Predicting new-onset persistent conduction block following transcatheter aortic valve replacement: The usefulness of FEOPS finite element analysis

Predicting new-onset persistent conduction block following transcatheter aortic valve replacement: The usefulness of FEOPS finite element analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predicting new-onset persistent conduction block following transcatheter aortic valve replacement: The usefulness of FEOPS finite element analysis Maode Wang, Yong Wang, Nic Debusschere, Giorgia Rocatello, Jun Jin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4913973/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Oct, 2024 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 16 You are reading this latest preprint version Abstract Background: Despite the frequency of persistent new-onset conduction blocks after transcatheter aortic valve replacement (TAVR), few preoperative methods of prediction exist. Methods: Patients who underwent TAVR in the Department of Cardiology of the Second Affiliated Hospital of the Army Medical University from December 2020 to September 2021 and postoperative aortic root modeling via the FEOPS finite element analysis were included in this single-center case-control study, divided into persistent conduction blocks (PCB)and non-PCB groups according to their pre- and postoperative electrocardiograms in the first month post-surgery. Risk factors affecting PCB were identified by comparing the baseline data of these two groups, including echocardiograms, computed tomography angiography of the aortic root, surgical decision-making, and FEOPS data. Independent risk factors were screened using logistic regression modeling, and the receiver operating characteristic (ROC) curve was used to test the predictive ability. Results: A total of 56 patients were included in this study, 37 with bicuspid aortic valve (BAV) and 19 with trileaflet aortic valve (TAV), with 17 cases of PCB. FEOPS of the contact pressure index (CPI), valve oversize ratio, differences between membranous interventricular septum length and implantation depth (ΔMSID), valve implantation depth were statistically different (P < 0.05). CPI could be used as an independent risk factor for PCB (P < 0.05), and the ROC curve comparison showed that the CPI was more predictive (AUC = 0.806, 95% CI: 0.684-0.928, P = 0.001). Conclusions: FEOPS has better predictive value for new-onset conduction block after TAVR compared to other known predictors. transcatheter aortic valve replacement TAVR transcatheter aortic valve implantation TAVI FEOPS conduction block Figures Figure 1 Figure 2 1. Introduction Aortic valve replacement (AVR) constituted the sole treatment for severe aortic stenosis (AS) until the advent of transcatheter aortic valve replacement (TAVR) in 2002. The latter has offered a novel treatment option for AS, combining convenience and minimal invasiveness, which has facilitated its rapid development and adoption as an alternative to AVR in high-risk groups. Following years of development, TAVR has become the primary treatment for AS. The indications for TAVR have been expanded to include low-risk patients with severe AS, as well as some patients with pure severe aortic regurgitation (AR). As research on TAVR continues, improvements have been made to the surgical approach and valve type, with the incidence of most complications decreasing significantly as a result. However, the incidence of postoperative new conduction blocks has not decreased as significantly. The occurrence of postoperative conduction block following TAVR, particularly in the form of high atrioventricular block and persistent complete left bundle-branch block, has been identified as a significant prognostic indicator. Studies have demonstrated that this is a crucial factor influencing the prognostic outcomes of patients at high risk of all-cause mortality, rehospitalisation rates, patient quality of life, and other key aspects. Implantation of the valve at an excessive depth, the use of oversized valves, excessive aortic angulation, and shorter membranous interventricular septum length are generally regarded as risk factors affecting postoperative conduction block after TAVR. However, these factors have limited ability to serve as predictors of conduction system damage. FEOPS, a finite element analysis prediction cloud platform based on patients’ preoperative computed tomography angiography (CTA), was employed for the 3D visual modelling of the patient's aortic root. This allowed for the arbitrary simulation of the different depths of each prosthetic aortic valve model and the associated valve implantation, as well as the simulation of periprosthetic leakage and compression in sensitive areas of the conduction system can be numerically expressed after TAVR. Therefore, this platform is expected to be an effective predictive model for persistent conduction blocks (PCB)after TAVR. 2. Methods 2.1 Study population This was a single-center case-control study. The inclusion criteria were as follows: a total of 62 patients with severe AS who underwent TAVR at the Department of Cardiology, Second Affiliated Hospital of the Army Medical University from December 2020 to September 2021 and who had completed CTA with FEOPS preoperatively and within 1 month postoperatively were selected. The exclusion criteria were as follows: (1) patients with previous AVR; (2) patients with preoperative high-degree atrioventricular block or persistent complete left bundle branch block; (3) patients with preoperative permanent pacemaker implantation; (4) patients with missing preoperative and postoperative electrocardiograms or ambulatory electrocardiograms to assess cardiac activity; (5) FEOPS could not be successfully modelled. A total of 56 patients were eventually enrolled in this study. This study adhered to the Declaration of Helsinki and was approved by the Medical Ethics Committee of the Second Affiliated Hospital of the Army Medical University (2020-research no. 068-03). The signing of the informed consent form was waived with the approval of the Ethics Committee. 2.2 TAVR surgical procedure All patients underwent transfemoral valve replacement under general anesthesia in the hybrid operating room or cardiac catheterization laboratory with transoesophageal echocardiography monitoring. Among a total of 56 patients, 13 received the Venus-A Plus valve and 43 received the Venus-A valve. The decision regarding the intraoperative balloon pre-expansion, as well as the selection of the balloon and valve model, was discussed by the heart valve interventional team. Following the release of the valve, an immediate aortic root angiography and TEE were conducted to evaluate the impact of prosthetic valve release. The operator conducted a comprehensive analysis of the results, taking into account the degree of aortic regurgitation, the pressure difference between the left ventricle and the ascending aorta, the risk of coronary obstruction, and the characteristics of the calcification distribution. 2.3 Conduction block subgroup Based on previous reports of excessive pacemaker implantation following TAVR, in the present study, conduction block is defined as the occurrence of postoperative and persistent manifestation of high atrioventricular block or persistent complete left bundle branch block during follow-up. All patients underwent electrocardiography before TAVR, postoperatively, both before discharge and one month after discharge. In accordance with the VARC-3 criteria set forth by the Valve Academic Research Consortium in 2021, the electrocardiograms of all patients were subjected to analysis. The results indicated that 21 patients developed postoperative complete left bundle branch block, three patients developed second-degree and higher atrioventricular block, and five patients underwent permanent pacemaker implantation. Of these, seven were found to have converted from complete left bundle branch block to a normal electrocardiogram before discharge and during follow-up. Accordingly, in the present study, persistent complete left bundle branch block and high-degree atrioventricular block were employed as outcome events, with a total of 17 patients exhibiting postoperative PCB. 2.4 Clinical information data collection The baseline patient information, electrocardiograms, pre- and postoperative echocardiograms, intraoperative aortic root angiograms, aortic balloon dilatation procedures, and post-valve release angiograms were extracted from the hospital's electronic medical record system for analysis. A Society of Thoracic Surgeons (STS) score was calculated. Baseline patient information was recorded, including sex, age, body mass index, and the prevalence of chronic diseases. The pre- and postoperative electrocardiogram and echocardiogram results were obtained for all patients by experienced electrocardiographers and ultrasonographers, respectively. 