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Machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters

BACKGROUND: Bacterial bloodstream infection is responsible for the majority of cases of sepsis and septic shock. Early recognition of the causative pathogen is pivotal for administration of adequate empiric antibiotic therapy and for the survival of the patients. In this study, we developed a feasib...

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Autores principales: Zhang, Fan, Wang, Hao, Liu, Liyu, Su, Teng, Ji, Bing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10566101/
https://www.ncbi.nlm.nih.gov/pubmed/37817106
http://dx.doi.org/10.1186/s12879-023-08602-4
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author Zhang, Fan
Wang, Hao
Liu, Liyu
Su, Teng
Ji, Bing
author_facet Zhang, Fan
Wang, Hao
Liu, Liyu
Su, Teng
Ji, Bing
author_sort Zhang, Fan
collection PubMed
description BACKGROUND: Bacterial bloodstream infection is responsible for the majority of cases of sepsis and septic shock. Early recognition of the causative pathogen is pivotal for administration of adequate empiric antibiotic therapy and for the survival of the patients. In this study, we developed a feasible machine learning (ML) model to predict gram-positive and gram-negative bacteremia based on routine laboratory parameters. METHODS: Data for 2118 patients with bacteremia were obtained from the Medical Information Mart for Intensive Care dataset. Patients were randomly split into the training set and test set by stratified sampling, and 374 routine laboratory blood test variables were retrieved. Variables with missing values in more than 40% of the patients were excluded. Pearson correlation test was employed to eliminate redundant features. Five ML algorithms were used to build the model based on the selected features. Additionally, 132 patients with bacteremia who were treated at Qilu Hospital of Shandong University were included in an independent test cohort to evaluate the model. RESULTS: After feature selection, 32 variables remained. All the five ML algorithms performed well in terms of discriminating between gram-positive and gram-negative bacteremia, but the performance of convolutional neural network (CNN) and random forest (RF) were better than other three algorithms. Consider of the interpretability of models, RF was chosen for further test (ROC-AUC = 0.768; 95%CI = 0.715–0.798, with a sensitivity of 75.20% and a specificity of 63.79%). To expand the application of the model, a decision tree (DT) was built utilizing the major variables, and it achieved an AUC of 0.679 (95%CI = 0.632–0.723), a sensitivity of 66%, and a specificity of 67.82% in the test cohort. When tested in the Qilu Hospital cohort, the ROC-AUC of the RF and DT models were 0.666 (95%CI = 0.579–0.746) and 0.615 (95%CI = 0.526–0.698), respectively. Finally, a software was developed to make the RF- and DT-based prediction models easily accessible. CONCLUSION: The present ML-based models could effectively discriminate between gram-positive and gram-negative bacteremia based on routine laboratory blood test results. This simple model would be beneficial in terms of guiding timely antibiotic selection and administration in critically ill patients with bacteremia before their pathogen test results are available. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-023-08602-4.
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spelling pubmed-105661012023-10-12 Machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters Zhang, Fan Wang, Hao Liu, Liyu Su, Teng Ji, Bing BMC Infect Dis Research BACKGROUND: Bacterial bloodstream infection is responsible for the majority of cases of sepsis and septic shock. Early recognition of the causative pathogen is pivotal for administration of adequate empiric antibiotic therapy and for the survival of the patients. In this study, we developed a feasible machine learning (ML) model to predict gram-positive and gram-negative bacteremia based on routine laboratory parameters. METHODS: Data for 2118 patients with bacteremia were obtained from the Medical Information Mart for Intensive Care dataset. Patients were randomly split into the training set and test set by stratified sampling, and 374 routine laboratory blood test variables were retrieved. Variables with missing values in more than 40% of the patients were excluded. Pearson correlation test was employed to eliminate redundant features. Five ML algorithms were used to build the model based on the selected features. Additionally, 132 patients with bacteremia who were treated at Qilu Hospital of Shandong University were included in an independent test cohort to evaluate the model. RESULTS: After feature selection, 32 variables remained. All the five ML algorithms performed well in terms of discriminating between gram-positive and gram-negative bacteremia, but the performance of convolutional neural network (CNN) and random forest (RF) were better than other three algorithms. Consider of the interpretability of models, RF was chosen for further test (ROC-AUC = 0.768; 95%CI = 0.715–0.798, with a sensitivity of 75.20% and a specificity of 63.79%). To expand the application of the model, a decision tree (DT) was built utilizing the major variables, and it achieved an AUC of 0.679 (95%CI = 0.632–0.723), a sensitivity of 66%, and a specificity of 67.82% in the test cohort. When tested in the Qilu Hospital cohort, the ROC-AUC of the RF and DT models were 0.666 (95%CI = 0.579–0.746) and 0.615 (95%CI = 0.526–0.698), respectively. Finally, a software was developed to make the RF- and DT-based prediction models easily accessible. CONCLUSION: The present ML-based models could effectively discriminate between gram-positive and gram-negative bacteremia based on routine laboratory blood test results. This simple model would be beneficial in terms of guiding timely antibiotic selection and administration in critically ill patients with bacteremia before their pathogen test results are available. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-023-08602-4. BioMed Central 2023-10-10 /pmc/articles/PMC10566101/ /pubmed/37817106 http://dx.doi.org/10.1186/s12879-023-08602-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access 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. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhang, Fan
Wang, Hao
Liu, Liyu
Su, Teng
Ji, Bing
Machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters
title Machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters
title_full Machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters
title_fullStr Machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters
title_full_unstemmed Machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters
title_short Machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters
title_sort machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10566101/
https://www.ncbi.nlm.nih.gov/pubmed/37817106
http://dx.doi.org/10.1186/s12879-023-08602-4
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