Details

Autor(en) / Beteiligte

• Strelkova, D.
• Rachina, S.
• Fedina, L.
• Vlasenko, A.
• Tetevina, M.
• Drogashevskaya, D.
• Chesnokova, M.
• Kuleshov, V.
• Burmistrova, E.
• Sychev, I.
• Ananicheva, N.

Titel

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

Ist Teil von

• The Journal of hospital infection, 2023-09, Vol.139, p.150-157

Ort / Verlag

England: Elsevier Ltd

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

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