1 and trees with maximized balanced accuracy (average of sensitivity and specificity) were reported. For illustration and interpretation, single decision trees and fast frugal trees were trained on imputed dataset No. As opposed to tree-based models, logistic regression models become unstable in the presence of multicollinearity, therefore, variance inflation factor was computed for explanatory variables and variables excluded if they exceeded a score of two. The DeLong test was used to test for significant difference between the AUC of different models. ![]() Area under the curve (AUC) and the receiver operating characteristic curve were computed on the independent validation dataset and used to compare model performances. For each method, final model performance estimates were retrieved by pooling results from the 10 imputed data sets. All models are described in more detail with literature suggestions in Table S1 (Supplemental File). To compare and choose different approaches describing the relationship of the found explanatory variables on FO at day three, methods were applied to the imputed data sets, namely (i) logistic regression, (ii) random forest (randomForest 4.6-14, ), (iii) fast frugal trees, and classification decision trees. Setting & Study DesignĪll variables of the univariable analysis that showed a significant association with FO ( p-value < 0.05) were selected and used for final inference of the binary outcome of FO at day three. Therefore, the aim of this retrospective cohort study is to identify factors contributing to FO in the critically ill and derive “FO phenotypes” by using machine learning techniques. Identifying a subgroup of patients especially particularly prone to FO in intensive care could be an essential step to optimize fluid management in the critically ill. recently discussed the importance of hemodynamic phenotypes to individualize the management of patients with septic shock. Further, adult ICU patients are an extremely heterogenic group of patients and current trends in critical care research go towards characterizing “phenotypes” of critically ill patients. However, such an analysis is crucial to gain further insights on how FO in the critically ill can be minimized. While awareness for the detrimental effects of FO in the critically ill has risen considerably during the last decade, and strategies to minimize FO were developed and are currently under investigation, less effort has been undertaken to investigate factors that lead to FO in the critically ill. Conclusion: The FO phenotypes consist of patients admitted after surgery or with sepsis/septic shock with high lactate and low bicarbonate. Sepsis/septic shock was identified as a risk factor in the MV and RF analysis. The most important predictors identified in all models were lactate and bicarbonate at admission and postsurgical ICU admission. Results: Out of 1772 included patients, 387 (21.8%) met the FO definition. Data was analyzed by multivariable logistic regression, fast and frugal trees (FFT), classification decision trees (DT), and a random forest (RF) model. ![]() Methods: Retrospective single center study including adult intensive care patients with a length of stay of ≥3 days and sufficient data to compute FO. The aim of this cohort study was to derive “FO phenotypes” in the critically ill by using machine learning techniques. However, research to identify subgroups of patients particularly prone to fluid overload is scarce. Background: The detrimental impact of fluid overload (FO) on intensive care unit (ICU) morbidity and mortality is well known.
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