Abstract
Background
The need for postoperative permanent pacemaker implantation (PPMI) remains one of the most frequent complications after transcatheter aortic valve implantation (TAVI). This study aimed to develop a novel, 2‐step risk score to predict PPMI probability after TAVI and implement it into a user‐friendly website. Our risk score addresses the data gap on current prosthesis generations and provides a new, clinically motivated approach to calculating PPMI risk.
Methods and Results
Between January 2019 and December 2020, 1039 patients underwent TAVI at our institution. We retrospectively evaluated clinical, electrocardiographic, echocardiographic, computed tomographic, and periprocedural data. Patients with prior PPMI were excluded. We developed a prediction model for PPMI occurrence, using 55 patient and procedural characteristics. With exclusion criteria applied, 836 patients (mean age 80.3±9.1 years; 50.6% female) were included. Of these, 149 (17.8%) required PPMI within 30 days after TAVI. Fourteen preprocedural parameters, including preexisting right bundle‐branch block, atrioventricular block, left bundle‐branch block, bradycardia, interventricular septum thickness, New York Heart Association class, and aortic annulus perimeter, were identified as PPMI risk factors and used to calculate the baseline risk in the first step of the TAVI PACER score. The second step includes intraprocedural variables to demonstrate how PPMI risk can vary based on valve type and implantation depth. The TAVI PACER score predicts PPMI with a sensitivity of 76% and specificity of 72% (area under the curve=0.8).
Conclusions
The TAVI PACER score provides a novel tool for daily clinical practice, predicting individual PPMI risk after TAVI based on various patient and procedural characteristics.
Keywords: aortic valve, pacemaker, risk score, TAVI
Subject Categories: Pacemaker, Aortic Valve Replacement/Transcather Aortic Valve Implantation, Risk Factors, Primary Prevention
Nonstandard Abbreviations and Acronyms
- PPMI
postoperative permanent pacemaker implantation
- TAVI
transcatheter aortic valve implantation
- THV
transcatheter heart valve
Clinical Perspective.
What Is New?
We developed a new 2‐step risk score called the TAVI PACER score, which aims to close the gap in existing data on current prosthesis generations.
In the first step, it considers various patient characteristics, and in the second step, it evaluates the impact of different procedural options on the necessity for pacemaker implantation.
What Are the Clinical Implications?
By calculating the individual pacemaker risk before the procedure and integrating it into treatment planning, our risk score aims to improve transparency in patient care, facilitate shared decision‐making, and ultimately enhance the treatment outcomes of transcatheter aortic valve implantation.
Over the past 2 decades, transcatheter aortic valve implantation (TAVI) has emerged as a safe and effective treatment for patients with severe symptomatic aortic stenosis and high risk for cardiac surgery. 1 Since its first implementation in 2002, TAVI and its outcomes have steadily improved. 2 , 3 Even in patients with low surgical risk, current data show that TAVI is noninferior to conventional surgical aortic valve replacement in terms of 1‐year mortality. 4 , 5 However, due to the proximity of the atrioventricular node and the His bundle to the landing zone of the transcatheter heart valve (THV), rhythmological complications remain common with TAVI. 6 Postoperative permanent pacemaker implantation (PPMI) is required in 3.4% to 25.9% of patients undergoing TAVI, which is significantly higher than after surgical aortic valve replacement. 7 , 8
Numerous individual risk constellations contribute to the heart team's interdisciplinary decision‐making process, highlighting the importance of patient‐centered precision medicine. Although the factors leading to an increased risk of PPMI have already been examined in several studies, an established risk score to estimate the individual risk of pacemaker implantation after TAVI is still lacking. 9 , 10 Two published risk scores 11 , 12 have not yet gained acceptance in everyday clinical practice due to their underperformance in external validation cohorts. 13 , 14 Recently, Lauten et al. renewed their call for the development of a novel straightforward risk score to predict pacemaker implantation following TAVI. 15
Based on data from a contemporary population who underwent TAVI, our study aimed to develop an innovative tool that assists interventional cardiologists in both selecting the appropriate THV and determining the intraoperative implantation strategy, considering the preexisting individual risk for PPMI. Our risk score is thus intended to facilitate decision‐making processes within the heart team and to support informed decision‐making between patients and their physicians.
METHODS
The data that support the findings of this study are available from the corresponding author upon request.
