Cardiac ResynchronizationTherapy Mechanisms in AtrialFibri l lation

Zachary I. Whinnett, BMedSci, BMBS, MRCP, PhD*,Darrel P. Francis, FRCP, MD

KEYWORDS

� Biventricular pacing � Cardiac resynchronization therapy � Atrial fibrillation

KEY POINTS

� The process of comparing multiple settings is the acid test of the resynchronization hypothesis,largely unacknowledged by the clinical and scientific community.

� Lack of a visible prominent effect of interventricular (VV) adjustment on hemodynamics is proof thateither the resynchronization concept is plain wrong or the measurement protocol was designed tobe so vulnerable to error that it cannot detect this important effect.

� The hazard of VV delay optimization lies in the widespread failure to perceive natural biologic vari-ability, which is often mistaken for failure of the operator to make measurements correctly. Mea-surement variability causes false optima to appear with VV delay adjustment and makes the sizeof the optimization benefit become exaggerated.

� Using sensitive (narrow-error-bar) methods, VV delay optimization produces relativelysmall increments in hemodynamic effects over and above programming a nominal setting ofVV 0 ms.

INTRODUCTION

It has been commonly assumed that the mecha-nism through which biventricular pacemakers(BVPs) improve cardiac function is by resynchroni-zation of ventricular activation, which is why itis commonly referred to as cardiac resynchroniza-tion therapy (CRT). Resynchronization meansrestoration of simultaneity, which can only meanof the ventricles (because the atria are never sup-posed to contract simultaneously with the ventri-cles). The mental picture conjured by that termand conveyed when speaking to nonspecialistsis almost always that of ventricular walls contract-ing incoordinately under native conduction and

Funding Sources: The authors would like to acknowledge38/28268).International Centre for Circulatory Health, National HeaNorth Wharf Road, London W2 1LA, UK* Corresponding author.E-mail address: z.whinnett@imperial.ac.uk

Heart Failure Clin 9 (2013) 475–488http://dx.doi.org/10.1016/j.hfc.2013.07.0051551-7136/13/$ – see front matter � 2013 Elsevier Inc. All

then brought back into correct mutual timing bythe pacemaker. The visual impact and cognitivecatchiness of the concept is overwhelming.

Under this hypothesis, patients with atrial fibril-lation (AF) should be in as strong a position tobenefit from BVP as those in sinus rhythm,because both groups are equally liable to ventric-ular dyssynchrony.

In routine clinical practice, BVPs are frequentlyimplanted into patients who are in permanent AF(23% in a large European survey1).

After implantation of a BVP in a patient with AF,there are 2 main targets for optimizing therapy.First, measures can be taken to ensure high per-centages of ventricular pacing that allow adequate

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delivery of therapy. Second, the relative timing ofstimulation of the 2 pacing leads can be adjusted(VV delay). If the mechanism through which BVPdelivers its beneficial effect is predominantly ven-tricular resynchronization, then adjusting VV delaywould be expected to produce large changes incardiac function.This article examines how to assess the reli-

ability of potential techniques for performing opti-mization and explores whether the effort requiredfor optimization is likely worthwhile.

CRT IN PATIENTS WITH ATRIAL FIBRILLATION

Would a patient with AF and left bundle branchblock (LBBB) stand to benefit from BVP? Even adevotee of the resynchronization concept acceptsthat the presence of AF might impair the ability ofBVP to give benefit. The variable intrinsic atrioven-tricular (AV) conduction might cause some beatsto not have BV capture by the pacemaker and,therefore, incomplete delivery of biventricularpacing.Therefore, it is not possible to simply assume

that the benefit observed in patients with sinusrhythm also applies to patients in AF.The evidence base for BVP in patients with AF

has been predominantly derived from observa-tional case series.2–10 These data suggest thatthe benefits seem attenuated compared with pa-tients in sinus rhythm.11,12

Only small numbers of patients with permanentAF have been included in the landmark random-ized studies to assess the impact of CRT onclinical outcome measures. It is difficult to under-stand why these randomized trials—outwardlydesignated trials of cardiac resynchronization—were not designed to cover a full spread of pa-tients with heart failure and LBBB. Until newstudies are carried out, guidance is based on inter-pretation of the data that did arise.13,14

