AUTHOR=Li Peng , Li Yan , Zhang Youjian , Zhu Shichao , Pei Yongju , Zhang Qi , Liu Junping , Bao Junzhe , Sun Mingjie 

TITLE=A dynamic nomogram to predict invasive fungal super-infection during healthcare-associated bacterial infection in intensive care unit patients: an ambispective cohort study in China

JOURNAL=Frontiers in Cellular and Infection Microbiology

VOLUME=Volume 14 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2024.1281759

DOI=10.3389/fcimb.2024.1281759

ISSN=2235-2988

ABSTRACT=Objectives: Invasive fungal super-infection (IFSI) is an added diagnostic and therapeutic dilemma. We aimed to develop and assess a nomogram of IFSI in patients with healthcare-associated bacterial infection (HABI).Methods: An ambispective cohort study was conducted in ICU patients with HABI from a tertiary hospital of China. Predictors of IFSI were selected by both the least absolute shrinkage and selection operator (LASSO) method and the two-way stepwise method. The predictive performance of two models built by logistic regression was internal-validated and compared. Then external validity was assessed and a web-based nomogram was deployed.Results: Between Jan 1, 2019 and June 30, 2023, 12,305 patients with HABI were screened in 14 ICUs, of whom 372 (3.0%) developed IFSI. Among the fungal strains causing IFSI, the most common was C.albicans (34.7%) with a decreasing proportion, followed by C.tropicalis (30.9%), A.fumigatus (13.9%) and C.glabrata (10.1%) with increasing proportions year by year. Compared with LASSO-model that included five predictors (combination of priority antimicrobials, immunosuppressant, MDRO, aCCI and S.aureus), the discriminabilitydiscriminative ability of stepwise-model was improved by 6.8% after adding two more predictors of COVID-19 and microbiological test before antibiotics use (P<0.01). And the stepwise-model showed similar discriminability in the derivation (the area under curve, AUC=0.87) and external validation cohorts (AUC=0.84, P=0.46).Similar discrimination of stepwise-model were achieved in derivation cohort (the area under curve, AUC=0.87) and in external validation cohort (AUC=0.84, P=0.46). No significant gaps existed between the proportion of actual diagnosed IFSI and the frequency of IFSI predicted by both two models in derivation cohort and by stepwise-model in external validation cohort (P=0.16, 0.30 and 0.35, respectively). Conclusions: The incidence of IFSI in ICU patients with HABI appeared to be a temporal rising, and our externally validated nomogram will facilitate the development of targeted and timely prevention and control measures based on specific risks of IFSI. would assist in impact study design and clinical decision making for the prevention and control of IFSI.