2.5 FEOPS finite element analysis A three-dimensional visual simulation of the aortic root was constructed using the FEOPS finite element analysis platform based on the preoperative CTA images of the aortic root. The postoperative CTA images of the patients were incorporated into the platform to obtain the final model of the role of the prosthetic valves in the FEOPS visualization model. This allows for the direct analysis of the depth of implantation of the valves, the perivalvular leakage index, and the contact pressure index (CPI). However, CPI were not analysed in seven patients due to poor right ventricular contrast enhancement in some patients. The distance from the distal end of the metal stent on the uncinate sinus side to the ipsilateral sinus floor, as determined from the postoperative CTA, was defined as the depth of valve implantation. The region of interest was defined as the conduction system at the inferior edge of the septal membrane. The percentage of the area where the stent was predicted to exert a pressure greater than 0.1 MPa in this region was used as the CPI. （ Figure 1 ） 2.6 CT scanning and analysis Patients underwent aortic CTA examination pre- and post-surgery, and CTA imaging of the aortic root was performed in our hospital (128-row dual-source CT DEFINITION FLASH; Siemens, Germany). The scanning method comprised retrospective electrocardiographic gated scanning with a layer thickness of 0.75 mm and an increment of 0.4 mm, and encompassed the aortic root and the entire aorta. The results were stored on a dedicated hard drive and analyzed using 3mensio software. Images were captured of 30% of the ventricular systole and used to determine the long and short diameters, mean diameters, perimeter, and areas of the annulus, sinotubular junction, and left ventricular outflow tract (LVOT), as well as the angle of aortic angulation and the leaflet calcification score. The threshold value for calculating calcification was set at 850 hundredths of a millimeter. The aortic valve was classified according to Sievers staging criteria, which defines the bicuspid aortic valve. Valve oversize ratio = (artificial valve circumference/CT-measured valve circumference ‒ 1) × 100%. LVOT coverage ratio = (1 ‒ artificial valve circumference/CT-measured LVOT circumference) × 100%. Where the membranous interventricular septum (MS) length refers to the distance between the annular plane of the aortic valve and the highest point of the muscular septum measured in a standard coronal view. The difference between membranous interventricular septum length and implantation depth (ΔMSID) was calculated as follows: ΔMSID = valve implantation depth ‒ MS length. 2.7 Statistical analysis Data were analyzed using the statistical software package SPSS 27.0. Data that exhibited a normal distribution were expressed as the mean ± standard deviation ( ±s), whereas those that did not conform to a normal distribution were expressed as M (Q 1 ，Q 3 ). Independent sample t-tests or Mann-Whitney U tests were employed for comparisons between groups. In the case of count data, the figures were expressed as cases (%). In instances where the total number of cases was less than 20, cases (ratios) were analyzed using Pearson’s Chi-squared test, continuity-corrected Chi-squared test, or Fisher’s exact probability method test according to the sample size and the theoretical frequency of the grouped cases. The PCB data obtained following TAVR were classified into distinct subgroups, and the baseline levels of patients, electrocardiograms, echocardiograms, aortic root CTAs, and the results of finite element analysis of FEOPS were compared between the overall patient cohort and the subgroups of bilobed and trilobed valves, respectively. Following comparison between the two groups, variables with P < 0.05 were introduced into a multifactorial logistic regression model for analysis of the independent risk factors for PCB. Receiver operating characteristic (ROC) curves were employed to assess the predictive value of the risk factors that were significant in the unifactorial analysis of PCB. The maximum Yoden index of the CPI value was identified as the predictive threshold for the CPI value. With this threshold, the predictive accuracy of the FEOPS finite element analysis was grouped, with a single factor analysis conducted to ascertain the predictive value influencing the accuracy of this model. A univariate analysis was employed to ascertain the factors influencing the accuracy of the model. All results were subjected to a two-sided test with a test level α = 0.05, and differences were considered statistically significant at P < 0.05. 3. Results 3.1 Comparison of baseline data and preoperative echocardiograms A total of 56 patients were included in this study, comprising 37 patients with bicuspid aortic valve (BAV) (22 Type 0, 14 Type 1, and 1 Type 2) and 19 patients with trileaflet aortic valve (TAV). Postoperative PCB was observed in 17 (30.4%) patients, of whom 14 (25.0%) exhibited persistent complete left bundle branch block and three demonstrated third-degree atrioventricular block. Among these 17 patients, five received a permanent pacemaker prior to discharge. A total of 39 (69.6%) patients did not experience postoperative PCB, or exhibited postoperative conduction block but recovered to normal before discharge or during follow-up. No statistically significant differences were identified in the comparison of patients' baseline data (P > 0.05). The prevalence of diabetes mellitus was found to be statistically different between the two groups of patients with TAV (P < 0.05), but not among patients with BAV or all patients. A comparison of the preoperative echocardiograms revealed a statistically significant difference in the prevalence of preoperative severe regurgitation among patients with TAV (P = 0.05). However, no statistically significant difference was observed among the overall patient cohort or patients within the BAV group (P＞0.05). Furthermore, no statistically significant difference was identified in the remaining indicators (P＞0.05). （ additional files 1 ） 3.2 Comparison of preoperative CTA data, surgical selection, and FEOPS data A statistically significant difference was observed in the LVOT area between patients with TAV (P 0.05). However, the valve oversize ratio and ΔMSID were observed to be statistically different within the overall patient cohort (P < 0.05), although no differences were identified in the bicuspid and trileaflet valve subgroups. The valve implantation depth was found to be statistically different within the overall patient cohort and the subgroup of patients with BAV (P < 0.05). However, no statistically significant differences were observed in the TAV group. Furthermore, the CPI was found to be statistically different (P < 0.05) both within the overall population and in the subgroups of bicuspid and trileaflet valves. （ additional files 2 ） 3.3 Logistic multifactor regression analysis The univariate analysis revealed statistically significant differences in the effects of CPI, valve implantation depth, valve implantation depth, ΔMSID, LVOT area, and diabetes mellitus on PCB. However, given that the differences in LVOT area and diabetes mellitus were only observed in the trileaflet subgroup, the absence of differences in the overall subgroup or the diaphragm subgroup, and the relatively small and inadequately represented sample size of the trileaflet subgroup, these two factors were not included in the multifactorial analysis. A binary logistic regression analysis utilising the input method was conducted, and the analysis yielded no statistically significant differences (P > 0.05), with the exception of the CPI (P < 0.05). （ Table 1 ） 3.4 ROC curve analysis The predictive value of the factors was analyzed using ROC curves. The results demonstrated that the CPI (AUC = 0.806, 95% CI: 0.684-0.928, P = 0.001), valve implantation depth (AUC = 0.677, 95% CI: 0.521-0.833, P = 0.46), valve oversize ratio (AUC = 0.681, 95% CI: 0.523-0.839, P = 0.043), and ΔMSID (AUC = 0.669, 95% CI: 0.502-0.835, P = 0.058) were also identified as factors with predictive value. （ Figure 2 ） 3.5 FEOPS predictive model accuracy analysis The CPI was calculated by conducting a ROC curve analysis to identify the optimal Jordon index (sensitivity = 81.25%, specificity = 66.67%) when the CPI was set at 32. This analysis also informed the accuracy grouping, whereby a CPI value of ≥32 was designated as PCB following TAVR, while a CPI value of <32 