Validation of a New Method for Stroke Volume Variation Assessment: a Comparaison with the PiCCO Technique

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Variation Assessment: a Comparaison with the PiCCO Technique

Taous-Meriem Laleg-Kirati, Claire Médigue, Yves Papelier, François Cottin, Andry van de Louw

To cite this version:

Taous-Meriem Laleg-Kirati, Claire Médigue, Yves Papelier, François Cottin, Andry van de Louw.

Validation of a New Method for Stroke Volume Variation Assessment: a Comparaison with the PiCCO Technique. [Research Report] RR-7172, INRIA. 2010, pp.22. �inria-00429496v3�

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a p p o r t

d e r e c h e r c h e

N0249-6399ISRNINRIA/RR--7172--FR+ENG

Validation of a New Method for Stroke Volume Variation Assessment: a Comparison with the

PiCCO Technique

Taous-Meriem Laleg-Kirati — Claire Médigue — Yves Papelier — François Cottin — Andry Van de Louw

N° 7172

Janvier 2010

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Centre de recherche INRIA Paris – Rocquencourt

PiCCO Tehnique

Taous-Meriem Laleg-Kirati

∗

, ClaireMédigue

†

, YvesPapelier

‡

,

FrançoisCottin

§

, Andry Van de Louw

¶

Thème: Observation, modélisationet ommandepourlevivant

Équipe-ProjetSISYPHE

Rapportdereherhe n°7172Janvier201022pages

Abstrat: Thispaperproposesanovel,simpleandminimallyinvasivemethod

for strokevolume variation assessmentusing arterial blood pressure measure-

ments. Thearterialbloodpressuresignalisreonstrutedusingasemi-lassial

signalanalysismethodallowingtheomputationofaparameter,alledtherst

systoli invariant IN V S1^. ^We^show^that IN V S1 ^is ^linearly^related ^to ^stroke

volume. Tovalidate this approah, astatistial omparaisonbetweenIN V S1

andstrokevolumemeasuredwiththePiCCOtehniquewasperformedduringa

15-mnreordingin21mehaniallyventilatedpatientsinintensiveare. In94%

ofthewhole reordings,astrongorrelationwasestimatedbyross-orrelation

analysis (mean oeient=0.9) and linearregression (mean oeient=0.89).

One the linear relation had been veried, a Bland-Altman test showed the

verygoodagreementbetweenthetwoapproahesand theirinterhangeability.

Fortheremaining 6%,IN V S1 ^and^the ^PiCCO^stroke^volume ^were ^not^orre-

lated at all, and this disrepany, interpretedwith the help of meanpressure,

heartrateandperipheralvasularresistanes,wasinfavorofIN V S1^.

Key-words: Arterial blood pressure, rst systoli invariant, PiCCO, semi-

lassialsignalanalysis,strokevolumevariation

∗

Taous-MeriemLaleg-KiratiiswithINRIABordeauxSud-Ouest,MAGIQUE-3Dprojet

team,UFRSienes,BâtimentB1,UniversitédePauetdesPaysdel'AdourBP1155,64013

Pau,Frane,(e-mail:Taous-Meriem.Laleginria.fr).

†

ClaireMédigueiswithINRIA-Roquenourt,B.P.105,78153LeChesnayedex,Frane,

(e-mail:Claire.Medigueinria.fr).

‡

Yves Papelieriswith EA3544 EFMHpital Antoine Bélère 92141, Clamart,Frane,

(e-mail:yves.papelierkb.u-psud.fr)

§

FrançoisCottiniswithUnitédeBiologieIntégrativedesAdaptationsàl'Exerie(IN-

SERM902EA3872,Genopole),91000Evry,Frane,(e-mail:franois.ottinbp.univ-evry.fr)

¶

AndryVandeLouwiswithIntensiveCareUnit,CentreHospitalierSud-Franilien,91014

Evry,Frane,(e-mail:andry.vandelouwh-sud-franilien.fr)

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omparaison ave le PiCCO

Résumé: Cet artileproposeune nouvelleméthodepourl'estimation duvo-

lumed'ejetionsystolique par desmesuresde pressionartérielle. Le signalde

pressionestreonstruitàl'aided'uneméthoded'analysesemi-lassiquepermet-

tant le alul d'un paramètre, appelé le premier invariantsystolique IN V S1^.

Onmontre que IN V S1 ^est linéairementrelié auvolume d'ejetionsystolique.

