A system theory based digital model for predicting the cumulative fluid balance course in intensive care patients

Frontiers in Physiology

Physiology

Michael Schörghuber¹  Martin Horn²  Christian Baumgartner³  Mathias Polz³  Theresa Rienmüller³  Jasmina Lozanović³  Katharina Bergmoser⁴ 

[1] Department of Anesthesiology and Intensive Care Medicine, Medical University of Graz, Graz, STM, Austria;Institute of Automation and Control, Graz University of Technology, Graz, STM, Austria;Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, STM, Austria;Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, STM, Austria;CBmed Center for Biomarker Research in Medicine, Graz, STM, Austria;

关键词: fluid balance;    system theory;    transfer function model;    prediction;    intensive care;    decision support;   

DOI  :  10.3389/fphys.2023.1101966

 received in 2022-11-18, accepted in 2023-04-04,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Polz, Bergmoser, Horn, Schörghuber, Lozanović, Rienmüller and Baumgartner.