A system theory based digital model for predicting the cumulative fluid balance course in intensive care patients Article Swipe
Mathias Polz
,
Katharina Bergmoser
,
Martin Horn
,
Michael Schörghuber
,
Jasmina Lozanović Šajić
,
Theresa Rienmüller
,
Christian Baumgärtner
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fphys.2023.1101966
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fphys.2023.1101966
Background: Surgical interventions can cause severe fluid imbalances in patients undergoing cardiac surgery, affecting length of hospital stay and survival. Therefore, appropriate management of daily fluid goals is a key element of postoperative intensive care in these patients. Because fluid balance is influenced by a complex interplay of patient-, surgery- and intensive care unit (ICU)-specific factors, fluid prediction is difficult and often inaccurate. Methods: A novel system theory based digital model for cumulative fluid balance (CFB) prediction is presented using recorded patient fluid data as the sole parameter source by applying the concept of a transfer function. Using a retrospective dataset of n = 618 cardiac intensive care patients, patient-individual models were created and evaluated. RMSE analyses and error calculations were performed for reasonable combinations of model estimation periods and clinically relevant prediction horizons for CFB. Results: Our models have shown that a clinically relevant time horizon for CFB prediction with the combination of 48 h estimation time and 8–16 h prediction time achieves high accuracy. With an 8-h prediction time, nearly 50% of CFB predictions are within ±0.5 L, and 77% are still within the clinically acceptable range of ±1.0 L. Conclusion: Our study has provided a promising proof of principle and may form the basis for further efforts in the development of computational models for fluid prediction that do not require large datasets for training and validation, as is the case with machine learning or AI-based models. The adaptive transfer function approach allows estimation of CFB course on a dynamically changing patient fluid balance system by simulating the response to the current fluid management regime, providing a useful digital tool for clinicians in daily intensive care.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fphys.2023.1101966
- https://www.frontiersin.org/articles/10.3389/fphys.2023.1101966/pdf
- OA Status
- gold
- Cited By
- 1
- References
- 97
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4365455427
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4365455427Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fphys.2023.1101966Digital Object Identifier
- Title
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A system theory based digital model for predicting the cumulative fluid balance course in intensive care patientsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-13Full publication date if available
- Authors
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Mathias Polz, Katharina Bergmoser, Martin Horn, Michael Schörghuber, Jasmina Lozanović Šajić, Theresa Rienmüller, Christian BaumgärtnerList of authors in order
- Landing page
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https://doi.org/10.3389/fphys.2023.1101966Publisher landing page
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https://www.frontiersin.org/articles/10.3389/fphys.2023.1101966/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/articles/10.3389/fphys.2023.1101966/pdfDirect OA link when available
- Concepts
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Intensive care, Intensive care unit, Balance (ability), Medicine, Computer science, Estimation, Intensive care medicine, Physical therapy, Engineering, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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