An de valider ette approhe, une omparaison statistique entre IN V S1 ^et

levolumed'ejetionsystolique mesuréparlatehniquePiCCOaétéeetuée

pourunenregistrementde15minutespour21patientsméaniquementventilés

et ensoinsintensifs. Pour94%de l'enregistrementomplet,uneforte orréla-

tionaétéestimée parune analyseross-orrélation(oeientmoyen=0.9)et

une regressionlinéaire(oeient moyen =0.89). Une fois larelation linéaire

vériée,untestdeBland-Altmanamontréunebonneorrespondaneentreles

deux approhes et leur interhangeabilité. Pourles 6% restant, IN V S1 ^et ^le

volumed'éjetionaluléparPiCCOn'ontpasétéorrélés,et ettediérene,

interprétée à l'aide de la pression moyenne, de la fréquene ardiaque et des

résistanesvasulairespériphériquesaétéenfaveurdeIN V S1^.

Mots-lés: Pressionartérielle,premierinvariantsystolique,PiCCO,analyse

semi-lassiquedusignal,variationsduvolumed'éjetion

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Contents

1 Introdution 3

2 Materialsand Methods 5

2.1 Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Dataaquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.3 Signalanalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.4 Statistialanalysis . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3 Results 11 3.1 Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.2 Cross-orrelationanalysis . . . . . . . . . . . . . . . . . . . . . . 12

3.3 Linearregression . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.4 Bland-Altmanmethod . . . . . . . . . . . . . . . . . . . . . . . . 13

4 Disussion 15

1 Introdution

Hemodynami monitoring is ruial for ritial are patient management. A

reentinternationalonsensusonferenereommendedagainsttheroutineuse

ofstati preloadedmeasurementsalone topredituidresponsiveness[2℄, and

dynami assessmentnowseems moreuseful. Several studieshavedoumented

the ability of respiratory stroke volume variation (SV V⁾ ^to ^predit ^the ^uid

responsiveness in hemodynamially ompromised patients[8℄-[13℄. Measuring

respiratorySV V ^requiresâôntinuous^monitoringôf^stroke^volume⁽SV⁾^whih

anbeobtainedusinginvasiveornon-invasivemethods. Currentinvasivemeth-

ods used havethe disadvantage of requiring theinsertion of a entral venous

atheter and the alibration of the ardia output measure with a old iso-

tonisodiumhloridebolus(PiCCOtehnology)[3℄oralithiumhloridebolus

(LiDCO tehnology) [9℄. An alternative method, whih does not require ve-

nous atheter insertionor alibrationhas been proposed (Flotra Vigileo) [5℄,

but severallinial studieshavepointedoutitspooragreementwithreferene

tehniques[12℄,[16℄. Esophagealeho-doppleristhemainnon-invasivemethod,

alulating aorti blood ow from the eho-derived aorti diameter and the

doppler-derivedaortibloodveloity[11℄. Nevertheless,thistehniquehaspo-

tentialontraindiations,suhasesophagealvariesoresophagealsurgery,and

several limitations: for instane, it measures theblood owin thedesending

aorta and not the whole ardia output. Moreover,the preisionof themea-

surement depends on aurate probe positioning, whih is not always easyto

obtain[4℄. Thus,eahoftheabovemethodshasitsowndrawbaks,andthereis

stillaneedforaneasilyappliable,minimallyinvasive,aurateandaordable

method toestimateSV V^.

Due to thefat that Arterial Blood Pressure (ABP) anbe measured us-

ingminimallyinvasiveornoninvasivemethods,theideaofestimatingSV ^from

ABP hasaptured sientistsfora longtime. Thus, manymethods havebeen

developedand whoseobjetiveisto nd arelation betweenoneorseveralpa-

rameters haraterizing the shape of the pressure and SV ôr ârdia ôutput

(CO^),^see^for^instane^[7℄,^[15℄,^[22℄,^[23℄^and^the^referenes^quoted^there. ^These

