Key result
A system theory-based digital model using a 48-hour estimation time and an 8-hour prediction time predicted cumulative fluid balance within the clinically acceptable range of ±1.0 L in 77% of predictions.
Why the study?
Fluid balance in cardiac surgery patients is influenced by complex factors, making cumulative fluid balance prediction difficult and often inaccurate despite its importance for postoperative intensive care.
Does a system theory based digital model accurately predict cumulative fluid balance in cardiac intensive care patients?
Observational (n=618)
No
Does a system theory based digital model accurately predict cumulative fluid balance in cardiac intensive care patients?
A novel system theory-based digital model using only cumulative fluid intake data can predict cumulative fluid balance in cardiac ICU patients with high accuracy over an 8-16 hour horizon.
Hypothesis-generating for digital CFB models in cardiac ICU; prospective validation needed before any practice consideration.
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, patient-individual 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.
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Polz et al. (2023) conducted an observational in Postoperative cardiac intensive care (n=618). System theory based digital model for cumulative fluid balance prediction was evaluated on Accuracy of cumulative fluid balance prediction within ±1.0 L at 8-hour prediction time. A system theory-based digital model using a 48-hour estimation time and an 8-hour prediction time predicted cumulative fluid balance within the clinically acceptable range of ±1.0 L in 77% of predictions.
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