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

• DOI: 10.3389/fcimb.2024.1281759
• Publication Date: 2024-02-26T04:39:54Z
• Authors: Peng Li; Yan Li; Youjian Zhang; Shichao Zhu; Yongju Pei; Qi Zhang; Junping Liu; Junzhe Bao; Mingjie Sun
• Keywords: Nomogram

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