was excluded. A statistically significant difference was observed in the right ventricular transverse diameter between the two groups (P ≤ 0.05). Additionally, a notable difference was noted in the right atrial transverse diameter and moderate or more AR, although this did not reach statistical significance (P > 0.05). （ Table 2 ） 4. Discussion In this study, we sought to identify predictors of PCB following TAVR. Our findings indicate that FEOPS has a high predictive value for this outcome. Specifically, our results demonstrate that a CPI threshold of ≥14% is predictive of conduction block after TAVR. The accuracy of this threshold as a predictive parameter was found to be only 53.1% in our centre's clinical practice. It is possible that this result may be due to differences in patients’ ethnicity. A notable feature of the Chinese population is the high prevalence of BAV, which is not observed in non-Asian populations. Patients with BAV are frequently considered a challenging subset for TAVR procedures. Computer simulation platforms are often employed to simulate the procedure and mitigate potential intraoperative and postoperative complications. Given the observed deficiencies in the accuracy of FEOPS in predicting conduction blocks at our centre, this study undertook a novel investigation into the prediction boundaries of FEOPS. As previously outlined, excessive pacemaker implantation was evident following TAVR. The present study concluded that postoperative PCB was a more clinically significant outcome. Therefore, postoperative PCB after TAVR was used as an outcome indicator. The influences of patients’ baseline data, preoperative echocardiograms, and preoperative CTA were also explored. Furthermore, the impact of surgical choices on conduction blocks was investigated, and the predictive ability of FEOPS was evaluated by comparing these influencing factors. As a result, FEOPS could not only be employed as an independent risk factor for conduction block, but also exhibited a high predictive capacity. In the analysis of the predictive accuracy of FEOPS, it was found that the size of the right ventricle and the presence of aortic regurgitation had a degree of influence on the predictive ability of FEOPS. TAVR represents the initial treatment option for patients with AS. However, it is associated with inherent complications when compared with AVR. As the procedure gains popularity and valves improve, all related complications are expected to decrease. However, postoperative conduction block is not expected to decline, which provides an opportunity to identify predictors of this complication. The available evidence indicates that valve implantation depth, oversize rate, and ΔMSID can be used as risk factors for postoperative conduction block. Conversely, non-coronary flap calcification and right coronary flap calcification have been identified as protective factors. However, the lack of specificity regarding these factors limits their utility in clinical practice. The higher prevalence of BAV in the Chinese population renders TAVR more challenging, underscoring the urgent need for preoperative simulation devices to assist clinicians in evaluating the surgical procedure. FEOPS enables the percentage of compressed area in the region of interest below the membranous septum to be quantified, thus providing a numerical risk assessment of postoperative conduction block. Furthermore, it offers a visual 3D model that can be used to assist the operator in simulating the surgical procedure prior to the operation, thereby offering excellent clinical guidance. Prior research has indicated that several factors may contribute to the risk of cardiac conduction system injury following valve implantation. These include excessive valve implantation depth, excessive oversize rate, excessive ΔMSID, shorter MS, larger aortic angle of formation, smaller aortic annulus, and smaller LVOT. The depth of valve implantation, oversize ratio, and ΔMSID were consistent with the results observed in this study. Furthermore, smaller LVOTs were found to be more likely to develop conduction blocks in patients with TAV. However, no statistically significant differences in septal length, annulus size, and aortic angle of formation size were observed in this study. In the present study, the CPI was found to be statistically significantly different in both the overall patient cohort and in the patients belonging to the subgroups of TAV and BAV. In this study, ROC curves were constructed for each of the influencing factors. The results demonstrated that CPI exhibited superior predictive efficacy compared to valve implantation depth, valve oversize ratio, and ΔMSID (AUC = 0.806). A logistic multifactorial analysis revealed that the CPI could be employed as an independent influencing factor, exhibiting greater representativeness compared to other influencing factors. The MS plays a pivotal role in connecting the muscular interventricular septum to the aortic outflow tract. The Hicks' bundle and left bundle branch traverse this structure, lacking muscle protection, rendering the cardiac conduction system susceptible to damage at this site. During TAVR surgery, the implantation of oversized valves, the use of valves with an oversized ΔMSID, and the depth of valve implantation may increase the stresses on this site to the point of damaging the conduction system, resulting in conduction block. The region of interest delineated by FEOPS exhibits a high degree of concordance with the membranous septum. The percentage of compressed area (denoted by the CPI) of the region of interest, as analyzed by FEOPS, is a more representative indicator of the stress situation of the relevant regions of the membranous septum conduction system. Consequently, it has a favorable effect in PCB. Additionally, this study identified some limitations of FEOPS. Firstly, the platform was unable to complete aortic root modelling when dealing with poor right ventriculography filling. Secondly, when performing valve matching, seven patients in this study could not be analyzed for CPI when the prosthetic valve acted in conjunction with the aortic root model. These two deficiencies were the main reasons that limited the application of the platform. In examining the predictive accuracy of CPI, it was observed that the length of the right ventricular transverse diameter exhibited a statistically significant correlation with the predictive accuracy. As the diameter increased, the accuracy declined. While no statistically significant difference was noted in the size of the right atrium, a similar trend was evident, which may be attributed to the larger right heart, influencing right heart contrast filling and potentially introducing bias in aortic root modelling. Furthermore, the predictive accuracy was observed to be marginally inferior in patients with moderate or greater regurgitation. However, this finding was not statistically significant, potentially due to the limited sample size of this study. This observation can be validated in subsequent studies with a larger patient cohort. The limitations of this study include its single-center retrospective design, small sample size, and the insufficient sample size of certain groups when making subgroup comparisons of bicuspid and trileaflet valves. This resulted in a lack of credibility for certain factors with differences, while certain factors with instructive significance were not analyzed for differences. Secondly, only a small proportion of patients included in this study had the new-generation retrievable system, the Venus A Plus valve. This is not in line with current trends in the use of mainstream valve types, which limits the utility of this study. Ultimately, this study examined an insufficient number of aspects related to the defects predicted by FEOPS, and there is a lack of evidence demonstrating its efficacy. This may limit its capacity to inform clinical practice. To address these shortcomings, future studies should expand the sample size, include more valve types, and focus on exploring the deficiencies of different prediction models and ways to improve them to reduce PCB occurrence after TAVR. 5. Conclusion In this study, we explored the predictors of PB after TAVR and compared the predictive ability of FEOPS finite element analysis with other predictors. As a result, FEOPS finite element analysis was found to better predict PB after TAVR, although some shortcomings were identified. Declarations Acknowledgements: We would like to thank Editage (www.editage.cn) for English language editing. Conflict of interest: Nic Debusschere and Giorgia Rocatello are employees of Feops NV. The author declare that there is no conflict of interest. Author contributions: MW and YW contributed equally to study design, data acquisition, statistical analysis, and drafted the manuscript. JJ and SY approved the submission of the final version. ND and GR contributed greatly to computer simulation. All authors contributed to the article and approved the submitted version. Funding: This work was funded by the Chongqing Talents Project (Jin Jun) and Young Doctor Incubation Program of Xinqiao Hospital (2022YQB094). 