In a predefined substudy of the RAFT (Resynch-ronization–Defibrillation for Ambulatory Heart Fail-ure Trial), patients with permanent AF, New YorkHeart Association class II or III heart failure, leftventricular (LV) ejection fraction less than or equalto 30%, and QRS duration greater than or equalto 120 ms were randomized to receive BVP or noBVP, with all patients receiving a defibrillator; 229patients were randomized. The principal result ofthe study is that the event rate was low in botharms and, therefore, theCI for the primary outcomeof death or heart failure hospitalization betweenthose assigned to CRT–implantable cardioverterdefibrillator (ICD) and those assigned ICD waswide,15 ranging from 0.65 to 1.41. The point esti-mate was 0.96 (P 5 .82), but the CI includes an

effect as strong as seen in CARE-HF (Cardiac Re-synchronization-Heart failure) trial, for instance.The MUSTIC-AF (Multisite Stimulation in Car-

diomyopathy Atrial Fibrillation) trial, which deliber-ately focused on AF and addressed symptoms,did find evidence of symptomatic advantage ofBVP over pure right ventricular (RV) pacing13 butit should be borne in mind that RV pacing on itsown was found harmful in the DAVID (Dual Cham-ber and VVI Implantable Defibrillator) trial,16 so thefavorable outcome in the BVP arm should not beassumed due to a salutary effect of BVP: it maysimply be a neutral alternative to harmful RVpacing.The absence of conclusive evidence of benefit

from randomized studies to support the use ofCRT in patients with AF may be explained by thefollowing:

1. Insufficient numbers of patients entered intorandomized studies

Many more sinus rhythm patients than AF pa-tients have been entered into randomized studies.Therefore, even if BVP is equally effective inboth groups, the AF studies are likely to beunderpowered.

2. Lack of consistent biventricular capture throughlack of AV node ablation

In AF, native RR interval varies because of vari-able arrival of atrial wavefronts and possibly alsovariable AV node conduction. This means that aregular programmed ventricular pacing rate mayfail to capture every beat consistently, becauseof intermittent breakthrough of native conduc-tion.17 If there is reduced delivery of therapy,perhaps in the 60% to 75% range, then a studyapproximately 2.5 times as large as those per-formed in sinus rhythm is required. In reality, AFstudies have been smaller than their sinus rhythmcounterparts.

3. The effect may be smaller in AF

Even if ventricular resynchronization is the pri-mary driver of the benefit of CRT in sinus rhythm,there may be a contribution from coordination ofatrial versus ventricular contraction (this is not re-synchronization but might be called euchroniza-tion).18 The beneficial effect of BVP in AF might,therefore, be smaller than in sinus rhythm.

OPTIMIZING DELIVERY OF BIVENTRICULARPACING

In order to ensure high percentages of ventricularpacing, hence, the opportunity for adequate

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delivery of therapy, it is important to ensure thatnative conduction does not unfavorably competewith BVP, so that ventricular resynchronization issuccessfully delivered.

High percentages of ventricular pacing may beachieved pharmacologically by adequate uptitra-tion of medication to control ventricular rate.Relying on the percent biventricular pacing datastored in the pacemaker to determine this may bemisleading, because these rates may includefusion and pseudofusion between pacing from thedevice and intrinsic atrioventricular conduction.19

AV node ablation can deliver a more powerfulguarantee of the effectiveness of adequate ratecontrol. Some operators are anxious about the po-tential adverse consequences of converting a pa-tient with stable status, who is nonpacemakerdependent, into a patient who is totally dependenton the pacemaker for survival.

Although observational studies report strongsymptomatic responses to ablation plus BVP3,6–9,20

there are 2 limitations. First, these procedures areby their nature only likely to be carried out by groupswith a strong belief in the rationale and, therefore,any outcome markers that require human judgmentshould not be assumed free of innocent bias. Sec-ond, they have all covered only the short term.Adverse consequences that take longer to materi-alize, such as the undesirable sequelae of total pac-ing dependency, may not have manifested at thetime the studies were evaluated.

The net clinical effect of biventricular pacing inAF is difficult to guess from the clinical outcomedata that exist to date. The randomized experi-ence is small, and the nonrandomized experience(like all observational data) is open to an unquanti-fiable degree of natural observer bias.

What is needed are reliable experimental data,which may take the form of physiologic measure-ments with and without BVP, with careful attentionto bias-resistance and reliability of the data withinindividual patients. Alternatively, they may take theform of outcome trials with event endpoints,although, for the reasons described previously,these have to be much larger than their sinusrhythm predecessors to achieve the same degreeof statistical power.

OPTIMIZATION OF INTERVENTRICULARDELAY

The typical nominal VV delay is close to 0 ms (ie,virtually simultaneous onset of stimulation). Ad-justing the VV delay is expected to change the de-gree of ventricular resynchronization. There aretheoretic reasons why it may be necessary toadjust the VV delay in order to obtain maximal

improvements in ventricular resynchronization.For example, if the LV lead is positioned in anarea of slow conduction, there may be a significanttime delay before the pacing stimulus spreads toactivate the left ventricle. In this case, it may berational to program the LV lead to pace beforethe RV lead in order to allow a greater bulk of theventricular myocardium to be activated approxi-mately synchronously.

