIVES

To identify risk factors for nosocomial bloodstream infections (BSIs) in intensive care unit (ICU) patients with COVID-19 and to build a predictive model for BSIs.

PATIENTS AND METHODS

The retrospective case-control study included 236 ICU COVID-19 patients with BSIs group and 234 patients in the control group. Demographic and laboratory data, comorbidities, drug use, invasive procedures and identified pathogens were recorded separately for patients directly admitted and transferred to ICU. Fine and Gray's multi-variate competing risk model was used to build a predictive model for patients transferred to ICU.

RESULTS

The risk factors were: interleukin inhibitors (HR = 6.1 (95% CI: 2.0-18.5)) and dexamethasone (HR = 3.0 (95% CI: 1.3-7.1)) use in previous hospitalization, glomerular filtration rate <60 mL/min per 1.73 m2 (HR = 4.0 (95% CI: 2.1-7.6)) and blood glucose >9 mmol/L (HR = 2.5 (95% CI: 1.4-4.6)) in patients directly admitted to ICU; and dexamethasone use in previous hospitalization (HR = 4.5 (95% CI: 1.8-11)), the total dexamethasone dose before transfer to ICU (HR = 1.2 (95% CI: 1.06-1.37)), diabetes mellitus (HR = 1.4 (95% CI: 1.1-1.9)), alanine transaminase (ALT) ≥35.5 U/L on hospital admission (HR = 1.5 (95% CI: 1.1-2.1)), and the use of low-flow oxygen versus high-flow oxygen therapy or non-invasive mechanical ventilation on admission to ICU ((HR = 2.7 (95% CI: 5.6-11.1)) in patients transferred to ICU. A predictive model had sensitivity of 63-73% and specificity of 71-83% at different times of ICU stay.

CONCLUSIONS

Our findings may help clinicians detect patients at high risk of developing BSIs.

An Observational Study to Develop a Predictive Model for Bacterial Pneumonia Diagnosis in Severe COVID-19 Patients-C19-PNEUMOSCORE

In COVID-19 patients, antibiotics overuse is still an issue. A predictive scoring model for the diagnosis of bacterial pneumonia at intensive care unit (ICU) admission would be a useful stewardship tool. We performed a multicenter observational study including 331 COVID-19 patients requiring invasive mechanical ventilation at ICU admission; 179 patients with bacterial pneumonia; and 152 displaying negative lower-respiratory samplings. A multivariable logistic regression model was built to identify predictors of pulmonary co-infections, and a composite risk score was developed using β-coefficients. We identified seven variables as predictors of bacterial pneumonia: vaccination status (OR 7.01; 95% CI, 1.73-28.39); chronic kidney disease (OR 3.16; 95% CI, 1.15-8.71); pre-ICU hospital length of stay ≥ 5 days (OR 1.94; 95% CI, 1.11-3.4); neutrophils ≥ 9.41 × 10⁹/L (OR 1.96; 95% CI, 1.16-3.30); procalcitonin ≥ 0.2 ng/mL (OR 5.09; 95% CI, 2.93-8.84); C-reactive protein ≥ 107.6 mg/L (OR 1.99; 95% CI, 1.15-3.46); and Brixia chest X-ray score ≥ 9 (OR 2.03; 95% CI, 1.19-3.45). A predictive score (C19-PNEUMOSCORE), ranging from 0 to 9, was obtained by assigning one point to each variable, except from procalcitonin and vaccine status, which gained two points each. At a cut-off of ≥3, the model exhibited a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 84.9%, 55.9%, 69.4%, 75.9%, and 71.6%, respectively. C19-PNEUMOSCORE may be an easy-to-use bedside composite tool for the early identification of severe COVID-19 patients with pulmonary bacterial co-infection at ICU admission. Its implementation may help clinicians to optimize antibiotics administration in this setting.

Procalcitonin Values Fail to Track the Presence of Secondary Bacterial Infections in COVID-19 ICU Patients

The development of secondary bacterial infections in COVID-19 patients has been associated with increased mortality and worse clinical outcomes. Consequently, many patients have received empirical antibiotic therapies with the potential to further exacerbate an ongoing antimicrobial resistance crisis. The pandemic has seen a rise in the use of procalcitonin testing to guide antimicrobial prescribing, although its value remains elusive. This single-centre retrospective study sought to analyse the efficacy of procalcitonin in identifying secondary infections in COVID-19 patients and evaluate the proportion of patients prescribed antibiotics to those with confirmed secondary infection. Inclusion criteria comprised patients admitted to the Grange University Hospital intensive care unit with SARS-CoV-2 infection throughout the second and third waves of the pandemic. Data collected included daily inflammatory biomarkers, antimicrobial prescriptions, and microbiologically proven secondary infections. There was no statistically significant difference between PCT, WBC, or CRP values in those with an infection versus those without. A total of 57.02% of patients had a confirmed secondary infection, with 80.2% prescribed antibiotics in Wave 2, compared to 44.07% with confirmed infection and 52.1% prescribed antibiotics in Wave 3. In conclusion, procalcitonin values failed to indicate the emergence of critical care-acquired infection in COVID-19 patients.

Diagnostic significance of secondary bacteremia in patients with COVID-19

OBJECTIVES

We investigated the occurrence of non-respiratory bacterial and fungal secondary infections, causative organisms, impact on clinical outcomes, and association between the secondary pathogens and mortality in hospitalized patients with coronavirus disease 2019 (COVID-19).

METHODS

This was a retrospective cohort study that included data from inpatients with COVID-19 from multiple centers participating in the Japan COVID-19 Taskforce (April 2020 to May 2021). We obtained demographic, epidemiological, and microbiological data throughout the course of hospitalization and analyzed the cases of COVID-19 complicated by non-respiratory bacterial infections.

RESULTS

Of the 1914 patients included, non-respiratory bacterial infections with COVID-19 were diagnosed in 81 patients (4.2%). Of these, 59 (3.1%) were secondary infections. Bacteremia was the most frequent bacterial infection, occurring in 33 cases (55.9%), followed by urinary tract infections in 16 cases (27.1%). Staphylococcus epidermidis was the most common causative organism of bacteremia. Patients with COVID-19 with non-respiratory secondary bacterial infections had significantly higher mortality, and a multivariate logistic regression analysis demonstrated that those with bacteremia (aOdds Ratio = 15.3 [5.97-39.1]) were at higher risk of death. Multivariate logistic regression analysis showed that age, male sex, use of steroids to treat COVID-19, and intensive care unit admission increased the risk for nosocomial bacteremia.

CONCLUSIONS

Secondary bacteremia is an important complication that may lead to poor prognosis in cases with COVID-19. An appropriate medical management strategy must be established, especially for patients with concomitant predisposing factors.