ArticleJournal of clinical medicine2022
Fluid Overload Phenotypes in Critical Illness-A Machine Learning Approach.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 25 citations in OpenAlex.
- The association of midregional pro-adrenomedullin (MR-proADM) at ICU admission and fluid overload in patients post elective cardiac surgery.Scientific reports · 2024Trial
- CUI-Curate: a GraphRAG-based framework for automated clinical concept curation for NLP applications.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Fluids in sepsis.BJA education · 2026Review
- Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.Pharmacotherapy · 2025Article
- Fluid accumulation syndrome in sepsis and septic shock: pathophysiology, relevance and treatment-a comprehensive review.Annals of intensive care · 2024Review
- Influence of fluid accumulation on major adverse kidney events in critically ill patients - an observational cohort study.Annals of intensive care · 2024Article
- Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.medRxiv : the preprint server for health sciences · 2024Article
- Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU.Scientific reports · 2023Observational
- The application of artificial intelligence in the management of sepsis.Medical review (2021) · 2023Review
- Protocolised early de-resuscitation in septic shock (REDUCE): protocol for a randomised controlled multicentre feasibility trial.BMJ open · 2023Article
- Cluster analysis driven by unsupervised latent feature learning of medications to identify novel pharmacophenotypes of critically ill patients.Scientific reports · 2023Article
- Using Machine-Learning to Assess the Prognostic Value of Early Enteral Feeding Intolerance in Critically Ill Patients: A Retrospective Study.Nutrients · 2023Article
- Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model.Critical care (London, England) · 2023Observational
- Fluid overload and mortality in critically ill patients with severe heart failure and cardiogenic shock-An observational cohort study.Frontiers in medicine · 2022Article
- Artificial intelligence-assisted phenotyping of sepsis: Research progress, clinical challenges, and translational prospects.Digital healthReview
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsRetrospective 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.
resultsOut 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.
conclusionThe FO phenotypes consist of patients admitted after surgery or with sepsis/septic shock with high lactate and low bicarbonate.
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