Several different methods have been proposedto guide the process of assessing the impact ofadjusting the VV delay. Because supporting evi-dence on the long-term benefit of BVP optimiza-tion from large clinical trials is lacking, guidelinesprovide little scientifically supported guidance onhow to program VV delay in AF.21

The lack of supporting evidence for optimizationmay be because there is no or little incrementalbenefit to be obtained from optimizing VV delayover and above the nominal delay. Alternatively,it may be that there are advantageous VV delays,but the methods used for optimization were unre-liable and did not identify them correctly.

In order to establish which of these explanationsis most likely, there must be a mechanism for as-sessing the reliability of an optimization method.Before proceeding to clinical outcome studies, sim-ple stepscanbe taken todeterminewhetheranopti-mization method has a high likelihood of deliveringreliable results. A rigorously conducted large-scaleclinical outcome study assessing an optimizationmethod that does not reliably identify the trueoptimal VV delay will not find a beneficial effect forVV optimization. It will not be possible to determinewhether the lack of benefit is because VV optimiza-tion is not important orwhether it is simplydue to thefailure of the optimization method tested.

IS A PROPOSED OPTIMIZATION METHODSUFFICIENTLY RELIABLE TO DELIVERCLINICALLY USEFUL RESULTS?

It is unwise to proceed directly to a clinical trialwith an optimization scheme before establishingits reliability; this can be done cheaply. This articlesets out a series of steps that can be used toassess any optimization method, beginning withsimple tests that can be done in a few minutes in1 or 2 patients.

Step 1: Does the Proposed OptimizationScheme Identify a Singular Optimal Value?

An optimization scheme must select a single VVdelay (or a narrow range of VV delays) if it is tomake any meaningful claim of being an optimiza-tion scheme at all.

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For example, a scheme that selects a broadrange, such as “anywhere from LV first by 80 msto RV first by 80 ms,” is so vague as to be little bet-ter than completely uninformative. Taken to itsextreme, a scheme could declare all possible VVdelays to be optimal: there is no reason to considerthis an optimization scheme.Ideally, a scheme should provide the optimum to

the level of precision to which a device can be pro-grammed. For devices that can only be pro-grammed to the nearest 10-ms step, there is noneed to know the optimum to any greater degreeof precision than this. Despite being easy to deter-mine, the majority of optimization schemes do notreport a degree of precision for the value deter-mined as the optimum.Aside from a too-wide range of proposed opti-

mality, the second undesirable pattern is thedual-peaked optimum. How can LV first by20 ms and RV first by 40 ms be equally optimal,with intervening settings worse? Uncritical accep-tance of dual-peaked optima allows virtually anypattern to be taken as a valid set of optimizationmeasurements. There is little physiologic justifica-tion for dual peaks. At the least, a dual-peakedpattern should be the subject of blinded verifica-tion from separate data by an independentobserver, and, if confirmed, patients should un-dergo detailed assessment of the physiologic pro-cess underlying the phenomenon, which mightturn out to be a landmark finding in fundamentalmechanisms.The ability to identify a singular region on the VV

delay spectrum as optimal is the most basicrequirement for an optimization scheme. It takesonly a handful of patients to test singularity ofmost of the proposed methods. For example, toassess whether optimization performed using leftventricular outflow tract (LVOT) velocity-time inte-gral (VTI) measurements identify a singular regionwould only require about 7 minutes per patient (3beats per VV delay, across 7 VV delays, allowing20 seconds per VTI measurement). Readers areencouraged to test this.

Step 2: Is It Reproducible?

Once a scheme has demonstrated adequate sin-gularity, it can then go forward for the slightlymore time-consuming test of reproducibility. If atest is not singular, there is no point assessing itfor reproducibility. The reason for this is as follows.A scheme that is not singular either provides toobroad a territory of supposed optimality or pro-vides multiple disconnected regions of optimality.In either case, if a second test is carried out,whether the second result is consistent with the

first in the first case is always judged successfulor in the second case is impossible to judge.For example, if the scheme addresses a spec-

trum of possibilities from “LV first by 120 ms” to“RV first by 80 ms” and declares that “LV first by80 ms to RV first by 40 ms” are all optimal, thena second test that declares the optimum is “LV firstby 40 ms to RV first by 80 ms” is judged a match—even though in both cases they cover half therange.In another example, if the first test declares the

optimum to be “LV first by 120 ms or RV first by40 ms” and the second test declares it “LV firstby 40 ms or RV first by 80 ms,” they may at firstbe considered almost concordant (due to thenear match of RV 40 ms and RV 80 ms) but onfurther reflection it becomes clear that almostany finding on the second test would be consid-ered a perfect or near match.So singularity is a vital prerequisite, but how

exactly should reproducibility itself be judged?

