Developing Machine-Learning Prediction Algorithm for Bacteremia in Admitted Patients
Received 23 November 2020
Accepted for publication 14 January 2021
Published 25 February 2021 Volume 2021:14 Pages 757—765
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Suresh Antony
Ebrahim Mahmoud,1 Mohammed Al Dhoayan,2,3 Mohammad Bosaeed,1,4,5 Sameera Al Johani,5,6 Yaseen M Arabi5,7
1Department of Infectious Disease, Department of Medicine, King Abdulaziz Medical City, Riyadh, Saudi Arabia; 2Department of Health Informatics, CPHHI, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; 3Data and Business Intelligence Management Department, ISID, King Abdulaziz Medical City, Riyadh, Saudi Arabia; 4King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia; 5College of Medicine, King Saud Bin Abdulaziz University For Health Sciences, Riyadh, Saudi Arabia; 6Department of Pathology & Laboratory Medicine, King Abdulaziz Medical City, Riyadh, Saudi Arabia; 7Department of Intensive Care, King Abdulaziz Medical City, Riyadh, Saudi Arabia
Correspondence: Ebrahim Mahmoud
Division of Infectious Diseases, Department of Medicine, King Abdulaziz Medical City, Riyadh, Riyadh, Saudi Arabia
Tel +966 500081418
Email [email protected]
Purpose: Bloodstream infection among hospitalized patients is associated with serious adverse outcomes. Blood culture is routinely ordered in patients with suspected infections, although 90% of blood cultures do not show any growth of organisms. The evidence regarding the prediction of bacteremia is scarce.
Patients And Methods: A retrospective review of blood cultures requested for a cohort of admitted patients between 2017 and 2019 was undertaken. Several machine-learning models were used to identify the best prediction model. Additionally, univariate and multivariable logistic regression was used to determine the predictive factors for bacteremia.
Results: A total of 36,405 blood cultures of 7157 patients were done. There were 2413 (6.62%) positive blood cultures. The best prediction was by using NN with the high specificity of 88% but low sensitivity. There was a statistical difference in the following factors: longer admission days before the blood culture, presence of a central line, and higher lactic acid—more than 2 mmol/L.
Conclusion: Despite the low positive rate of blood culture, machine learning could predict positive blood culture with high specificity but minimum sensitivity. Yet, the SIRS score, qSOFA score, and other known factors were not good prognostic factors. Further improvement and training would possibly enhance machine-learning performance.
Keywords: bacteremia, blood culture prediction, machine learning, predictive medicine
This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.Download Article [PDF] View Full Text [HTML][Machine readable]