Association between Atrial Fibrillation and the risk of New-Onset Left bundle branch block following Transcatheter Mitral Valve Replacement: a retrospective analysis of the National Inpatient Sample

Association between Atrial Fibrillation and the risk of New-Onset Left bundle branch block following Transcatheter Mitral Valve Replacement: a retrospective analysis of the National Inpatient Sample | 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 Association between Atrial Fibrillation and the risk of New-Onset Left bundle branch block following Transcatheter Mitral Valve Replacement: a retrospective analysis of the National Inpatient Sample Qing Chen, Ying Cui, Ying Guo, Lin He, Ting Liang, Shizhe Fu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7380737/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Transcatheter mitral valve replacement (TMVR) offers an alternative for high-risk patients with mitral valve disease. Atrial fibrillation (AF) frequently coexists with mitral valve disease, while new-onset left bundle branch block (NLBBB) following transcatheter valve interventions may adversely affect cardiac function and necessitate permanent pacemaker implantation. However, the association between pre-existing AF and NLBBB following TMVR remains inadequately characterized. Methods This retrospective study analyzed TMVR hospitalizations with or without AF using National Inpatient Sample data from 2016 to 2022. The primary outcome was the risk of NLBBB following TMVR. The association between AF and NLBBB risk was evaluated using multivariable logistic regression, adjusting for sociodemographic characteristics and comorbidities. Sensitivity analyses were performed using inverse probability of treatment weighting (IPTW) and interaction test to validate the robustness of our findings. Results Among 1,754 patients undergoing TMVR, 65.6% (n = 1,150) had documented AF. AF patients were significantly older (74.63 ± 9.91 vs. 68.24 ± 13.88 years; P < 0.001) and exhibited higher prevalence of peripheral vascular disease (P = 0.004) and hypothyroidism (P = 0.024). The incidence of NLBBB was 4.61% in the AF cohort. Multivariable analysis demonstrated that AF was independently associated with increased NLBBB risk (aOR 2.09; 95% CI 1.13–3.84; P = 0.018), the finding that remained consistent after IPTW analysis (ATT 1.90; 95% CI 1.07–3.69; P = 0.030). Further stratification revealed that persistent AF significantly correlated with NLBBB development (ATT 2.74; 95% CI 1.23–6.09; P = 0.014), whereas paroxysmal AF did not show statistical significance (ATT 1.84; 95% CI 0.88–3.82; P = 0.100). Conclusions Patients with AF, particularly persistent AF, demonstrated significantly higher risk of NLBBB following TMVR. Transcatheter Mitral Valve Replacement New-Onset Left Bundle Branch Block Atrial Fibrillation the National Inpatient Sample Figures Figure 1 Figure 2 Introduction The global prevalence of mitral valve disease has exhibited a significant increasing trend, particularly in aging populations[ 1 ]. The current therapeutic modalities for mitral valve disease each present distinct clinical limitation[ 2 ]. Transcatheter mitral valve replacement (TMVR), a minimally invasive percutaneous technique for mitral valve replacement, has shown superior safety profiles compared to conventional surgery in high-risk surgical patients (STS score > 8%) over the past decade[ 3 ]. Previous studies have identified atrial fibrillation (AF), cardiac functional status, and left atrial dimension as significant predictors of postoperative outcomes following mitral valve surgery [ 4 , 5 ]. AF represents a prevalent comorbidity in patients with mitral valve disease, with a preoperative incidence of approximately 40%[ 6 ]. Mechanistically, AF may influence cardiac conduction through multiple mechanisms, including the promotion of fibrotic changes, exacerbation of congestive heart failure, and induction of electrical and structural atrial remodeling[ 7 , 8 ]. Consequently, this shared pathophysiology may potentially induce bundle branch remodeling, thereby precipitating the development of left bundle branch block (LBBB). As an established independent mortality predictor in cardiovascular populations, LBBB represents a clinically significant conduction abnormality, particularly following transcatheter valve interventions. There is sufficient evidence to suggest that it primarily occurs through multiple pathophysiological mechanisms, including the induction of ventricular asynchrony, abnormal septal motion, microstructural remodeling of myocardial fibrosis, or mitral regurgitation. These factors contribute to left ventricular dysfunction, ultimately precipitating adverse cardiovascular events such as clinical heart failure, and permanent pacemaker implantation[ 9 – 11 ]. When coexisting with AF, LBBB and AF exhibit synergistic detrimental effects, further significantly reducing left ventricular ejection fraction (LVEF) and worsening prognosis[ 12 ]. According to the literature, the incidence of LBBB after TAVR reaches 10%-22%[ 13 – 15 ], which significantly increases the risk of all-cause mortality and cardiovascular mortality, severely affecting patient prognosis[ 16 – 18 ]. However, the incidence and risk factors for LBBB following TMVR remain poorly characterized, and the electrophysiological impact of pre-existing AF on conduction system vulnerability requires further elucidation. Given the paucity of evidence regarding the impact of AF on the occurrence of new-onset left bundle branch block (NLBBB) following TMVR, we conducted this nationwide analysis utilizing the National Inpatient Sample (NIS) database to systematically investigate differences in demographic and clinical characteristics, incidence of NLBBB, and associated clinical outcomes between patients with AF and non-AF undergoing TMVR. Methods Data Source This retrospective study utilized data from the NIS database spanning January 1, 2016, to December 31, 2022. As the largest publicly available all-payer inpatient database under the Healthcare Cost and Utilization Project (HCUP), the NIS encompasses approximately 97% of inpatient discharges from 46 U.S. states, with an annual sample size exceeding 7 million records representing a 20% stratified sample of U.S. community hospital discharges[ 19 ]. The NIS systematically captures clinical data such as patient demographics, diagnostic codes, comorbidities, surgical procedures, in-hospital complications, and healthcare costs, all coded using the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) and Procedure Coding System (ICD-10-PCS). Since the NIS database is publicly accessible and contains no personally identifiable information, it is exempt from requiring informed consent by Institutional Review Boards (IRBs). Study Population Data from the NIS spanning the years 2016 to 2022 were utilized in this study. We included all hospitalizations involving TMVR for patients (aged ≥ 18 years), who were identified using specific International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) procedure codes:02UG3JH, 02UG3JZ, 02QG3ZZ, and 02QG3ZE. Hospitalizations for AF were identified using ICD-10-CM codes I48.0, I48.1, I48.2, and I48.9. NLBBB during hospitalization was ascertained using ICD-10-CM codes I44.4, I44.5, and I44.7. Patients with missing baseline data or pre-existing LBBB before TMVR were excluded from the analysis (Fig. 1 ). For each included hospitalization, baseline demographic characteristics (e.g. age, sex), hospital features (e.g. bed size, teaching status), and clinically relevant comorbidities were extracted and analyzed. Comorbidities were assessed using the Elixhauser Comorbidity Index and corresponding ICD-10-CM codes ( Supplemental Table 1 ). Study outcome The primary endpoint was the risk of NLBBB following TMVR patients with AF compared to non-AF patients. Data analysis National estimates of hospitalizations and patient numbers were calculated by applying NIS sampling weights to ensure representativeness[ 20 ]. Descriptive statistics summarized categorical variables as frequencies (%) and continuous variables as mean ± standard deviation (SD). Group comparisons employed weighted linear regression for continuous variables and weighted chi-square tests for categorical variables. Multivariable regression analyses were conducted to evaluate the independent effect of AF on outcomes after adjusting for confounders. The regression model included covariates that demonstrated significant between-group differences in baseline characteristics or univariate analyses: age; sex; race; payment type; median household income based on the patient’s ZIP code; smoking status; dyslipidemia; prior stroke; prior myocardial infarction; prior percutaneous coronary intervention; prior coronary artery bypass grafting; pulmonary circulation disorders; peripheral vascular disorders; neurological disorders; chronic pulmonary disease; hypothyroidism; renal failure; liver disease; rheumatoid arthritis/collagen vascular diseases; coagulopathy; weight loss; obesity; diabetes; and hypertension. Results were expressed as unadjusted and adjusted odds ratios (aORs) with corresponding 95% confidence intervals (CIs). To enhance the robustness of our findings, we conducted a sensitivity analysis using IPTW by excluding patients with right bundle branch block (RBBB) or third-degree atrioventricular block (3°AVB). Subsequently, we stratified the AF population into paroxysmal and persistent AF subgroups to evaluate the differential impact on postoperative NLBBB. Stabilized inverse probability weights were calculated using predicted probabilities derived from the propensity score model[ 21 ]. Subgroup analyses were conducted to investigate the association between AF and NLBBB across various patient subgroups stratified by age; sex; chronic lung disease; renal disease; smoking status; hyperlipidemia; obesity; weight loss; and diabetes. For each subgroup analysis formal interaction tests were performed using likelihood ratio tests to compare models with and without interaction terms between AF stratification variables assessing whether associations varied significantly