Study Population
Between January 2019 and December 2020, 1039 patients underwent TAVI at our center. After excluding patients with previous device implantation, valve‐in‐valve procedure, and patients who had received rarely used or discontinued valves (e.g., ALLEGRA, NVT AG, Morges, Switzerland; LOTUS Edge, Boston Scientific, Natick, MA; CENTERA, Edwards Lifesciences, Irvine, CA), a total of 836 patients were included in the analysis (Figure 1). Following current guidelines, every patient undergoing TAVI received an initial assessment by a multidisciplinary heart team. This study conforms to the principles outlined in the 2014 Declaration of Helsinki and was approved by our institutional review board (EA1/341/21). All patients had provided their informed consent for data collection, TAVI, and the potential implantation of a pacemaker before the procedure.
Figure 1. Study workflow.
CRT indicates cardiac resynchronization therapy; ICD, implantable cardioverter defibrillator; PPM, permanent pacemaker; and TAVI, transcatheter aortic valve implantation.
Procedures
Patient baseline data, preoperative echocardiographic data, and postoperative complications were retrospectively obtained from electronic medical records. Twelve‐lead ECG analysis was performed at baseline and within 48 hours after TAVI. Conduction disorders were defined according to the standard definitions of the American Heart Association, the American College of Cardiology Foundation, and the recommendations of the Heart Rhythm Society for the standardization and interpretation of ECGs. 16 Aortic valve assessment was performed using Pie Medical Imaging's 3Mensio software at 25% to 40% of the cardiac cycle. Computed tomography data were also used to assess THV calcification, to measure the membranous septum length, and to calculate valve oversizing. The implantation depth of the prosthesis in the left ventricular outflow tract was measured using the images of stored intraprocedural fluoroscopic aortography. The measurements were performed with MERLIN Diagnostic software (Phönix‐PACS GmbH, Freiburg, Germany). The portion of the prosthetic stent located below the annulus in the left ventricle was measured orthogonally to the aortic annulus at the noncoronary cusp and left coronary cusp. During data acquisition, all investigators were blinded to the postoperative need for a pacemaker. The data were collected retrospectively.
Statistical Analysis
Normally distributed continuous variables are presented as mean±SD, and nonnormally distributed continuous variables are reported as median with interquartile range. Categorical variables are shown as absolute (n) and relative frequencies (%).
For predicting PPMI, a total of 55 variables were considered. For variable selection, we ran logistic regression models after imputing missing values. Of all data entries, 5.3% were missing. We used multiple imputation with 100 imputed data sets. Lasso regression was used to identify relevant predictors. For each imputation, the recovered relevant predictors under minimal penalty were recorded after running a 10‐fold cross‐validation. Predictors were selected if they were included in the final model in at least 50% of all imputations. To further improve the final predictive model, all first‐order interactions of the preselected variables from the first step were scanned in a second Lasso selection process akin to the first one. Positively selected interactions were added to the final model. The predictive quality of the model was assessed by estimating the likelihood of post‐TAVI pacemaker implantation using a logistic regression model and calculating the receiver operating characteristic curve for the derived score. The area under the curve served as an indicator of model fit (Figure 2). Again, these analyses were pooled for all imputations. All statistical analyses were conducted using R Statistical Software (version 4.2.1; Core Team 2022).
Figure 2. Receiver operating characteristic curve analysis for the final model including periprocedural characteristics and interactions.
AUC indicates area under the curve.
RESULTS
Patient Characteristics
The study included 836 patients with a mean age of 80.3±9.1 years and a balanced sex ratio (50.6% female, n=423). On average, patients had a low surgical risk (Society of Thoracic Surgeons score 3.4% [interquartile range, 2.3–5.2]), along with severe aortic valve stenosis characterized by a mean pressure gradient of 42.0±14.4 mm Hg and an aortic valve area of 0.74±0.20 cm2. Additional clinical and procedural characteristics are shown in Table 1. Within 30 days after TAVI, 149 patients (17.8%) underwent PPMI, primarily due to third‐degree atrioventricular block (69%, n=102). Other indications for PPMI included bradyarrhythmia (14.8%, n=22), second‐degree atrioventricular block type Mobitz II (10.7%, n=16), and sinus node dysfunction (3.4%, n=5).
Table 1.