1. Independent data sets acquired separately

The key to reproducibility is the identification ofequivalence or near-equivalence of VV delay opti-mum by independent observers who acquireseparate data blinded to each other’s findings.It is grossly inadequate to ask 2 observers toexamine the same acquired set of data (it makesno more sense than asking 2 observers to look atthe same reading displayed on a blood pressuremachine and call it reproducibility).The enemy of optimization is biologic variability

that is continually producing fluctuations in anymeasured variable. In this environment, there isgreat opportunity for differences between observa-tions at different VV delays to be the result of thesenatural biologic fluctuations and not the VV delay it-self. For this reason, the 2 operators should acquireseparate data. If under these circumstances theyobtain the same optimum, then the scheme is suf-ficiently robust to overcome this noise.

2. Blinding

In clinical practice, most workers have aninherent recognition that the techniques they useare less good than claimed, although they rarelyvoice this concern. The principal hallmark of thepresence of this gestalt knowledge is the behaviorof peeking at previous results before making a newmeasurement. This happens in assessment of LVfunction and of valvular function22 and is evencondoned in guidelines.When practitioners conduct peeking, to ensure

that values make physiologic sense in the context,they are recognizing that the noise in the

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measurement is so great that they have to selectbetween the various measurements, using someexternal knowledge in order to achieve an ex-pected pattern. In such an environment, oncethey conduct one optimization, they tend to tunethe results of the second to match the results ofthe first.

A simple solution to this is to ask 2 independentoperators to conduct entirely separate optimiza-tions on the same patient immediately succes-sively without the 2 operators communicating orviewing each other’s data. Alternatively, the pro-cess could be automated so that the human ten-dency to manipulate the interpretation of one setof data to match that of the other is avoided.

Again, although this process of testing repro-ducibility takes a little forethought to understandwhy it is necessary, its actual execution is easyand quick.

3. Willingness to publish unpleasant results

Individuals testing reproducibility should bewilling to publish the findings even if they do notfit their prejudice or a department’s liking. It shouldnot be assumed that findings that discord withprejudice are incorrect. Instead, it may be the fail-ure of previous workers to report findings that mayhave resulted in the present worker having a wrongprejudice. In any case, a biologic observation is abiologic observation and not an assay of the staffmember, so no shame should be accrued fromrealizing that a scheme is not reproducible.

4. Calculate and display the relevant variable, notan irrelevant one

When the data of the 2 optimizations arecollected in a handful of patients, the relevantcalculation is the SD of difference between theoptimal VV delays. This is carried out by listingthe VV delay selected by operator 1 in the first col-umn, the VV delay selected by operator 2 in thesecond column, and the difference in the third col-umn. The difference should be signed (ie, if oper-ator 2 picked a more positive VV delay in aparticular patient, it would be marked as a positivedifference; meanwhile if that operator picked amore negative VV delay in another patient, it wouldbe marked as a negative difference).

On average (unless an operator is biased towardreporting a higher or lower value consistently), themean of those differences tends to be distributedat approximately 0. The relevant question is howwide that distribution is, which can be convenientlycalculated as the SD of those differences. Thereare several variables that there is no point incalculating.

Whether operator 1 and operator 2 aresignificantly differentIt isof nouse tocalculatewhetheroperator 2 reportsa significantly higher optimum or not than operator1. This is only of interest if there was a concernthat one operator had a consistent tendency topick higher or lower values. If theywere both select-ing purely randomly, without making any biologicmeasurement, they would have no difference intheir mean optima (because they are drawn fromidentical distribution) even though such an optimi-zation process would be completely useless.

What proportion of patients is within the 95%Bland-Altman CI bands?There is essentially no point reporting this becausethe CI bands are designed to capture approxi-mately 95% of the cases on a parametric basis.Deviations from 95% occur by chance due to theshapes of the distribution and give no useful infor-mation on the reliability of the technique.

Difference in LVOT VTI or other measurementbetween settingsThe question is how widely the repeat VV optimaare distributed, not the measured consequenceson the VTI or other physiologic variables. Althoughit might be intuitively attractive for a clinician toreport whether operator 1 and operator 2 gener-ated similar physiologic consequences, selectingsettings entirely at random, even with physiologythat is strongly dependent on VV delay, results ina near-0 difference between the 2 operators. Againthis is because the physiologic responses aredrawn from the same distribution, not becausethe VV delays are equal.

