Machine learning methods to improve bedside fluid responsiveness prediction in severe sepsis or septic shock: An observational study
Open accessAbstract
Background Passive leg raising (PLR) predicts fluid responsiveness in critical illness, although restrictions in mobilising patients often preclude this haemodynamic challenge being used. We investigated whether machine learning applied on transthoracic echocardiography (TTE) data might be used as a tool for predicting fluid responsiveness in critically ill patients.Methods We studied, 100 critically ill patients (mean age: 62 yr [standard deviation: 14]) with severe sepsis or septic shock prospectively over 24 months. Transthoracic echocardiography measurements were performed at baseline, after PLR, and before and after a standardised fluid challenge in learning and test populations (n=50 patients each). A 15% increase in stroke volume defined fluid responsiveness. The machine learning methods used were classification and regression tree (CART), partial least-squares regression (PLS), neural network (NNET), and linear discriminant analysis (LDA). Each method was applied offline to determine whether fluid responsiveness may be predicted from left and right cardiac ventricular physiological changes detected by cardiac ultrasound. Predictive values for fluid responsiveness were compared by receiver operating characteristics (area under the curve [AUC]; mean [95% confidence intervals]).Results In the learning sample, the AUC values were PLR 0.76 (0.62-0.89), CART 0.83 (0.73-0.94), PLS 0.97 (0.93-1), NNET 0.93 (0.85-1), and LDA 0.90 (0.81-0.98). In the test sample, the AUC values were PLR 0.77 (0.64-0.91), CART 0.68 (0.54-0.81), PLS 0.83 (0.71-0.96), NNET 0.83 (0.71-0.94), and LDA 0.85 (0.74-0.96) respectively. The PLS model identified inferior vena cava collapsibility, velocity-time integral, S-wave, E/Ea ratio, and E-wave as key echocardiographic parameters.Conclusions Machine learning generated several models for predicting fluid responsiveness that were comparable with the haemodynamic response to PLR.
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Bataille et al. found that machine learning models (particularly partial least-squares and neural networks) demonstrated strong predictive performance (test AUROC ~0.83-0.85) for fluid responsiveness, comparable to passive leg raising.
“Machine learning models—particularly partial least-squares and neural networks—demonstrated strong predictive performance (test AUROC ~0.83–0.85), comparable to passive leg raising.”
From Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis (2026) · cites this paper as [69]
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- Authors
- Bataille, B., de Selle, J., Moussot, P.-E., Marty, P., Silva, S., & Cocquet, P.
- Affiliations
- Médipole Garonne (FR)
- Université Toulouse III - Paul Sabatier (FR)
- Corresponding author
- Bataille, Benoît
- Venue
- British Journal of Anaesthesia
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 126
- Issue
- 4
- Pages
- 826-834
- ISSN
- 0007-0912 (ISSN-L)
- OpenAlex
- W3120666205
- Cited by
- 56
- References
- 34
- Citations by year
- 2026: 16 · 2025: 15 · 2024: 6 · 2023: 10 · 2022: 6 · 2021: 3
- FWCI
- 5.87
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- British Journal of Anaesthesia (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Topics
- Health Sciences › Medicine › Surgery › Hemodynamic Monitoring and Therapy
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Ultrasound in Clinical Applications
- Keywords
- fluid responsiveness, septic shock, severe sepsis, passive leg raising, transthoracic echocardiography, machine learning, partial least squares regression, classification and regression trees, artificial neural networks, linear discriminant analysis, inferior vena cava collapsibility, stroke volume, fluid challenge, echocardiography
- MeSH terms
- Machine Learning, Aged, Echocardiography, Female, Fluid Therapy, Humans, Male, Middle Aged, Predictive Value of Tests, Prospective Studies, Shock, Septic, Critical Illness, Sepsis, Point-of-Care Systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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