across subgroups ( P <0.05 considered significant). All analyses accounted for complex survey design incorporating survey weights stratification clustering variables[ 22 – 24 ]. Statistical analyses utilized R software ( http://www.R-project.org , The R Foundation) and Empower Stats ( http://www.empowerstats.com , X&Y Solution, Inc., Boston, MA). Results Baseline demographics of TMVR patients with AF or non-AF The NIS database identified 1,754 patients who underwent TMVR between 2016 and 2022, comprising 1,150 (65.6%) with AF and 604 (34.4%) without AF. Notably, the proportion of AF patients undergoing TMVR demonstrated a significant temporal increase from 57.14% in 2016 to 66.0% in 2022 (P-trend<0.001)( Supplemental Fig. 1 ). Comparative analysis revealed that AF patients were significantly older than non-AF patients (74.63 ± 9.91 vs. 68.24 ± 13.88s; P<0.001) and exhibited higher prevalence rates of peripheral vascular disease(P = 0.004) and hypothyroidism (P = 0.024) (Table 1 ). Table 1 Baseline Demographics in patients with TMVR between 2016 to 2022: stratified by AF Variable No AF (n = 604) AF (n = 1150) P-value Demographics Age (mean ± SD) 68.24 ± 13.88 74.63 ± 9.91 < 0.001** Female 321 (53.15%) 644 (56.00%) 0.254 Race < 0.001** White 433 (71.69%) 925 (80.43%) Black 82 (13.58%) 94 (8.17%) Hispanic 46 (7.62%) 52 (4.52%) Other 43 (7.12%) 79 (6.87%) Primary Payer, n (%) < 0.001** Medicare/Medicaid 437 (72.35%) 952 (82.78%) Private 50 (8.28%) 47 (4.09%) Other 96 (15.89%) 124 (10.78%) Self-pay 21 (3.48%) 27 (2.35%) Median Household Income Quartile, n (%) 0.017* First (lowest) 144 (23.84%) 241 (20.96%) Second 172 (28.48%) 270 (23.48%) Third 150 (24.83%) 341 (29.65%) Fourth (highest) 138 (22.85%) 298 (25.91%) Smoking 220 (36.42%) 366 (31.83%) 0.052 Dyslipidemia 361 (59.77%) 726 (63.13%) 0.168 Hospital Teaching Status, n (%) 0.474 Non-teaching 4 (0.66%) 13 (1.13%) Teaching 33 (5.46%) 53 (4.61%) Urban teaching 567 (93.87%) 1084 (94.26%) Hospital Bed Size, n (%) 0.893 Small 45 (7.45%) 80 (6.96%) Medium 93 (15.40%) 184 (16.00%) Large 466 (77.15%) 886 (77.04%) Medical History , n (%) Prior stroke 60 (9.93%) 140 (12.17%) 0.161 Prior MI 73 (12.09%) 109 (9.48%) 0.089 Prior PCI 61 (10.10%) 94 (8.17%) 0.177 Prior CABG 153 (25.33%) 244 (21.22%) 0.050 Comorbidities , n (%) Pulmonary circulation Disorders 250 (41.39%) 518 (45.04%) 0.143 Peripheral vascular disorders 94 (15.56%) 245 (21.30%) 0.004* Chronic pulmonary disease 170 (28.15%) 323 (28.09%) 0.979 Hypothyroidism 116 (19.21%) 275 (23.91%) 0.024* Renal failure 303 (50.17%) 586 (50.96%) 0.753 Liver disease 56 (9.27%) 82 (7.13%) 0.114 Rheumatoid arthritis/ collagen vascular diseases 20 (3.31%) 46 (4.00%) 0.471 Coagulopathy 143 (23.68%) 292 (25.39%) 0.429 Obesity 77 (12.75%) 174 (15.13%) 0.176 Weight loss 40 (6.62%) 103 (8.96%) 0.090 Hypertension 499 (82.62%) 955 (83.04%) 0.821 Diabetes 192 (31.79%) 316 (27.48%) 0.059 The outcome of the risk of NLBBB in AF patients who underwent TMVR The analysis revealed a significantly higher incidence of postoperative NLBBB in AF patients (4.61%) compared to non-AF patients (2.48%) within the overall TMVR cohort (P = 0.028). The temporal patterns of postoperative LBBB incidence among TMVR patients over the seven-year study period are depicted in Fig. 2 . Multivariable regression analysis, after adjustment for potential confounders, established AF as an independent predictor of NLBBB following TMVR (unadjusted OR 1.90, 95% CI 1.06–3.39, P = 0.031; model I aOR 2.24, 95% CI 1.18–4.24, P = 0.031; model II aOR 2.09, 95% CI 1.13–3.84, P = 0.018). Notably, after excluding patients with RBBB or t3°AVB, the AF group demonstrated a 2.21-fold increased risk of postoperative LBBB compared to non-AF patients (unadjusted OR 2.21, 95% CI 1.19–4.08, P = 0.012; model I aOR 2.24, 95% CI 1.18–4.24, P = 0.014; model II aOR 2.33, 95% CI 1.22–4.43, P = 0.010) (Table 2 ). Table 2 New-Onset LBBB in patients undergoing TMVR with AF compared with non-AF: Multiple regression Analysis In-hospital outcome AF n = 1150 Non-AF n = 604 Non-adjusted p-value Adjust-I p-value Adjust-II p-value LBBB 53(4.61%) 15(2.48%) 1.90 (1.06, 3.39) 0.031* 2.24 (1.18,4.24) 0.031* 2.09 (1.13,3.84) 0.018* Excluding of RBBB &3° AVB AF n = 1047 Non-AF n = 551 Non-adjusted p-value Adjust-I p-value Adjust-II p-value LBBB 53(5.06%) 13(2.36%) 2.21 (1.19, 4.08) 0.012* 2.24 (1.18,4.24) 0.014* 2.33 (1.22,4.43) 0.010* Non-adjusted model adjusts for: None; Adjust I model adjust for: age; female; race; primary payer; median household income quartile; Adjust II model adjust for: age; female; race; primary payer; median household income quartile; smoking; dyslipidemia; prior stroke; prior MI; prior PCI; prior CABG; pulmonary circulation disorders;peripheral vascular disorders༛neurological disorders; chronic pulmonary disease༛hypothyroidism༛renal failure༛liver disease༛rheumatoid arthritis/collagen vascular diseases༛coagulopathy༛weight loss༛obesity༛diabetes༛hypertension. *Indicates statistically significant differences at P < 0.05. The robustness of clinical outcome through sensitivity analyses Using IPTW analysis, patients with AF revealed a significantly elevated risk of postoperative NLBBB compared to non-AF patients (ATT 1.99; 95%CI:1.07–3.69; P = 0.030; ATC 1.86; 95%CI:1.01–3.44; P = 0.046; ATE 1.91; 95%CI:1.01–3.43; P = 0.030). This association remained robust after excluding patients with pre-existing RBBB or 3°AVB (ATT 2.41;95%CI:1.20–4.81; P = 0.013; ATC 2.25; 95%CI:1.19–4.27; P = 0.013; ATE 2.35; 95%CI:1.23–4.50; P = 0.010) (Table 3 ). Table 3 Outcome of new-onset LBBB in patients undergoing TMVR with AF compared with non-AF: Inverse Probability of Treatment Weighting In-hospital Outcome IPTW AF Patients AF Patients (Excluding RBBB & 3° AV Block) LBBB ATT 1.99 (1.07, 3.69) 2.41 (1.20, 4.81) p = 0.030* p = 0.013* ATC 1.86 (1.01, 3.44) 2.25 (1.19, 4.27) p = 0.046* p = 0.013* ATE 1.91 (1.07, 3.43) 2.35 (1.23, 4.50) p = 0.030* p = 0.010* Results in table: ATT (95%CI) P-value; ATC (95%CI) P-value; ATE (95%CI) P-value; AF patients were stratified into paroxysmal AF (n = 448) and persistent AF (n = 379) subgroups, after excluding those with unspecified AF (ICD-10-CM code I48.91). The impact of different AF subtypes on postoperative NLBBB was reanalyzed using IPTW. The results revealed a significantly higher risk of NLBBB in the persistent AF group compared to the non-AF group (ATT 2.74; 95%CI: 1.23–6.09; P = 0.014; ATC 2.67; 95%CI: 1.15–6.20; P = 0.022; ATE 2.70; 95%CI: 1.23–5.89; P = 0.013). Although the risk of NLBBB was higher in the paroxysmal AF group compared to the non-AF group, no statistically significant difference was observed (ATT 1.84; 95%CI: 0.88–3.82; P = 0.100; ATC 1.76; 95%CI: 0.85–3.65; P = 0.131; ATE 1.79; 95% CI: 0.88–3.64; P = 0.108) (Table 4 ). Table 4 Results of different atrial fibrillation subtypes on LBBB: Inverse Probability of Treatment Weighting Clinical outcome IPTW Paroxysmal AF N = 448 Persistent AF N = 379 LBBB ATT 1.84 (0.88, 3.82) 2.74 (1.23, 6.09) p = 0.100 p = 0.014* ATC 1.76 (0.85, 3.65) 2.67 (1.15, 6.20) p = 0.131 p = 0.022* ATE 1.79 (0.88, 3.64) 2.70 (1.23, 5.89) p = 0.108 p = 0.013* Results in table: OR (95%CI) P-value The outcomes of potential effect modification between AF and other clinical variables on NLBBB risk Interaction analyses using likelihood ratio tests revealed no statistically significant effect modification by age, sex, chronic lung disease, chronic kidney disease, smoking status, hyperlipidemia, obesity, weight loss, and diabetes mellitus (all P-interactions>0.05) on the AF-LBBB association (Table 5 ). These findings indicate the consistent association between AF and NLBBB following TMVR across diverse patient subgroups, suggesting an effect independent of age, sex, or comorbid conditions. Table 5 Subgroup analysis of AF patients on new-onset LBBB Stratification Variable Subgroups Odds Ratio (95% CI) P-value P for Interaction Gender 0.284 Female 2.96 (1.17, 7.52) 0.022* Male 2.19 (0.85, 5.62) 0.104 Age 0.559 < 60 Years 3.22 (0.56, 18.60) 0.191 ≥ 60 Years 2.20 (0.49, 9.94) 0.306 Chronic Lung Disease 0.517 No 2.46 (1.10, 5.47) 0.028* Yes 3.27 (1.36, 7.84) 0.008** Kidney Disease 0.846 No 1.98 (0.90, 4.32) 0.088 Yes 1.36 (0.60, 3.11) 0.460 Smoking Status 0.508 No 2.40 (1.12, 5.14) 0.024* Yes 1.60 (0.67, 3.85) 0.292 Hyperlipidemia 0.786 No 1.83 (0.63, 5.33) 0.265 Yes 2.20 (0.81, 5.99) 0.121 Obesity 0.280 No 1.81 (0.95, 3.45) 0.072 Yes 2.33 (1.01, 5.38) 0.048* Weight loss 0.760 No 2.12 (1.13, 3.97) 0.019* Yes 1.71 (0.46, 6.33) 0.419 Diabetes 0.144 No 1.49 (0.72, 3.07) 0.283 Yes 2.56 (1.17, 5.57) 0.018* Note: *p < 0.05; **p < 0.01 Discussion The association between AF and NLBBB following TMVR remains unexplored in the literature. We analyzed patients who underwent TMVR between 2016 and 2022, categorizing them into AF and non-AF groups to evaluate the risk of intraoperative NLBBB. The key findings are summarized below:1) In patients undergoing TMVR, the incidence of postoperative NLBBB was 4.61% in AF patients versus 2.48% in those without AF; 2) The risk of NLBBB in the AF group was 2.09-fold higher compared to the non-AF group, suggesting that AF serves as an independent predictor of NLBBB following the procedure; 3) Compared with paroxysmal AF, persistent AF has a more significant impact on the occurrence of NLBBB after TMVR, suggesting that distinct AF phenotypes may differentially influence the incidence of NLBBB post-intervention AF represents a prevalent complication of mitral valve disease. Mitral valve disorders, particularly mitral regurgitation and stenosis, induce volume and pressure overload in the left atrium, promoting left atrial dilation and fibrosis. These structural alterations create both electrophysiological and anatomical substrates conducive to AF development. The findings of this study indicate that the proportion of patients with AF was significantly higher than non-AF patients among those undergoing TMVR(65.6% versus 34.4%), This innovative observation not only fills a critical gap in the current literature but also provides clinicians with valuable data references and practical guidance for managing TMVR patients. LBBB, due to its anatomical