ArticleVirology journal2026
Risk factors for intensive care unit coronavirus disease 2019 pneumonia-associated invasive fungal infections: a Bayesian evidence synthesis integrating external and real-world data.
Article in Virology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe incidence of Coronavirus Disease 2019 (COVID-19) pneumonia-associated Invasive Fungal Infections (IFI) has risen markedly. However, current evidence regarding associated risk factors remains inconsistent across studies and limited by small sample sizes, underscoring the need for systematically integrated evidence.
methodsThis retrospective study integrated published external evidence with Real-World Data (RWD) within a Bayesian framework to derive more accurate and robust estimates of potential risk factors for IFI associated with COVID-19 pneumonia in Intensive Care Unit (ICU) patients.
resultsBased on the study-derived prior distributions for 25 potential risk factors, the Bayesian integrated analysis further suggested that male sex, advanced age, diabetes mellitus, chronic pulmonary disease, hepatic or renal dysfunction, malignancy, increased disease severity (Sequential Organ Failure Assessment (SOFA) scores, Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, and Charlson Comorbidity Index (CCI)), and prolonged mechanical ventilation were possible associated with IFI in ICU patients with COVID-19 pneumonia. In addition, prior glucocorticoid exposure, high-dose and prolonged glucocorticoid therapy, vasopressor use, renal replacement therapy, and mechanical ventilation were also potentially linked to higher IFI incidence. Subgroup analyses revealed that factors associated with Aspergillus infection in COVID-19 patients were largely consistent with the overall cohort.
conclusionThis observational study systematically explored potential factors associated with IFI in ICU patients with COVID-19 pneumonia by integrating published external evidence with RWD within a Bayesian framework, thereby providing more reliable evidence to support early identification and prevention in high-risk individuals.
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