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Licensed Unlicensed Requires Authentication Published by De Gruyter January 25, 2017

Physiological closed-loop control of mechanical ventilation and extracorporeal membrane oxygenation

  • Christian Brendle EMAIL logo , Thorsten Mülders , Jan Kühn , Thorsten Janisch , Rüdger Kopp , Rolf Rossaint , Andre Stollenwerk , Stefan Kowalewski , Berno Misgeld , Steffen Leonhardt and Marian Walter

Abstract

A new concept is presented for cooperative automation of mechanical ventilation and extracorporeal membrane oxygenation (ECMO) therapy for treatment of acute respiratory distress syndrome (ARDS). While mechanical ventilation is continuously optimized to promote lung protection, extracorporeal gas transfer rates are simultaneously adjusted to control oxygen supply and carbon dioxide removal using a robust patient-in-the-loop control system. In addition, the cooperative therapy management uses higher-level algorithms to adjust both therapeutic approaches. The controller synthesis is derived based on the introduced objectives, the experimental setup and the uncertain models. Finally, the autonomous ARDS therapy system capabilities are demonstrated and discussed based on in vivo data from animal experiments.

Acknowledgment

The authors gratefully acknowledge the contribution of the German Research Foundation DFG (Grant PAK 138/2; LE 817/15-1; KO 1430/14-1; RO 2000/18-1).

Appendix

A Equations and parameters

A.1 Extracorporeal gas transfer

Time delays: TQO2,d=14.92 s and TQCO2,d=5.03 s; variable range of in silico study: QCO2[0.15,0.3] l/min, QO2[0,0.25] l/min, Sv,O2[45,70]%, and pv,CO2[44,70] mm Hg; parameters are presented in Tables 2 and 3.

(7)GQ,nom=KQ(1+sz0,Q)TQ2s2+2TQDQs+1,
(8)WQ=i=0Nbisii=0Naisi.
Table 2:

Nominal transfer function parameter (see Eq. 7).

GasKQz0,QTQDQ
CO20.9960.231 s−10.913 s45.74
O20.9870.0995 s−126.03 s0.795
Table 3:

Weight function parameter (see Eq. 8).

iO2: aiO2: biCO2: aiCO2: bi
04.05·10−62.33·10−70.03860.00285
10.0004060.00013688.09.25
20.01740.01051.68·1041.02·104
30.1610.1941.01·1053.59·105
40.6730.6061.99·1054.59·105
50.7710.8648.55·1041.07·105
610.94826903218
7128.47

A.2 Circulatory gas exchange model

Concentration c=[cCO2,cO2]T; time delays: TO2,d,pat=27.4 s and TCO2,d,pat=28 s; parameters are presented in Table 4; variable range of in silico study:

(9)Sp,O2[88,98]%;pv,CO2[44,70]mm Hg;QCO[6,10]l/min;QT,O2[0.27,0.29] l/min;VA[2.5,3.5]l;Vt[31.2,46.8]l;hct[19,21]%.
(10)Td,a=Va/QCOTd,v=Vv/QCO,
(11)F˙A=1VA[863(1fs)pbarpH2OQCO(cmvca),+RRVtfd(FinspFA)]
(12)pa*=pA=(pbarpH2O)FA,
(13)ca=ca*(1fs)+cmvfs,
(14)c˙v=1VT[(QT,O2VmolRQQO2,TVmol)+QCO(cacv)],
(15)c˙mv=1Voxy[(QCO2,yVmolQO2,yVmol)+QCO(cvcmv)].
Table 4:

Patient model parameters.

ParameterValueDescription
fs0.23Pulmonary blood shunt
fd0.8Reduced alveolar space
QO2,T0.27 l/minCell’s O2 consumption
pbar760 mm HgAmbient pressure [38]
pH2O47 mm HgVapor pressure [38]
RQ0.9Respiratory quotient
VA3 lAlveolar volume [12]
Va1.82 lArterial volume [12]
VT39 lTissue volume [12]
Voxy0.2 lOxygenator blood volume
Vv3.188 lVenous volume [12]

A.3 Sensor models

(16)GSp,O2(s)=11s+1e5ss,
(17)Gpv,CO2(s)=120s+1e10se6ss.