Comparison of Clinical and Procedural Characteristics between Patients With and Without PPMI after TAVI
| All patients (N=836) | PPMI (N=149) | No PPMI (N=687) | |
|---|---|---|---|
| Age, y | 80.3±9.1 | 81.5±5.2 | 80.0±9.6 |
| Female sex | 423 (50.6) | 73 (49.0) | 350 (50.9) |
| Body mass index, kg/m2 (N=834) | 27.1±5.6 | 27.8±6.2 | 27.0±5.4 |
| Logistic European System for Cardiac Operative Risk Evaluation II, % (N=755) | 3.4 (2.1–5.8) | 4.3 (2.3–6.7) | 3.3 (2.1–5.4) |
| Society of Thoracic Surgeons score, % (N=746) | 3.4 (2.3–5.2) | 3.9 (2.5–5.5) | 3.3 (2.2–5.1) |
| New York Heart Association class ≥III (N=814) | 566 (67.7) | 94 (63.1) | 472 (68.7) |
| Comorbidities | |||
| Diabetes | 265 (31.7) | 57 (38.3) | 208 (30.3) |
| Arterial hypertension | 763 (91.3) | 137 (91.9) | 626 (91.1) |
| Coronary artery disease (N=819) | 511 (61.1) | 92 (61.7) | 419 (61.0) |
| Coronary artery bypass graft | 57 (6.8) | 15 (10.1) | 42 (6.1) |
| Percutaneous coronary intervention | 347 (41.5) | 65 (43.6) | 282 (41.0) |
| Myocardial infarction | 101 (12.1) | 20 (13.4) | 81 (11.8) |
| Nicotine abuse | 180 (21.5) | 41 (27.5) | 139 (20.2) |
| Chronic lung disease | 185 (22.1) | 34 (22.8) | 151 (22.0) |
| Stroke | 87 (10.4) | 15 (10.1) | 72 (10.5) |
| Peripheral arterial disease | 122 (14.6) | 26 (17.4) | 96 (14.0) |
| Estimated glomerular filtration rate (N=817) | 58.0±20.9 | 54.1±21.3 | 58.8±20.7 |
| ECG | |||
| Heart rate, bpm (N=814) | 71 (63–82) | 71 (63–82) | 72 (63–82) |
| QRS complex, ms (N=815) | 98 (90–114) | 104 (92–137) | 97 (90–112) |
| PQ interval, ms (N=608) | 178 (156–200) | 190 (165–219) | 176 (155–197) |
| Preoperative atrioventricular block I° (N=818) | 156 (18.7) | 42 (28.2) | 114 (16.6) |
| Preoperative left BBB (N=818) | 69 (8.3) | 9 (6.0) | 60 (8.7) |
| Preoperative right BBB (N=818) | 80 (9.6) | 42 (28.2) | 38 (5.5) |
| Preoperative, left anterior hemiblock (N=818) | 113 (13.5) | 29 (19.5) | 84 (12.2) |
| Preoperative left posterior hemiblock (N=818) | 0 | 0 | 0 |
| Echocardiography | |||
| Ejection fraction, % (N=780) | 60 (50–60) | 59 (50–60) | 60 (51–60) |
| Interventricular septum in diastole, mm (N=691) | 13.6±3.2 | 14.4±5.7 | 13.4±2.2 |
| Left ventricular diameter in diastole, mm (N=660) | 45.4±7.1 | 44.8±7.3 | 45.5±7.1 |
| Aortic valve area, cm2 (N=803) | 0.74±0.20 | 0.76±0.18 | 0.74±0.20 |
| Pressure mean gradient over the aortic valve, mm Hg (N=776) | 42.0±14.4 | 41.6±12.9 | 42.1±14.7 |
| Cardiac computed tomography | |||
| Aortic annulus perimeter, mm (N=707) | 76 (71–82) | 76 (72–81) | 77 (71–82) |
| Membranous septum length, mm (N=723) | 2.4 (1.3–3.8) | 2.4 (1.2–3.4) | 2.4 (1.3–3.9) |
| Procedural data | |||
| Alternative access | 60 (7.2) | 9 (6.0) | 51 (7.4) |
| Balloon predilatation | 506 (60.5) | 95 (63.8) | 411 (59.8) |
| Balloon postdilatation | 217 (26.0) | 43 (28.9) | 174 (25.3) |
| Implantation depth (mean), mm | 5.1±2.3 | 5.6±2.5 | 5.0±2.2 |
| Implantation depth (noncoronary cusp), mm | 5.1±2.4 | 5.6±2.6 | 5.0±2.4 |
| Implantation depth (left coronary cusp), mm | 5.2±2.5 | 5.5±2.7 | 5.1±2.4 |
| Edwards Sapien 3 | 379 (45.3) | 53 (35.6) | 326 (47.5) |
| Size 20 | 3 (0.8) | 0 (0.0) | 3 (0.9) |
| Size 23 | 102 (26.9) | 11 (20.8) | 91 (27.9) |
| Size 26 | 142 (37.5) | 21 (39.6) | 121 (37.1) |
| Size 29 | 132 (34.8) | 21 (39.6) | 111 (34.0) |
| Abbott Portico | 361 (43.2) | 78 (52.3) | 283 (41.2) |
| Size 23 | 23 (6.4) | 9 (11.5) | 14 (5.0) |
| Size 25 | 114 (31.6) | 17 (21.8) | 97 (34.3) |
| Size 27 | 129 (35.7) | 27 (34.6) | 102 (36.0) |
| Size 29 | 95 (26.3) | 25 (32.1) | 70 (24.7) |
| Medtronic Evolut R/PRO | 96 (11.5) | 18 (12.1) | 78 (11.4) |
| Size 26 | 41 (42.7) | 6 (33.3) | 35 (44.9) |
| Size 29 | 42 (43.8) | 11 (61.1) | 31 (39.7) |
| Size 34 | 13 (13.5) | 1 (5.6) | 12 (15.4) |
| Self‐expanding valves | 457 (54.7) | 96 (64.4) | 361 (52.5) |
| Balloon‐expandable valves | 379 (45.3) | 53 (35.6) | 326 (47.5) |
BBB indicates bundle‐branch block; PPMI, postoperative permanent pacemaker implantation; and TAVI, transcatheter aortic valve implantation.
