[en] Positive end-expiratory pressure results in a sustained positive intrathoracic pressure, which exerts pressure on intrathoracic vessels, resulting in cardiopulmonary interactions. This sustained positive intrathoracic pressure is known to decrease cardiac preload, and thus, decrease venous return, ultimately reducing both the stroke volume and stressed blood volume of the cardiovascular system. Currently, cardiovascular and pulmonary care are provided independently of one another. That positive end-expiratory pressure alters both stroke volume and stressed blood volume suggests both the pulmonary and cardiovascular state should be conjointly optimised. Optimising these systems in isolation may benefit one system, but have highly detrimental effects on the other. A combined cardiopulmonary model has the potential to provide a better understanding of patient specific pulmonary and cardiovascular state, as well as resulting cardiopulmonary interactions. This would enable simultaneous optimisation of all cardiovascular and pulmonary parameters. Cardiopulmonary interactions are highly patient specific and unpredictable, making accurate modelling of these interactions challenging. A previously validated cardiopulmonary model was found to have increasing errors at high positive end-expiratory pressures. A new iteration, the alpha model, was introduced to resolve this issue. This paper aims to review the alpha model against its predecessors, the previous cardiopulmonary model, and the original three chamber cardiovascular system model. All models are used to identify cardiovascular system parameters from measurements of 4 pigs during a preload reduction manoeuvre. Outputs and parameter estimations from models are compared to assess the relative performance of the alpha model against its predecessors. The novel alpha model was able to reduce model errors under high positive end-expiratory pressure, resulting in more accurate model outputs. At high positive end-expiratory pressures (20cmH2O), the alpha model had an average error of 11.24%, while the original cardiopulmonary model had a much higher error of 52.21%. Furthermore, identified outputs of the alpha model more closely matched those of the 3 chamber model than the previous cardiopulmonary model. On average, at high positive end-expiratory levels, identified model parameters from the alpha model showed a 6.21% difference to those of the 3 chamber model, while the cardiopulmonary model displayed a 39.43% difference. The alpha model proved to be more stable than the original cardiopulmonary model, making it a good candidate for model based care. However, it produced similar parameter outputs to the simpler three chamber cardiovascular model, bringing into question whether the additional complexity is justified, especially considering the low availability of clinical data in the ICU. There is a critical need for model based care to guide important procedures in ICU, such as fluid therapy. Candidate models should be continuously reviewed in order to guarantee the best possible care.
Disciplines :
Cardiovascular & respiratory systems Electrical & electronics engineering
Author, co-author :
Cushway, James ; Université de Liège - ULiège > Aérospatiale et Mécanique (A&M) ; Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
Murphy, Liam; Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
Chase, J Geoffrey ; Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
Shaw, Geoffrey; Dept of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
Desaive, Thomas ; Université de Liège - ULiège > Département d'astrophysique, géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles
Zhou, Cong; Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
Language :
English
Title :
Model based care in the ICU: A review of potential combined cardio-pulmonary models.
This work was supported by EU H2020
ERA Permed JTC2021, “Personalised perfusion
guided fluid therapy”. The project was supported
by EU H2020 R\&I programme (MSCA-RISE-2019
call) under grant agreement \#872488—DCPM.
Pinsky MR. Cardiopulmonary Interactions: Physiologic Basis and Clinical Applications. Annals of the American Thoracic Society. 2018; 15:S45–S48. https://doi.org/10.1513/AnnalsATS.201704-339FR PMID: 28820609
Pinsky MR, Desmet JM, Vincent JL. Estimating Left Ventricular Filling Pressure during Positive End-Expiratory Pressure in Humans. American Review of Respiratory Disease. 1991; 143(1). https://doi.org/10.1164/ajrccm/143.1.25 PMID: 1986680
Pinsky MR. The Effects of Mechanical Ventilation on the Cardiovascular System. Critical Care Clinics. 1990; 6(3):663–678. https://doi.org/10.1016/S0749-0704(18)30360-9 PMID: 2199000
Pinsky MR, Desmet JM, Vincent JL. Effect of Positive End-expiratory Pressure on Right Ventricular Function in Humans. American Review of Respiratory Disease. 1992; 146(3). https://doi.org/10.1164/ajrccm/146.3.681 PMID: 1519848
Magder S, Malhotra A, Hibbert KA, Hardin CC. Cardiopulmonary Monitoring: Basic Physiology, Tools, and Bedside Management for the Critically Ill. Springer; 2021.
Mahmood SS, Pinsky MR. Heart-lung interactions during mechanical ventilation: the basics. Annals of translational medicine. 2018; 6(18). https://doi.org/10.21037/atm.2018.04.29 PMID: 30370276
Pironet A, et al. Model-Based Stressed Blood Volume is an Index of Fluid Responsiveness. IFACPapersOnLine. 2015; 48(20):291–296. https://doi.org/10.1016/j.ifacol.2015.10.154
Desaive T., Horikawa O., Ortiz J. P. and Chase J. G. Model-based management of cardiovascular failure: Where medicine and control systems converge. Annual Reviews in Control, vol. 48, pp. 383–91, 2019. https://doi.org/10.1016/j.arcontrol.2019.05.003
Murphy L, Davidson S, Chase JG, Knopp JL, Zhou T, Desaive T. Patient-Specific Monitoring and Trend Analysis of Model-Based Markers of Fluid Responsiveness in Sepsis: A Proof-of-Concept Animal Study. Ann Biomed Eng. 2020; 48:682–694. https://doi.org/10.1007/s10439-019-02389-9 PMID: 31768794
Knopp JL, Chase JG, Kim KT, Shaw GM. Model-based estimation of negative inspiratory driving pressure in patients receiving invasive NAVA mechanical ventilation. Computer Methods and Programs in Biomedicine. 2021; 208:106300. https://doi.org/10.1016/j.cmpb.2021.106300 PMID: 34348200
Chiew YS, Chase JG, Shaw GM, Sundareson A, Desaive T. Model-based PEEP optimisation in mechanical ventilation. BioMedical Engineering Online. 2011; 10. https://doi.org/10.1186/1475-925X-10-111 PMID: 22196749
Bates JHT. Lung mechanics: an inverse modeling approach. Cambridge University Press; 2009.