A.4 Physiological target controller

KPID(s)=Kp+KIs1+KDsTfs+1 (see Table 5).(18)

Table 5:

Controller parameters.

Kp(mlmin)Ki(mlmin2)KD(ml%)Tf (min)
KO2,low3.41%1.81%0.07217.5
KO2,high31%3.481%0.0487.77
KCO20.0511mm Hg3.3361mm Hg

A.5 PEEP titration protocol

The PEEP titration protocol length has been limited to a minimal number of breath cycles required for reliable parameter estimation (see Table 6). A minimum compliance difference of 0.5 ml/mbar is significantly distinguishable using 5 samples assuming a test power of 80% and a normal distributed compliance noise with a standard deviation of 0.278 ml/mbar (measured steady-state value).

Table 6:

PEEP titration protocol parameters.

PEEPPEEP rangeStep size (mbar)Step length (breath cycles)
Titration[PEEPmin , PEEPmax ]−26
ReviewPEEPbest ± 2 mbar−0.56

whiletruedo

  open lung: 10 breath cycles PEEP= PEEPmax;

  decremental PEEP titration;

  reopen lung: PEEP=PEEPmax for 1 min;

  set best PEEP={PEEPbest|max(C̅rs,step)};

  wait 1 min; set tstart=tact; set review=true;

  whileC¯rs,10(tact)>maxt[tstart,tact]C¯rs,10(t)0.8do

    ifreviewthen

     PEEP review;

     set review=false; set trev=tact;

    else iftacttrev> 5 min then

     set review=true;

    end if

  end while

end while

A.6 Blood gas values

Used constants are presented in Table 7.

(19)cH+=KCO2αCO2pCO2cCO2αCO2pCO2,
(20)pH=9log(cH+lnmol),
(21)cCO2=cCO2erycHbcHbery+cCO2pla(1cHbcHbery),
(22)cCO2ery=αCO2erypO2(1+10pHerypKery),
(23)pKery=6,125log(1+10pHery7,840,06SO2),
(24)pHery=7,19+0,77(pH7,4),+0,035(1SO2)
(25)cCO2pla=αCO2plapO2(1+10pHpKpla),
(26)pKpla=6,125log(1+10pH8,7),
(27)cO2=αO2pO2+capO2SO2,
(28)capO2=kHMCHChctVmol,
(29)SO2=ef(pO2)1001+ef(pO2),
(30)f(pO2)=ln(SO2,01SO2,0)+ln(pO2PO2,0)+ktanh(h1kln(pO2PO2,0)),
(31)pO2,0=1,955p507,410pH7,4,
(32)pO2=p507,410pH7,4SO2100SO2h.
Table 7:

Blood gas parameters.

ParameterValueDescription
αCO232.6598 μmollmm HgSolubility of CO2 in water [42]
αCO2ery0.195 mmollkPaSolubility of CO2 in erythrocyte [42]
αCO2pla0.23 mmollkPaSolubility of CO2 in plasma [42]
αO21.457 μmollmm HgSolubility of O2 in water [42]
cHb9.3 mmollHemoglobin concentration in blood [42]
cHbery21 mmollHemoglobin concentration in erythrocyte [42]
h2.87Hill coefficient [42]
KCO2795 mmol/lDissociation constant of carbonic acid [12]
kH1.34 ml/gMaximum O2 uptake [26]
k3.5Half horizontal distance [42]
MCHC340 g/lMean corpuscular hemoglobin concentration [26]
p507,426.86 mm HgHalf O2 saturation pressure [42]
SO2,00.867Maximum of Hill slope [42]
Vmol22.414 l/molMolar volume

References

[1] Amato MB, Meade MO, Slutsky AS, et al. Driving pressure and survival in the acute respiratory distress syndrome. New Engl J Med 2015; 372: 747–755.10.1056/NEJMsa1410639Search in Google Scholar

[2] Apkarian P. Nonsmooth μ-synthesis. Int J Robust Nonlin 2011; 21: 1493–1508.10.1002/rnc.1644Search in Google Scholar

[3] Arnal JM, Garnero A, Novonti D, et al. Feasibility study on full closed-loop control ventilation (IntelliVent-ASVTM) in icu patients with acute respiratory failure: a prospective observational comparative study. Crit Care 2013; 17: R196.10.1186/cc12890Search in Google Scholar