Step 3: Is the Value Identified as OptimalBiologically Plausible?

For any scheme that passes both singularity andreproducibility criteria, it is time to test its physio-logic plausibility. For example, a scheme thatalways defines the optimum VV delay in womenas RV first by 60 ms and in men as LV first by80 ms would meet criteria of singularity and repro-ducibility, because each patient is always allo-cated a consistent value, but would not bebiologically plausible.

There are several aspects to biologic plausibil-ity. A scheme that for most patients picked RV firstby a high degree (eg, 60 ms or 80 ms) would sufferfrom the apprehension that it is delivering largelyRV pacing, which, as is known from trials, suchas DAVID, is likely harmful.16

Plotting the distribution of optima obtained maygive clues to plausibility. It would be expected,from induction from the concept behind BVP,

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that VV optima most commonly are in the vicinityof VV 0 ms or slightly left first. It would be ex-pected that RV first or very extreme LV first wouldbe identified as optimal in only a minority ofpatients.This 3-step process (singularity, reproducibility,

and plausibility) can be checked inexpensivelyand can be conducted for multiple variables inthe same study without loss of power. The authorshave conducted such a study for markers that areelectrical (QRS duration), flow based (LVOT VTI),and pressure based (noninvasive beat-by-beatblood pressure) to show how it could work in thisscreening process.23

Box 1Process for identifying a reliable scheme foroptimization of VV delay

Step 1: Does the proposed optimization schemeidentify a singular optimal value?

Step 2: Is it reproducible?

Step 3: Is the value identified as optimal biolog-ically plausible?

Step 4: Does the optimization method result inimprovements in cardiac function?

Step 5: Choosing an optimization scheme

Step 4: Does the Optimization Method Resultin Improvements in Cardiac Function?

Singular, reproducible, and plausible optimizationschemes are still not yet necessarily ready forlarge-scale trailing.Optima based on intracardiac measurements

need not necessarily agree with each other or pro-duce overall improvements in cardiac function,because there are many potential variables, andmaximization of one may be at the expense ofanother. Outside the heart, however, what gener-ates pressure is what generates flow, so these 2variables tend to be affected in the same directionby any change in VV delay timing. All other periph-erally measured variables are downstream conse-quences of pressure and/or flow and, therefore,are likely to be affected concordantly. It is implau-sible for a peripheral variable to have a VV delayoptimum that is substantially different from theoptimum for pressure and flow.Optimization should be expected to deliver an

increment in cardiac function. The size of the incre-ment can be determined in relation to the status ata reference VV delay, such as 0 ms. For example, ifa VV delay of LV first 60 ms is identified as optimal,it can be compared with VV 0 ms in order to deter-mine themagnitude of the increment in blood pres-sure or flow that is gained by programming theoptimized setting. It is important to ensure that ifusing ameasure of cardiac function to both identifythe optimal delay and assess the impact of pro-gramming the optimal delay, that separate mea-surements are used to identify the optimal VVdelay from those used to determine the hemody-namic impact. Otherwise, there is likely to be apositive bias, which results in an overestimationof the magnitude of improvement in cardiac func-tion.24 Therefore, one set of measurements shouldbe made to identify the optimal delay and separatemeasurements made to compare this optimaldelay with the reference.

Step 5: Choosing an Optimization Scheme

After these stages, only methods that identify asingular optimum, are reproducible, and are plau-sible are left. They have good agreement withmeasures of cardiac function. It is likely that opti-mization methods demonstrating these propertieswill show clustering with regard to the VV delayidentified as optimal. These schemes will thereforeshow good agreement with each other with regardto the VV delay determined as optimal. Eachscheme would be already validated against allthe others in that cluster. With all schemes in thecluster reporting similar optima, any one of theschemes could be chosen for clinical use, perhapsbased on cost or local convenience.No endpoint trials need be carried out before

this stage. At this stage, if 2 different measuresof cardiac function consistently identify differentoptima, to assist in choosing between theschemes it may be helpful to conduct a clinicaltrial. Box 1 shows the process for identifying areliable scheme for optimization of VV delay.