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Supplementary Files Tables.docx additionalfiles1Comparisonofbaselineandpreoperativeechocardiographicdata.docx additionalfiles2ComparisonofpreoperativeCTAintraoperativeconditionsandFEOPSfiniteelementanalysis.docx Cite Share Download PDF Status: Published Journal Publication published 31 Oct, 2024 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 23 Sep, 2024 Reviews received at journal 19 Sep, 2024 Reviews received at journal 17 Sep, 2024 Reviews received at journal 15 Sep, 2024 Reviewers agreed at journal 08 Sep, 2024 Reviewers agreed at journal 08 Sep, 2024 Reviewers agreed at journal 03 Sep, 2024 Reviewers agreed at journal 01 Sep, 2024 Reviews received at journal 29 Aug, 2024 Reviewers agreed at journal 25 Aug, 2024 Reviewers agreed at journal 25 Aug, 2024 Reviewers invited by journal 25 Aug, 2024 Editor invited by journal 23 Aug, 2024 Editor assigned by journal 22 Aug, 2024 Submission checks completed at journal 22 Aug, 2024 First submitted to journal 14 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4913973","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":356964739,"identity":"6fdd7344-69ba-45aa-bb16-66b63ce1bdd9","order_by":0,"name":"Maode Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Maode","middleName":"","lastName":"Wang","suffix":""},{"id":356964740,"identity":"65ca8a59-6726-4f00-b59a-73b9479d9c0c","order_by":1,"name":"Yong Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Wang","suffix":""},{"id":356964742,"identity":"b7cec6a1-2ea8-464a-b20d-f1135be2db12","order_by":2,"name":"Nic Debusschere","email":"","orcid":"","institution":"FEops NV","correspondingAuthor":false,"prefix":"","firstName":"Nic","middleName":"","lastName":"Debusschere","suffix":""},{"id":356964744,"identity":"148aabcd-d24a-4489-acd0-19556733baec","order_by":3,"name":"Giorgia Rocatello","email":"","orcid":"","institution":"FEops NV","correspondingAuthor":false,"prefix":"","firstName":"Giorgia","middleName":"","lastName":"Rocatello","suffix":""},{"id":356964747,"identity":"50441a93-bb51-49e9-8265-72a5f70a8be0","order_by":4,"name":"Jun Jin","email":"","orcid":"","institution":"The Second Affiliated Hospital of Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Jin","suffix":""},{"id":356964749,"identity":"ce902813-aed5-44dd-87c6-97d7642f076c","order_by":5,"name":"Shiyong Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYDCCAzCGBGMDiJJjY28/gEMtXAtEKUyLMR/PmQRitUD4ifMkHAzw6uA73vz8wcc9hxPnz25uk/i4oza9TYIhgeFHxTacWiTPHDNsnPHscOKGOwfbJGeeOZ7bJt14gLHnzG2cWgxu5DA28xwAapFIbJPmbTuW2yZzIIGZsY0ILfNnQLSks0kkGBCnpeEGWEtNAkEtIL/MnHEg3XjDjcRmy5ltBwzbgIF8EJ9fgCH24MOHA9ay82ekP7zxsa1OXr69/eCDHxW4tUBBM4hgAUbNYTD3ACH1QFAHIpg/QBmjYBSMglEwClAAABRCZX+pkW7RAAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Army Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shiyong","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2024-08-14 13:27:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4913973/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4913973/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-024-04302-2","type":"published","date":"2024-10-31T15:57:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66738787,"identity":"5b5bea78-d00b-4e7e-a190-c898ec213d6e","added_by":"auto","created_at":"2024-10-16 05:26:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":408417,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of FEOPS-sensitive areas in patients with and without conduction blocks. (a, b, c, d, e) Patient with conduction blocks. (f, g, h, I, j) Patient without conduction blocks.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4913973/v1/ce909b604b9ec8f50e3d8b29.png"},{"id":66738788,"identity":"ea666807-d63d-45dc-8f56-1f6868ee8b97","added_by":"auto","created_at":"2024-10-16 05:26:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33829,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis. CPI, contact pressure index;\u003cem\u003e \u003c/em\u003eΔMSID, difference between membranous interventricular septum length and implantation depth.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4913973/v1/221c6dc1976c1623d9271980.png"},{"id":68206606,"identity":"dacf6a79-28a2-46ba-b045-0d5b7e450394","added_by":"auto","created_at":"2024-11-04 16:33:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":777608,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4913973/v1/ef64d3d8-cd75-46ca-a5b7-6ce9be37007a.pdf"},{"id":66738791,"identity":"fc96ed28-9057-4cdd-8887-c1f7477c7443","added_by":"auto","created_at":"2024-10-16 05:26:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24947,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4913973/v1/0c6e35774de373defe3a8de8.docx"},{"id":66739642,"identity":"8075a661-2def-45d5-95aa-a8344a8bcbd5","added_by":"auto","created_at":"2024-10-16 05:34:41","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22076,"visible":true,"origin":"","legend":"","description":"","filename":"additionalfiles1Comparisonofbaselineandpreoperativeechocardiographicdata.docx","url":"https://assets-eu.researchsquare.com/files/rs-4913973/v1/e638251d0ecaa77eb2f4363d.docx"},{"id":66738790,"identity":"1a7c497f-55a8-4b31-8e22-22f40adec973","added_by":"auto","created_at":"2024-10-16 05:26:41","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":22770,"visible":true,"origin":"","legend":"","description":"","filename":"additionalfiles2ComparisonofpreoperativeCTAintraoperativeconditionsandFEOPSfiniteelementanalysis.docx","url":"https://assets-eu.researchsquare.com/files/rs-4913973/v1/e7ae783d9c07b66a5ebdca8c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting new-onset persistent conduction block following transcatheter aortic valve replacement: The usefulness of FEOPS finite element analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAortic valve replacement (AVR) constituted the sole treatment for severe aortic stenosis (AS) until the advent of\u0026nbsp;transcatheter aortic valve replacement\u0026nbsp;(TAVR) in 2002. The latter has offered a novel treatment option for AS, combining convenience and minimal invasiveness, which has facilitated its rapid development and adoption as an alternative to AVR in high-risk groups. Following years of development, TAVR has become the primary treatment for AS. The indications for TAVR have been expanded to include low-risk patients with severe AS, as well as some patients with pure severe aortic regurgitation (AR).\u003cem\u003e\u0026nbsp;\u003c/em\u003e \u003cem\u003e\u0026nbsp;\u003c/em\u003eAs research on TAVR continues, improvements have been made to the surgical approach and valve type, with the incidence of most complications decreasing significantly as a result. However, the incidence of postoperative new conduction blocks has not decreased as significantly.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026nbsp; The occurrence of postoperative conduction block following TAVR, particularly in the form of high atrioventricular block and persistent complete left bundle-branch block, has been identified as a significant prognostic indicator. Studies have demonstrated that this is a crucial factor influencing the prognostic outcomes of patients at high risk of all-cause mortality, rehospitalisation rates, patient quality of life, and other key aspects. Implantation of the valve at an excessive depth, the use of oversized valves, excessive aortic angulation, and shorter membranous interventricular septum length are generally regarded as risk factors affecting postoperative conduction block after TAVR.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026nbsp; However, these factors have limited ability to serve as predictors of conduction system damage. FEOPS, a finite element analysis prediction cloud platform based on patients\u0026rsquo; preoperative computed tomography angiography (CTA), was employed for the 3D visual modelling of the patient\u0026apos;s aortic root. This allowed for the arbitrary simulation of the different depths of each prosthetic aortic valve model and the associated valve implantation, as well as the simulation of periprosthetic leakage and compression in sensitive areas of the conduction system can be numerically expressed after TAVR.