predisposition, is a frequent conduction disorder following TAVR. This conduction disturbance can further exacerbate pre-existing cardiac conditions through multiple ventricular-level electrophysiological remodeling processes and mechanical dyssynchrony, while also inducing progressive ventricular remodeling in structurally normal hearts. These pathological changes ultimately lead to cardiac dysfunction, culminating in decompensation and cardiomyopathy development[ 25 , 26 ]. To our knowledge, the probability of postoperative LBBB occurrence in patients undergoing TMVR and the specific clinical impact of AF on postoperative LBBB have not been reported in the literature with definitive conclusions. Utilizing the NIS database, we conducted a comprehensive retrospective analysis of all patients undergoing TMVR between 2016 and 2022. The cohort was systematically stratified into AF and non-AF groups to investigate the association with postoperative NLBBB. Our findings revealed that AF is significantly associated with an increased risk of NLBBB following TMVR compared to non-AF patients. Notably, persistent AF exhibited a more pronounced effect on postoperative NLBBB development than paroxysmal AF, indicating differential impacts of AF phenotypes on conduction disturbances. These findings provide new clinical data regarding the occurrence of conduction disorders after TMVR and their associated risk factors and potential clinical outcomes. Previous studies report that LBBB typically presents at a mean age of 70 ± 10 years, with prevalence increasing with age[ 27 – 30 ]. In our cohort, TMVR with AF patients were older (mean age: 74.63 ± 9.91 years), which may partially explain the higher risk of postoperative LBBB in this population. AF induces structural and electrophysiological remodeling in atrial and sinoatrial nodal tissues. Experimental and clinical evidence further demonstrates that sustained AF promotes sinus node remodeling[ 31 – 33 ]. The characteristic rapid atrial rate in AF increases susceptibility to ventricular arrhythmias, while the atrioventricular node serves a critical protective role by regulating ventricular impulse conduction. However, persistent high-frequency atrial depolarization may induce electrophysiological remodeling of the AV node itself[ 34 ]. Similar pathophysiological mechanisms could potentially contribute to bundle branch remodeling in AF[ 35 ]. The evolution of transcatheter valve replacement techniques has highlighted the contribution of anatomical characteristics (e.g. annular calcification, implantation depth) to postoperative conduction abnormalities[ 36 , 37 ]. Comprehensive anatomical assessment before TMVR is essential, including evaluation of atrial dimensions[ 38 – 40 ], left ventricular outflow tract obstruction (LVOTO), and valve displacement risk. The eft ventricular outflow tract (LVOT), located at the basal interventricular septum, lies in close anatomical proximity to the left bundle branch (LBB). Post-implantation, the anterior mitral leaflet is displaced, forming a "neo-LVOT." A reduced neo-LVOT area may precipitate acute LVOTO, manifesting as immediate hemodynamic instability post-deployment, with intraprocedural echocardiography revealing valve malposition or anterior leaflet-induced obstruction[ 41 ]. The irregular cardiac rhythm and structural atrial remodeling in AF may increase procedural complexity, potentially exacerbating LVOTO and valve displacement risks, which may subsequently result in conduction system injury and bundle branch block. In contrast to paroxysmal AF, persistent AF induces progressive structural remodeling (including fibrotic deposition and ion channel dysregulation) that results in irreversible pathological alterations[ 42 ]. Electrophysiological mapping studies demonstrate that patients with persistent AF present with tripled atrial dilatation magnitude and a 40% reduction in conduction velocity compared to paroxysmal AF cases[ 43 ]. This may partially explain why persistent AF demonstrates a more pronounced association with new-onset LBBB following TMVR. Our retrospective study reveals a correlation without establishing causation. Further research is necessary to better understand the underlying mechanisms and nature of the relationship among the three variables. Our study offers several methodological strengths, employing a comprehensive sensitivity analysis through IPTW, and stratified analysis to repeatedly validate the stability of our research findings, thereby enhancing the result's reliability. By leveraging the NIS database, we analyzed a nationally representative cohort of 1,754 TMVR patients from 2016 to 2022, which ensures the generalizability of our data. The key innovations of our research include: (1) This is the first study to investigate the differential impact of AF on the risk of LBBB following TMVR, providing additional clinical evidence regarding the influence of AF on postoperative LBBB occurrence and offering important insights into the factors contributing to conduction abnormalities after TMVR. (2) confirming differential effects of AF subtypes on postoperative LBBB development, with persistent AF showing a more significant influence on LBBB occurrence compared to paroxysmal AF. However, the potential pathophysiological mechanisms underlying the increased LBBB risk in AF patients undergoing TMVR remain incompletely understood. Future research directions should focus on prospectively assessing the impact of AF on postoperative outcomes in TMVR patients experiencing LBBB and exploring the potential causal relationship between AF and postoperative LBBB development. Limitations Several limitations should be acknowledged. First, certain clinically relevant variables were unavailable, including echocardiographic data (to assess structural differences), LBBB duration, electrocardiographic confirmation, TMVR access route, intraprocedural hemodynamics, valve type, and coexisting complications. The absence of these data may limit a comprehensive evaluation of patient outcomes. Second, as the study relied on ICD-10 coding within the NIS database, potential misclassification or incomplete documentation could affect accuracy. Although NIS data quality has been extensively validated, this inherent limitation may introduce bias. Finally, the study spanned a period of evolving TMVR techniques (2016–2022), during which procedural refinements, valve technologies, and patient selection criteria underwent significant advancements. Consequently, early practices may differ from current standards, potentially limiting the direct applicability of our findings to contemporary clinical settings. Conclusions Our study indicates that patients with AF, particularly those with persistent AF, exhibited a significantly higher risk of developing NLBBB following TMVR. The formation of NLBBB can lead to further deterioration of cardiac function affecting the patient's prognosis. These findings suggest that personalized management strategies should be implemented for AF patients undergoing TMVR to mitigate the risk of postoperative conduction abnormalities. Additionally, this study highlights the imperative for further exploration into the underlying mechanisms contributing to these observed disparities, thereby providing significant guidance for future research endeavors. Abbreviations AF Atrial Fibrillation LBBB Left bundle branch block LBBB Left bundle branch LVOTO left ventricular outflow tract obstruction LVOTO left ventricular outflow tract NLBBB New-onset Left bundle branch block NIS National Inpatient Sample ICD-10-CM/PCS The International Classification of Diseases, Tenth Revision Clinical IPTW Inverse Probability of Treatment Weighting OR odds ratio aOR adjusted odds ratio RBBB Right bundle branch block TMVR Transcatheter Mitral Valve Replacement 3°AVB Third-degree Atrioventricular block Declarations Ethics approval and consent to participate The data for this study were derived from the National Inpatient Sample (NIS) database, a publicly accessible healthcare resource that contains no personally identifiable information. Under the Health Insurance Portability and Accountability Act (HIPAA) regulations, our institutional review board determined that patient consent was not required for this analysis. Conflicts of interest: The authors declare that they have no conflicts of interest Consent for publication: Not applicable. Funding: This work was supported by the Central government guided local science and technology project (Project No. 2022FRD05046, 2024FRD05139), Research Project of Ningxia Medical University (Project No. XM2023038), the Natural Science Foundation of Ningxia Province (Project No. 2023AAC02071), and the National Natural Science Foundation of China (grand number 82260086). Author Contribution Q. Ch: Formal analysis and Writing – original draft.Xp. M, Shb.J, and Gzh. C: Funding acquisition, Resources and Supervision.Shzh. F, B. Sh, and Cy. Y: Formal analysis and Software.Y. C., Y.G, L. H, T. L, and R. Y: Project administration and Investigation.All authors reviewed the manuscript. Acknowledgments: Not applicable. Availability of data and materials The data for this research was derived from the National Inpatient Sample (NIS) of the Healthcare Cost and Utilization Project, which can be accessed at https://www.hcup-us.ahrq.gov/nisoverview.jsp for usage agreements and associated fees. With authorization from the U.S. Department of Health and Human Services, the dataset is also available upon reasonable request from the corresponding author. References Nkomo VT, Gardin JM, Skelton TN, Gottdiener JS, Scott CG, Enriquez-Sarano M. Burden of valvular heart diseases: a population-based study. Lancet (London England). 2006;368(9540):1005–11. http://doi.org/10.1016/s0140-6736(06)69208-8 . Stone GW, Lindenfeld J, Abraham WT, Kar S, Lim DS, Mishell JM, Whisenant B, Grayburn PA, Rinaldi M, Kapadia SR, et al. Transcatheter mitral-valve repair in patients with heart failure. N Engl J Med. 2018;379(24):2307–18. http://doi.org/10.1056/NEJMoa1806640 . 