Values are presented as mean±SD, median (interquartile range), or n (%).
Predictors for Pacemaker Implantation
The first lasso regression step revealed 14 preprocedural and 2 periprocedural predictors (see Table 2) that correlated with PPMI. When including periprocedural characteristics the model yielded an area under the curve=0.78 in our cohort (95% CI, 0.74–0.82). In the second lasso step, where interactions between all primarily selected variables were allowed, 16 interaction terms were added. For the prediction of PPMI, the final model (see Table S1) achieved an area under the curve of 0.8 (95% CI, 0.76–0.84) with a sensitivity of 0.76 and a specificity of 0.72. We implemented the final model in an online calculator that gives individual patient and procedural risks based on our sample (https://biometrycharite.shinyapps.io/pmrisk/).
Table 2.
Prediction of Pacemaker Implantation After TAVI Depending on Different Risk Factors (Prefinal Model Without Interactions)
| Recovery* | Model A | Model B | |||
|---|---|---|---|---|---|
| Atrioventricular block | 100 | 1.921† | P=0.014 | 2.048‡ | P=0.009 |
| Right BBB | 100 | 5.357§ | P<0.001 | 6.557§ | P<0.001 |
| Atrioventricular block and right BBB | 80 | 1.276 | P=0.705 | 1.215 | P=0.770 |
| Atrioventricular block and left BBB | 76 | 0.258 | P=0.229 | 0.212 | P=0.201 |
| Interventricular septum in diastole | 100 | 1.068 | P=0.080 | 1.080† | P=0.040 |
| Left ventricular diameter in diastole | 83 | 0.971 | P=0.058 | 0.973 | P=0.086 |
| NYHA class 2 (vs 1) | 100 | 2.555 | P=0.065 | 2.772† | P=0.050 |
| NYHA class 3 (vs 1) | 0 | 1.520 | P=0.396 | 1.629 | P=0.333 |
| NYHA class 4 (vs 1) | 69 | 0.785 | P=0.716 | 0.839 | P=0.798 |
| Atrial fibrillation and bradycardia | 100 | 4.776‡ | P=0.005 | 4.976‡ | P=0.005 |
| Aortic valve perimeter | 69 | 1.002 | P=0.552 | 1.004 | P=0.502 |
| MR mild (vs none) | 0 | 1.125 | P=0.641 | 1.097 | P=0.715 |
| MR moderate/severe (vs none) | 100 | 2.028† | P=0.021 | 1.884† | P=0.041 |
| Estimated glomerular filtration rate | 55 | 0.995 | P=0.341 | 0.996 | P=0.460 |
| Logistic European System for Cardiac Operative Risk Evaluation II | 100 | 1.049† | P=0.015 | 1.054‡ | P=0.009 |
| Body mass index | 99 | 1.035 | P=0.051 | 1.041† | P=0.026 |
| Nicotine abuse | 99 | 1.398 | P=0.164 | 1.444 | P=0.136 |
| Edwards Sapien 3 (vs Abbott Portico) | 100 | 0.515‡ | P=0.005 | ||
| Implantation depth, noncoronary cusp | 100 | 1.118† | P=0.010 | ||
| Constant | 0.033‡ | P=0.005 | 0.012§ | P=0.001 | |
Model A without procedural factors, Model B including procedural factors. BBB indicates bundle‐branch block; MR, mitral regurgitation; NYHA, New York Heart Association; and TAVI, transcatheter aortic valve implantation.
Number of imputations where the predictor was selected.
P<0.05.
P<0.01.
P<0.001.
