Advertisement

Patient-Specific Monitoring and Trend Analysis of Model-Based Markers of Fluid Responsiveness in Sepsis: A Proof-of-Concept Animal Study

  • Liam MurphyEmail author
  • Shaun Davidson
  • J. Geoffrey Chase
  • Jennifer L. Knopp
  • Tony Zhou
  • Thomas Desaive
Original Article
  • 50 Downloads

Abstract

Total stressed blood volume (\(SBV_{\text{T}}\)) and arterial elastance (\(E_{\text{a}}\)) are two potentially important, clinically applicable metrics for guiding treatment in patients with altered hemodynamic states. Defined as the total pressure generating blood in the circulation, \(SBV_{\text{T}}\) is a potential direct measurement of tissue perfusion, a critical component in treatment of sepsis. \(E_{\text{a}}\) is closely related to arterial tone thus provides insight into cardiac efficiency. However, it is not clinically feasible or ethical to measure \(SBV_{\text{T}}\) in patients, so a three chambered cardiovascular system model using measured left ventricle pressure and volume, aortic pressure and central venous pressure is implemented to identify \(SBV_{\text{T}}\) and \(E_{\text{a}}\) from clinical data. \(SBV_{\text{T}}\) and \(E_{\text{a}}\) are identified from clinical data from six (6) pigs, who have undergone clinical procedures aimed at simulating septic shock and subsequent treatment, to identify clinically relevant changes. A novel, validated trend analysis method is used to adjudge clinically significant changes in state in the real-time \(E_{\text{a}}\) and \(SBV_{\text{T}}\) traces. Results matched hypothesised increases in \(SBV_{\text{T}}\) during fluid therapy, with a mean change of + 21% during initial therapy, and hypothesised decreases during endotoxin induced sepsis, with a mean change of − 29%. \(E_{\text{a}}\) displayed the hypothesised reciprocal behaviour with a mean changes of − 12 and + 30% during initial therapy and endotoxin induced sepsis, respectively. The overall results validate the efficacy of \(SBV_{\text{T}}\) in tracking changes in hemodynamic state in septic shock and fluid therapy.

