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.

Abstract

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). 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. 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 discriminability 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). 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). 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.