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3. Toeg HD, Abessi O, Al-Atassi T, et al. Finding the ideal biomaterial for aorticvalve repair with ex vivo porcine left heart simulator and finite elementmodeling. J Thorac Cardiovasc Surg 2014;148:1739–45.

4. Qiao A, Pan Y, Dong N. Modeling study of aortic root for Ross pro-cedure: a structural finite element analysis. J Heart Valve Dis 2014;23:683–7.

5. Antoniadis AP, Mortier P, Kassab G, et al. Biomechanical modeling toimprove coronary artery bifurcation stenting: expert review document ontechniques and clinical implementation. J Am Coll Cardiol Intv 2015;8:1281–96.

Patient-Specific ComputerModeling to Predict AorticRegurgitation AfterTranscatheter AorticValve Replacement

Outcome of transcatheter aortic valve replacement(TAVR) depends on a combination of patient-,procedure-, and operator-related variables. Specificdevice–host-related interactions may also beinvolved and may result in, for instance, incompleteand/or nonuniform frame expansion that in turn maylead to aortic regurgitation (AR) (1). Due to the largevariability of the aortic root anatomy, the occurrenceand severity of AR is hard to predict, indicating theneed of tools that help the physician to select thetype and size of valve that best fits the individualpatient in addition to the optimal landing zone.Computer simulation of a TAVR procedure that isbased upon the integration of the patient-specificanatomy, the physical and (bio)mechanical proper-ties of the valve, and recipient anatomy may servethis goal (2). We herein describe such a modelfor AR prediction that was validated in a series of60 patients who underwent TAVR with the MedtronicCoreValve Revalving System (MCS) (Medtronic,Dublin, Ireland).

For that purpose, pre-operative multislice com-puted tomography (MSCT) was used to generatepatient-specific 3-dimensional models of the nativeaortic root using image segmentation techniques(Mimics v17.0, Materialise, Leuven, Belgium). Subse-quently, implantation of virtual CoreValve models inthese aortic root models was retrospectively simu-lated using finite-element computer modelling(Abaqus v6.12, Dassault Systèmes, Paris, France),resulting in a prediction of frame deformation andnative leaflet displacement. Details of this method,as well as the validation of the predicted framedeformation, have been described before (3). Ineach computer-simulated implantation, all steps

of the clinical implantation were respected, con-sisting of pre-dilation, valve size selection, depth ofimplantation, and post-dilation if applied. The depthof implantation was matched with the actual depthof implantation derived from contrast angiographyperformed immediately after TAVR.

The blood flow domain including the paravalvularleakage channels (if any) was then derived from thepredicted frame and aortic root deformation, andcomputational fluid dynamics (OpenFOAM v2.1.1,OpenCFD, Bracknell, United Kingdom) was used tomodel blood flow during diastole with the aim ofassessing the severity of aortic regurgitation afterTAVR. For this purpose, a fixed pressure difference of32 mm Hg was imposed from the ascending aorta tothe left ventricle. The actual pressure difference post-TAVR was intentionally not used as the aim is tovalidate a model predicting AR based on pre-operative MSCT only (i.e., when the pressure post-TAVR is unknown). The value of 32 mm Hg is anaverage obtained from a large group of patients. Theresulting flow, expressed in ml/s, was compared withthe clinically assessed AR. The modelling of AR isillustrated in Figure 1 showing 2 patients withdifferent severities of AR.

Contrast angiography and Doppler echocardiogra-phy were used for the assessment of AR. Analogousto the CHOICE (A Comparison of Transcatheter HeartValves in High Risk Patients With Severe Aortic Ste-nosis) study, AR severity by contrast angiographywas defined by visual estimation of the contrastdensity in the left ventricle using the Sellers classi-fication (0 ¼ none/trace, 1 ¼ mild, 2 ¼ moderate,3 ¼ severe; the latter comprised grades 3 and 4 ac-cording to Sellers) (4). Two observers independentlyfrom one another scored the angiograms. In case ofdiscrepancies, consensus was reached by consulting asenior cardiologist. The intraobserver and interob-server variability for the assessment of AR post-TAVRaccording to the Sellers classification were k 0.70 and0.78, respectively. Doppler echocardiography wasperformed before discharge. AR severity was definedby the circumferential extent of the Doppler signal atthe inflow of the MCS frame in the parasternal short-axis view (VARC-2 [Valve Academic ResearchConsortium-2]) (5). Echocardiography was availablein 56 of the 60 patients. Distinction was made be-tween none (grade 0), mild (<10%, grade 1), moderate(10% to 29%, grade 2), and severe ($30%, grade 3)AR. Physicians performing TAVR and engineersperforming the simulations were blinded to oneanother’s results.