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026nbsp; Therefore, this platform is expected to be an effective predictive model for persistent conduction blocks (PCB)after TAVR.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e2.1 Study population\u003c/p\u003e\n\u003cp\u003eThis was a single-center case-control study. The inclusion criteria were as follows: a total of 62 patients with severe AS who underwent TAVR at the Department of Cardiology, Second Affiliated Hospital of the Army Medical University from December 2020 to September 2021 and who had completed CTA with FEOPS preoperatively and within 1 month postoperatively were selected. The exclusion criteria were as follows: (1) patients with previous AVR; (2) patients with preoperative high-degree atrioventricular block or persistent complete left bundle branch block; (3) patients with preoperative permanent pacemaker implantation; (4) patients with missing preoperative and postoperative electrocardiograms or ambulatory electrocardiograms to assess cardiac activity; (5) FEOPS could not be successfully modelled. A total of 56 patients were eventually enrolled in this study. This study adhered to the Declaration of Helsinki and was approved by the Medical Ethics Committee of the Second Affiliated Hospital of the Army Medical University (2020-research no. 068-03). The signing of the informed consent form was waived with the approval of the Ethics Committee.\u003c/p\u003e\n\u003cp\u003e2.2 TAVR surgical procedure\u003c/p\u003e\n\u003cp\u003eAll patients underwent transfemoral valve replacement under general anesthesia in the hybrid operating room or cardiac catheterization laboratory with transoesophageal echocardiography monitoring. Among a total of 56 patients, 13 received the Venus-A Plus valve and 43 received the Venus-A valve. The decision regarding the intraoperative balloon pre-expansion, as well as the selection of the balloon and valve model, was discussed by the heart valve interventional team. Following the release of the valve, an immediate aortic root angiography and TEE were conducted to evaluate the impact of prosthetic valve release. The operator conducted a comprehensive analysis of the results, taking into account the degree of aortic regurgitation, the pressure difference between the left ventricle and the ascending aorta, the risk of coronary obstruction, and the characteristics of the calcification distribution.\u003c/p\u003e\n\u003cp\u003e2.3 Conduction block subgroup\u003c/p\u003e\n\u003cp\u003eBased on previous reports of excessive pacemaker implantation following TAVR,\u003cem\u003e\u0026nbsp;\u003c/em\u003e \u0026nbsp;in the present study, conduction block is defined as the occurrence of postoperative and persistent manifestation of high atrioventricular block or persistent complete left bundle branch block during follow-up. All patients underwent electrocardiography before TAVR, postoperatively, both before discharge and one month after discharge. In accordance with the VARC-3 criteria set forth by the Valve Academic Research Consortium in 2021,\u003cem\u003e\u0026nbsp;\u003c/em\u003e \u0026nbsp;the electrocardiograms of all patients were subjected to analysis. The results indicated that 21 patients developed postoperative complete left bundle branch block, three patients developed second-degree and higher atrioventricular block, and five patients underwent permanent pacemaker implantation. Of these, seven were found to have converted from complete left bundle branch block to a normal electrocardiogram before discharge and during follow-up. Accordingly, in the present study, persistent complete left bundle branch block and high-degree atrioventricular block were employed as outcome events, with a total of 17 patients exhibiting postoperative PCB.\u003c/p\u003e\n\u003cp\u003e2.4 Clinical information data collection\u003c/p\u003e\n\u003cp\u003eThe baseline patient information, electrocardiograms, pre- and postoperative echocardiograms, intraoperative aortic root angiograms, aortic balloon dilatation procedures, and post-valve release angiograms were extracted from the hospital\u0026apos;s electronic medical record system for analysis. A Society of Thoracic Surgeons (STS) score was calculated. Baseline patient information was recorded, including sex, age, body mass index, and the prevalence of chronic diseases. The pre- and postoperative electrocardiogram and echocardiogram results were obtained for all patients by experienced electrocardiographers and ultrasonographers, respectively.\u003c/p\u003e\n\u003cp\u003e2.5 FEOPS finite element analysis\u003c/p\u003e\n\u003cp\u003eA three-dimensional visual simulation of the aortic root was constructed using the FEOPS finite element analysis platform based on the preoperative CTA images of the aortic root. The postoperative CTA images of the patients were incorporated into the platform to obtain the final model of the role of the prosthetic valves in the FEOPS visualization model. This allows for the direct analysis of the depth of implantation of the valves, the perivalvular leakage index, and the\u0026nbsp;contact pressure index (CPI).\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026nbsp; However, CPI were not analysed in seven patients due to poor right ventricular contrast enhancement in some patients. The distance from the distal end of the metal stent on the uncinate sinus side to the ipsilateral sinus floor, as determined from the postoperative CTA, was defined as the depth of valve implantation. The region of interest was defined as the conduction system at the inferior edge of the septal membrane. The percentage of the area where the stent was predicted to exert a pressure greater than 0.1 MPa in this region was used as the CPI. \u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eFigure 1\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e2.6 CT scanning and analysis\u003c/p\u003e\n\u003cp\u003ePatients underwent aortic CTA examination pre- and post-surgery, and CTA imaging of the aortic root was performed in our hospital (128-row dual-source CT DEFINITION FLASH; Siemens, Germany). The scanning method comprised retrospective electrocardiographic gated scanning with a layer thickness of 0.75 mm and an increment of 0.4 mm, and encompassed the aortic root and the entire aorta. The results were stored on a dedicated hard drive and analyzed using 3mensio software. Images were captured of 30% of the ventricular systole and used to determine the long and short diameters, mean diameters, perimeter, and areas of the annulus, sinotubular junction, and left ventricular outflow tract (LVOT), as well as the angle of aortic angulation and the leaflet calcification score. The threshold value for calculating calcification was set at 850 hundredths of a millimeter. The aortic valve was classified according to Sievers staging criteria, which defines the bicuspid aortic valve.\u003cem\u003e\u0026nbsp;\u003c/em\u003e \u0026nbsp;Valve oversize ratio = (artificial valve circumference/CT-measured valve circumference ‒ 1)\u0026nbsp;\u0026times;\u0026nbsp;100%. LVOT coverage ratio = (1 ‒ artificial valve circumference/CT-measured LVOT circumference)\u0026nbsp;\u0026times;\u0026nbsp;100%. Where the membranous interventricular septum (MS) length refers to the distance between the annular plane of the aortic valve and the highest point of the muscular septum measured in a standard coronal view.\u003cem\u003e\u0026nbsp;\u003c/em\u003e \u0026nbsp;The difference between membranous interventricular septum length and implantation depth (\u0026Delta;MSID) was calculated as follows:\u0026nbsp;\u0026Delta;MSID = valve implantation depth ‒ MS length.