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methods, whih are based on some models of systemi irulation, are alled

pulseontourmethods. Aomparisonbetweensomeofthepulseontourmeth-

ods has been proposed in [1℄, [26℄, [27℄, [29℄. The simplest model supposes a

proportionalitybetweenCO ând ^the ^Mean Ârterial ^Pressure ⁽M AP^). Ôther

approahes, based onwindkessel models, link SV ^to ^dierent ^lumped ^param-

eters suh as pulse pressure, the systoli and diastoli pressures[7℄. However

theseapproahesonsiderthearterialsystemasalumpedsystemwhihappears

notsuientlyaurate. So, othermethods resultingfromdistributed arterial

modelsusethepressureareasothatSV ^is^often^supposed^to^beproportionalto theareaunderthesystolipartofthepressureurve. Corretedversionsofthis

relationhavebeenalsoproposed[15℄. However,thisapproahrequiresdeteting

theendofthesystolewhihisompletelynontrivial,partiularlyinperipheral

ABP waveforms. Moreover, approahes taking into aountthe nonlinear as-

pets of the arterial system have been proposed, for example modelow [28℄,

butsomestudieshaverevealedthepooreieny ofthis methodin anumber

ofases[24℄.

In this paper we introdue a novel tehnique for SV V ^assessment ^using

ABPmeasurements. Thismethod isbasedontheanalysis ofABPwithanew

signal analysis method that was reently proposed in [21℄, and alled Semi-

Classial Signal Analysis (SCSA). The new spetral parameters provided by

SCSA,eigenvaluesandinvariants,havealreadygivenpromisingresultsinsome

otherappliations,assummarized inthefollowing.

On the one hand,weassessed their ability to disriminate between dier-

entsituations. Intherstsituation,nineheartfailure subjetswereompared

to nine healthy subjets. In the seond situation, eight highly t triathletes

were ompared before and after training. SCSA parameters always provided

moresigniant results than lassial parameters, regardingtemporal as well

asspetralparameters([20℄, [21℄). On theotherhand,wetested theabilityof

theinvariants to represent physiologialparameters of great interest, partiu-

larlySV V^,ⁱⁿ ^two^well-known^onditions: ^the^head-up ⁶⁰^degrees^tilt-test^and

the handgrip-test [21℄. Let us fous on the rst invariants. The rst global

invariant(IN V1⁾ îs,^by ^denition,^the^mean^valueôf ^theÂBP^signal,^whihîs

astandardparameterinlinialpratie. Therstsystoli(IN V S1⁾^and^dias-

toli(IN V D1⁾învariantsâre^lessôbvious. ^They^result^from^thedeomposition ofthepressureintoitssystolianddiastoliparts. Inpartiular,IN V S1ôrre-

spondsto theintegraloftheestimated systolipressurewith SCSA.Referring

to the pulse ontour method stating that the area under the systoli part of

thepressureurveisproportionaltoSV âs^desribedâbove,ôneân^show^that IN V S1 ^variations^giveinformationonSV V^.

WestudyinthispapertheorrelationbetweenIN V S1^and^measuredSV V

using areferene method; thePiCCO tehnique. ThePiCCO tehnique uses

the pulse ontour method with a alibration by a transpulmonary thermodi-

lution and is onsidered areliable tehnique. In what follows, we presentthe

experimentalprotooland reallsomebasiaspetsoftheSCSAmethod. We

introdueIN V S1^and^its^relation^toSV V^. ^Then,^we^present^statistial^results

on21patients'reordings.

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2 Materials and Methods

This prospetivestudywasonduted in the16-bedmedial-surgialintensive

areunit(ICU)oftheSud-FranilienGeneralHospital(Evry,Frane).

2.1 Patients

Inlusionriterion: allmehaniallyventilatedpatientswhoseardia

output was ontinuously monitored with a transpulmonary thermodilu-

tionatheter(PiCCO,PulsionMedialSystems,Munih,Germany)were

inluded,exeptthosesatisfyingthefollowingexludingriteria. PiCCO

isroutinely used in this unit to monitorhemodynamially ompromised

patients.

Exlusionriteria: patientspresentingardiaarrhythmiasorbreath-

ing spontaneously were exluded beause the SVV is not appliable for

suhpatients.

Protool: all patients were sedated with midazolam and fentanyl in

dosages that were titrated to ahieve full adaptation to the ventilator.

Ventilatorsettingswereasfollows: volumeassist-ontrolmode;tidalvol-

ume (Vt), 6ml/kg ^ideal ^body ^weight; ^breathing ^rate, ²⁰ yles/minute;

inspiratory/expiratoryratio,

1

2^; ^andF iO2^adjusted ^to ^maintain^transu-

taneousoxygensaturationinblood94%. Positiveend-expiratorypressure

(PEEP) wasset at 5cm H2O ^but ^some^hypoxemi ^patients^required ^an

inreaseinPEEP to 10m H2Oduring thedataaquisition,to improve

arterial oxygenation. The inrease in PEEP was left to the disretion

oftheattending physiian, aswellasthe adaptationofvasoativedrugs

dosages,adjusted to maintainan adequateirulatorystatus during the

protool.