DISCUSSION
Our study aimed to develop an innovative tool that assists interventional cardiologists in both selecting the appropriate THV and determining the implantation strategy based on the individual patient's risk for PPMI. So far, most studies analyzing PPMI risk factors reported odds ratios indicating relative risks associated with certain factors. To help patients better understand their own risk—thereby facilitating shared decision‐making—we designed our score to calculate the individual probability of PPMI. The risk score is easily accessible via a website. After entering the unmodifiable, preprocedural risk factors, the tool provides an initial PPMI risk assessment. In the next step, the tool calculates potential risk changes based on valve selection and intraprocedural implantation depth, allowing interventional cardiologists to adjust procedural planning according to the patient's pacemaker risk. With this approach, we hope to contribute to a long‐term reduction in the key complication of TAVI: postoperative pacemaker implantation.
The included patient group represents older individuals with a low‐risk profile. The observed pacemaker implantation rate of 17.8% in our study aligns with current literature, which reports rates ranging from 3.4% to 25.9% following TAVI. 17 , 18 , 19
In recent years, 2 risk scores for predicting PPMI after TAVI have gained attention: the risk calculation by Maeno et al. and the Emory score by Kiani et al. 11 , 12 Despite promising results of the original studies, the performance of both scores in external validation cohorts still requires improvement. 13 , 14 In our study we attempted to improve certain aspects of the scores and apply a new calculating approach. The studies of Maeno et al. and Kiani et al. are both single‐center studies from large US medical institutions, focusing on patients who received balloon‐expandable valve types. To enhance applicability in daily clinical routine, we included both balloon‐expandable and self‐expanding valve types. Furthermore, the studies conducted by Maeno et al. and Kiani et al. spanned earlier periods. Considering the rapid technological advancements in recent years, our study updates the findings of Maeno et al. and Kiani et al. with more contemporary data. All our patients received treatment within a 2‐year time frame, ensuring consistency in the employed valve systems and minimizing potential biases associated with postoperative conduction anomalies. Additionally, the large size of our study population and limited exclusion criteria reduced the risk of distortion. Notably, the number of patients who underwent PPMI was relatively low in both studies: 35 patients (14.6%) in Maeno et al. and 57 patients (7.3%) in Kiani et al. This smaller sample size might have made the scores highly applicable to the specific patient cohorts studied but may not fully represent the general population. With 149 patients who underwent PPMI after TAVI, we offer a larger and potentially more representative sample size.
Our statistical approach differs in 2 key ways. First, we used multiple imputation to handle missing values. This enabled us to minimize information loss due to missing variables and to reduce bias caused by missing patterns. Second, we did not develop our score by using logistic regression models but rather improved its predictive power by adding lasso regression. Lasso regression is well suited for developing risk scores because it incorporates a penalty term to prevent the model from becoming overly complex and depending too much on the training data. This approach reduces the risk of overfitting, and enhances predictive performance on new data sets.
One focus of our study was on the implantation depth as a risk factor. Maeno et al. also identified implantation depth at the noncoronary cusp as a risk factor. However, Kiani et al. excluded implantation depth from their score, arguing it is measured retrospectively and therefore does not contribute to preoperative risk assessment. We attempted to address this aspect in our 2‐stage score by incorporating implantation depth in the second step. This approach aims to both acknowledge the point made by Kiani et al. and emphasize the significant role of implantation depth as a risk factor for PPMI.
In our risk score, we identified various variables that had a significant impact on PPMI after TAVI. Atrioventricular block and right bundle‐branch block, well‐known risk factors for PPMI after TAVI, were also strongly associated with PPMI in our analysis. 20 , 21 For anatomical reasons, THV implantation primarily leads to pressure on the left Tawara branch, so preexisting right Tawara branch block or preexisting atrioventricular node conduction disturbances can lead to a correspondingly high risk for PPMI. 22
A lower THV implantation has also been confirmed as a significant risk factor in numerous studies. 23 , 24 The proximity of the conduction pathways to the distal landing zone of the THV increases their susceptibility to injury. 22 , 25 The implantation depth plays a key role in avoiding PPMI as it is one of the few modifiable procedural aspects. In our study, measurement of the implantation depth between the aortic annulus and the distal end of the prosthesis at the noncoronary cusp was identified as the method with the most precise differentiation of postoperative pacemaker dependency. Choosing the noncoronary cusp as a reference point seems reasonable due to its anatomical proximity to the cardiac conduction system. 26
As previously described, the implantation of a balloon‐expandable valve in our study was associated with a lower risk of PPMI than the implantation of a self‐expanding valve. 27 , 28
The high association between logistic European System for Cardiac Operative Risk Evaluation II, elevated body mass index, and nicotine abuse with PPMI suggests that patients with multiple clinical morbidities may have an increased risk of PPMI after TAVI. Similarly, in our study, anatomical risk factors such as the thickness of the interventricular septum and a smaller left ventricular diameter were strongly associated with PPMI. To the best of our knowledge, these risk factors have not previously been described in the context of TAVI.