HAZARDS OF JUDGING A BOOK BY ITSCOVER OR AN ARTICLE BY ITS TITLE

Several different methods have been proposed toguide VV delay optimization. Although little recog-nized, it is not sufficient to settle on a parameterandmeasure it at each setting, choosing the settingwith the best value, because the measurementsmay vary spontaneously with time due to naturalbiologic fluctuation. If thought is not applied, thesebiologic fluctuationsmay bemistaken for the effectof VV delay; thereby, a random VV delay settingmay unknowingly become selected.The protocol used to acquire data needs careful

planning so that enough steps are taken to mini-mize the effects of biologic variability. If thesesteps have not been taken, then the findings maybe incorrect. If the planning has not been carriedout, then the investigators will not have realized

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that they need to take these steps. If signal hasbeen overwhelmed by noise, a study’s investiga-tors are unlikely to report it (they may not evenknow). The only sign of this that a reader can real-istically expect to see is the absence of evidenceof meticulous quantification of natural biologicvariability by the investigators.

It is tempting to classify articles on optimizationby their choice of variable measured for optimiza-tion (eg, VTI of Doppler, pulse pressure, or LV dP/dtmax). But the crucial distinction is not the choiceof variable but the choice to measure the variablesufficiently precisely to make the optimization sin-gular, reproducible, and plausible.25 For each var-iable there is noise. By measuring (not guessing)the magnitude of the noise, it is possible tocalculate how many replicate measurements arerequired for an optimization of any desired degreeof precision.26,27

If the steps taken to minimize the effect of noisehave not been carefully planned, then even mea-sures that have the potential to be excellent candi-dates as a means for guiding optimization may fail.

dP/dtmax, for example, has strong theoreticgrounds for being a good marker of cardiac func-tion. Many different protocols have been used foracquiring invasive dP/dtmax to guide AV delay opti-mization. Some investigators have simply used asingle 30-second recording and not comparedthis to a reference setting28 whereas others havecompared single measurements of calculation ofthe relative change in dP/dtmax with a referencesetting29 and others have used multiple measure-ments of the relative change.30–32 Reproducibilityis poor if only single measurements are madeand is much improved by making multiplemeasurements.25

The foregoing makes it clear that it is insufficientto choose a marker that has strong a priori validity,and insufficient to simply measure it invasively. It isessential that carefully preplanned steps are takento minimize the effect of noise that occurs due tobackground spontaneous variations that occureven in dP/dtmax measurements.

It is not only the parameter proposed as an opti-mization marker but also the way in which it is re-corded (ie, precisely what noise reduction stepsare included and quantitatively how these weredecided on) that determines whether it can be areliable method to guide optimization.

EXAMPLES OF VARIABLES THAT CAN BEMEASURED FOR OPTIMIZATION

A wide variety of variables can be proposedfor optimization, including electrical, pressure-based, and flow-based.

LVOT VTI

The intuitively most attractive variable to maximizewhile optimizing VV delay is cardiac output.33–35 Ata fixed heart rate, this is equivalent to maximizingstroke volume. For a constant outflow tract diam-eter, this is also equivalent to maximizing strokedistance or VTI. Typically this is recorded noninva-sively using Doppler echocardiography, althoughit can be recorded invasively using a flow wire.36

At each pacemaker setting, a sample of pulsedwave Doppler traces is acquired from the LVOT,while keeping the probe position constant be-tween settings. It is often recommended toaverage the VTIs of 3 beats, although articlesdescribing the technique commonly show the pro-cess carried out on a single beat per setting. Incre-ments of 20 ms in the VV delay are commonlyrecommended. The VV delay setting that yieldsthe highest VTI is selected as the optimum.

The authors’ group has assessed noninvasiveoutflow tract VTI as a tool for VV delay optimizationin AF.23 Six consecutive beats of LVOT flow wereacquired (Fig. 1). The average of the 6 beats wastaken as the value for that setting. The authors as-sessed singularity, reproducibility, and plausibilityof the data.

SingularityThe number of optimizations in which there is asingle peak of clearly maximum VTI (judged fromthe raw points, not the curve) is few. Applyingcurve fitting increases the proportion but thereare still many of the 40 optimizations in which thecurves are noncurved or inverted. Each invertedcurve is noise rather than physiologically meaning-ful, but what may not be obvious is that it has acounterpart that is correctly oriented but alsonoise, because it is the nature of random noiseto be equal in each direction. Therefore, the num-ber of curves that are biologic meaningful may beas few as 50% or less of those acquired.

ReproducibilityIn Fig. 1, it can be seen that only 9 of the 20 pa-tients tested have a pair of curves that are bothcorrectly oriented. In the remaining 11, it is notpossible to establish reproducibility. In the 9,most have fairly good agreement between the op-tima indicated by the 2 curves. This is an exampleof the phenomenon explained earlier: in theabsence of strong singularity, there is little pointin addressing reproducibility of the optimum. TheSD of difference was 34 ms.