[4] Ashbaugh DG, Bigelow DB, Petty TL, Levine BE. Acute respiratory distress in adults. Lancet 1967; 290: 319–323.10.1016/S0140-6736(67)90168-7Search in Google Scholar

[5] Bellani G, Laffey J, Pham T, et al. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. J Am Med Assoc 2016; 315: 788–800.10.1001/jama.2016.0291Search in Google Scholar PubMed

[6] Brendle C, Hackmack KF, Kühn J, et al. In silico evaluation of gas transfer estimation during extracorporeal membrane oxygenation. 9th IFAC Symposium on Biological and Medical Systems, Berlin, Germany 2015; 499–504.10.1016/j.ifacol.2015.10.190Search in Google Scholar

[7] Brendle C, Hackmack KF, Kühn J, et al. Closed-loop control of extracorporeal oxygen and carbon dioxide gas transfer. Control Eng Pract 2017; 59: 173–182.10.1016/j.conengprac.2016.09.016Search in Google Scholar

[8] Brendle C, Hackmack KF, Kühn J, et al. Continuous gas transfer monitoring during extracorporeal membrane oxygenation. Biomed Signal Proces 2017; 31: 321–330.10.1016/j.bspc.2016.08.023Search in Google Scholar

[9] Brower RG, Matthay MA, Morris A, Schoenfeld D, Thompson BT, Wheeler A. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. the acute respiratory distress syndrome network. N Eng J Med 2000; 342: 1301–1308.10.1056/NEJM200005043421801Search in Google Scholar PubMed

[10] Buehler S, Schumann S, Lichtwarck-Aschoff M, Lozano S, Guttmann J. The shape of intratidal resistance-volume and compliance-volume curves in mechanical ventilation–an animal study. Biomed Tech 2013; 58 (Suppl. 1). Available at: https://www.degruyter.com/view/j/bmte.2013.58.issue-s1-D/bmt-2013-4118/bmt-2013-4118.xml.10.1515/bmt-2013-4118Search in Google Scholar PubMed

[11] Fick A. Über diffusion. Ann Phys-Berlin 1855; 170: 59–86.10.1002/andp.18551700105Search in Google Scholar

[12] Grodins F, Buell J, Bart A. Mathematical analysis and digital simulation of the respiratory control system. Santa Monica: Rand Corporation 1967.10.1152/jappl.1967.22.2.260Search in Google Scholar

[13] Hexamer M, Werner J. A mathematical model for the gas transfer in an oxygenator. IFAC Modelling and Control in Biomedical Systems, Melbourne, Australia 2003; 409–414.10.1016/S1474-6670(17)33538-3Search in Google Scholar

[14] Hill J, O’Brien T, Murray J, et al. Prolonged extracorporeal oxygenation for acute post-traumatic respiratory failure (shock-lung syndrome). New Engl J Med 1972; 286: 629–634.10.1056/NEJM197203232861204Search in Google Scholar PubMed

[15] Hodgson C, Keating JL, Holland AE, et al. Recruitment manoeuvres for adults with acute lung injury receiving mechanical ventilation. Cochrane Db Syst Rev 2009; 2: CD006667.10.1002/14651858.CD006667.pub2Search in Google Scholar PubMed

[16] Isermann R, Münchhof M. Identification of dynamic systems: an introduction with applications. Heidelberg: Springer 2011.10.1007/978-3-540-78879-9Search in Google Scholar

[17] Jandre FC, Pino AV, Lacorte I, Neves JH, Giannella-Neto A. A closed-loop mechanical ventilation controller with explicit objective functions. IEEE Trans Biomed Eng 2004; 51: 823–831.10.1109/TBME.2004.826678Search in Google Scholar PubMed

[18] Karbing DS, Kjærgaard S, Andreassen S, Espersen K, Rees SE. Minimal model quantification of pulmonary gas exchange in intensive care patients. Med Eng Phys 2011; 33: 240–248.10.1016/j.medengphy.2010.10.007Search in Google Scholar PubMed