\u003c/p\u003e\n\u003cp\u003e2.7 Statistical analysis\u003c/p\u003e\n\u003cp\u003eData were analyzed using the statistical software package SPSS 27.0. Data that exhibited a normal distribution were expressed as the mean\u0026nbsp;\u0026plusmn;\u0026nbsp;standard deviation (\u0026nbsp;\u0026plusmn;s), whereas those that did not conform to a normal distribution were expressed as\u0026nbsp;M (Q\u003csub\u003e1\u003c/sub\u003e，Q\u003csub\u003e3\u003c/sub\u003e). Independent sample t-tests or Mann-Whitney U tests were employed for comparisons between groups. In the case of count data, the figures were expressed as cases (%). In instances where the total number of cases was less than 20, cases (ratios) were analyzed using Pearson\u0026rsquo;s Chi-squared test, continuity-corrected Chi-squared test, or Fisher\u0026rsquo;s exact probability method test according to the sample size and the theoretical frequency of the grouped cases. The PCB data obtained following TAVR were classified into distinct subgroups, and the baseline levels of patients, electrocardiograms, echocardiograms, aortic root CTAs, and the results of finite element analysis of FEOPS were compared between the overall patient cohort and the subgroups of bilobed and trilobed valves, respectively. Following comparison between the two groups, variables with P \u0026lt; 0.05 were introduced into a multifactorial logistic regression model for analysis of the independent risk factors for PCB. Receiver operating characteristic (ROC) curves were employed to assess the predictive value of the risk factors that were significant in the unifactorial analysis of PCB. The maximum Yoden index of the CPI value was identified as the predictive threshold for the CPI value. With this threshold, the predictive accuracy of the FEOPS finite element analysis was grouped, with a single factor analysis conducted to ascertain the predictive value influencing the accuracy of this model. A univariate analysis was employed to ascertain the factors influencing the accuracy of the model. All results were subjected to a two-sided test with a test level \u0026alpha; = 0.05, and differences were considered statistically significant at P \u0026lt; 0.05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Comparison of baseline data and preoperative echocardiograms\u003c/p\u003e\n\u003cp\u003eA total of 56 patients were included in this study, comprising 37 patients with\u0026nbsp;bicuspid aortic valve (BAV) (22 Type 0, 14 Type 1, and 1 Type 2) and 19 patients with\u0026nbsp;trileaflet aortic valve (TAV). Postoperative PCB was observed in 17 (30.4%) patients, of whom 14 (25.0%) exhibited persistent complete left bundle branch block and three demonstrated third-degree atrioventricular block. Among these 17 patients, five received a permanent pacemaker prior to discharge. A total of 39 (69.6%) patients did not experience postoperative PCB, or exhibited postoperative conduction block but recovered to normal before discharge or during follow-up. No statistically significant differences were identified in the comparison of patients\u0026apos; baseline data (P \u0026gt; 0.05). The prevalence of diabetes mellitus was found to be statistically different between the two groups of patients with TAV (P \u0026lt; 0.05), but not among patients with BAV or all patients. A comparison of the preoperative echocardiograms revealed a statistically significant difference in the prevalence of preoperative severe regurgitation among patients with TAV (P = 0.05). However, no statistically significant difference was observed among the overall patient cohort or patients within the BAV group (P＞0.05). Furthermore, no statistically significant difference was identified in the remaining indicators (P＞0.05).\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eadditional files 1\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3.2 Comparison of preoperative CTA data, surgical selection, and FEOPS data\u003c/p\u003e\n\u003cp\u003eA statistically significant difference was observed in the LVOT area between patients with TAV (P \u0026lt; 0.05), but no difference was observed in the overall patient cohort or those patients with BAV. The remaining CTA indicators were not found to be statistically different (P \u0026gt; 0.05). However, the valve oversize ratio and \u0026Delta;MSID were observed to be statistically different within the overall patient cohort (P \u0026lt; 0.05), although no differences were identified in the bicuspid and trileaflet valve subgroups. The valve implantation depth was found to be statistically different within the overall patient cohort and the subgroup of patients with BAV (P \u0026lt; 0.05). However, no statistically significant differences were observed in the TAV group. Furthermore, the CPI was found to be statistically different (P \u0026lt; 0.05) both within the overall population and in the subgroups of bicuspid and trileaflet valves.\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eadditional files 2\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3.3 Logistic multifactor regression analysis\u003c/p\u003e\n\u003cp\u003eThe univariate analysis revealed statistically significant differences in the effects of CPI, valve implantation depth, valve implantation depth,\u0026nbsp;\u0026Delta;MSID, LVOT area, and diabetes mellitus on PCB. However, given that the differences in LVOT area and diabetes mellitus were only observed in the trileaflet subgroup, the absence of differences in the overall subgroup or the diaphragm subgroup, and the relatively small and inadequately represented sample size of the trileaflet subgroup, these two factors were not included in the multifactorial analysis. A binary logistic regression analysis utilising the input method was conducted, and the analysis yielded no statistically significant differences (P \u0026gt; 0.05), with the exception of the CPI (P \u0026lt; 0.05).\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3.4 ROC curve analysis\u003c/p\u003e\n\u003cp\u003eThe predictive value of the factors was analyzed using ROC curves. The results demonstrated that the CPI (AUC = 0.806, 95% CI: 0.684-0.928, P = 0.001), valve implantation depth (AUC = 0.677, 95% CI: 0.521-0.833, P = 0.46), valve oversize ratio (AUC = 0.681, 95% CI: 0.523-0.839, P = 0.043), and\u0026nbsp;\u0026Delta;MSID (AUC = 0.669, 95% CI: 0.502-0.835, P = 0.058) were also identified as factors with predictive value.\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eFigure 2\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3.5 FEOPS predictive model accuracy analysis\u003c/p\u003e\n\u003cp\u003eThe CPI was calculated by conducting a ROC curve analysis to identify the optimal Jordon index (sensitivity = 81.25%, specificity = 66.67%) when the CPI was set at 32. This analysis also informed the accuracy grouping, whereby a CPI value of\u0026nbsp;\u0026ge;32 was designated as PCB following TAVR, while a CPI value of \u0026lt;32 was excluded. A statistically significant difference was observed in the right ventricular transverse diameter between the two groups (P\u0026nbsp;\u0026le;\u0026nbsp;0.05). Additionally, a notable difference was noted in the right atrial transverse diameter and moderate or more AR, although this did not reach statistical significance (P \u0026gt; 0.05).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we sought to identify predictors of PCB following TAVR. Our findings indicate that FEOPS has a high predictive value for this outcome. Specifically, our results demonstrate that a CPI threshold of\u0026nbsp;\u0026ge;14% is predictive of conduction block after TAVR.\u003cem\u003e\u0026nbsp;\u003c/em\u003e \u0026nbsp;The accuracy of this threshold as a predictive parameter was found to be only 53.1% in our centre\u0026apos;s clinical practice. It is possible that this result may be due to differences in patients\u0026rsquo;\u0026nbsp;ethnicity.\u003cem\u003e\u0026nbsp;\u003c/em\u003e \u0026nbsp;A notable feature of the Chinese population is the high prevalence of BAV, which is not observed in non-Asian populations. Patients with BAV are frequently considered a challenging subset for TAVR procedures. Computer simulation platforms are often employed to simulate the procedure and mitigate potential intraoperative and postoperative complications.\u003cem\u003e\u0026nbsp;\u003c/em\u003e \u0026nbsp;Given the observed deficiencies in the accuracy of FEOPS in predicting conduction blocks at our centre, this study undertook a novel investigation into the prediction boundaries of FEOPS. As previously outlined, excessive pacemaker implantation was evident following TAVR. The present study concluded that postoperative PCB was a more clinically significant outcome. Therefore, postoperative PCB after TAVR was used as an outcome indicator. The influences of patients\u0026rsquo;\u0026nbsp;baseline data, preoperative echocardiograms, and preoperative CTA were also explored. Furthermore, the impact of surgical choices on conduction blocks was investigated, and the predictive ability of FEOPS was evaluated by comparing these influencing factors. As a result, FEOPS could not only be employed as an independent risk factor for conduction block, but also exhibited a high predictive capacity. In the analysis of the predictive accuracy of FEOPS, it was found that the size of the right ventricle and the presence of aortic regurgitation had a degree of influence on the predictive ability of FEOPS.\u003c/p\u003e\n\u003cp\u003eTAVR represents the initial treatment option for patients with AS. However, it is associated with inherent complications when compared with AVR. As the procedure gains popularity and valves improve, all related complications are expected to decrease. However, postoperative conduction block is not expected to decline, which provides an opportunity to identify predictors of this complication.