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Bax JJ, Debonnaire P, Lancellotti P, Ajmone Marsan N, Tops LF, Min JK, Piazza N, Leipsic J, Hahn RT, Delgado V. Transcatheter interventions for mitral regurgitation: multimodality imaging for patient selection and procedural guidance. JACC Cardiovasc imaging. 2019;12(10):2029–48. http://doi.org/10.1016/j.jcmg.2019.03.036 . Gheorghe LL, Mobasseri S, Agricola E, Wang DD, Milla F, Swaans M, Pandis D, Adams DH, Yadav P, Sievert H, et al. Imaging for native mitral valve surgical and transcatheter interventions. JACC Cardiovasc imaging. 2021;14(1):112–27. http://doi.org/10.1016/j.jcmg.2020.11.021 . Reid A, Ben Zekry S, Turaga M, Tarazi S, Bax JJ, Wang DD, Piazza N, Bapat VN, Ihdayhid AR, Cavalcante JL, et al. Neo-LVOT and transcatheter mitral valve replacement: expert recommendations. JACC Cardiovasc imaging. 2021;14(4):854–66. http://doi.org/10.1016/j.jcmg.2020.09.027 . Stone GW, Adams DH, Abraham WT, Kappetein AP, Généreux P, Vranckx P, Mehran R, Kuck KH, Leon MB, Piazza N, et al. Clinical trial design principles and endpoint definitions for transcatheter mitral valve repair and replacement: part 2: endpoint definitions: a consensus document from the mitral valve academic research consortium. J Am Coll Cardiol. 2015;66(3):308–21. http://doi.org/10.1016/j.jacc.2015.05.049 . Heijman J, Voigt N, Nattel S, Dobrev D. Cellular and molecular electrophysiology of atrial fibrillation initiation, maintenance, and progression. Circ Res. 2014;114(9):1483–99. http://doi.org/10.1161/circresaha.114.302226 . Verma A, Jiang CY, Betts TR, Chen J, Deisenhofer I, Mantovan R, Macle L, Morillo CA, Haverkamp W, Weerasooriya R, et al. Approaches to catheter ablation for persistent atrial fibrillation. N Engl J Med. 2015;372(19):1812–22. http://doi.org/10.1056/NEJMoa1408288 . Additional Declarations No competing interests reported. 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07:11:48","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":177880,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7380737/v1/849032c6b0ff76946c27ab52.html"},{"id":92474418,"identity":"c61c7623-b50f-42b8-a422-34a39a930585","added_by":"auto","created_at":"2025-09-30 07:11:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":119008,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flowchart\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e NIS: national inpatient sample; TMVI: transcatheter mitral valve replacement; AF: atrial fibrillation; LBBB: left bundle branch block; RBBB: right bundle branch block; 3°AVB: third-degree atrioventricular block.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7380737/v1/56958fd85c884ac2e6a8c2d2.png"},{"id":92476064,"identity":"5852401d-8925-4cf5-bc71-4fbd510dc460","added_by":"auto","created_at":"2025-09-30 07:19:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26364,"visible":true,"origin":"","legend":"\u003cp\u003eTrend of New-onset LBBB during TMVR patients with/without AF from 2016 to 2022\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7380737/v1/8f128e5e28c90e4645e912f4.png"},{"id":108512970,"identity":"5ddd2a80-de33-4b8d-873b-d4a510c96140","added_by":"auto","created_at":"2026-05-05 12:57:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":677173,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7380737/v1/28b96b0c-7965-4bdd-bb09-ac17bbfd4c9d.pdf"},{"id":92474422,"identity":"912b25ef-eb32-43e4-89a7-1800299d3178","added_by":"auto","created_at":"2025-09-30 07:11:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":181674,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7380737/v1/fe6a65b4a2179ff73211e98b.docx"},{"id":92474420,"identity":"468b0be9-8bed-44d9-931c-17c75dfcdd58","added_by":"auto","created_at":"2025-09-30 07:11:48","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":31550,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7380737/v1/5f4534ade1c92230f6107da1.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Atrial Fibrillation and the risk of New-Onset Left bundle branch block following Transcatheter Mitral Valve Replacement: a retrospective analysis of the National Inpatient Sample","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global prevalence of mitral valve disease has exhibited a significant increasing trend, particularly in aging populations[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The current therapeutic modalities for mitral valve disease each present distinct clinical limitation[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Transcatheter mitral valve replacement (TMVR), a minimally invasive percutaneous technique for mitral valve replacement, has shown superior safety profiles compared to conventional surgery in high-risk surgical patients (STS score\u0026thinsp;\u0026gt;\u0026thinsp;8%) over the past decade[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Previous studies have identified atrial fibrillation (AF), cardiac functional status, and left atrial dimension as significant predictors of postoperative outcomes following mitral valve surgery [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAF represents a prevalent comorbidity in patients with mitral valve disease, with a preoperative incidence of approximately 40%[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Mechanistically, AF may influence cardiac conduction through multiple mechanisms, including the promotion of fibrotic changes, exacerbation of congestive heart failure, and induction of electrical and structural atrial remodeling[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Consequently, this shared pathophysiology may potentially induce bundle branch remodeling, thereby precipitating the development of left bundle branch block (LBBB). As an established independent mortality predictor in cardiovascular populations, LBBB represents a clinically significant conduction abnormality, particularly following transcatheter valve interventions. There is sufficient evidence to suggest that it primarily occurs through multiple pathophysiological mechanisms, including the induction of ventricular asynchrony, abnormal septal motion, microstructural remodeling of myocardial fibrosis, or mitral regurgitation. These factors contribute to left ventricular dysfunction, ultimately precipitating adverse cardiovascular events such as clinical heart failure, and permanent pacemaker implantation[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. When coexisting with AF, LBBB and AF exhibit synergistic detrimental effects, further significantly reducing left ventricular ejection fraction (LVEF) and worsening prognosis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. According to the literature, the incidence of LBBB after TAVR reaches 10%-22%[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which significantly increases the risk of all-cause mortality and cardiovascular mortality, severely affecting patient prognosis[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, the incidence and risk factors for LBBB following TMVR remain poorly characterized, and the electrophysiological impact of pre-existing AF on conduction system vulnerability requires further elucidation.\u003c/p\u003e\u003cp\u003eGiven the paucity of evidence regarding the impact of AF on the occurrence of new-onset left bundle branch block (NLBBB) following TMVR, we conducted this nationwide analysis utilizing the National Inpatient Sample (NIS) database to systematically investigate differences in demographic and clinical characteristics, incidence of NLBBB, and associated clinical outcomes between patients with AF and non-AF undergoing TMVR.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Source\u003c/h2\u003e\u003cp\u003eThis retrospective study utilized data from the NIS database spanning January 1, 2016, to December 31, 2022. As the largest publicly available all-payer inpatient database under the Healthcare Cost and Utilization Project (HCUP), the NIS encompasses approximately 97% of inpatient discharges from 46 U.S. states, with an annual sample size exceeding 7\u0026nbsp;million records representing a 20% stratified sample of U.S. community hospital discharges[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The NIS systematically captures clinical data such as patient demographics, diagnostic codes, comorbidities, surgical procedures, in-hospital complications, and healthcare costs, all coded using the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) and Procedure Coding System (ICD-10-PCS). Since the NIS database is publicly accessible and contains no personally identifiable information, it is exempt from requiring informed consent by Institutional Review Boards (IRBs).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eData from the NIS spanning the years 2016 to 2022 were utilized in this study. We included all hospitalizations involving TMVR for patients (aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years), who were identified using specific International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) procedure codes:02UG3JH, 02UG3JZ, 02QG3ZZ, and 02QG3ZE. Hospitalizations for AF were identified using ICD-10-CM codes I48.0, I48.1, I48.2, and I48.9. NLBBB during hospitalization was ascertained using ICD-10-CM codes I44.4, I44.5, and I44.7. Patients with missing baseline data or pre-existing LBBB before TMVR were excluded from the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For each included hospitalization, baseline demographic characteristics (e.g. age, sex), hospital features (e.g. bed size, teaching status), and clinically relevant comorbidities were extracted and analyzed. Comorbidities were assessed using the Elixhauser Comorbidity Index and corresponding ICD-10-CM codes (\u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eStudy outcome\u003c/h3\u003e\n\u003cp\u003eThe primary endpoint was the risk of NLBBB following TMVR patients with AF compared to non-AF patients.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eNational estimates of hospitalizations and patient numbers were calculated by applying NIS sampling weights to ensure representativeness[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Descriptive statistics summarized categorical variables as frequencies (%) and continuous variables as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Group comparisons employed weighted linear regression for continuous variables and weighted chi-square tests for categorical variables.