Describing Cardiac ICU Patientsu2019 Fluid Transfer Characteristics Using System Analysis u2013 a Proof of Concept
Morressier, 2017
Online
unknown
Describing Cardiac ICU Patientsu2019 Fluid Transfer Characteristics Using System Analysis u2013 a Proof of ConceptKatharina Bergmoser1, 2, Lucas Pflanzl-Knizacek1,3, Sonja Langthaler2, Christian Baumgartner2 1CBmed u2013 Center for Biomarker Research in Medicine, Graz, Austria 2Graz University of Technology u2013 Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz, Austria3Medical University of Graz u2013 Division of Endocrinology and Metabolism, Graz, AustriaContact: katharina.bergmoser@cbmed.atBackground: Especially in an intensive care setting, the cumulative fluid balance (CFB) provides easy-to-assess and valuable information on the patientu2019s current health status [1] and the amount of excess fluid currently accumulated within the body. A fluid overload of 10% of a patientu2019s baseline body weight is associated with an increased mortality [2-4] and should therefore be avoided. Estimating a patientu2019s CFB course as a response to different fluid application regimes may be difficult. Modeling an individual patientu2019s fluid transfer characteristics by considering as many relevant patient parameters as possible can be challenging and easily results in high dimensional and complex models, whose introduction into clinical practice can be difficult. Control system analysis provides efficient tools for the description of complex systems and is commonly used in other areas aiming to model physiological behavior [5-9].Objectives: The identification of individual transfer functions commonly used in system analysis may help in detecting patients being non-responsive to late conservative fluid therapy at an early stage of postoperative fluid management. Clustering the individual patientsu2019 transfer functions within a large patient population with respect to diagnosis or other patient features might furthermore allow the definition of cohort-specific model parameters. The use of cohort transfer functions in decision support systems might assist in assessing each patientu2019s actual fluid needs, facilitating fluid management by preventing severe fluid overloads and minimizing the risk of therapies such as renal replacement therapy in advance. Materials and Methods: The CFB course of critically ill patients recovering from trauma has already been described qualitatively in literature [10-13]. Malbrain et al. suggested the ROSE model, which divides the recovery process into four subsequent stages: Resuscitation, Optimization, Stabilization and Evacuation. Figure 1 shows the CFB course to be targeted in intensive care. In general, the lengths of the four subsequent recovery phases depend on the clinical course of the respective patient. A second order discrete-time transfer function was identified using a selected cardiac patientu2019s individual cumulative fluid intake (CFI) and CFB as input series and output series respectively. The patientu2019s transfer function was estimated using the MATLAB System Identification Toolbox. Model verification was performed using MATLAB Simulink, whereby an approximated intake function fitted to the patientu2019s CFI was used as input series.Results: The identified transfer function comprises a holistic description of the patientu2019s characteristics influencing the individual reaction to administered fluids without necessity for measuring multiple and/or complex vital parameters. The model output of the estimated transfer function for the selected patient after application of the approximated CFI compared to the patientu2019s actual CFB versus the averaged CFBs including four patients with similar lengths of stay are shown in Figure 2. Conclusions: Second order transfer function models provide a valuable option for describing fluid transfer characteristics of ICU patients. The estimated transfer function shows a good congruence with the documented preliminary patient data. A transfer function of higher order does not result in a justifying increase of goodness of fit. Patient-specific transfer functions might act as a key tool reflecting the actual patient within control loops being an essential base for providing decision support in fluid administration.Acknowledgments
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Describing Cardiac ICU Patientsu2019 Fluid Transfer Characteristics Using System Analysis u2013 a Proof of Concept
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Autor/in / Beteiligte Person: | Pflanzl-Knizacek, Lucas |
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Veröffentlichung: | Morressier, 2017 |
Medientyp: | unknown |
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