Messmer, Anna S.; Moser, Michel; Zuercher, Patrick; Schefold, Joerg C.; Müller, Martin; Pfortmüller, Carmen A. (2022). Fluid Overload Phenotypes in Critical Illness—A Machine Learning Approach. Journal of clinical medicine, 11(2), p. 336. MDPI 10.3390/jcm11020336
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2022_-_Messmer_-_CliMed_-_DOI_10.3390__jcm11020336.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (1MB) | Preview |
Background: The detrimental impact of fluid overload (FO) on intensive care unit (ICU) morbidity and mortality is well known. However, research to identify subgroups of patients particularly prone to fluid overload is scarce. The aim of this cohort study was to derive “FO phenotypes” in the critically ill by using machine learning techniques. Methods: Retrospective single center study including adult intensive care patients with a length of stay of ≥3 days and sufficient data to compute FO. Data was analyzed by multivariable logistic regression, fast and frugal trees (FFT), classification decision trees (DT), and a random forest (RF) model. Results: Out of 1772 included patients, 387 (21.8%) met the FO definition. The random forest model had the highest area under the curve (AUC) (0.84, 95% CI 0.79–0.86), followed by multivariable logistic regression (0.81, 95% CI 0.77–0.86), FFT (0.75, 95% CI 0.69–0.79) and DT (0.73, 95% CI 0.68–0.78) to predict FO. The most important predictors identified in all models were lactate and bicarbonate at admission and postsurgical ICU admission. Sepsis/septic shock was identified as a risk factor in the MV and RF analysis. Conclusion: The FO phenotypes consist of patients admitted after surgery or with sepsis/septic shock with high lactate and low bicarbonate.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > Clinic of Intensive Care 04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > University Emergency Center |
UniBE Contributor: |
Messmer, Anna Sarah, Moser, Michel, Zürcher, Patrick, Schefold, Jörg Christian, Müller, Martin (B), Pfortmüller, Carmen |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
2077-0383 |
Publisher: |
MDPI |
Language: |
English |
Submitter: |
Doris Straub |
Date Deposited: |
04 Feb 2022 10:05 |
Last Modified: |
30 Jan 2024 02:57 |
Publisher DOI: |
10.3390/jcm11020336 |
PubMed ID: |
35054030 |
BORIS DOI: |
10.48350/164500 |
URI: |
https://boris.unibe.ch/id/eprint/164500 |
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