\u003cem\u003e\u0026nbsp;\u003c/em\u003e \u0026nbsp;The available evidence indicates that valve implantation depth, oversize rate, and\u0026nbsp;\u0026Delta;MSID can be used as risk factors for postoperative conduction block. Conversely, non-coronary flap calcification and right coronary flap calcification have been identified as protective factors.\u003cem\u003e\u0026nbsp;\u003c/em\u003e \u0026nbsp;However, the lack of specificity regarding these factors limits their utility in clinical practice. The higher prevalence of BAV in the Chinese population renders TAVR more challenging, underscoring the urgent need for preoperative simulation devices to assist clinicians in evaluating the surgical procedure. FEOPS enables the percentage of compressed area in the region of interest below the membranous septum to be quantified, thus providing a numerical risk assessment of postoperative conduction block. Furthermore, it offers a visual 3D model that can be used to assist the operator in simulating the surgical procedure prior to the operation, thereby offering excellent clinical guidance.\u003c/p\u003e\n\u003cp\u003ePrior research has indicated that several factors may contribute to the risk of cardiac conduction system injury following valve implantation. These include excessive valve implantation depth, excessive oversize rate, excessive\u0026nbsp;\u0026Delta;MSID, shorter MS, larger aortic angle of formation, smaller aortic annulus, and smaller LVOT. The depth of valve implantation, oversize ratio, and\u0026nbsp;\u0026Delta;MSID were consistent with the results observed in this study. Furthermore, smaller LVOTs were found to be more likely to develop conduction blocks in patients with TAV. However, no statistically significant differences in septal length, annulus size, and aortic angle of formation size were observed in this study. In the present study, the CPI was found to be statistically significantly different in both the overall patient cohort and in the patients belonging to the subgroups of TAV and BAV. In this study, ROC curves were constructed for each of the influencing factors. The results demonstrated that CPI exhibited superior predictive efficacy compared to valve implantation depth, valve oversize ratio, and\u0026nbsp;\u0026Delta;MSID (AUC = 0.806). A logistic multifactorial analysis revealed that the CPI could be employed as an independent influencing factor, exhibiting greater representativeness compared to other influencing factors. The MS plays a pivotal role in connecting the muscular interventricular septum to the aortic outflow tract. The Hicks\u0026apos; bundle and left bundle branch traverse this structure, lacking muscle protection, rendering the cardiac conduction system susceptible to damage at this site.\u003cem\u003e\u0026nbsp;\u003c/em\u003e \u0026nbsp;During TAVR surgery, the implantation of oversized valves, the use of valves with an oversized\u0026nbsp;\u0026Delta;MSID, and the depth of valve implantation may increase the stresses on this site to the point of damaging the conduction system, resulting in conduction block. The region of interest delineated by FEOPS exhibits a high degree of concordance with the membranous septum. The percentage of compressed area (denoted by the CPI) of the region of interest, as analyzed by FEOPS, is a more representative indicator of the stress situation of the relevant regions of the membranous septum conduction system. Consequently, it has a favorable effect in PCB.\u003c/p\u003e\n\u003cp\u003eAdditionally, this study identified some limitations of FEOPS. Firstly, the platform was unable to complete aortic root modelling when dealing with poor right ventriculography filling.\u003cem\u003e\u0026nbsp;\u003c/em\u003e \u0026nbsp;Secondly, when performing valve matching, seven patients in this study could not be analyzed for CPI when the prosthetic valve acted in conjunction with the aortic root model. These two deficiencies were the main reasons that limited the application of the platform. In examining the predictive accuracy of CPI, it was observed that the length of the right ventricular transverse diameter exhibited a statistically significant correlation with the predictive accuracy. As the diameter increased, the accuracy declined. While no statistically significant difference was noted in the size of the right atrium, a similar trend was evident, which may be attributed to the larger right heart, influencing right heart contrast filling and potentially introducing bias in aortic root modelling. Furthermore, the predictive accuracy was observed to be marginally inferior in patients with moderate or greater regurgitation. However, this finding was not statistically significant, potentially due to the limited sample size of this study. This observation can be validated in subsequent studies with a larger patient cohort.\u003c/p\u003e\n\u003cp\u003eThe limitations of this study include its single-center retrospective design, small sample size, and the insufficient sample size of certain groups when making subgroup comparisons of bicuspid and trileaflet valves. This resulted in a lack of credibility for certain factors with differences, while certain factors with instructive significance were not analyzed for differences. Secondly, only a small proportion of patients included in this study had the new-generation retrievable system, the Venus A Plus valve. This is not in line with current trends in the use of mainstream valve types, which limits the utility of this study. Ultimately, this study examined an insufficient number of aspects related to the defects predicted by FEOPS, and there is a lack of evidence demonstrating its efficacy. This may limit its capacity to inform clinical practice. To address these shortcomings, future studies should expand the sample size, include more valve types, and focus on exploring the deficiencies of different prediction models and ways to improve them to reduce PCB occurrence after TAVR.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, we explored the predictors of PB after TAVR and compared the predictive ability of FEOPS finite element analysis with other predictors. As a result, FEOPS finite element analysis was found to better predict PB after TAVR, although some shortcomings were identified.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe would like to thank Editage (www.editage.cn) for English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eNic Debusschere and Giorgia Rocatello are employees of Feops NV. The author declare that there is\u0026nbsp;no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e MW and YW contributed equally to study design, data acquisition, statistical analysis, and drafted the manuscript. JJ and SY approved the submission of the final version. ND and GR contributed greatly to computer simulation. All authors contributed to the article and approved the submitted version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was funded by the Chongqing Talents Project (Jin Jun) and Young Doctor Incubation Program of Xinqiao Hospital (2022YQB094).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOtto CM, Nishimura RA, Bonow RO, et al. 2020 ACC/AHA Guideline for the Management of Patients With Valvular Heart Disease: Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. \u003cem\u003eCirculation\u003c/em\u003e. 2021;143(5):e35-e71. doi:10.1161/CIR.0000000000000932\u003c/li\u003e\n\u003cli\u003eZhang J, Pan Y, Wang B, Fu G. Current Opinions on New-Onset Left Bundle Branch Block after Transcatheter Aortic Valve Replacement and the Search for Physiological Pacing. \u003cem\u003eRev Cardiovasc Med\u003c/em\u003e. 2022;23(3):90. doi:10.31083/j.rcm2303090\u003c/li\u003e\n\u003cli\u003eBadertscher P, M\u0026auml;der N, Serban T, et al. Incidence of infranodal conduction delay in patients with left bundle branch block after transcatheter aortic valve replacement: Impact of the 2021 ESC guidelines for cardiac pacing. \u003cem\u003eHeart Rhythm\u003c/em\u003e. 2023;20(4):646-647. doi:10.1016/j.hrthm.2023.01.019\u003c/li\u003e\n\u003cli\u003eRod\u0026eacute;s-Cabau J, Ellenbogen KA, Krahn AD, et al. Management of Conduction Disturbances Associated With Transcatheter Aortic Valve Replacement: JACC Scientific Expert Panel. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e. 2019;74(8):1086-1106. doi:10.1016/j.jacc.2019.07.014\u003c/li\u003e\n\u003cli\u003eMangieri A, Montalto C, Pagnesi M, et al. 