[19] Kopp R, Bensberg R, Stollenwerk A, et al. Automatic control of veno-venous extracorporeal lung assist. Artif Organs 2016; 40: 992–998.10.1111/aor.12664Search in Google Scholar PubMed

[20] Lachmann B. Open up the lung and keep the lung open. Intens Care Med 1992; 18: 319–321.10.1007/978-88-470-2203-4_30Search in Google Scholar

[21] Lachmann B, Robertson B, Vogel J. In vivo lung lavage as an experimental model of the respiratory distress syndrome. Acta Anaesth Scand 1980; 24: 231–236.10.1111/j.1399-6576.1980.tb01541.xSearch in Google Scholar PubMed

[22] Lewandowski K. Extracorporeal membrane oxygenation for severe acute respiratory failure. Crit Care 2000; 4: 156–168.10.1186/cc689Search in Google Scholar PubMed PubMed Central

[23] Luepschen H, Meier T, Grossherr M, Leibecke T, Leonhardt S. Optimization of artificial ventilation therapy for ARDS based on automatic identification of lung properties. 3rd European Medical and Biological Engineering Conference, Prague, Czech 2005; 1727–1983.Search in Google Scholar

[24] Marhong JD, Munshi L, Detsky M, Telesnicki T, Fan E. Mechanical ventilation during extracorporeal life support (ECLS): a systematic review. Intens Care Med 2015; 41: 994–1003.10.1007/s00134-015-3716-2Search in Google Scholar PubMed

[25] Mendoza García A, Krane M, Baumgartner B, et al. Automation of a portable extracorporeal circulatory support system with adaptive fuzzy controllers. Med Eng Phys 2014; 36: 981–990.10.1016/j.medengphy.2014.04.009Search in Google Scholar PubMed

[26] Meyrowitz G. Automatisierung der Herz-Lungen-Maschine. Berlin: Mensch & Buch Verlag 2005. Available at: https://publikationen.bibliothek.kit.edu/1000003288.Search in Google Scholar

[27] Misgeld BJ, Werner J, Hexamer M. Simultaneous automatic control of oxygen and carbon dioxide blood gases during cardiopulmonary bypass. Artif Organs 2010; 34: 503–512.10.1111/j.1525-1594.2009.00890.xSearch in Google Scholar PubMed

[28] Noah MA, Peek GJ, Finney SJ, et al. Referral to an extracorporeal membrane oxygenation center and mortality among patients with severe 2009 influenza A(H1N1). J Am Med Assoc 2011; 306: 1659–1668.10.1001/jama.2011.1471Search in Google Scholar PubMed

[29] Otis AB, McKerrow CB, Bartlett RA, et al. Mechanical factors in distribution of pulmonary ventilation. J Appl Physiol 1956; 8: 427–443.10.1152/jappl.1956.8.4.427Search in Google Scholar PubMed

[30] Peek GJ, Elbourne D, Mugford M, et al. Randomised controlled trial and parallel economic evaluation of conventional ventilatory support versus extracorporeal membrane oxygenation for severe adult respiratory failure (CESAR). Health Technol Assess 2010; 14: 1–46.10.3310/hta14350Search in Google Scholar PubMed

[31] Pham T, Combes A, Rozé H, et al. Extracorporeal membrane oxygenation for pandemic influenza A(H1N1)-induced acute respiratory distress syndrome: a cohort study and propensity-matched analysis. Am J Resp Crit Care 2013; 187: 276–285.10.1164/rccm.201205-0815OCSearch in Google Scholar PubMed

[32] Piraino T. Decremental peep titration: a step away from the table. Respir Care 2013; 58: 886–888.10.4187/respcare.02453Search in Google Scholar PubMed

[33] Pomprapa A, Schwaiberger D, Lachmann B, Leonhardt S. A mathematical model for carbon dioxide elimination: an insight for tuning mechanical ventilation. Eur J Appl Physiol 2014; 114: 165–175.10.1007/s00421-013-2754-0Search in Google Scholar PubMed

[34] Pomprapa A, Muanghong D, Köny M, et al. Artificial intelligence for closed-loop ventilation therapy with hemodynamic control using the open lung concept. Int J Intell Comput Cybernetics 2015; 8: 50–68.10.1108/IJICC-05-2014-0025Search in Google Scholar