2.2 Data aquisition

One-leadeletroardiogram,arterialpressure,andrespiratoryowsignalswere

reorded during a15-min period using aBiopa 100system (Biopasystems,

Goleta, CA, USA). All data were sampled at 1000Hz ând ^stored ôn â ^hard

disk. Cardiaoutputwasalibratedjustbeforethedataaquisitionwithaold

isotoni sodium hloride bolus of 20 ml. Then, CO ^and ^peripheral ^vasular

resistanes(P V R⁾^were^delivered^every³⁰^seonds^during ^the^15-min^period.

2.3 Signal analysis

SignalproessingwasperformedusingtheSilab andMatlabenvironmentsat

the Frenh National institute for Researh in Computer Siene and Control

(INRIA-Sisyphe team).

A Semi-ClassialSignal Analysis method

Inthissetion,weintroduetheSCSAtehniqueandsomeresultsofitsappli-

ationtoABPanalysis. WealsoshowtherelationbetweenIN V S1 ^andSV V^.

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TheSCSApriniple Lety:t7−→y(t)^be^a^real^valued^funtionrepresenting thesignaltobeanalyzedsuhthat:

y∈L¹1(R), y(t)≥0, ∀t∈R,

∂^my

∂x^m ∈L¹(R), m= 1,2, ⁽¹⁾

with,

L¹1(R) ={V| Z ^+∞

−∞

|V(t)|(1 +|t|)dt <∞}. ⁽²⁾

The main ideain the SCSAonsists in interpreting the signaly ^as^a ^mul-

tipliation operator, φ → y.φ^, ôn ^some^funtion ^spae. ^Then, înstead ôf ^the

standard Fourier Transform, we use the spetrum of a regularized version of

thisoperator,knownas theShrödingeroperatorin L²(R)^, ^for^the ^analysis^of y^:

H(h;y) =−h²d²

dt² −y, ⁽³⁾

forasmall h > 0^. ^The^SCSA ^method îs^better^suited ^to ^theânalysis ôf ^some

pulseshapedsignalsthantheFourierTransform[21℄.

Inthisapproah,thesignalisapotentialoftheShrödingeroperatorH(h;y)^.

Weareinterestedinthespetralproblemofthis operatorwhih isgivenby:

−h²d²ψ

dt² −yψ=λψ, t∈R, ⁽⁴⁾

whereλ^, λ∈R^andψ^,ψ∈H²(R)¹^arerespetivelytheeigenvaluesofH(h;y)

andtheassoiatedeigenfuntions. Underequation(1),thespetrumofH(h;y)

onsistsof:

aontinuousspetrumλ≥0^,

adisretespetrumomposedofnegativeeigenvalues. Thereisanon-zero,

nitenumberNh ^of^negativeeigenvaluesoftheoperatorH(h;y)^. ^We^put λ=−κ²_nh ^withκnh >0 ^andκ1h> κ2h >· · · > κnh^,n= 1,· · ·, Nh^. ^Let

ψnh^,n= 1,· · ·, Nh ^be^the^assoiatedL²-normalizedeigenfuntions[21℄.

TheSCSAtehniqueonsistsinreonstrutingthesignaly^with^the^disrete

spetrumofH(h;y)^using^the^following^formula:

yh(t) = 4h

Nh

X

n=1

κnhψ_nh² (t), t∈R. ⁽⁵⁾

Here,theparameterh^playsânîmportant^role. Âsh^dereases,^theâpproxi-

mationofthesignalimproves. However,ash^dereases,^the^number^of^negative

eigenvaluesNh înreases ând ^hene ^the ^time ^required ^to ^perform ^the ômpu-

tation inreases. So, in pratie, what weare looking foris a valueof h ^that

providesasuientlysmallestimationerrorwithareduednumberofnegative

eigenvalues. Wesummarizethemain stepsforreonstruting asignalwiththe

SCSAasfollows[21℄:

1H²(R)^denotes^the^Sobolev^spae^of^order²

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1. Interpret the signal to be analyzed y âs â ^potential ôf ^the ^Shrödinger

operatorH(h;y)⁽³⁾^;

2. omputethenegativeeigenvaluesandtheassoiatedL²-normalizedeigen- funtionsofH(h;y)^;

3. omputeyh^aording^to ^equation⁽⁵⁾^;

4. lookforavalueofh^to^obtain^a^goodapproximationwithasmallnumber ofnegativeeigenvalues.