Morton SE, Knopp JL, Chase JG, Docherty P, Howe SL, Möller K, et al. Optimising mechanical ventilation through model-based methods and automation. Annual Reviews in Control. 2019; 48:369–382. https://doi.org/10.1016/j.arcontrol.2019.05.001 PMID: 36911536
Chiew YS, Chase JG, Lambermont B, Janssen N, Schranz C, Moeller K, et al. Physiological relevance and performance of a minimal lung model – an experimental study in healthy and acute respiratory distress syndrome model piglets. BMC Pulmonary Medicine. 2012; 12. https://doi.org/10.1186/1471-2466-12-59 PMID: 22999004
Morton SE, Knopp JL, Tawhai MH, Docherty P, Heines SJ, Bergmans DC, et al. Prediction of lung mechanics throughout recruitment maneuvers in pressure-controlled ventilation. Computer Methods and Programs in Biomedicine. 2020; 197:105696. https://doi.org/10.1016/j.cmpb.2020.105696 PMID: 32798977
de Bournonville S, Pironet A, Pretty C, Chase JG, Desaive T. Parameter estimation in a minimal model of cardio-pulmonary interactions. Mathematical Biosciences. 2019; 313:81–94. https://doi.org/10.1016/j.mbs.2019.05.003 PMID: 31128126
Cushway J, Murphy L, Chase JG, Shaw GM, Desaive T. Physiological trend analysis of a novel cardiopulmonary model during a preload reduction manoeuvre. Computer Methods and Programs in Biomedicine. 2022; 220:106819. https://doi.org/10.1016/j.cmpb.2022.106819 PMID: 35461125
Cushway J, Murphy L, Chase JG, Shaw GM, Desaive T. Modelling patient specific cardiopulmonary interactions. Computers in Biology and Medicine. 2022; 151:106235. https://doi.org/10.1016/j.compbiomed.2022.106235 PMID: 36334361
Suga H, Sagawa K, Shoukas AA. Load Independence of the Instantaneous Pressure-Volume Ratio of the Canine Left Ventricle and Effects of Epinephrine and Heart Rate on the Ratio. Circulation Research. 1973; 32(3):314–322. https://doi.org/10.1161/01.RES.32.3.314 PMID: 4691336
Davidson S, Prett C, Pironet A, Kamoi S, Balmer J, Desaive T, et al. Minimally invasive, patient specific, beat-by-beat estimation of left ventricular time varying elastance. BioMedical Engineering OnLine. 2017; 16(42). https://doi.org/10.1186/s12938-017-0338-7 PMID: 28407773
Katz AI, Chen Y, Moreno AH. Flow through a Collapsible Tube: Experimental Analysis and Mathematical Model. Biophysical Journal. 1969; 9(10):1261–1279. https://doi.org/10.1016/S0006-3495(69) 86451-9 PMID: 5824415
Bronicki RA, Anas NG. Cardiopulmonary interaction. Pediatric Critical Care Medicine. 2009; 10(3):313–322. https://doi.org/10.1097/PCC.0b013e31819887f0 PMID: 19307810
Guyton AC, Hall JE. Textbook of Medical Physiology. 11th ed. Elsevier Saunders; 2006.
Pironet A, Docherty PD, Dauby PC, Chase JG, Desaive T. Practical identifiability analysis of a minimal cardiovascular system model. Computer Methods and Programs in Biomedicine. 2019; 171:53–65. https://doi.org/10.1016/j.cmpb.2017.01.005 PMID: 28153466
Pironet A, Dauby PC, Chase JG, Docherty PD, Revie JA, Desaive T. Structural identifiability analysis of a cardiovascular system model. Medical Engineering & Physics. 2016; 38(5):433–441. https://doi.org/10.1016/j.medengphy.2016.02.005 PMID: 26970891
Zhou C, Chase JG, Knopp J, Sun Q, Tawhai M, Möller K, et al. Virtual patients for mechanical ventilation in the intensive care unit. Computer Methods and Programs in Biomedicine. 2021; 199:105912. https://doi.org/10.1016/j.cmpb.2020.105912 PMID: 33360683
Sun Q, Chase JG, Zhou C, Tawhai MH, Knopp JL, Möller K, et al. Over-distension prediction via hysteresis loop analysis and patient-specific basis functions in a virtual patient model. Computers in Biology and Medicine. 2022; 141:105022. https://doi.org/10.1016/j.compbiomed.2021.105022 PMID: 34801244
Grübler MR, Wigger O, David B, Bloechlinger S. Basic concepts of heart-lung interactions during mechanical ventilation. Swiss Medical Weekly. 2017; 147. PMID: 28944931
Buda AJ, Pinsky MR, Ingels Neil B J, Daughters GT, Stinson EB, Alderman EL. Effect of Intrathoracic Pressure on Left Ventricular Performance. N Engl J Med. 1979; 301:453–459. https://doi.org/10.1056/ NEJM197908303010901 PMID: 460363