Our study cohort is distinguished by a relatively high usage of Portico valve systems. This may be a contributing factor to the discovery of new risk factors for pacemaker implantation in this study.
The presented single‐center retrospective data served as an initial attempt at developing a predictive model for post‐TAVI pacemaker implantation. However, we could not yet validate the model with data from another source with comparable richness in patient information. As such, our model is susceptible to overfitting, that is, to possibly carry variables that are sample specific and might not replicate in other cohorts. We took measures to prevent overfitting. First, we limited the model's complexity to main effects and first‐order‐interactions. Second, we strictly chose only those predictors that showed up in at least half of all iterations due to missing value imputation. The validation of the score in a different cohort is crucial for its future clinical use.
In everyday clinical practice, procedural planning for TAVI is performed by the heart team based on individual patient characteristics. We aimed to reflect this aspect of individual risk stratification in our score by developing the 2‐stage procedure. This provides interventionalists with an overview of how certain procedural characteristics affect individual risk alongside the inherent preexisting risk for a pacemaker.
Study Limitations
The results of this retrospective cohort study serve as empirical evidence to strengthen hypotheses, but they cannot prove a causal relationship. The THV choice was the responsibility of the interventional cardiologist. Individual patient risk profiles, anatomical characteristics, and the current knowledge of postoperative conduction anomaly risk factors likely influenced THV selection and may have affected the pacemaker rate after TAVI. Notably, we lacked information regarding the pacing rate after TAVI. Besides the identified risk factors, there may be unknown factors that have influenced the pacemaker rate after TAVI.
CONCLUSIONS
Our risk score fills the existing data gap regarding newer prosthesis generations and introduces a novel approach to risk score calculation. The 2‐step nature of our score allows interventional cardiologists to initially calculate the patient's individual pacemaker risk before TAVI based on unmodifiable clinical risk factors. Building upon this outcome, our risk score enables them to assess how the pacemaker risk of their patient can effectively be influenced by specific valve types or implantation techniques. This 2‐step risk score may provide a novel and valuable tool for daily clinical practice.
Sources of Funding
None.
Disclosures
Henryk Dreger received research support from Abbott, advisory board, and speaker's fees from Abbott and Edwards. Verena Tscholl received research support from Abbott and Zoll, as well as speaker fees from Novartis, Biotronik, Medtronic, and Astra Zeneca.
Supporting information
Table S1
Acknowledgments
David Leistner with Henryk Dreger had the idea for this study. Data acquisition was performed by a group of 7 investigators (Alexandra Janiszewski, Julia Lueg, Benjamin Juri, Louis Morell, Maria Hajduczenia, Pierre Hennig). The statistical analysis was performed by Daniel Schulze. The article was mainly authored by Alexandra Janiszewski and Julia Lueg, all authors discussed the results and contributed to the final article.
A. Janiszewski, J. Lueg, V. Tscholl, and H. Dreger contributed equally.
This article was sent to Amgad Mentias, MD, Associate Editor, for review by expert referees, editorial decision, and final disposition.
Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.124.039036
For Sources of Funding and Disclosures, see page 7.
REFERENCES
- 1. Vahanian A, Beyersdorf F, Praz F, Milojevic M, Baldus S, Bauersachs J, Capodanno D, Conradi L, De Bonis M, de Paulis R, et al. 2021 ESC/EACTS guidelines for the management of valvular heart disease. Eur Heart J. 2022;43:561–632. doi: 10.1093/eurheartj/ehab395 [DOI] [PubMed] [Google Scholar]
- 2. Ciobanu AO, Gherasim L, Vinereanu D. Risk of stroke after transcatheter aortic valve implantation: epidemiology, mechanism, and management. Am J Ther. 2021;28:e560–e572. doi: 10.1097/MJT.0000000000001413 [DOI] [PubMed] [Google Scholar]
