Mamandipoor, B., Yeung, W., Agha-Mir-Salim, L. et al. (3 more authors) (2022) Prediction of blood lactate values in critically ill patients: a retrospective multi-center cohort study. Journal of Clinical Monitoring and Computing, 36 (4). pp. 1087-1097. ISSN 1387-1307
Abstract
Elevations in initially obtained serum lactate levels are strong predictors of mortality in critically ill patients. Identifying patients whose serum lactate levels are more likely to increase can alert physicians to intensify care and guide them in the frequency of tending the blood test. We investigate whether machine learning models can predict subsequent serum lactate changes. We investigated serum lactate change prediction using the MIMIC-III and eICU-CRD datasets in internal as well as external validation of the eICU cohort on the MIMIC-III cohort. Three subgroups were defined based on the initial lactate levels: (i) normal group (< 2 mmol/L), (ii) mild group (2–4 mmol/L), and (iii) severe group (> 4 mmol/L). Outcomes were defined based on increase or decrease of serum lactate levels between the groups. We also performed sensitivity analysis by defining the outcome as lactate change of > 10% and furthermore investigated the influence of the time interval between subsequent lactate measurements on predictive performance. The LSTM models were able to predict deterioration of serum lactate values of MIMIC-III patients with an AUC of 0.77 (95% CI 0.762–0.771) for the normal group, 0.77 (95% CI 0.768–0.772) for the mild group, and 0.85 (95% CI 0.840–0.851) for the severe group, with only a slightly lower performance in the external validation. The LSTM demonstrated good discrimination of patients who had deterioration in serum lactate levels. Clinical studies are needed to evaluate whether utilization of a clinical decision support tool based on these results could positively impact decision-making and patient outcomes.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Author(s), under exclusive licence to Springer Nature B.V. This is an author-produced version of a paper subsequently published in Journal of Clinical Monitoring and Computing. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Critical illness; Deep learning; Lactate; Resuscitation; Time series; Cohort Studies; Critical Illness; Humans; Lactic Acid; Retrospective Studies |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 13 Mar 2024 11:08 |
Last Modified: | 13 Mar 2024 14:29 |
Status: | Published |
Publisher: | Springer Science and Business Media LLC |
Refereed: | Yes |
Identification Number: | 10.1007/s10877-021-00739-4 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:210193 |