Keywords

Stressed blood volume Arterial elastance Cardiovascular Fluid therapy 

Notes

References

  1. 1.
    Bagshaw, S. M., P. D. Brophy, D. Cruz, and C. Ronco. Fluid balance as a biomarker: impact of fluid overload on outcome in critically ill patients with acute kidney injury. Crit. care 12(4):169, 2008.  https://doi.org/10.1186/cc6948.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Byrne, L., and F. Haren. Fluid resuscitation in human sepsis: time to rewrite history? Ann. Intensive Care 7(1):4, 2017.  https://doi.org/10.1186/s13613-016-0231-8.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Cavallaro, F., C. Sandroni, and M. Antonelli. Functional hemodynamic monitoring and dynamic indices of fluid responsiveness. Miner. Anestesiol. 74(4):123–135, 2008.Google Scholar
  4. 4.
    Cecconi, M., D. De Backer, M. Antonelli, R. Beale, J. Bakker, C. Hofer, R. Jaeschke, A. Mebazaa, M. R. Pinsky, J. L. Teboul, et al. Consensus on circulatory shock and hemodynamic monitoring. Task force of the European society of intensive care medicine. Intensive Care Med. 40(12):1795–1815, 2014.  https://doi.org/10.1007/s00134-014-3525-z.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Chase, J. G., A. J. Le Compte, J.-C. Preiser, G. M. Shaw, S. Penning, and T. Desaive. Physiological modeling, tight glycemic control, and the icu clinician: what are models and how can they affect practice? Ann. Intensive Care 1(1):11, 2011.  https://doi.org/10.1186/2110-5820-1-11.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Davidson, S., C. Pretty, A. Pironet, T. Desaive, N. Janssen, B. Lambermont, P. Morimont, and J. G. Chase. Minimally invasive estimation of ventricular dead space volume through use of frank-starling curves. PLoS ONE 12(4):e0176302, 2017.  https://doi.org/10.1371/journal.pone.0176302.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Davidson, S., C. Pretty, A. Pironet, S. Kamoi, J. Balmer, T. Desaive, and J. G. Chase. Minimally invasive, patient specific, beat-by-beat estimation of left ventricular time varying elastance. BioMed. Eng. Online 16 (1):42, 2017. ISSN 1475-925X.  https://doi.org/10.1186/s12938-017-0338-7.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Davidson, S. M., D. O. Kannangara, C. G. Pretty, S. Kamoi, T. Desaive, and J. G. Chase. A novel approach for deriving a patient specific beat-to-beat estimate of the cardiac driver function. IFAC Pap. Online 48(20):348–353, 2015.  https://doi.org/10.1016/j.ifacol.2015.10.164.CrossRefGoogle Scholar
  9. 9.
    Davidson, A. M., C. Pretty, S. Kamoi, J. Balmer, T. Desaive, and J. G. Chase. Real-time, minimally invasive, beat-to-beat estimation of end-systolic volume using a modified end-systolic pressure–volume relation. IFAC Pap. Online 50(1):5456–5461, 2017.  https://doi.org/10.1016/j.ifacol.2017.08.1082.CrossRefGoogle Scholar
  10. 10.
    Dellinger, R. P., M. M. Levy, A. Rhodes, D. Annane, H. Gerlach, S. M. Opal, J. E. Sevransky, C. L. Sprung, I. S. Douglas, R. Jaeschke, et al. Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock, 2012. Intensive Care Med. 39(2):165–228, 2013.  https://doi.org/10.1007/S00134-012-2769-8.CrossRefPubMedGoogle Scholar
  11. 11.
    Dickson, J. L., C. A. Gunn, and J. G. Chase. Humans are horribly variable. Int. J. Clin. Med. Imaging 1(2):1–1000142, 2014.Google Scholar
  12. 12.
    Dickson, J. L., C. A. Gunn, and J. G. Chase. Clinical & medical imaging. Int. J. 1(2):1000142, 2014.Google Scholar
  13. 13.
    Drosatos, K., A. Lymperopoulos, P. J. Kennel, N. Pollak, P. C. Schulze, and I. J. Goldberg. Pathophysiology of sepsis-related cardiac dysfunction: driven by inflammation, energy mismanagement, or both? Curr. Heart Fail. Rep. 12(2):130–140, 2015.  https://doi.org/10.1007/s11897-014-0247-z.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Guarracino, F., R. Baldassarri, and M. R. Pinsky. Ventriculo-arterial decoupling in acutely altered hemodynamic states. Crit. Care 17(2):213, 2013.  https://doi.org/10.1186/cc12522.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Hariyanto, H., C. Q. Yahya, M. Widiastuti, P. Wibowo, and O. E. Tampubolon. Fluids and sepsis: changing the paradigm of fluid therapy: a case report. J Med. Case Rep. 11(1):30, 2017.  https://doi.org/10.1186/s13256-016-1191-1.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Howell, M. D., and A. M. Davis. Management of sepsis and septic shock. Jama, 317(8):847–848, 2017.  https://doi.org/10.1001/jama.2017.0131.CrossRefPubMedGoogle Scholar
  17. 17.
    Kamoi, S., C. Pretty, J. Balmer, S. Davidson, A. Pironet, T. Desaive, G. M. Shaw, and J. G. Chase. Improved pressure contour analysis for estimating cardiac stroke volume using pulse wave velocity measurement. Biomed. Eng. Online 16(1):51, 2017.  https://doi.org/10.1186/s12938-017-0341-z.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Kelm, D. J., J. T. Perrin, R. Cartin-Ceba, O. Gajic, L. Schenck, and C. C. Kennedy. Fluid overload in patients with severe sepsis and septic shock treated with early-goal directed therapy is associated with increased acute need for fluid-related medical interventions and hospital death. Shock 43(1):68, 2015.  https://doi.org/10.1001/jama.2016.0288.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Kumar, A., J. E. Parrillo, and A. Kumar, et al. Clinical review: myocardial depression in sepsis and septic shock. Crit. Care 6(6):500, 2002.  https://doi.org/10.1186/cc1822.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Maas, J. J., M. R. Pinsky, L. P. Aarts, and J. R. Jansen. Bedside assessment of total systemic vascular compliance, stressed volume, and cardiac function curves in intensive care unit patients. Anesth. Analg. 115(4):880–887, 2012.  https://doi.org/10.1213/ANE.0b013e31825fb01d.CrossRefPubMedGoogle Scholar