- 3. Mangieri A, Montalto C, Poletti E, Sticchi A, Crimi G, Giannini F, Latib A, Capodanno D, Colombo A. Thrombotic versus bleeding risk after transcatheter aortic valve replacement: JACC review topic of the week. J Am Coll Cardiol. 2019;74:2088–2101. doi: 10.1016/j.jacc.2019.08.1032 [DOI] [PubMed] [Google Scholar]
- 4. Anantha‐Narayanan M, Kandasamy VV, Reddy YN, Megaly M, Baskaran J, Pershad A, Suri RM, Garcia S. Low‐risk transcatheter versus surgical aortic valve replacement—an updated meta‐analysis of randomized controlled trials. Cardiovasc Revasc Med. 2020;21:441–452. doi: 10.1016/j.carrev.2019.08.003 [DOI] [PubMed] [Google Scholar]
- 5. Blankenberg S, Seiffert M, Vonthein R, Baumgartner H, Bleiziffer S, Borger MA, Choi YH, Clemmensen P, Cremer J, Czerny M, et al. Transcatheter or surgical treatment of aortic‐valve stenosis. N Engl J Med. 2024;390:1572–1583. doi: 10.1056/NEJMoa2400685 [DOI] [PubMed] [Google Scholar]
- 6. Reiter C, Lambert T, Kellermair J, Blessberger H, Fellner A, Strasser B, Grund M, Nahler A, Steinwender C. Intraprocedural dynamics of cardiac conduction during transcatheter aortic valve implantation: assessment by simultaneous electrophysiological testing. Heart Rhythm. 2021;18:419–425. doi: 10.1016/j.hrthm.2020.10.018 [DOI] [PubMed] [Google Scholar]
- 7. Glikson M, Nielsen JC, Kronborg MB, Michowitz Y, Auricchio A, Barbash IM, Barrabés JA, Boriani G, Braunschweig F, Brignole M, et al. 2021 ESC guidelines on cardiac pacing and cardiac resynchronization therapy. Eur Heart J. 2021;42:3427–3520. doi: 10.1093/eurheartj/ehab364 [DOI] [PubMed] [Google Scholar]
- 8. Reardon MJ, van Mieghem NM, Popma JJ, Kleiman NS, Søndergaard L, Mumtaz M, Adams DH, Deeb GM, Maini B, Gada H, et al. Surgical or transcatheter aortic‐valve replacement in intermediate‐risk patients. N Engl J Med. 2017;376:1321–1331. doi: 10.1056/NEJMoa1700456 [DOI] [PubMed] [Google Scholar]
- 9. Du F, Zhu Q, Jiang J, Chen H, Liu X, Wang J. Incidence and predictors of permanent pacemaker implantation in patients who underwent transcatheter aortic valve replacement: observation of a chinese population. Cardiology. 2020;145:27–34. doi: 10.1159/000502792 [DOI] [PubMed] [Google Scholar]
- 10. Sammour Y, Krishnaswamy A, Kumar A, Puri R, Tarakji KG, Bazarbashi N, Harb S, Griffin B, Svensson L, Wazni O, et al. Incidence, predictors, and implications of permanent pacemaker requirement after transcatheter aortic valve replacement. JACC Cardiovasc Interv. 2021;14:115–134. doi: 10.1016/j.jcin.2020.09.063 [DOI] [PubMed] [Google Scholar]
- 11. Maeno Y, Abramowitz Y, Kawamori H, Kazuno Y, Kubo S, Takahashi N, Mangat G, Okuyama K, Kashif M, Chakravarty T, et al. A highly predictive risk model for pacemaker implantation after TAVR. JACC Cardiovasc Imaging. 2017;10:1139–1147. doi: 10.1016/j.jcmg.2016.11.020 [DOI] [PubMed] [Google Scholar]
- 12. Kiani S, Kamioka N, Black GB, Lu ML, Lisko JC, Rao B, Mengistu A, Gleason PT, Stewart JP, Caughron H, et al. Development of a risk score to predict new pacemaker implantation after transcatheter aortic valve replacement. JACC Cardiovasc Interv. 2019;12:2133–2142. doi: 10.1016/j.jcin.2019.07.015 [DOI] [PubMed] [Google Scholar]
- 13. Hyde EK, Throndson K, Arcinas LA, Shah AH, Hiebert B, Yamashita MH, Lee Samson D, Maric A, Love R, Poettcker D, et al. Validation of the Emory risk score in the transcatheter aortic valve implantation population: a Canadian perspective. CJC Open. 2022;4:1060–1068. doi: 10.1016/j.cjco.2022.08.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Spring AM, Catalano MA, Prasad V, Rutkin B, Koss E, Hartman A, Yu PJ. Evaluating the validity of risk scoring in predicting pacemaker rates following transcatheter aortic valve replacement. J Interv Cardiol. 2020;2020:1807909. doi: 10.1155/2020/1807909 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Lauten PC, Goebel B, Gonzalez‐Lopez D, Schreiber M, Kuntze T, Al Jassem M, Lapp H. Transcatheter aortic valve implantation: addressing the subsequent risk of permanent pacemaker implantation. J Cardiovasc Dev Dis. 2023;10:230. doi: 10.3390/jcdd10060230 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Kusumoto FM, Schoenfeld MH, Barrett C, Edgerton JR, Ellenbogen KA, Gold MR, Goldschlager NF, Hamilton RM, Joglar JA, Kim RJ, et al. 2018 ACC/AHA/HRS guideline on the evaluation and management of patients with bradycardia and cardiac conduction delay: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical practice guidelines, and the Heart Rhythm Society. J Am Coll Cardiol. 2019;74:932–987. doi: 10.1016/j.jacc.2018.10.043 [DOI] [PubMed] [Google Scholar]