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In-hospital outcomes of TAVR patients with a bundle branch block: Insights from the National Inpatient Sample 2011\u0026ndash;2018. \u003cem\u003eCatheter Cardiovasc Interv\u003c/em\u003e. 2022;100(3):424-436. doi:10.1002/ccd.30341\u003c/li\u003e\n\u003cli\u003eHalim J, Brouwer J, Lycke M, Swaans MJ, Van der Heyden J. Transcatheter aortic valve replacement: impact of pre-procedural FEops HEARTguide assessment on device size selection in borderline annulus size cases. \u003cem\u003eNeth Heart J Mon J Neth Soc Cardiol Neth Heart Found\u003c/em\u003e. 2021;29(12):654-661. doi:10.1007/s12471-021-01620-4\u003c/li\u003e\n\u003cli\u003eRocatello G, El Faquir N, De Santis G, et al. 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Valve Academic Research Consortium 3: Updated Endpoint Definitions for Aortic Valve Clinical Research. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e. 2021;77(21):2717-2746. doi:10.1016/j.jacc.2021.02.038\u003c/li\u003e\n\u003cli\u003eLiu X, Fan J, Mortier P, et al. Sealing Behavior in Transcatheter Bicuspid and Tricuspid Aortic Valves Replacement Through Patient-Specific Computational Modeling. \u003cem\u003eFront Cardiovasc Med\u003c/em\u003e. 2021;8. doi:10.3389/fcvm.2021.732784\u003c/li\u003e\n\u003cli\u003eKalra A, Das R, Alkhalil M, et al. Bicuspid Aortic Valve Disease: Classifications, Treatments, and Emerging Transcatheter Paradigms. \u003cem\u003eStruct Heart J Heart Team\u003c/em\u003e. 2024;8(1):100227. doi:10.1016/j.shj.2023.100227\u003c/li\u003e\n\u003cli\u003eChen YH, Chang HH, Liao TW, et al. Membranous septum length predicts conduction disturbances following transcatheter aortic valve replacement. \u003cem\u003eJ Thorac Cardiovasc Surg\u003c/em\u003e. 2022;164(1):42-51.e2. doi:10.1016/j.jtcvs.2020.07.072\u003c/li\u003e\n\u003cli\u003eChandra S, Lang RM, Nicolarsen J, et al. Bicuspid aortic valve: inter-racial difference in frequency and aortic dimensions. \u003cem\u003eJACC Cardiovasc Imaging\u003c/em\u003e. 2012;5(10):981-989. doi:10.1016/j.jcmg.2012.07.008\u003c/li\u003e\n\u003cli\u003eAbdelshafy M, Elkoumy A, Elzomor H, et al. Predictors of Conduction Disturbances Requiring New Permanent Pacemaker Implantation following Transcatheter Aortic Valve Implantation Using the Evolut Series. \u003cem\u003eJ Clin Med\u003c/em\u003e. 2023;12(14):4835. doi:10.3390/jcm12144835\u003c/li\u003e\n\u003cli\u003eBrown JA, Lee JH, Smith MA, et al. Patient-Specific Immersed Finite Element-Difference Model of Transcatheter Aortic Valve Replacement. \u003cem\u003eAnn Biomed Eng\u003c/em\u003e. 2023;51(1):103-116. doi:10.1007/s10439-022-03047-3\u003c/li\u003e\n\u003cli\u003eDowling C, Bavo AM, Faquir NE, et al. Patient-Specific Computer Simulation of Transcatheter Aortic Valve Replacement in Bicuspid Aortic Valve Morphology. \u003cem\u003eCirc Cardiovasc Imaging\u003c/em\u003e. Published online October 2019. doi:10.1161/CIRCIMAGING.119.009178\u003c/li\u003e\n\u003cli\u003eDargan J, Kenawi A, Khan F, Firoozi S, Brecker S. Patient-Specific Computer Modeling to Guide Redo Transcatheter Aortic Valve Replacement. \u003cem\u003eJACC Cardiovasc Interv\u003c/em\u003e. 2023;16(18):2332-2334. doi:10.1016/j.jcin.2023.07.034\u003c/li\u003e\n\u003cli\u003eDowling C, Gooley R, McCormick L, et al. Ongoing Experience With Patient-Specific Computer Simulation of Transcatheter Aortic Valve Replacement in Bicuspid Aortic Valve. \u003cem\u003eCardiovasc Revascularization Med Mol Interv\u003c/em\u003e. 2023;51:31-37. doi:10.1016/j.carrev.2023.01.015\u003c/li\u003e\n\u003cli\u003eAuffret V, Puri R, Urena M, et al. Conduction Disturbances After Transcatheter Aortic Valve Replacement: Current Status and Future Perspectives. \u003cem\u003eCirculation\u003c/em\u003e. 2017;136(11):1049-1069. doi:10.1161/CIRCULATIONAHA.117.028352\u003c/li\u003e\n\u003cli\u003eLi YM, Xiong TY, Xu K, et al. Characteristics and outcomes following transcatheter aortic valve replacement in China: a report from China aortic valve transcatheter replacement registry (CARRY). \u003cem\u003eChin Med J (Engl)\u003c/em\u003e. 2021;134(22):2678-2684. doi:10.1097/CM9.0000000000001882\u003c/li\u003e\n\u003cli\u003eKawashima T, Sasaki H. A macroscopic anatomical investigation of atrioventricular bundle locational variation relative to the membranous part of the ventricular septum in elderly human hearts. \u003cem\u003eSurg Radiol Anat SRA\u003c/em\u003e. 2005;27(3):206-213. doi:10.1007/s00276-004-0302-7\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"transcatheter aortic valve replacement, TAVR, transcatheter aortic valve implantation, TAVI, FEOPS, conduction block","lastPublishedDoi":"10.21203/rs.3.rs-4913973/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4913973/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Despite the frequency of persistent new-onset conduction blocks after transcatheter aortic valve replacement (TAVR), few preoperative methods of prediction exist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Patients who underwent TAVR in the Department of Cardiology of the Second Affiliated Hospital of the Army Medical University from December 2020 to September 2021 and postoperative aortic root modeling via the FEOPS finite element analysis were included in this single-center case-control study, divided into persistent conduction blocks (PCB)and non-PCB groups according to their pre- and postoperative electrocardiograms in the first month post-surgery. Risk factors affecting PCB were identified by comparing the baseline data of these two groups, including echocardiograms, computed tomography angiography of the aortic root, surgical decision-making, and FEOPS data. Independent risk factors were screened using logistic regression modeling, and the receiver operating characteristic (ROC) curve was used to test the predictive ability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 56 patients were included in this study, 37 with bicuspid aortic valve (BAV) and 19 with trileaflet aortic valve (TAV), with 17 cases of PCB. FEOPS of the contact pressure index (CPI), valve oversize ratio, differences between membranous interventricular septum length and implantation depth (ΔMSID), valve implantation depth were statistically different (P \u0026lt; 0.05). CPI could be used as an independent risk factor for PCB (P \u0026lt; 0.05), and the ROC curve comparison showed that the CPI was more predictive (AUC = 0.806, 95% CI: 0.684-0.928, P = 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eFEOPS has better predictive value for new-onset conduction block after TAVR compared to other known predictors.\u003c/p\u003e","manuscriptTitle":"Predicting new-onset persistent conduction block following transcatheter aortic valve replacement: The usefulness of FEOPS finite element analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-16 05:26:36","doi":"10.21203/rs.3.rs-4913973/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-23T04:44:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-19T19:28:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-17T18:57:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-15T11:23:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11606567705249143173276494020703650299","date":"2024-09-08T19:46:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326207166768219933460130307668016752230","date":"2024-09-08T15:23:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240672297252766904779720598114473143300","date":"2024-09-03T07:52:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60491606299579362647161928432202053883","date":"2024-09-01T11:51:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-29T04:25:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74635327902329424717721789208370582751","date":"2024-08-25T10:37:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159954960861841556882739818895906442431","date":"2024-08-25T08:54:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-25T08:53:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-23T05:59:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-22T11:01:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-22T10:58:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2024-08-14T13:25:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"389eed90-bce5-46bf-a8c5-5f603d4df5b8","owner":[],"postedDate":"October 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-04T16:23:00+00:00","versionOfRecord":{"articleIdentity":"rs-4913973","link":"https://doi.org/10.1186/s12872-024-04302-2","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2024-10-31 15:57:08","publishedOnDateReadable":"October 31st, 2024"},"versionCreatedAt":"2024-10-16 05:26:36","video":"","vorDoi":"10.1186/s12872-024-04302-2","vorDoiUrl":"https://doi.org/10.1186/s12872-024-04302-2","workflowStages":[]},"version":"v1","identity":"rs-4913973","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4913973","identity":"rs-4913973","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}