Abstract
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.
Highlights
IntroductionIntravenous fluids are one of the most commonly applied therapies in the intensive care unit (ICU), and it is not surprising that optimizing this therapy is an ongoing issue in the management of the critically ill [1,2]
Comparing the random forest model, the fast and frugal tree, the classification decision tree, and the logistic regression, the best area under the curve (AUC) for predicting fluid overload (FO) on day three in critically ill patients was the random forest model with 0.84
Our analysis identifies high lactate to be a major determinate for FO at intensive care unit (ICU) day three Our analysis identifies high lactate to be a major determinate for FO at ICU day three well reflect current clinical practice, as lactate has traditionally been used to guide fluid well reflect current clinical practice, as lactate has traditionally been used to guide fluid resuscitation therapy in critically ill patients [43,44,45]
Summary
Intravenous fluids are one of the most commonly applied therapies in the intensive care unit (ICU), and it is not surprising that optimizing this therapy is an ongoing issue in the management of the critically ill [1,2]. The detrimental impact of fluid overload (FO) on intensive care unit (ICU). Research to identify subgroups of patients 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.
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