Biologic PlausibilityThere is no point addressing biologic plausibilitybecause singularity is poor as is reproducibility.

Fig. 1. Data from 20 patients who had VV delay optimization performed twice on the same day, using LVOT VTI.Data for the first optimization is displayed in black and the second in gray. A parabola was fitted and the peak ofthe parabola was considered to represent the optimal VV delay (optimum 5 largest VTI). (From Kyriacou A,Li Kam Wa ME, Pabari PA, et al. A systematic approach to designing reliable VV optimization methodology:assessment of internal validity of echocardiographic, electrocardiographic and haemodynamic optimization ofcardiac resynchronization therapy. Int J Cardiol 2012 Mar 26. [Epub ahead of print]; with permission.)

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Fig. 2. Data from 20 patients who had VV delay optimization performed twice on the same day, using 12-leadECG QRS width. Data for the first optimization is displayed in black and the second optimization is displayedin gray. A parabola was fitted for optimization session and the trough of the parabola was considered to repre-sent the optimal VV delay (optimum 5 narrowest QRS). (From Kyriacou A, Li Kam Wa ME, Pabari PA, et al. A sys-tematic approach to designing reliable VV optimization methodology: assessment of internal validity ofechocardiographic, electrocardiographic and haemodynamic optimization of cardiac resynchronization therapy.Int J Cardiol 2012 Mar 26. [Epub ahead of print]; with permission.)

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This analysis does not show that measurementof flow is doomed to be ineffective for VV optimiza-tion. It only shows that the protocol carried out,measuring 6 beats, cannot reliably identify a singu-lar or reproducible optimum. It is entirely possiblethat an approach that averaged more beats mightvanquish the noise sufficiently to make theapproach feasible. This does not seem, however,to have yet been tested. Efforts to optimize VVdelay by LVOT VTI have often claimed to use 3beats per setting, and lectures have sometimesshown examples using only 1 beat per setting. Itis hard to understand how such attempts couldbe anything other than futile.

QRS Minimization

With wide QRS a prime criterion for implantation ofCRT, it is intellectually attractive to aim to adjustVV delay in a manner to restore QRS width to asclose as possible to a normal narrow duration.Whether competitive narrowing of QRS is a

physiologically ideal target is not known, but theauthors’ experiments may have cast some light.

SingularityQRS duration can easily be measured precisely,as readily seen from the error bars in Fig. 2 too nar-row to see in many cases (just visible for the gray).In almost every case, there is a clear optimum (ie,VV delay which minimizes QRS duration).

ReproducibilityThe replicate data sets are closely concordant, asevidenced by the almost overlapping curves inmany cases.

Biologic PlausibilityThe concern for QRS duration is that some of theVV delays identified as apparently optimal, in

Fig. 3. Plot of the distribution of VV delays identified as ooptima with significant (�40 ms) RV pre-excitation was idPabari PA, et al. A systematic approach to designing reliabnal validity of echocardiographic, electrocardiographic anzation therapy. Int J Cardiol 2012 Mar 26. [Epub ahead of

terms of QRS minimization, are not VV delaysthat spring to mind as physiologically desirable.For example, in patients 3, 5, 8, 15, 18, and 19,the VV delay consistently selected as optimalby both replicate VV optimization processeswas RV first by 60 ms. If 30% of cases requiresuch extreme RV preactivation for optimality,then some of the understanding of the mecha-nism of CRT and cardiac function must be incor-rect. Fig. 3 shows plot of the distribution of VVdelays identified as optimal using QRSminimization.

Pressure

The authors’ group has conducted a series ofexperiments over the past 10 years on the poten-tial for using blood pressure, which can bemeasured beat-to-beat noninvasively, as a markerfor optimization of BVPs.30–32 Pressure has theadvantage over flow in that it can be acquiredautomatically with no human intervention re-quired, which makes it more convenient thanecho Doppler.

SingularityMost of the 40 optimizations shown in Fig. 4 iden-tified a clear optimum VV delay.

ReproducibilityThe test-retest variability of the optimum by thisBP protocol was an SD of differences of 10.2 ms.

PlausibilityEighteen of the 20 patients showed on both opti-mization sessions an optimum that was in thecentral region that biologically might appearmost plausible. No post hoc data editing tookplace.

ptimal using QRS minimization. A high proportion ofentified as optimal. (From Kyriacou A, Li Kam Wa ME,le VV optimization methodology: assessment of inter-d haemodynamic optimization of cardiac resynchroni-print]; with permission.)

