Machine‑learning based prediction of prognostic risk factors in patients with invasive candidiasis infection and bacterial bloodstream infection: a singled centered retrospective study

Li, Yaling, Wu, Yutong, Gao, Yali, Niu, Xueli, Li, Jingyi, Tang, Mingsui, Fu, Chang, Qi, Ruiqun, Song, Bing ORCID: https://orcid.org/0000-0001-9356-2333, Chen, Hongduo, Gao, Xinghua, Yang, Ying and Guan, Xiuhao 2022. Machine‑learning based prediction of prognostic risk factors in patients with invasive candidiasis infection and bacterial bloodstream infection: a singled centered retrospective study. BMC Infectious Diseases 22 , 150. 10.1186/s12879-022-07125-8

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Abstract

Background Invasive candidal infection combined with bacterial bloodstream infection is one of the common nosocomial infections that is also the main cause of morbidity and mortality. The incidence of invasive Candidal infection with bacterial bloodstream infection is increasing year by year worldwide, but data on China is still limited. Methods We included 246 hospitalised patients who had invasive candidal infection combined with a bacterial bloodstream infection from January 2013 to January 2018; we collected and analysed the relevant epidemiological information and used machine learning methods to find prognostic factors related to death (training set and test set were randomly allocated at a ratio of 7:3). Results Of the 246 patients with invasive candidal infection complicated with a bacterial bloodstream infection, the median age was 63 years (53.25–74), of which 159 (64.6%) were male, 109 (44.3%) were elderly patients (> 65 years), 238 (96.7%) were hospitalised for more than 10 days, 168 (68.3%) were admitted to ICU during hospitalisation, and most patients had records of multiple admissions within 2 years (167/246, 67.9%). The most common blood index was hypoproteinemia (169/246, 68.7%), and the most common inducement was urinary catheter use (210/246, 85.4%). Moreover, the most frequently infected fungi and bacteria were Candida parapsilosis and Acinetobacter baumannii, respectively. The main predictors of death prognosis by machine learning method are serum creatinine level, age, length of stay, stay in ICU during hospitalisation, serum albumin level, C-Reactive protein (CRP), leukocyte count, neutrophil count, Procalcitonin (PCT), and total bilirubin level. Conclusion Our results showed that the most common candida and bacteria infections were caused by Candida parapsilosis and Acinetobacter baumannii, respectively. The main predictors of death prognosis are serum creatinine level, age, length of stay, stay in ICU during hospitalisation, serum albumin level, CRP, leukocyte count, neutrophil count, PCT and total bilirubin level.

Item Type:	Article
Date Type:	Published Online
Schools:	Dentistry
Additional Information:	This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Publisher:	BioMed Central
ISSN:	1471-2334
Date of First Compliant Deposit:	4 March 2022
Date of Acceptance:	1 February 2022
Last Modified:	13 May 2023 21:29
URI:	https://orca.cardiff.ac.uk/id/eprint/147837

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