Comparison of in-hospital complication rates after transcatheter aortic valve replacement in patients with bicuspid versus tricuspid aortic valves: a retrospective cohort study

Comparison of in-hospital complication rates after transcatheter aortic valve replacement in patients with bicuspid versus tricuspid aortic valves: a retrospective cohort study | 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 Article Comparison of in-hospital complication rates after transcatheter aortic valve replacement in patients with bicuspid versus tricuspid aortic valves: a retrospective cohort study Tingxi Zhu, Jiawei Luo, Xuan Huang, Lulu Liu, Kehan Li, Wei He, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4793214/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 aortic valve replacement (TAVR) has become a popular alternative to surgical aortic valve replacement (SAVR) for patients with valvular heart disease (VHD), particularly for those with aortic anomalies. Objectives: The study aimed to compare the risks of post-TAVR complications between patients with bicuspid and tricuspid aortic valves and to identify associated risk factors. Methods: The association between complications and valve type (bicuspid or tricuspid) was assessed. The study also explored various combinations of factors to understand their impact on complications. Separate analyses were conducted to identify specific risk factors for each complication. Results: Out of the 1154 eligible patients, 508 had bicuspid aortic valves (BAV) and 646 had tricuspid aortic valves (TAV). The study identified 40 cases of acute kidney injury, 134 instances of new-onset permanent pacemaker implantations, 129 occurrences of paravalvular leak, and 30 device failures. The comprehensive logistic regression revealed no statistically significant association between complications and valve type (ORs: 0.52 (95%CI, 0.23–1.09) for acute kidney injury, 1.29 (95%CI, 0.87–1.92) for permanent pacemaker implantation, 1.29 (95%CI, 0.82–2.02) for paravalvular leak, and 0.54 (95%CI, 0.22–1.23) for device failure). Total bilirubin (TBIL), albumin (ALB), age, and New York Heart Association (NYHA) scores, among other factors, were associated with specific post-TAVR complications. Conclusions: The incidence of acute kidney injury, new permanent pacemaker implantations, paravalvular leaks, and device failures did not differ significantly between patients with BAV and TAV following TAVR. Specific risk factors for these complications were identified, highlighting the importance of careful clinical monitoring in post-TAVR management. Health sciences/Risk factors Health sciences/Medical research/Epidemiology Health sciences/Diseases/Cardiovascular diseases/Valvular disease Transcatheter aortic valve replacement (TAVR) Bicuspid aortic valves (BAV) Tricuspid aortic valves (TAV) Complications Cohort study Figures Figure 1 Figure 2 Introduction Aortic stenosis (AS) and aortic regurgitation (AR) are most common cause of valvular heart disease (VHD) in the Western world. These conditions has affected more than 41 million people, leading to significant premature mortality since 2000 and reducing quality of life [ 1 – 3 ] . Historically, the primary treatment for VHD was the high-risk open-heart procedure known as surgical aortic valve replacement (SAVR) [ 4 , 5 ] , However, transcatheter aortic valve replacement (TAVR) has recently emerged as a viable interventional treatment modality. It is now a well-established therapy, particularly for inoperable and high-risk patients with clinical VHD [ 5 – 8 ] . In the general population, the aortic valve typically comprises three nearly identical, semilunar-shaped aortic cusps, termed the tricuspid aortic valve (TAV). In contrast, some individuals possess only two leaflets of unequal size, referred to as a bicuspid aortic valve (BAV) [ 9 ] . BAV is the most prevalent congenital heart valve anomaly, present in approximately 0.5–2% of the global population [ 10 , 11 ] . Notably, in our data, the ratio of BAV to TAV is approximately 0.79, indicating a significant proportion of BAV cases. The distinct valve anatomy in BAV patients can lead to an "annulus-valve" mismatch, complicating the selection of an appropriate TAVR system size for physicians. Additionally, certain BAV patients may exhibit associated aortic abnormalities, such as a dilated ascending aorta, potentially resulting in macrovascular complications [ 9 ] . Although some randomized controlled trials (RCTs) have investigated the post-TAVR complications in BAV patients, the findings have been inconsistent [ 12 – 14 ] . Over recent years, numerous researchers have advocated for the broader adoption of TAVR in BAV patients. However, given the novelty of TAVR and its associated high costs, many relevant studies have been constrained by small sample sizes. Based on an early-stage literature review by our team, while the number of studies on TAVR in BAV patients has been on the rise from 2014 to 2022, over 60% of these investigations included fewer than 100 BAV patients, and 85% had fewer than 500 BAV participants ( Sup Table S1 ). West China Hospital, the preeminent general hospital in southwest China, was among the pioneers in introducing TAVR procedures in the country. Its comprehensive electronic medical record (EMR) system, encompassing both the Hospital Information System (HIS) and the Laboratory Information System (LIS), has chronicled medical histories of over 7 million patients annually since 2000. This wealth of data, which includes detailed valve information, sociodemographic profiles, socioeconomic statuses, and a substantial record of TAVR cases, offers a unique research opportunity. In this study, our objective is to evaluate the post-TAVR complication risks in VHD patients with BAV in comparison to those with TAV. We also delve into the specific risk factors associated with each complication. Materials and methods All methods were performed in accordance with the relevant guidelines and regulations. Study population The HIS and LIS recorded data on 2393 middle-aged patients who underwent Transcatheter Aortic Valve Replacement (TAVR) at West China Hospital from 2010 to 2022. The hospital's EMR system automatically logged patient information during their stay, including sociodemographic details such as age and sex, health-related metrics like Body Mass Index (BMI) and New York Heart Association (NYHA) score, laboratory indices including