Fig. 4. Data from 20 patients who had VV delay optimization performed twice on the same day, using noninva-sive systolic blood pressure (SBP). In order to minimize noise, each tested VV delay was compared with the refer-ence VV delay of 0 ms and the relative change in systolic blood pressure was calculated by subtracting the meanSBP of the 10 beats immediately after the transition from the 10 beats immediately before the transition. A meanof 8 replicate measurements was used. Data for the first optimization is displayed in black and the second opti-mization is displayed in gray. A parabola was fitted in all optimization sessions and the peak of the parabola wasconsidered to represent the optimal VV delay (optimum5 highest relative SBP). (From Kyriacou A, Li KamWaME,Pabari PA, et al. A systematic approach to designing reliable VV optimization methodology: assessment of inter-nal validity of echocardiographic, electrocardiographic and haemodynamic optimization of cardiac resynchroni-zation therapy. Int J Cardiol 2012 Mar 26. [Epub ahead of print]; with permission.)

CRT Mechanisms in Atrial Fibrillation 485

Whinnett & Francis486

IS TIME SPENT OPTIMIZING VV DELAYRATHER THAN SIMPLY PROGRAMMINGSIMULTANEOUS RV AND LV PACING LIKELYTO DELIVER CLINICAL USEFULIMPROVEMENTS IN CARDIAC FUNCTION?

Although many clinicians consider it not worth-while to optimize VV delay, the articles that reportthe results of VV optimization characteristicallyindicate large increments in physiologic variablesmeasured. How can these be reconciled?There is a serious trap into which it is easily

possible to fall when optimizing VV delay by maxi-mizing a physiologic variable. Consider an extremecase where the biologic variable measured has norelation to VV delay at all and only shows randombiologic variability. If N settings are tested, 1 ofwhich is the 0 VV delay that is the reference state,then because of natural variability, there is always1 VV delay whose measurement is highest. Onaverage, in 1 out of N cases, this is VV 0; in the re-maining (N-1)/N cases, it is someother delay. Thus,as more settings are tested, the greater the chancethat a non-0 setting will be selected as optimal.But the problem has even worse ramifications.

Clinicians mindful of the need for clinical auditmight attempt to calculate the average benefitin the variable achieved by VV optimization.Because, in each case, they would use the incre-ment from VV 0 ms to the optimal VV delay, andin each case that increment would be positive(except in the 1/N cases, in which it is 0 becausethe VV 0-ms setting happened by chance to givethe highest measurement), the increment wouldbe a list of exclusively positive numbers (with afew 0s). Necessarily, such a list would be statisti-cally significantly larger than 0.Therefore, in any statistical test, the optimization

process would seem to have statistically signifi-cantly increased the measurement, such as ofLVOT VTI, even though in reality the optimizationwas no more than selecting a VV delay setting atrandom. In real life, VV delay may be making acontribution, but the size of it is unknown unlessspecial steps are taken to eradicate the noisecomponent.Therefore, estimating the increment achieved by

VV optimization must not be carried out using datathat have wide error bars. Using a high degree ofreplication, such as can be achievedwith pressure,and curve fitting to minimize the effect of noise,27 itis possible to visualize the effect of adjusting VVdelay and gauge the likely magnitude of contribu-tion of moving VV delay away from 0 ms.From the authors’ AF study of VV optimization, it

seems that in most cases the optimum VV delaygives systolic blood pressure less than 1 mm Hg

higher than would be achieved by VV 0-ms pacing.Because blood pressure is approximately 100 mmHg, the authors interpret this as an increment incardiac output or stroke volume of less than 1%.To put this into context, simply switching on CRT(in sinus rhythm) to optimal AV delay (in patientsin SR) increases blood pressure by approximately6%. Thus, the amount gained by most patientsfrom VV optimization is less than one-sixth theeffect of CRT itself.

SUMMARY

VV delay optimization is a fascinating and poten-tially hazardous pursuit. The fascination lies inthat the process of comparing multiple settings isthe acid test of the resynchronization hypothesis,largely unacknowledged by the clinical and scien-tific community. Lack of a visible prominent effectof VV adjustment on hemodynamics is proofthat either the resynchronization concept is plainwrong, or the measurement protocol design is sovulnerable to error that it cannot detect this impor-tant effect. The hazard lies in thewidespread failureto perceive natural biologic variability, which isoften mistaken for failure of the operator to makemeasurements correctly. Measurement variabilitycauses false optima to appear with VV delayadjustment and makes the size of the optimizationbenefit become exaggerated.Using sensitive (narrow-error-bar) methods, VV

delay optimization produces small increments inhemodynamic effects over and above program-ming a nominal setting of VV 0 ms.

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