[35] Ranieri VM, Rubenfeld GD, Thompson BT, et al. Acute respiratory distress syndrome: the berlin definition. J Am Med Assoc 2012; 307: 2526–2533.Search in Google Scholar

[36] Sangalli F, Patroniti N, Pesenti A. ECMO-extracorporeal life support in adults. Milan: Springer 2014.10.1007/978-88-470-5427-1Search in Google Scholar

[37] Schmidt M, Pellegrino V, Combes A, Scheinkestel C, Cooper D, Hodgson C. Mechanical ventilation during extracorporeal membrane oxygenation. Crit Care 2014; 18: 1–10.10.1186/cc13702Search in Google Scholar PubMed PubMed Central

[38] Schmidt RF, Lang F, Heckmann M. Physiologie des Menschen: mit Pathophysiologie. 31. Auflage: Springer 2010.10.1007/978-3-642-01651-6Search in Google Scholar

[39] Schumann S, Vimlati L, Kawati R, Guttmann J, Lichtwarck-Aschoff M. Analysis of dynamic intratidal compliance in a lung collapse model. Anesthesiology 2011; 114: 1111–1117.10.1097/ALN.0b013e31820ad41bSearch in Google Scholar PubMed

[40] Shekar K, Mullany DV, Thomson B, Ziegenfuss M, Platts DG, Fraser JF. Extracorporeal life support devices and strategies for management of acute cardiorespiratory failure in adult patients: a comprehensive review. Crit Care 2014; 18: 219.10.1186/cc13865Search in Google Scholar PubMed PubMed Central

[41] Sidebotham D, Allen SJ, McGeorge A, Ibbott N, Willcox T. Venovenous extracorporeal membrane oxygenation in adults: practical aspects of circuits, cannulae, and procedures. J Cardiothor Vasc An 2012; 26: 893–909.10.1053/j.jvca.2012.02.001Search in Google Scholar PubMed

[42] Siggaard-Andersen O, Wimberley PD, Fogh-Andersen N, Gøthgen IH. Measured and derived quantities with modern ph and blood gas equipment: calculation algorithms with 54 equations. Scand J Clin Lab Inv 1988; 48: 7–15.10.1080/00365518809168181Search in Google Scholar

[43] Skogestad S, Postlethwaite I. Multivariable feedback control: analysis and design. 2nd ed. Wiley 2005.Search in Google Scholar

[44] Smith DJ, Porumamilla HV. LOG based robust tracking control of blood gases during extracorporeal membrane oxygenation. American Control Conference 2011; 324–329.10.1109/ACC.2011.5991330Search in Google Scholar

[45] Stollenwerk A, Göbe F, Walter M, et al. Smart data provisioning for model-based generated code in an intensive care application. High Confidence Medical Devices, Software, and Systems and Medical Device Plug-and-Play Interoperability, Chicago, IL, USA 2011; 8.Search in Google Scholar

[46] Terragni PP, Filippini C, Slutsky AS, et al. Accuracy of plateau pressure and stress index to identify injurious ventilation in patients with acute respiratory distress syndrome. Anesthesiology 2013; 119: 880–889.10.1097/ALN.0b013e3182a05bb8Search in Google Scholar

[47] Walter M, Wartzek T, Kopp R, et al. Automation of long term extracorporeal oxygenation systems. European Control Conference (ECC), Budapest, Hungary 2009; 2482–2486.10.23919/ECC.2009.7074778Search in Google Scholar

[48] Walter M, Brendle C, Bensberg R, et al. Closed loop physiological ECMO control. 5th European Conf of the Int Federation for Medical and Biological Engineering 2011; 319–322.10.1007/978-3-642-23508-5_83Search in Google Scholar

[49] Zia M, Davies F, Alston R. Advances in monitoring for cardiothoracic surgery oxygenator exhaust capnography: a method of estimating arterial carbon dioxide tension during cardiopulmonary bypass. J Cardiothor Vacs An 1992; 6: 42–45.10.1016/1053-0770(91)90043-SSearch in Google Scholar

Received: 2016-3-29
Accepted: 2016-11-28
Published Online: 2017-1-25
Published in Print: 2017-4-1

©2017 Walter de Gruyter GmbH, Berlin/Boston

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