  21. 21.
    Magder, S., and B. De Varennes. Clinical death and the measurement of stressed vascular volume. Crit. Care Med. 26(6):1061–1064, 1998.CrossRefGoogle Scholar
  22. 22.
    Malbrain, M. L. N. G., N. Van Regenmortel, B. Saugel, B. De Tavernier, P.-J. Van Gaal, O. Joannes-Boyau, J.-L. Teboul, T. W. Rice, M. Mythen, and X. Monnet. Principles of fluid management and stewardship in septic shock: it is time to consider the four d’s and the four phases of fluid therapy. Ann. Intensive Care 8(1):66, 2018.  https://doi.org/10.1186/s13613-018-0402-x.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Marik, P., and R. Bellomo. A rational approach to fluid therapy in sepsis. Br. J. Anaesth. 116(3):339–349, 2015.  https://doi.org/10.1093/bja/aev349.CrossRefPubMedGoogle Scholar
  24. 24.
    Merx, M. W., and C. Weber. Sepsis and the heart. Circulation 116(7):793–802, 2007.  https://doi.org/10.1161/circulationaha.106.678359.CrossRefPubMedGoogle Scholar
  25. 25.
    Monnet, X., P. E. Marik, and J.-L. Teboul. Prediction of fluid responsiveness: an update. Ann. Intensive Care 6(1):111, 2016.  https://doi.org/10.1186/s13613-016-0216-7.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Mouncey, P. R., T. M. Osborn, G. S. Power, D. A. Harrison, M. Z. Sadique, R. D. Grieve, R. Jahan, S. E. Harvey, D. Bell, J. F. Bion, et al. Trial of early, goal-directed resuscitation for septic shock. N. Engl. J. Med. 372(14):1301–1311, 2015.  https://doi.org/10.1056/NEJMoa1500896.CrossRefPubMedGoogle Scholar
  27. 27.
    Pironet, A., P. C. Dauby, J. G. Chase, S. Kamoi, N. Janssen, P. Morimont, B. Lambermont, and T. Desaive. Model-based stressed blood volume is an index of fluid responsiveness. IFAC Pap. Online 48(20):291–296, 2015.  https://doi.org/10.1016/j.ifacol.2015.10.154.CrossRefGoogle Scholar
  28. 28.
    Pironet, A., P. C. Dauby, S. Paeme, S. Kosta, J. G. Chase, and T. Desaive. Simulation of left atrial function using a multi-scale model of the cardiovascular system. PLoS ONE 8(6):e65146, 2013.  https://doi.org/10.1371/journal.pone.0065146.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Pironet, A., T. Desaive, J. G. Chase, P. Morimont, and P. C. Dauby. Model-based computation of total stressed blood volume from a preload reduction manoeuvre. Math. Biosci. 265:28–39, 2015. ISSN 0025-5564.  https://doi.org/10.1016/j.mbs.2015.03.015.CrossRefGoogle Scholar
  30. 30.
    Pironet, A., T. Desaive, P. C. Dauby, J. G. Chase, and P. D. Docherty. Parameter identification methods in a model of the cardiovascular system. IFAC Pap. Online 48(20):366–371, 2015.  https://doi.org/10.1016/j.ifacol.2015.10.167.CrossRefGoogle Scholar
  31. 31.
    Rothe, C. F. Mean circulatory filling pressure: its meaning and measurement. J. Appl. Physiol. 74(2):499–509, 1993.  https://doi.org/10.1152/jappl.1993.74.2.499.CrossRefGoogle Scholar
  32. 32.
    Seymour, C. W., V. X. Liu, T. J. Iwashyna, F. M. Brunkhorst, T. D. Rea, A. Scherag, G. Rubenfeld, J. M. Kahn, M. Shankar-Hari, M. Singer, et al. Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (sepsis-3). Jama 315(8):762–774, 2016.  https://doi.org/10.1001/jama.2016.0288.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Silva, J. M., A. M. R. R. de Oliveira, F. A. M. Nogueira, P. M. M. Vianna, M. C. P. Filho, L. F. Dias, V. P. L. Maia, C. de Souza Neucamp, C. P. Amendola, M. J. C. Carmona, et al. The effect of excess fluid balance on the mortality rate of surgical patients: a multicenter prospective study. Crit. Care 17(6):R288, 2013.  https://doi.org/10.1186/cc13151.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Smith, B. W., J. G. Chase, R. I. Nokes, G. M. Shaw, and T. David. Velocity profile method for time varying resistance in minimal cardiovascular system models. Phys. Med. Biol. 48(20):3375, 2003.  https://doi.org/10.1088/0031-9155/48/20/008/meta.CrossRefPubMedGoogle Scholar
  35. 35.
    Starfinger, C., C. E. Hann, J. G. Chase, T. Desaive, A. Ghuysen, and G. M. Shaw. Model-based cardiac diagnosis of pulmonary embolism. Comput. Methods Programs Biomed. 87(1):46–60, 2007.  https://doi.org/10.1016/j.cmpb.2007.03.010.CrossRefPubMedGoogle Scholar
  36. 36.
    Stevenson, D., J. Revie, J. G. Chase, C. E. Hann, G. M. Shaw, B. Lambermont, A. Ghuysen, P. Kolh, and T. Desaive. Algorithmic processing of pressure waveforms to facilitate estimation of cardiac elastance. Biomed. Eng. Online 11(1):28, 2012.  https://doi.org/10.1186/1475-925X-11-28.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Stevenson, D., J. Revie, J. G. Chase, C. E. Hann, G. M. Shaw, B. Lambermont, A. Ghuysen, P. Kolh, and T. Desaive. Beat-to-beat estimation of the continuous left and right cardiac elastance from metrics commonly available in clinical settings. Biomed. Eng. Online 11(1):73, 2012.  https://doi.org/10.1186/1475-925X-11-73.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Suga, H., K. Sagawa, and A. A. Shoukas. Load independence of the instantaneous pressure–volume ratio of the canine left ventricle and effects of epinephrine and heart rate on the ratio. Circ. Res. 32(3):314–322, 1973.CrossRefGoogle Scholar
  39. 39.
    Vincent, J.-L., and M. R. Pinsky. We should avoid the term “fluid overload”, 2018.Google Scholar
  40. 40.
    Zhou, T., J. Knopp, C. J. D. McKinlay, G. D. Gamble, J. E. Harding, J. G. Chase, CHYLD Study Group, et al. Glycaemic state analysis from continuous glucose monitoring measurements in infants. IFAC Pap. Online 51(27):276–281, 2018.  https://doi.org/10.1016/j.ifacol.2018.11.629.CrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of CanterburyChristchurchNew Zealand
  2. 2.Institute of Biomedical EngineeringUniversity of OxfordOxfordUK
  3. 3.Liege UniversityLiegeBelgium

Personalised recommendations