- 17. Beyersdorf F, Bauer T, Freemantle N, Walther T, Frerker C, Herrmann E, Bleiziffer S, Möllmann H, Landwehr S, Ensminger S, et al. Five‐year outcome in 18010 patients from the German Aortic Valve Registry. Eur J Cardiothoracic Surg. 2021;60:1139–1146. [DOI] [PubMed] [Google Scholar]
- 18. Wang T, Ou A, Xia P, Tian J, Wang H, Cheng Z. Predictors for the risk of permanent pacemaker implantation after transcatheter aortic valve replacement: a systematic review and meta‐analysis. J Card Surg. 2022;37:377–405. doi: 10.1111/jocs.16129 [DOI] [PubMed] [Google Scholar]
- 19. Maier O, Piayda K, Afzal S, Polzin A, Westenfeld R, Jung C, Zeus T, Antoch G, Kelm M, Veulemans V. Computed tomography derived predictors of permanent pacemaker implantation after transcatheter aortic valve replacement: a meta‐analysis. Catheter Cardiovasc Interv. 2021;98:E897–e907. doi: 10.1002/ccd.29805 [DOI] [PubMed] [Google Scholar]
- 20. Guetta V, Goldenberg G, Segev A, Dvir D, Kornowski R, Finckelstein A, Hay I, Goldenberg I, Glikson M. Predictors and course of highdegree atrioventricular block after transcatheter aortic valve implantation using the corevalve revalving system. Am J Cardiol. 2011;108:1600–1605. doi: 10.1016/j.amjcard.2011.07.020 [DOI] [PubMed] [Google Scholar]
- 21. Laish‐Farkash A. Right bundle branch block in patients undergoing transcatheter aortic valve implantation: are there clinical implications? Israel Med Assoc J. 2019;21:829–830. [PubMed] [Google Scholar]
- 22. Piazza N, de Jaegere P, Schultz C, Becker AE, Serruys PW, Anderson RH. Anatomy of the aortic valvar complex and its implications for transcatheter implantation of the aortic valve. Circ Cardiovasc Interv. 2008;1:74–81. doi: 10.1161/CIRCINTERVENTIONS.108.780858 [DOI] [PubMed] [Google Scholar]
- 23. Saad M, Klaus Y, Buhse P, Puehler T, Lutter G, Seoudy H, Frank D. Influence of deep implantation on conduction disturbances after transcatheter aortic valve implantation. Herzschrittmacherther + Elektrophysiol. 2021;32:371–379. doi: 10.1007/s00399-021-00784-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Sammour Y, Banerjee K, Kumar A, Lak H, Chawla S, Incognito C, Patel J, Kaur M, Abdelfattah O, Svensson LG, et al. Systematic approach to high implantation of SAPIEN‐3 valve achieves a lower rate of conduction abnormalities including pacemaker implantation. Circ Cardiovasc Interv. 2021;14:e009407. doi: 10.1161/CIRCINTERVENTIONS.120.009407 [DOI] [PubMed] [Google Scholar]
- 25. Chen S, Chau KH, Nazif TM. The incidence and impact of cardiac conduction disturbances after transcatheter aortic valve replacement. Ann Cardiothorac Surg. 2020;9:452–467. doi: 10.21037/acs-2020-av-23 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Young Lee M, Chilakamarri Yeshwant S, Chava S, Lawrence LD. Mechanisms of heart block after transcatheter aortic valve replacement: cardiac anatomy, clinical predictors and mechanical factors that contribute to permanent pacemaker implantation. Arrhythmia Electrophysiol Rev. 2015;4:81–85. doi: 10.15420/aer.2015.04.02.81 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Vlastra W, Chandrasekhar J, Muñoz‐Garcia AJ, Tchétché D, de Brito FS, Barbanti M, Kornowski R, Latib A, D'Onofrio A, Ribichini F, et al. Comparison of balloon‐expandable vs. self‐expandable valves in patients undergoing transfemoral transcatheter aortic valve implantation: from the CENTER‐collaboration. Eur Heart J. 2019;40:456–465. doi: 10.1093/eurheartj/ehy805 [DOI] [PubMed] [Google Scholar]
- 28. van Rosendael PJ, Delgado V, Bax JJ. Pacemaker implantation rate after transcatheter aortic valve implantation with early and new‐generation devices: a systematic review. Eur Heart J. 2018;39:2003–2013. doi: 10.1093/eurheartj/ehx785 [DOI] [PubMed] [Google Scholar]
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Supplementary Materials
Table S1