Rawson, Timothy M 
ORCID: 0000-0002-2630-9722, Hernandez, Bernard, Wilson, Richard C ORCID: 0000-0002-3275-6932, Ming, Damien ORCID: 0000-0003-3125-6378, Herrero, Pau, Ranganathan, Nisha, Skolimowska, Keira, Gilchrist, Mark, Satta, Giovanni, Georgiou, Pantelis et al (show 1 more authors)
(2021)
Supervised machine learning to support the diagnosis of bacterial infection in the context of COVID-19.
JAC-ANTIMICROBIAL RESISTANCE, 3 (1).
dlab002-.
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Abstract
<h4>Background</h4>Bacterial infection has been challenging to diagnose in patients with COVID-19. We developed and evaluated supervised machine learning algorithms to support the diagnosis of secondary bacterial infection in hospitalized patients during the COVID-19 pandemic.<h4>Methods</h4>Inpatient data at three London hospitals for the first COVD-19 wave in March and April 2020 were extracted. Demographic, blood test and microbiology data for individuals with and without SARS-CoV-2-positive PCR were obtained. A Gaussian Naive Bayes, Support Vector Machine (SVM) and Artificial Neural Network were trained and compared using the area under the receiver operating characteristic curve (AUCROC). The best performing algorithm (SVM with 21 blood test variables) was prospectively piloted in July 2020. AUCROC was calculated for the prediction of a positive microbiological sample within 48 h of admission.<h4>Results</h4>A total of 15 599 daily blood profiles for 1186 individual patients were identified to train the algorithms; 771/1186 (65%) individuals were SARS-CoV-2 PCR positive. Clinically significant microbiology results were present for 166/1186 (14%) patients during admission. An SVM algorithm trained with 21 routine blood test variables and over 8000 individual profiles had the best performance. AUCROC was 0.913, sensitivity 0.801 and specificity 0.890. Prospective testing on 54 patients on admission (28/54, 52% SARS-CoV-2 PCR positive) demonstrated an AUCROC of 0.960 (95% CI: 0.90-1.00).<h4>Conclusions</h4>An SVM using 21 routine blood test variables had excellent performance at inferring the likelihood of positive microbiology. Further prospective evaluation of the algorithms ability to support decision making for the diagnosis of bacterial infection in COVID-19 cohorts is underway.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Vaccine Related, Emerging Infectious Diseases, Infectious Diseases, Biodefense, Prevention, Infection, 3 Good Health and Well Being |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 28 Oct 2022 15:08 |
| Last Modified: | 12 Apr 2024 11:46 |
| DOI: | 10.1093/jacamr/dlab002 |
| Open Access URL: | https://doi.org/10.1093/jacamr/dlab002 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3165857 |
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