\u003c/p\u003e\u003cp\u003eMultivariable regression analyses were conducted to evaluate the independent effect of AF on outcomes after adjusting for confounders. The regression model included covariates that demonstrated significant between-group differences in baseline characteristics or univariate analyses: age; sex; race; payment type; median household income based on the patient\u0026rsquo;s ZIP code; smoking status; dyslipidemia; prior stroke; prior myocardial infarction; prior percutaneous coronary intervention; prior coronary artery bypass grafting; pulmonary circulation disorders; peripheral vascular disorders; neurological disorders; chronic pulmonary disease; hypothyroidism; renal failure; liver disease; rheumatoid arthritis/collagen vascular diseases; coagulopathy; weight loss; obesity; diabetes; and hypertension. Results were expressed as unadjusted and adjusted odds ratios (aORs) with corresponding 95% confidence intervals (CIs).\u003c/p\u003e\u003cp\u003eTo enhance the robustness of our findings, we conducted a sensitivity analysis using IPTW by excluding patients with right bundle branch block (RBBB) or third-degree atrioventricular block (3\u0026deg;AVB). Subsequently, we stratified the AF population into paroxysmal and persistent AF subgroups to evaluate the differential impact on postoperative NLBBB. Stabilized inverse probability weights were calculated using predicted probabilities derived from the propensity score model[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Subgroup analyses were conducted to investigate the association between AF and NLBBB across various patient subgroups stratified by age; sex; chronic lung disease; renal disease; smoking status; hyperlipidemia; obesity; weight loss; and diabetes. For each subgroup analysis formal interaction tests were performed using likelihood ratio tests to compare models with and without interaction terms between AF stratification variables assessing whether associations varied significantly across subgroups (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05 considered significant).\u003c/p\u003e\u003cp\u003eAll analyses accounted for complex survey design incorporating survey weights stratification clustering variables[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Statistical analyses utilized R software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, The R Foundation) and Empower Stats (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.empowerstats.com\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, X\u0026amp;Y Solution, Inc., Boston, MA).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBaseline demographics of TMVR patients with AF or non-AF\u003c/h2\u003e\u003cp\u003eThe NIS database identified 1,754 patients who underwent TMVR between 2016 and 2022, comprising 1,150 (65.6%) with AF and 604 (34.4%) without AF. Notably, the proportion of AF patients undergoing TMVR demonstrated a significant temporal increase from 57.14% in 2016 to 66.0% in 2022 (P-trend\u0026lt;0.001)( \u003cb\u003eSupplemental Fig.\u0026nbsp;1\u003c/b\u003e). Comparative analysis revealed that AF patients were significantly older than non-AF patients (74.63\u0026thinsp;\u0026plusmn;\u0026thinsp;9.91 vs. 68.24\u0026thinsp;\u0026plusmn;\u0026thinsp;13.88s; P\u0026lt;0.001) and exhibited higher prevalence rates of peripheral vascular disease(P\u0026thinsp;=\u0026thinsp;0.004) and hypothyroidism (P\u0026thinsp;=\u0026thinsp;0.024) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Demographics in patients with TMVR between 2016 to 2022: stratified by AF\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo AF\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;604)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1150)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDemographics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.24\u0026thinsp;\u0026plusmn;\u0026thinsp;13.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.63\u0026thinsp;\u0026plusmn;\u0026thinsp;9.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e321 (53.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e644 (56.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.254\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e433 (71.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e925 (80.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82 (13.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94 (8.17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46 (7.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (4.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43 (7.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79 (6.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary Payer, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedicare/Medicaid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e437 (72.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e952 (82.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (8.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (4.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96 (15.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124 (10.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-pay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (3.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (2.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian Household Income Quartile, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.017*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirst (lowest)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 (23.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e241 (20.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e172 (28.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e270 (23.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThird\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e150 (24.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e341 (29.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFourth (highest)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138 (22.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e298 (25.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e220 (36.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e366 (31.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e361 (59.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e726 (63.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.168\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital Teaching Status, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.474\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-teaching\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (0.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (1.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeaching\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (5.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (4.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban teaching\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e567 (93.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1084 (94.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital Bed Size, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.893\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 (7.45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80 (6.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (15.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e184 (16.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLarge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e466 (77.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e886 (77.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedical History\u003c/b\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior stroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60 (9.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140 (12.17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior MI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73 (12.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109 (9.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior PCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (10.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94 (8.17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.177\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior CABG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153 (25.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e244 (21.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePulmonary circulation Disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e250 (41.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e518 (45.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeripheral vascular disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94 (15.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e245 (21.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic pulmonary disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e170 (28.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e323 (28.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.979\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothyroidism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116 (19.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e275 (23.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.024*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenal failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e303 (50.17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e586 (50.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.753\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56 (9.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (7.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRheumatoid arthritis/\u003c/p\u003e\u003cp\u003ecollagen vascular diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (3.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (4.