Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Feb 26:14:1281759.
doi: 10.3389/fcimb.2024.1281759. eCollection 2024.

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

Peng Li  1 , Yan Li  1 , Youjian Zhang  1 , Shichao Zhu  2 , Yongju Pei  3 , Qi Zhang  4 , Junping Liu  5 , Junzhe Bao  6 , Mingjie Sun  1
Affiliations

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

Peng Li et al. Front Cell Infect Microbiol. .

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 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).

Conclusion: 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.

Keywords: COVID-19; healthcare-associated infection; intensive care unit; invasive fungal super-infection; nomogram.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow diagram of prediction modeling.
Figure 2
Figure 2
Epidemiological and microbiological characteristics of IFSI in ICU patients with HABI. (A) Trends in the prevalence of fungal strains causing IFSI in ICU, (B) Distribution of isolated pathogens among ICU patients with IFSI.
Figure 3
Figure 3
Screening process for potential predictors by LASSO method (The blue vertical dashed line showed the λ value at the minimum MSE, while the red showed the λ value at the the minimum MSE + SE. The labels from “a” to “e” represented combination of priority antimicrobials, inappropriate antimicrobials prescribing, MDRO, aCCI and S.aureus, in turn).
Figure 4
Figure 4
Forest plot for predictors of IFSI selected by stepwise and LASSO methods in multivariate model.
Figure 5
Figure 5
The predictive performance and external validity of models. (A) ROCs of the LASSO-model, stepwise-model on the derivation cohort and stepwise-model on the validation cohort, respectively. (B) Calibration plots of the LASSO-model, stepwise-model on the derivation cohort and stepwise-model on the validation cohort, respectively.
See this image and copyright information in PMC

References

    1. Abad C. L., Barker A. K., Safdar N. (2020). A systematic review of the effectiveness of cohorting to reduce transmission of healthcare-associated C. difficile and multidrug-resistant organisms. Infect. Cont. Hosp. Ep. 41, 691–709. doi: 10.1017/ice.2020.45 - DOI - PMC - PubMed
    1. Alba A. C., Agoritsas T., Walsh M., Iorio A., Devereaux P. J., McGinn T., et al. (2017). Discrimination and calibration of clinical prediction models: users’ guides to the medical literatur. JAMA 318, 1377–1384. doi: 10.1001/jama.2017.12126 - DOI - PubMed
    1. Au E. H., Francis A., Bernier-Jean A., Teixeira-Pinto A. (2020). Prediction modeling-part 1: regression modeling. Kidney Int. 97, 877–884. doi: 10.1016/j.kint.2020.02.007 - DOI - PubMed
    1. Bassetti M., Kollef M. H., Timsit J. F. (2020). Bacterial and fungal superinfections in critically ill patients with COVID-19. Intens. Care Med. 46, 2071–2074. doi: 10.1007/s00134-020-06219-8 - DOI - PMC - PubMed
    1. Baur D., Gladstone B. P., Burkert F., Carrara E., Foschi F., Döbele S., et al. (2017). Effect of antibiotic stewardship on the incidence of infection and colonisation with antibiotic-resistant bacteria and Clostridium difficile infection: a systematic review and meta-analysis. Lancet Infect. Dis. 17, 990–1001. doi: 10.1016/S1473-3099(17)30325-0 - DOI - PubMed

LinkOut - more resources