Cargando…
Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis
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...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9206432/ https://www.ncbi.nlm.nih.gov/pubmed/35726257 http://dx.doi.org/10.7717/peerj.13594 |
_version_ | 1784729336051400704 |
---|---|
author | Gao, Yali Tang, Mingsui Li, Yaling Niu, Xueli Li, Jingyi Fu, Chang Wang, Zihan Liu, Jiayi Song, Bing Chen, Hongduo Gao, Xinghua Guan, Xiuhao |
author_facet | Gao, Yali Tang, Mingsui Li, Yaling Niu, Xueli Li, Jingyi Fu, Chang Wang, Zihan Liu, Jiayi Song, Bing Chen, Hongduo Gao, Xinghua Guan, Xiuhao |
author_sort | Gao, Yali |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9206432 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92064322022-06-19 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 Chen, Hongduo Gao, Xinghua Guan, Xiuhao PeerJ Mycology 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. PeerJ Inc. 2022-06-15 /pmc/articles/PMC9206432/ /pubmed/35726257 http://dx.doi.org/10.7717/peerj.13594 Text en © 2022 Gao et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Mycology Gao, Yali Tang, Mingsui Li, Yaling Niu, Xueli Li, Jingyi Fu, Chang Wang, Zihan Liu, Jiayi Song, Bing Chen, Hongduo Gao, Xinghua Guan, Xiuhao Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis |
title | Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis |
title_full | Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis |
title_fullStr | Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis |
title_full_unstemmed | Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis |
title_short | Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis |
title_sort | machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis |
topic | Mycology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9206432/ https://www.ncbi.nlm.nih.gov/pubmed/35726257 http://dx.doi.org/10.7717/peerj.13594 |
work_keys_str_mv | AT gaoyali machinelearningbasedpredictionandanalysisofprognosticriskfactorsinpatientswithcandidemiaandbacteraemiaa5yearanalysis AT tangmingsui machinelearningbasedpredictionandanalysisofprognosticriskfactorsinpatientswithcandidemiaandbacteraemiaa5yearanalysis AT liyaling machinelearningbasedpredictionandanalysisofprognosticriskfactorsinpatientswithcandidemiaandbacteraemiaa5yearanalysis AT niuxueli machinelearningbasedpredictionandanalysisofprognosticriskfactorsinpatientswithcandidemiaandbacteraemiaa5yearanalysis AT lijingyi machinelearningbasedpredictionandanalysisofprognosticriskfactorsinpatientswithcandidemiaandbacteraemiaa5yearanalysis AT fuchang machinelearningbasedpredictionandanalysisofprognosticriskfactorsinpatientswithcandidemiaandbacteraemiaa5yearanalysis AT wangzihan machinelearningbasedpredictionandanalysisofprognosticriskfactorsinpatientswithcandidemiaandbacteraemiaa5yearanalysis AT liujiayi machinelearningbasedpredictionandanalysisofprognosticriskfactorsinpatientswithcandidemiaandbacteraemiaa5yearanalysis AT songbing machinelearningbasedpredictionandanalysisofprognosticriskfactorsinpatientswithcandidemiaandbacteraemiaa5yearanalysis AT chenhongduo machinelearningbasedpredictionandanalysisofprognosticriskfactorsinpatientswithcandidemiaandbacteraemiaa5yearanalysis AT gaoxinghua machinelearningbasedpredictionandanalysisofprognosticriskfactorsinpatientswithcandidemiaandbacteraemiaa5yearanalysis AT guanxiuhao machinelearningbasedpredictionandanalysisofprognosticriskfactorsinpatientswithcandidemiaandbacteraemiaa5yearanalysis |