Utilising routinely collected clinical data through time series deep learning to improve identification of bacterial bloodstream infections: a retrospective cohort study
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Author(s)
Type
Journal Article
Abstract
Background:
Blood cultures are the gold standard for diagnosing bacterial bloodstream infections, but test results are only available 24–48 h after sampling. We aimed to develop and evaluate models using health-care data to predict bloodstream infections in patients admitted to hospital.
Methods:
In this retrospective cohort study, we used routinely collected blood biomarkers and demographic data from patients who underwent blood sample collection for testing via culture between March 3, 2014, and Dec 1, 2021, at Imperial College Healthcare NHS Trust (London, UK) as model features. Data up to 14 days before blood sample collection were provided to long short-term memory (LSTM) or static logistic regression models. The primary outcome was prediction of blood culture results, defined as a pathogenic bloodstream infection (ie, isolation of pathogenic bacteria of interest) or no bloodstream infection (ie, no growth or contamination). Data collected up to Feb 28, 2021 (n=15 212) comprised the training set and were evaluated against a temporal hold-out test set comprising patients who were sampled after March 1, 2021 (n=5638).
Findings:
Among 20 850 patients with available data, pathogenic bacteria were observed in the cultured blood samples of 3866 (18·5%) patients. 2920 (62·2%) of 4897 patients who had their blood samples taken more than 48 h after admission to hospital had pathogenic bloodstream infections, and so were defined as having hospital-acquired bloodstream infections. Including data from the 7 days before admission (7-day window approach) and using five-fold cross validation in the training set gave an area under receiver operator curve (AUROC) of 0·75 (IQR 0·68–0·82) and an area under the precision recall curve (AUPRC) of 0·58 (0·46–0·77) for static models and an AUROC of 0·92 (0·91–0·93) and AUPRC of 0·75 (0·72–0·76) for the LSTM model. In the hold-out test set performances were: AUROC of 0·74 (95% CI 0·70–0·78) and AUPRC of 0·48 (0·43–0·53) for static models and AUROC of 0·97 (0·96–0·97) and AUPRC of 0·65 (0·60–0·70) for LSTM. Removal of time series information resulted in lower model performance, particularly for hospital-acquired bloodstream infections. Dynamics of C-reactive protein concentration, eosinophil count, and platelet count were important features for prediction of blood culture results.
Interpretation:
Deep learning models accounting for longitudinal changes could support individualised clinical decision making for patients at risk of bloodstream infections. Appropriate implementation into existing diagnostic pathways could enhance diagnostic stewardship and reduce unnecessary antimicrobial prescribing.
Funding:
UK Department of Health and Social Care, the National Institute for Health and Care Research, and the Wellcome Trust.
Blood cultures are the gold standard for diagnosing bacterial bloodstream infections, but test results are only available 24–48 h after sampling. We aimed to develop and evaluate models using health-care data to predict bloodstream infections in patients admitted to hospital.
Methods:
In this retrospective cohort study, we used routinely collected blood biomarkers and demographic data from patients who underwent blood sample collection for testing via culture between March 3, 2014, and Dec 1, 2021, at Imperial College Healthcare NHS Trust (London, UK) as model features. Data up to 14 days before blood sample collection were provided to long short-term memory (LSTM) or static logistic regression models. The primary outcome was prediction of blood culture results, defined as a pathogenic bloodstream infection (ie, isolation of pathogenic bacteria of interest) or no bloodstream infection (ie, no growth or contamination). Data collected up to Feb 28, 2021 (n=15 212) comprised the training set and were evaluated against a temporal hold-out test set comprising patients who were sampled after March 1, 2021 (n=5638).
Findings:
Among 20 850 patients with available data, pathogenic bacteria were observed in the cultured blood samples of 3866 (18·5%) patients. 2920 (62·2%) of 4897 patients who had their blood samples taken more than 48 h after admission to hospital had pathogenic bloodstream infections, and so were defined as having hospital-acquired bloodstream infections. Including data from the 7 days before admission (7-day window approach) and using five-fold cross validation in the training set gave an area under receiver operator curve (AUROC) of 0·75 (IQR 0·68–0·82) and an area under the precision recall curve (AUPRC) of 0·58 (0·46–0·77) for static models and an AUROC of 0·92 (0·91–0·93) and AUPRC of 0·75 (0·72–0·76) for the LSTM model. In the hold-out test set performances were: AUROC of 0·74 (95% CI 0·70–0·78) and AUPRC of 0·48 (0·43–0·53) for static models and AUROC of 0·97 (0·96–0·97) and AUPRC of 0·65 (0·60–0·70) for LSTM. Removal of time series information resulted in lower model performance, particularly for hospital-acquired bloodstream infections. Dynamics of C-reactive protein concentration, eosinophil count, and platelet count were important features for prediction of blood culture results.
Interpretation:
Deep learning models accounting for longitudinal changes could support individualised clinical decision making for patients at risk of bloodstream infections. Appropriate implementation into existing diagnostic pathways could enhance diagnostic stewardship and reduce unnecessary antimicrobial prescribing.
Funding:
UK Department of Health and Social Care, the National Institute for Health and Care Research, and the Wellcome Trust.
Date Issued
2025-03-01
Date Acceptance
2025-01-15
Citation
The Lancet: Digital Health, 2025, 7 (3), pp.E205-E215
ISSN
2589-7500
Publisher
Elsevier
Start Page
E205
End Page
E215
Journal / Book Title
The Lancet: Digital Health
Volume
7
Issue
3
Copyright Statement
© 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
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Publication Status
Published