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.471\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoagulopathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e143 (23.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e292 (25.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.429\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77 (12.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e174 (15.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (6.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103 (8.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e499 (82.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e955 (83.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e192 (31.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e316 (27.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eThe outcome of the risk of NLBBB in AF patients who underwent TMVR\u003c/h3\u003e\n\u003cp\u003eThe analysis revealed a significantly higher incidence of postoperative NLBBB in AF patients (4.61%) compared to non-AF patients (2.48%) within the overall TMVR cohort (P\u0026thinsp;=\u0026thinsp;0.028). The temporal patterns of postoperative LBBB incidence among TMVR patients over the seven-year study period are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Multivariable regression analysis, after adjustment for potential confounders, established AF as an independent predictor of NLBBB following TMVR (unadjusted OR 1.90, 95% CI 1.06\u0026ndash;3.39, P\u0026thinsp;=\u0026thinsp;0.031; model I aOR 2.24, 95% CI 1.18\u0026ndash;4.24, P\u0026thinsp;=\u0026thinsp;0.031; model II aOR 2.09, 95% CI 1.13\u0026ndash;3.84, P\u0026thinsp;=\u0026thinsp;0.018). Notably, after excluding patients with RBBB or t3\u0026deg;AVB, the AF group demonstrated a 2.21-fold increased risk of postoperative LBBB compared to non-AF patients (unadjusted OR 2.21, 95% CI 1.19\u0026ndash;4.08, P\u0026thinsp;=\u0026thinsp;0.012; model I aOR 2.24, 95% CI 1.18\u0026ndash;4.24, P\u0026thinsp;=\u0026thinsp;0.014; model II aOR 2.33, 95% CI 1.22\u0026ndash;4.43, P\u0026thinsp;=\u0026thinsp;0.010) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNew-Onset LBBB in patients undergoing TMVR with AF compared with non-AF: Multiple regression Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIn-hospital outcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;1150\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-AF\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;604\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-adjusted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAdjust-I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAdjust-II\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLBBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53(4.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15(2.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.90\u003c/p\u003e\u003cp\u003e(1.06, 3.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.031*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.24\u003c/p\u003e\u003cp\u003e(1.18,4.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.031*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003cp\u003e(1.13,3.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.018*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExcluding of RBBB \u0026amp;3\u0026deg; AVB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;1047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-AF\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-adjusted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAdjust-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAdjust-II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLBBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53(5.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13(2.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003cp\u003e(1.19, 4.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.012*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.24 (1.18,4.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.014*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.33\u003c/p\u003e\u003cp\u003e(1.22,4.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.010*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNon-adjusted model adjusts for: None;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eAdjust I model adjust for: age; female; race; primary payer; median household income quartile;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eAdjust II model adjust for: age; female; race; primary payer; median household income quartile; smoking; dyslipidemia; prior stroke; prior MI; prior PCI; prior CABG; pulmonary circulation disorders;peripheral vascular disorders༛neurological disorders; chronic pulmonary disease༛hypothyroidism༛renal failure༛liver disease༛rheumatoid arthritis/collagen vascular diseases༛coagulopathy༛weight loss༛obesity༛diabetes༛hypertension.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e*Indicates statistically significant differences at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eThe robustness of clinical outcome through sensitivity analyses\u003c/h3\u003e\n\u003cp\u003eUsing IPTW analysis, patients with AF revealed a significantly elevated risk of postoperative NLBBB compared to non-AF patients (ATT 1.99; 95%CI:1.07\u0026ndash;3.69; P\u0026thinsp;=\u0026thinsp;0.030; ATC 1.86; 95%CI:1.01\u0026ndash;3.44; P\u0026thinsp;=\u0026thinsp;0.046; ATE 1.91; 95%CI:1.01\u0026ndash;3.43; P\u0026thinsp;=\u0026thinsp;0.030). This association remained robust after excluding patients with pre-existing RBBB or 3\u0026deg;AVB (ATT 2.41;95%CI:1.20\u0026ndash;4.81; P\u0026thinsp;=\u0026thinsp;0.013; ATC 2.25; 95%CI:1.19\u0026ndash;4.27; P\u0026thinsp;=\u0026thinsp;0.013; ATE 2.35; 95%CI:1.23\u0026ndash;4.50; P\u0026thinsp;=\u0026thinsp;0.010) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOutcome of new-onset LBBB in patients undergoing TMVR with AF compared with non-AF: Inverse Probability of Treatment Weighting\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIn-hospital Outcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIPTW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAF Patients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAF Patients\u003c/p\u003e\u003cp\u003e(Excluding RBBB \u0026amp; 3\u0026deg; AV Block)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLBBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.99 (1.07, 3.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.41 (1.20, 4.81)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.030*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.013*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.86 (1.01, 3.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.25 (1.19, 4.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.046*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.013*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.91 (1.07, 3.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.35 (1.23, 4.50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.030*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.010*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eResults in table: ATT (95%CI) P-value; ATC (95%CI) P-value; ATE (95%CI) P-value;\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAF patients were stratified into paroxysmal AF (n\u0026thinsp;=\u0026thinsp;448) and persistent AF (n\u0026thinsp;=\u0026thinsp;379) subgroups, after excluding those with unspecified AF (ICD-10-CM code I48.91). The impact of different AF subtypes on postoperative NLBBB was reanalyzed using IPTW. The results revealed a significantly higher risk of NLBBB in the persistent AF group compared to the non-AF group (ATT 2.74; 95%CI: 1.23\u0026ndash;6.09; P\u0026thinsp;=\u0026thinsp;0.014; ATC 2.67; 95%CI: 1.15\u0026ndash;6.20; P\u0026thinsp;=\u0026thinsp;0.022; ATE 2.70; 95%CI: 1.23\u0026ndash;5.89; P\u0026thinsp;=\u0026thinsp;0.013). Although the risk of NLBBB was higher in the paroxysmal AF group compared to the non-AF group, no statistically significant difference was observed (ATT 1.84; 95%CI: 0.88\u0026ndash;3.82; P\u0026thinsp;=\u0026thinsp;0.100; ATC 1.76; 95%CI: 0.85\u0026ndash;3.65; P\u0026thinsp;=\u0026thinsp;0.131; ATE 1.79; 95% CI: 0.88\u0026ndash;3.64; P\u0026thinsp;=\u0026thinsp;0.108) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of different atrial fibrillation subtypes on LBBB: Inverse Probability of Treatment Weighting\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical outcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIPTW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eParoxysmal AF\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;448\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePersistent AF\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;379\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLBBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.84 (0.88, 3.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.74 (1.23, 6.09)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.014*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.76 (0.85, 3.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.67 (1.15, 6.20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.022*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.79 (0.88, 3.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.70 (1.23, 5.89)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.013*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eResults in table: OR (95%CI) P-value\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eThe outcomes of potential effect modification between AF and other clinical variables on NLBBB risk\u003c/h2\u003e\u003cp\u003eInteraction analyses using likelihood ratio tests revealed no statistically significant effect modification by age, sex, chronic lung disease, chronic kidney disease, smoking status, hyperlipidemia, obesity, weight loss, and diabetes mellitus (all P-interactions\u0026gt;0.05) on the AF-LBBB association (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e These findings indicate the consistent association between AF and NLBBB following TMVR across diverse patient subgroups, suggesting an effect independent of age, sex, or comorbid conditions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSubgroup analysis of AF patients on new-onset LBBB\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStratification Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubgroups\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOdds Ratio (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP for Interaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.96 (1.17, 7.