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
. 2022 Jun 2:2022:7896218.
doi: 10.1155/2022/7896218. eCollection 2022.

Prediction of Prognostic Risk Factors in Patients with Invasive Candidiasis and Cancer: A Single-Centre Retrospective Study

Jingyi Li  1 , Yaling Li  1   2 , Yali Gao  3 , Xueli Niu  1 , Mingsui Tang  1 , Chang Fu  1 , Zihan Wang  1 , Jiayi Liu  1 , Bing Song  1   2 , Hongduo Chen  1 , Xinghua Gao  1 , Xiuhao Guan  1
Affiliations

Prediction of Prognostic Risk Factors in Patients with Invasive Candidiasis and Cancer: A Single-Centre Retrospective Study

Jingyi Li et al. Biomed Res Int. .

Retraction in

Abstract

Background: Invasive candidiasis is a common cancer-related complication with a high fatality rate. If patients with a high risk of dying in the hospital are identified early and accurately, physicians can make better clinical judgments. However, epidemiological analyses and mortality prediction models of cancer patients with invasive candidiasis remain limited.

Method: A set of 40 potential risk factors was acquired in a sample of 258 patients with both invasive candidiasis and cancer. To begin, risk factors for Candida albicans vs. non-Candida albicans infections and persistent vs. nonpersistent Candida infections were analysed using classic statistical methods. Then, we applied three machine learning models (random forest, logistic regression, and support vector machine) to identify prognostic indicators related to mortality. Prediction performance of different models was assessed by precision, recall, F1 score, accuracy, and AUC.

Results: Of the 258 patients both with invasive candidiasis and cancer included in the analysis. The median age of patients was 62 years, and 95 (36.82%) patients were older than 65 years, of which 178 (66.28%) were male. And 186 (72.1%) patients underwent surgery 2 weeks before data collection, 100 (39.1%) patients stayed in ICU during hospitalisation, 99 (38.4%) patients had bacterial blood infection, 85 (32.9%) patients had persistent invasive candidiasis, and 41 (15.9%) patients died within 30 days. The usage of drainage catheter and prolonged length of hospitalisation are the dominant risk factors for non-Candida albicans infections and persistent Candida infections, respectively. Risk factors, such as septic shock, history of surgery within the past 2 weeks, usage of drainage tubes, length of stay in ICU, total parenteral nutrition, serum creatinine level, fungal antigen, stay in ICU during hospitalisation, and total bilirubin level, were significant predictors of death. The RF model outperformed the LR and SVM models. Precision, recall, F1 score, accuracy, and AUC for RF were 64.29%, 75.63%, 69.23%, 89.61%, and 91.28%.

Conclusions: In this study, the machine learning-based models accurately predicted the prognosis of cancer and invasive candidiasis patients. The algorithm could be used to help clinicians in high-risk patients' early intervention.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Flow chart of machine learning.
Figure 2
Figure 2
Tumour types of 258 patients.
Figure 3
Figure 3
Candida species in 258 patients.
Figure 4
Figure 4
The distribution of blood bacterial infection in 99 patients.
Figure 5
Figure 5
Receiver operating characteristic curve of different machine learning models.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Pfaller M. A., Diekema D. J. Epidemiology of invasive candidiasis: a persistent public health problem. Clinical Microbiology Reviews . 2007;20(1):133–163. doi: 10.1128/CMR.00029-06. - DOI - PMC - PubMed
    1. Bassetti M., Giacobbe D. R., Vena A., et al. Incidence and outcome of invasive candidiasis in intensive care units (ICUs) in Europe: results of the EUCANDICU project. Critical Care . 2019;23(1):p. 219. doi: 10.1186/s13054-019-2497-3. - DOI - PMC - PubMed
    1. Bassetti M., Righi E., Montravers P., Cornely O. A. What has changed in the treatment of invasive candidiasis? A look at the past 10 years and ahead. Journal of Antimicrobial Chemotherapy . 2018;73(suppl_1):i14–i25. doi: 10.1093/jac/dkx445. - DOI - PMC - PubMed
    1. Lortholary O., Renaudat C., Sitbon K., Desnos-Ollivier M., Bretagne S., Dromer F. The risk and clinical outcome of candidemia depending on underlying malignancy. Intensive Care Medicine . 2017;43(5):652–662. doi: 10.1007/s00134-017-4743-y. - DOI - PMC - PubMed
    1. Skrobik Y., Laverdiere M. Why _Candida_ sepsis should matter to ICU physicians. Critical Care Clinics . 2013;29(4):853–864. doi: 10.1016/j.ccc.2013.06.007. - DOI - PubMed

Publication types

Substances

Supplementary concepts