Moderate–severe AR (Sellers AR $2) post-TAVRwas seen in 15 patients (25%) by angiography. The

FIGURE 1 Illustration of the Computer Model to Predict AR

(Top row) Patient in whom the model predicted perfect sealing resulting in no streamlines from the aorta to the ventricle that corresponded

well with aortic regurgitation (AR) by angiography and echocardiography (both grade 0). (Bottom row) Patient with a predicted AR of 16 ml/s

(model) corresponding well with angiography (grade 3) and echocardiography (grade 2).

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agreement between the observed (i.e., Sellers,angiography) and predicted AR (i.e., ml/s, model) isshown in Table 1. Receiver-operating characteristiccurve analysis revealed that 16.25 ml/s is the cutoffvalue that best differentiated patients with none-to-mild and moderate-to-severe AR. Sensitivity,

TABLE 1 Comparison of Observed and Predicted AR

Observed AR Predicted AR, ml/s p Value

Angiography (Sellers) Simulation/model

0, n ¼ 14 4.3 [3.4–11.5]

1, n ¼ 31 8.7 [4.4–16.0] 0.002

$2, n ¼ 15 19.7 [16.7–22.2]

<2, n ¼ 45 7.8 [4.0–15.8] <0.001

$2, n ¼ 15 19.7 [16.7–22.2]

Echocardiographic (VARC-2) Simulation/model

0, n ¼ 22 6.5 [3.6–10.7]

1, n ¼ 25 13.7 [4.5–20.3] 0.012

$2, n ¼ 9 17.1 [16.3–19.7]

<2, n ¼ 47 8.9 [4.1–16.2] 0.070

$2, n ¼ 9 17.1 [16.3–19.7]

Values are median [interquartile range].

AR ¼ aortic regurgitation; VARC-2 ¼ Valve Academic Research Consortium-2.

specificity, positive predictive value, negative pre-dictive value, and accuracy were 0.80, 0.80, 0.57,0.92, and 0.80, respectively. By echocardiography,moderate–severe AR was seen in 9 patients (15%).The agreement between the observed and predictedAR is shown in Table 1. Receiver-operating charac-teristic curve analysis revealed that 16.0 ml/s is thecutoff value that best differentiated patients withnone-to-mild and moderate-to-severe AR. Sensi-tivity, specificity, positive predictive value, negativepredictive value, accuracy were 0.72, 0.78, 0.35,0.94, and 0.73, respectively. Besides simulating theactual TAVR procedure (i.e., device size and posi-tioning), a few alternative scenarios were investi-gated in a subset of cases in which the impact ofdevice sizing (Figure 2) and implantation depthwere investigated (Figure 3).

We, thus, found that computer simulation usingdedicated software integrating the MSCT-derivedpatient-specific anatomy and the geometric, andmechanical properties of the valve accurately pre-dicts the severity of AR that will occur after the im-plantation of the self-expanding MCS valve whenmeasured by angiography or echocardiography.

FIGURE 2 Impact of Device Sizing

(Top row) Patient in whom a 26 mm (left) and 29 mm (right) MCS implantation was simulated. The model predicts a minor impact of device

sizing (26 mm: 4 ml/s, 29 mm: 1 ml/s). (Bottom row) Patient in whom sizing had a significant impact on the predicted AR (bottom left: 26 mm

MCS valve, 16 ml/s, bottom right: 29 mm MCS, 6 ml/s). MCS ¼ Medtronic CoreValve Revalving System.