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.022*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.19 (0.85, 5.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;60 Years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.22 (0.56, 18.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60 Years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.20 (0.49, 9.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChronic Lung Disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.517\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.46 (1.10, 5.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.028*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.27 (1.36, 7.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.008**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKidney Disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.846\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.98 (0.90, 4.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.36 (0.60, 3.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.40 (1.12, 5.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.024*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.60 (0.67, 3.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHyperlipidemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.83 (0.63, 5.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.20 (0.81, 5.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eObesity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.280\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.81 (0.95, 3.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.33 (1.01, 5.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.048*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWeight loss\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.760\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.12 (1.13, 3.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.019*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.71 (0.46, 6.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.49 (0.72, 3.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.56 (1.17, 5.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.018*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe association between AF and NLBBB following TMVR remains unexplored in the literature. We analyzed patients who underwent TMVR between 2016 and 2022, categorizing them into AF and non-AF groups to evaluate the risk of intraoperative NLBBB. The key findings are summarized below:1) In patients undergoing TMVR, the incidence of postoperative NLBBB was 4.61% in AF patients versus 2.48% in those without AF; 2) The risk of NLBBB in the AF group was 2.09-fold higher compared to the non-AF group, suggesting that AF serves as an independent predictor of NLBBB following the procedure; 3) Compared with paroxysmal AF, persistent AF has a more significant impact on the occurrence of NLBBB after TMVR, suggesting that distinct AF phenotypes may differentially influence the incidence of NLBBB post-intervention\u003c/p\u003e\u003cp\u003eAF represents a prevalent complication of mitral valve disease. Mitral valve disorders, particularly mitral regurgitation and stenosis, induce volume and pressure overload in the left atrium, promoting left atrial dilation and fibrosis. These structural alterations create both electrophysiological and anatomical substrates conducive to AF development. The findings of this study indicate that the proportion of patients with AF was significantly higher than non-AF patients among those undergoing TMVR(65.6% versus 34.4%), This innovative observation not only fills a critical gap in the current literature but also provides clinicians with valuable data references and practical guidance for managing TMVR patients. LBBB, due to its anatomical predisposition, is a frequent conduction disorder following TAVR. This conduction disturbance can further exacerbate pre-existing cardiac conditions through multiple ventricular-level electrophysiological remodeling processes and mechanical dyssynchrony, while also inducing progressive ventricular remodeling in structurally normal hearts. These pathological changes ultimately lead to cardiac dysfunction, culminating in decompensation and cardiomyopathy development[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. To our knowledge, the probability of postoperative LBBB occurrence in patients undergoing TMVR and the specific clinical impact of AF on postoperative LBBB have not been reported in the literature with definitive conclusions. Utilizing the NIS database, we conducted a comprehensive retrospective analysis of all patients undergoing TMVR between 2016 and 2022. The cohort was systematically stratified into AF and non-AF groups to investigate the association with postoperative NLBBB. Our findings revealed that AF is significantly associated with an increased risk of NLBBB following TMVR compared to non-AF patients. Notably, persistent AF exhibited a more pronounced effect on postoperative NLBBB development than paroxysmal AF, indicating differential impacts of AF phenotypes on conduction disturbances. These findings provide new clinical data regarding the occurrence of conduction disorders after TMVR and their associated risk factors and potential clinical outcomes.\u003c/p\u003e\u003cp\u003ePrevious studies report that LBBB typically presents at a mean age of 70\u0026thinsp;\u0026plusmn;\u0026thinsp;10 years, with prevalence increasing with age[\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In our cohort, TMVR with AF patients were older (mean age: 74.63\u0026thinsp;\u0026plusmn;\u0026thinsp;9.91 years), which may partially explain the higher risk of postoperative LBBB in this population. AF induces structural and electrophysiological remodeling in atrial and sinoatrial nodal tissues. Experimental and clinical evidence further demonstrates that sustained AF promotes sinus node remodeling[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The characteristic rapid atrial rate in AF increases susceptibility to ventricular arrhythmias, while the atrioventricular node serves a critical protective role by regulating ventricular impulse conduction. However, persistent high-frequency atrial depolarization may induce electrophysiological remodeling of the AV node itself[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Similar pathophysiological mechanisms could potentially contribute to bundle branch remodeling in AF[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The evolution of transcatheter valve replacement techniques has highlighted the contribution of anatomical characteristics (e.g. annular calcification, implantation depth) to postoperative conduction abnormalities[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Comprehensive anatomical assessment before TMVR is essential, including evaluation of atrial dimensions[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], left ventricular outflow tract obstruction (LVOTO), and valve displacement risk. The eft ventricular outflow tract (LVOT), located at the basal interventricular septum, lies in close anatomical proximity to the left bundle branch (LBB). Post-implantation, the anterior mitral leaflet is displaced, forming a \"neo-LVOT.\" A reduced neo-LVOT area may precipitate acute LVOTO, manifesting as immediate hemodynamic instability post-deployment, with intraprocedural echocardiography revealing valve malposition or anterior leaflet-induced obstruction[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The irregular cardiac rhythm and structural atrial remodeling in AF may increase procedural complexity, potentially exacerbating LVOTO and valve displacement risks, which may subsequently result in conduction system injury and bundle branch block.\u003c/p\u003e\u003cp\u003eIn contrast to paroxysmal AF, persistent AF induces progressive structural remodeling (including fibrotic deposition and ion channel dysregulation) that results in irreversible pathological alterations[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Electrophysiological mapping studies demonstrate that patients with persistent AF present with tripled atrial dilatation magnitude and a 40% reduction in conduction velocity compared to paroxysmal AF cases[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This may partially explain why persistent AF demonstrates a more pronounced association with new-onset LBBB following TMVR. Our retrospective study reveals a correlation without establishing causation. Further research is necessary to better understand the underlying mechanisms and nature of the relationship among the three variables.\u003c/p\u003e\u003cp\u003eOur study offers several methodological strengths, employing a comprehensive sensitivity analysis through IPTW, and stratified analysis to repeatedly validate the stability of our research findings, thereby enhancing the result's reliability. By leveraging the NIS database, we analyzed a nationally representative cohort of 1,754 TMVR patients from 2016 to 2022, which ensures the generalizability of our data. The key innovations of our research include: (1) This is the first study to investigate the differential impact of AF on the risk of LBBB following TMVR, providing additional clinical evidence regarding the influence of AF on postoperative LBBB occurrence and offering important insights into the factors contributing to conduction abnormalities after TMVR. (2) confirming differential effects of AF subtypes on postoperative LBBB development, with persistent AF showing a more significant influence on LBBB occurrence compared to paroxysmal AF. However, the potential pathophysiological mechanisms underlying the increased LBBB risk in AF patients undergoing TMVR remain incompletely understood. Future research directions should focus on prospectively assessing the impact of AF on postoperative outcomes in TMVR patients experiencing LBBB and exploring the potential causal relationship between AF and postoperative LBBB development.