A System Theory Based Digital Model for Predicting the Cumulative Fluid Balance Course in Intensive Care Patients

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, patientindividual
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.