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These findings indicate both the feasibility andclinical utility of computer simulation of a TAVRprocedure with the objective to improve outcome byhelping the physician to select the size of valve thatbest fits the individual patient. AR, which was theoutcome of interest in this study, also depends on thedepth of implantation that in particular depends onphysician’s performance. As illustrated in the casestudy (Figure 3), the simulation can inform thephysician which size of valve at which optimal land-ing zone is associated with the least amount of AR.Although the choice of valve size is easy to follow inclinical practice, this is less so for the depth of im-plantation. The use of repositionable valve technol-ogies, however, may overcome this technical issue,thereby enforcing the clinical power and utility of theherein proposed simulation workflow.

Also, in order to meet the goal of tailored orpatient-specific treatment planning, all clinicallyavailable valves should be incorporated into thesimulation program that also should have the capac-ity to predict all clinically relevant device–host-related interactions or complications. The mostrecent valve technologies are reported to be associ-ated with substantially less AR, but possibly with ahigher than expected or accepted incidence of newconduction abnormalities. The simulation programshould, therefore, follow suit and provide a compre-hensive output containing all outcomes that arerelevant to the patient and physician.

The current validation is not without limitations,in particular because we used contrast angiographyand echocardiography for the assessment AR. Thesewidely used clinical tools have clear limitations and

FIGURE 3 Impact of Device Positioning

(Top row) Patient in whom simulation of a lower implantation (top left) results in a higher predicted aortic regurgitation (AR) (32 ml/s) than a

higher position (top right: 17 ml/s). (Bottom row) Simulation revealing that a higher implantation (bottom right: 15 ml/s) results in more severe

AR than a lower implantation (bottom left: 6 ml/s).

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are inferior to magnetic resonance imaging for theassessment of AR. Magnetic resonance imagingshould, therefore, have been used but is logisticallydemanding and difficult to perform in patients whounderwent TAVR. Nevertheless, patient-specificcomputer simulation using dedicated software accu-rately predicts the severity of AR and may improveoutcome of TAVR by helping the physician to selectthe size of valve that best fits the individual patient.

*Peter de Jaegere, MD, PhDGianluca De Santis, PhDRamon Rodriguez-Olivares, MDJohan Bosmans, MD, PhDNico Bruining, PhDTim Dezutter, MScZouhair Rahhab, BScNahid El Faquir, BScValérie Collas, MSc

Bart Bosmans, MScBenedict Verhegghe, PhDClaire Ren, MD, PhDMarcel Geleinse, MD, PhDCarl Schultz, MD, PhDNicolas van Mieghem, MD, PhDMatthieu De Beule, PhDPeter Mortier, PhD

*Thoraxcenter‘s-Gravendijkwal 230, 3015 CERotterdam, the NetherlandsE-mail: p.dejaegere@erasmusmc.nlhttp://dx.doi.org/10.1016/j.jcin.2016.01.003

Please note: Dr. Bosmans is supported by a PhD grant, partially funded byMaterialise N.V. Dr. de Jaegere is proctor for Medtronic. Dr. De Santis andMr. Dezutter are employees of FEops. Drs. Verhegghe, De Beule, and Mortierare cofounders of and shareholders in FEops. Dr. van Mieghem has receivedresearch grant support from Edwards Lifesciences, St. Jude Medical, AbbottVascular, Boston Scientific, and Medtronic. All other authors have reported thatthey have no relationships relevant to the contents of this paper to disclose.Drs. de Jaegere and De Santis contributed equally to this work.

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3. Schultz C, Rodriguez-Olivares R, Bosmans J, et al. Patient-specific image-based computer simulation for the prediction of valve morphology and

calcium displacement after TAVI with the Medtronic CoreValve and theEdwards Sapien valve. EuroIntervention 2016;11:1044–52.

4. Abdel-Wahab M, Mehilli J, Frerker C, et al., for the CHOICE investigators.Comparison of balloon-expandable vs self-expandable valves in patientsundergoing transcatheter aortic valve replacement: the CHOICE randomizedclinical trial. JAMA 2014;311:1503–14.

5. Kappetein AP, Head SJ, Généreux P, et al. Updated standardizedendpoint definitions for transcatheter aortic valve implantation: the ValveAcademic Research Consortium-2 consensus document. Eur Heart J 2012;33:2403–18.