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eSeveral limitations should be acknowledged. First, certain clinically relevant variables were unavailable, including echocardiographic data (to assess structural differences), LBBB duration, electrocardiographic confirmation, TMVR access route, intraprocedural hemodynamics, valve type, and coexisting complications. The absence of these data may limit a comprehensive evaluation of patient outcomes. Second, as the study relied on ICD-10 coding within the NIS database, potential misclassification or incomplete documentation could affect accuracy. Although NIS data quality has been extensively validated, this inherent limitation may introduce bias. Finally, the study spanned a period of evolving TMVR techniques (2016\u0026ndash;2022), during which procedural refinements, valve technologies, and patient selection criteria underwent significant advancements. Consequently, early practices may differ from current standards, potentially limiting the direct applicability of our findings to contemporary clinical settings.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study indicates that patients with AF, particularly those with persistent AF, exhibited a significantly higher risk of developing NLBBB following TMVR. The formation of NLBBB can lead to further deterioration of cardiac function affecting the patient's prognosis. These findings suggest that personalized management strategies should be implemented for AF patients undergoing TMVR to mitigate the risk of postoperative conduction abnormalities. Additionally, this study highlights the imperative for further exploration into the underlying mechanisms contributing to these observed disparities, thereby providing significant guidance for future research endeavors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAtrial Fibrillation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLBBB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLeft bundle branch block\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLBBB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLeft bundle branch\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLVOTO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eleft ventricular outflow tract obstruction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLVOTO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eleft ventricular outflow tract\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNLBBB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNew-onset Left bundle branch block\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNIS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Inpatient Sample\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICD-10-CM/PCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThe International Classification of Diseases, Tenth Revision Clinical\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIPTW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInverse Probability of Treatment Weighting\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eodds ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eaOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eadjusted odds ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRBBB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRight bundle branch block\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTMVR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTranscatheter Mitral Valve Replacement\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e3\u0026deg;AVB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThird-degree Atrioventricular block\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe data for this study were derived from the National Inpatient Sample (NIS) database, a publicly accessible healthcare resource that contains no personally identifiable information. Under the Health Insurance Portability and Accountability Act (HIPAA) regulations, our institutional review board determined that patient consent was not required for this analysis.\u003c/p\u003e\n\u003ch2\u003eConflicts of interest:\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Central government guided local science and technology project (Project No. 2022FRD05046, 2024FRD05139), Research Project of Ningxia Medical University (Project No. XM2023038), the Natural Science Foundation of Ningxia Province (Project No. 2023AAC02071), and the National Natural Science Foundation of China (grand number 82260086).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eQ. Ch: Formal analysis and Writing \u0026ndash; original draft.Xp. M, Shb.J, and Gzh. C: Funding acquisition, Resources and Supervision.Shzh. F, B. Sh, and Cy. Y: Formal analysis and Software.Y. C., Y.G, L. H, T. L, and R. Y: Project administration and Investigation.All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments:\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe data for this research was derived from the National Inpatient Sample (NIS) of the Healthcare Cost and Utilization Project, which can be accessed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.hcup-us.ahrq.gov/nisoverview.jsp\u003c/span\u003e\u003c/span\u003e for usage agreements and associated fees. With authorization from the U.S. Department of Health and Human Services, the dataset is also available upon reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNkomo VT, Gardin JM, Skelton TN, Gottdiener JS, Scott CG, Enriquez-Sarano M. Burden of valvular heart diseases: a population-based study. Lancet (London England). 2006;368(9540):1005\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/s0140-6736(06)69208-8\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(06)69208-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStone GW, Lindenfeld J, Abraham WT, Kar S, Lim DS, Mishell JM, Whisenant B, Grayburn PA, Rinaldi M, Kapadia SR, et al. Transcatheter mitral-valve repair in patients with heart failure. 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N Engl J Med. 2015;372(19):1812\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1056/NEJMoa1408288\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa1408288\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Transcatheter Mitral Valve Replacement, New-Onset Left Bundle Branch Block, Atrial Fibrillation, the National Inpatient Sample","lastPublishedDoi":"10.21203/rs.3.rs-7380737/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7380737/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eTranscatheter mitral valve replacement (TMVR) offers an alternative for high-risk patients with mitral valve disease. Atrial fibrillation (AF) frequently coexists with mitral valve disease, while new-onset left bundle branch block (NLBBB) following transcatheter valve interventions may adversely affect cardiac function and necessitate permanent pacemaker implantation. However, the association between pre-existing AF and NLBBB following TMVR remains inadequately characterized.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective study analyzed TMVR hospitalizations with or without AF using National Inpatient Sample data from 2016 to 2022. The primary outcome was the risk of NLBBB following TMVR. The association between AF and NLBBB risk was evaluated using multivariable logistic regression, adjusting for sociodemographic characteristics and comorbidities. Sensitivity analyses were performed using inverse probability of treatment weighting (IPTW) and interaction test to validate the robustness of our findings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 1,754 patients undergoing TMVR, 65.6% (n\u0026thinsp;=\u0026thinsp;1,150) had documented AF. AF patients were significantly older (74.63\u0026thinsp;\u0026plusmn;\u0026thinsp;9.91 vs. 68.24\u0026thinsp;\u0026plusmn;\u0026thinsp;13.88 years; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and exhibited higher prevalence of peripheral vascular disease (P\u0026thinsp;=\u0026thinsp;0.004) and hypothyroidism (P\u0026thinsp;=\u0026thinsp;0.024). The incidence of NLBBB was 4.61% in the AF cohort. Multivariable analysis demonstrated that AF was independently associated with increased NLBBB risk (aOR 2.09; 95% CI 1.13\u0026ndash;3.84; P\u0026thinsp;=\u0026thinsp;0.018), the finding that remained consistent after IPTW analysis (ATT 1.90; 95% CI 1.07\u0026ndash;3.69; P\u0026thinsp;=\u0026thinsp;0.030). Further stratification revealed that persistent AF significantly correlated with NLBBB development (ATT 2.74; 95% CI 1.23\u0026ndash;6.09; P\u0026thinsp;=\u0026thinsp;0.014), whereas paroxysmal AF did not show statistical significance (ATT 1.84; 95% CI 0.88\u0026ndash;3.82; P\u0026thinsp;=\u0026thinsp;0.100).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003ePatients with AF, particularly persistent AF, demonstrated significantly higher risk of NLBBB following TMVR.\u003c/p\u003e","manuscriptTitle":"Association between Atrial Fibrillation and the risk of New-Onset Left bundle branch block following Transcatheter Mitral Valve Replacement: a retrospective analysis of the National Inpatient Sample","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 07:11:43","doi":"10.21203/rs.3.rs-7380737/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f4b66cc-7eac-4c94-8338-a01eba385df3","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-05T12:40:25+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T12:56:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 07:11:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7380737","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7380737","identity":"rs-7380737","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}