ABP analysiswith the SCSA Now,weintroduesomeresultsontheap-

pliationoftheSCSAtoABPanalysis. WedenotebyP ^the^ABP^signal^andPˆ

itsestimationwiththeSCSAsuhthat:

Pˆ(t) = 4h

Nh

X

n=1

κnhψ²_nh(t), ⁽⁶⁾

where−κ²_nh^,n= 1,· · ·, Nh âre^theNh ^negativeeigenvaluesoftheShrödinger operator H(h;P)ândψnh ^theâssoiatedL²−^normalizedeigenfuntions.

The ABP signal wasestimated for several values of the parameter h ^and

heneNh^. ^Fig.1illustratesmeasuredandestimatedpressuresforonebeatofan ABPsignalandtheestimatederrorwithNh= 9^. ^Signals^measured^at^the^aorta

(invasively)andatthenger(noninvasively)respetivelywereonsidered. We

pointoutthat5^to9^negativeeigenvaluesaresuientforagoodestimationof anABPbeat[17℄,[19℄.

Oneappliation of the SCSA to ABP signals onsists in deomposing the

signal into its systoli and diastoli parts. This appliation was inspired by

a redued model of ABP based on solitons solutions of a Korteweg-de Vries

(KdV) equation 2

proposed in [10℄, [18℄. As desribed in [17℄, [21℄, the idea

onsists in deomposing (6) into twopartial sums: the rst one,omposed of

the Ns ⁽Ns = 1,2,3 ⁱⁿ ^general) ^largest κnh ând ^the ^seond ômposed ôf ^the

remainingomponents. Then,therstpartialsumrepresentsrapidphenomena

that predominateduring the systoliphase andthe seondone desribesslow

phenomenaofthediastoliphase. WedenotebyPˆs^andPˆd ^the^systoli^pressure

andthediastolipressurerespetivelyestimatedwiththeSCSA.Thenwehave:

Pˆs(t) = 4h

N^s

X

n=1

κnhψ²_nh(t), ⁽⁷⁾

Pˆd(t) = 4h

Nh

X

n=N^s+1

κnhψ_nh² (t). ⁽⁸⁾

Fig.2showsmeasuredpressureandestimatedsystolianddiastolipressures

respetively. WenotiethatPˆs^andPˆd^arerespetivelyloalizedduringthesys- toleandthediastole.

2

Solitonsaresolutionsofsomenon-linearpartialderivativeequationsliketheKdVequation

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1.6 1.8 2 2.2 2.4 2.6 2.8 3 50

55 60 65 70 75 80 85 90 95

t (s)

Arterial blood pressure (mmHg)

Estimated pressure Measured pressure

1.6 1.8 2 2.2 2.4 2.6 2.8 3

0 0.5 1 1.5 2 2.5

t (s)

Relative error (%)

(a)Aorta

1.6 1.8 2 2.2 2.4 2.6 2.8 3

50 60 70 80 90 100 110 120 130 140

t (s)

Aretrial blood pressure (mmHg)

Estimated pressure Measured pressure

1.6 1.8 2 2.2 2.4 2.6 2.8 3

0 0.5 1 1.5 2 2.5 3

t (s)

Relative error (%)

(b)Finger

Figure1: Estimationofthepressureattheaorta andthengerlevelwiththe

SCSAandNχ= 9^. Ôn^the^left,^theêstimatedând^measured^pressures. Ôn^the

right,therelativeerror

1.6 1.8 2 2.2 2.4 2.6 2.8 3

0 10 20 30 40 50 60 70 80 90 100

t (s)

ABP (mmHg)

Reconstructed systolic pressure Measured pressure

(a)

1.6 1.8 2 2.2 2.4 2.6 2.8 3

30 40 50 60 70 80 90 100

t (s)

ABP (mmHg)

Reconstructed diastolic pressure Measured pressure

(b)

Figure2: (a)Estimatedsystolipressure,(b)Estimateddiastolipressure