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. 2023 Jul 18;12(14):4751.
doi: 10.3390/jcm12144751.

The Artificial Neural Network as a Diagnostic Tool of the Risk of Clostridioides difficile Infection among Patients with Chronic Kidney Disease

Jakub Stojanowski  1 , Andrzej Konieczny  1 , Łukasz Lis  2 , Weronika Frosztęga  1 , Patrycja Brzozowska  1 , Anna Ciszewska  2 , Klaudia Rydzyńska  1 , Michał Sroka  2 , Kornelia Krakowska  1 , Tomasz Gołębiowski  1 , Zbigniew Hruby  2 , Mariusz Kusztal  1 , Magdalena Krajewska  1
Affiliations

The Artificial Neural Network as a Diagnostic Tool of the Risk of Clostridioides difficile Infection among Patients with Chronic Kidney Disease

Jakub Stojanowski et al. J Clin Med. .

Abstract

The majority of recently published studies indicate a greater incidence and mortality due to Clostridioides difficile infection (CDI) in patients with chronic kidney disease (CKD). Hospitalization, older age, the use of antibiotics, immunosuppression, proton pump inhibitors (PPI), and chronic diseases such as CKD are responsible for the increased prevalence of infections. The aim of the study is to identify clinical indicators allowing, in combination with artificial intelligence (AI) techniques, the most accurate assessment of the patients being at elevated risk of CDI.

Keywords: Clostridioides difficile; artificial intelligence; chronic kidney disease; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Random forest classifier with given input variables: length of antibiotics used before CDI, in days, status of ER stay before admission, Norton scale, care class, BMI showed very good ability to discriminate patients who developed CDI.
Figure 2
Figure 2
Due to the risk of bias due to the small number of samples, a leave-one-out cross validation analysis was performed which resulted in an averaged AUC of 0.9370.
See this image and copyright information in PMC

References

    1. McDonald L.C., Gerding D.N., Johnson S., Bakken J.S., Carroll K.C., Coffin S.E., Dubberke E.R., Garey K.W., Gould C.V., Kelly C., et al. Clinical Practice Guidelines for Clostridium difficile Infection in Adults and Children: 2017 Update by the Infectious Diseases Society of America (IDSA) and Society for Healthcare Epidemiology of America (SHEA) Clin. Infect. Dis. 2018;66:e1–e48. doi: 10.1093/cid/cix1085. - DOI - PMC - PubMed
    1. Chicco D., Jurman G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom. 2020;21:6. doi: 10.1186/s12864-019-6413-7. - DOI - PMC - PubMed
    1. Lis Ł., Konieczny A., Sroka M., Ciszewska A., Krakowska K., Gołębiowski T., Hruby Z. Clinical Determinants Predicting Clostridioides difficile Infection among Patients with Chronic Kidney Disease. Antibiotics. 2022;11:785. doi: 10.3390/antibiotics11060785. - DOI - PMC - PubMed
    1. Teng C., Reveles K.R., Obodozie-Ofoegbu O.O., Frei C.R. Clostridium difficile Infection Risk with Important Antibiotic Classes: An Analysis of the FDA Adverse Event Reporting System. Int. J. Med. Sci. 2019;16:630–635. doi: 10.7150/ijms.30739. - DOI - PMC - PubMed
    1. Abrahamian F.M., Talan D.A., Krishnadasan A., Citron D.M., Paulick A.L., Anderson L.J., Goldstein E.J.C., Moran G.J. Clostridium difficile Infection among US Emergency Department Patients with Diarrhea and No Vomiting. Ann. Emerg. Med. 2017;70:19–27.e14. doi: 10.1016/j.annemergmed.2016.12.013. - DOI - PubMed

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