Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis

Gao, Yali, Tang, Mingsui, Li, Yaling, Niu, Xueli, Li, Jingyi, Fu, Chang, Wang, Zihan, Liu, Jiayi, Song, Bing ORCID: https://orcid.org/0000-0001-9356-2333, Chen, Hongduo, Gao, Xinghua and Guan, Xiuhao 2022. Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis. PeerJ – the Journal of Life & Environmental Sciences 10 , e13594. 10.7717/peerj.13594

PDF - Published Version Available under License Creative Commons Attribution. Download (2MB)

Abstract

Bacteraemia has attracted great attention owing to its serious outcomes, including deterioration of the primary disease, infection, severe sepsis, overwhelming septic shock or even death. Candidemia, secondary to bacteraemia, is frequently seen in hospitalised patients, especially in those with weak immune systems, and may lead to lethal outcomes and a poor prognosis. Moreover, higher morbidity and mortality associated with candidemia. Owing to the complexity of patient conditions, the occurrence of candidemia is increasing. Candidemia-related studies are relatively challenging. Because candidemia is associated with increasing mortality related to invasive infection of organs, its pathogenesis warrants further investigation. We collected the relevant clinical data of 367 patients with concomitant candidemia and bacteraemia in the first hospital of China Medical University from January 2013 to January 2018. We analysed the available information and attempted to obtain the undisclosed information. Subsequently, we used machine learning to screen for regulators such as prognostic factors related to death. Of the 367 patients, 231 (62.9%) were men, and the median age of all patients was 61 years old (range, 52–71 years), with 133 (36.2%) patients aged >65 years. In addition, 249 patients had hypoproteinaemia, and 169 patients were admitted to the intensive care unit (ICU) during hospitalisation. The most common fungi and bacteria associated with tumour development and Candida infection were Candida parapsilosis and Acinetobacter baumannii, respectively. We used machine learning to screen for death-related prognostic factors in patients with candidemia and bacteraemia mainly based on integrated information. The results showed that serum creatinine level, endotoxic shock, length of stay in ICU, age, leukocyte count, total parenteral nutrition, total bilirubin level, length of stay in the hospital, PCT level and lymphocyte count were identified as the main prognostic factors. These findings will greatly help clinicians treat patients with candidemia and bacteraemia.

Item Type:	Article
Date Type:	Publication
Schools:	Dentistry
Publisher:	PeerJ
ISSN:	2167-8359
Date of First Compliant Deposit:	15 September 2022
Date of Acceptance:	25 May 2022
Last Modified:	24 May 2023 20:39
URI:	https://orca.cardiff.ac.uk/id/eprint/152629

Actions (repository staff only)

Edit Item