white blood cell count (WBC) and direct bilirubin (DBIL), as well as pre-existing conditions like hypertension and diabetes. Patients who lacked a definitive aortic valve type or were missing more than 75% of their laboratory tests were excluded from the study. Ultimately, 1154 patients were included in the research. The detailed patient selection process is shown in Fig. 1 . Valve types Valve data was extracted from medical records, discharge summaries, in-hospital examination results, and surgical reports. Cases with discrepancies across different records underwent further verification for study eligibility. Complications This study monitored four complications: acute kidney injury (AKI), new-onset permanent pacemaker implantation, paravalvular leak, and device failure. TAVR complications were defined according to the Valvular Academic Research Consortium-3 (VARC-3) guidelines. Acute kidney injury was defined as increase in serum creatinine ≥ 150–200% within 7 days compared with baseline or increase of ≥ 0.3mg/dL (≥ 26.4 µmol/L) within 48 hour of the index procedure. Permanent pacemaker implantation (PPI) was defined as a new permanent pacemaker implanted during hospitalization after TAVR; paravalvular leak was defined as any degree of paravalvular leak that occurs during hospitalization; device failure was defined as patients undertook an unsuccessfully TAVR with intraoperative conversion to open-heart surgery or in-hospital death. Covariates Data on sociodemographic characteristics such as age and sex, health-related factors like BMI and NYHA score, laboratory indices (including WBC, DBIL, among others), and underlying diseases (specifically hypertension and diabetes) were extracted from the EMR. BMI was determined using height (in centimeters) and weight (in kilograms) recorded at admission. NYHA scores of III or IV were categorized as high-level. In our main analysis, all factors, with the exception of valve type (BAV vs TAV) that might influence the incidence of complications, were treated as potential confounders, as suggested by prior literature. The covariates were classified into three categories: (1) Sociodemographic features, encompassing age, sex, and BMI. (2) Cardiac health indicators, such as hypertension, NYHA score, troponin, and myoglobin. (3) General health metrics of other organs, which included measures like hemoglobin, platelet count (PC), red blood cell (RBC) count, white blood cell (WBC) count, total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), Aspartate aminotransferase/alanine aminotransferase ratio (ASL/ALT ratio), albumin (ALB), glomerular filtration rate (GFR), creatine kinase-myoglobin binding (CK-MB), cholesterol, triglyceride, international normalized ratio (INR), prothrombin time (PT), and medical histories pertaining to hypertension and diabetes. In addition, based on the results of our main analysis, we pooled all the cases together, regardless of their valve type, to test the risk factors associated with the four complications mentioned above. Missing data Covariates with a missing rate exceeding 25% were considered inappropriate for imputation and were thus excluded from the study. We utilized the MICE package in R for multiple imputation, a popular method for addressing data gaps due to its ability to provide unbiased and consistent estimates of missing values [ 15 ] . After the imputation process, we observed no significant changes in the distributional characteristics (Refer to Sup Table S3 ). Statistical analysis The incidence rate of complications was determined using a 95% confidence interval (CI). A multivariable logistic model was deployed to evaluate the influence of various risk factors on the four observed complications. For our main analysis, we delved into the association between valve type and the aforementioned complications. We began with a basic model, centered solely around the valve type. Subsequently, the model was systematically expanded by introducing different groups of covariates: sociodemographic attributes, heart health metrics, and health indicators of other organs. Utilizing hierarchical multiple regression, we thoroughly assessed the effects of integrating these covariate groups in diverse sequences. Every possible sequence of introducing these covariate groups was meticulously explored. With the inclusion of each new group, we conducted Analysis of Variance (ANOVA) tests to discern statistical variations before and after their addition. Through this methodical approach, we derived six unique nested models, encompassing our foundational statistical model. In the additional analysis, building on the outcomes from the main analysis, we constructed separate multivariable logistic regression models for each complication. This allowed us to discern the specific risk factors for each complication. Statistical significance was pegged at a two-tailed P -value of less than 0.05. All computations and analyses were executed using R software, version 3.6.1. Results Baseline characteristics A total of 1239 persons were excluded according to the exclusion criteria, leaving 1154 eligible patients for further analysis, among which, 508 patients with diagnosis of BAV and 646 with TAV. The male-to-female ratio was approximately 1.51, and median age was 71.90 years. Compared with TAV patients, we found that, the proportion or means were statistical lower in BAV patients: Age (70.26 vs. 72.12), NYHA high level (81.7% vs. 92.6%), and higher in BAV patients: GFR (73.58 vs. 69.86), female proportion (43.5% vs. 36.8%), ALB (42.13vs. 40.98), Triglyceride (1.37 vs. 1.27), CK-MB (2.47 vs. 2.19). We also found that BAV patients had a higher proportion of diabetes (20.9% vs. 15.3%) and lower proportion of hypertension (41.9% vs. 58.7%) (Table 1 ). Table 1 Baseline characteristics and clinical parameters of BAV and TAV patients. Patients with BAV Patients with TAV P value All patients, n 508 646 Female, n (%) 221 (43.5) 238 (36.8) 0.025 Age, mean (SD) 70.26 (9.11) 72.12 (7.96) < 0.001 BMI, mean (SD) 22.74 (7.47) 22.65 (7.66) 0.838 NYHA high level, n (%) 415 (81.7) 598 (92.6) < 0.001 ASL/ALT, mean (SD) 1.36 (0.58) 1.44 (1.11) 0.142 Albumin (ALB), g/L mean (SD) 42.13 (4.34) 40.98 (4.27) < 0.001 white blood cell (WBC) count, HP mean (SD) 6.15 (1.75) 6.32 (2.06) 0.125 Cholesterol, mmol/L mean (SD) 4.20 (1.04) 4.11 (1.08) 0.15 Triglyceride, mmol/L mean (SD) 1.37 (0.69) 1.27 (0.68) 0.015 International Normalized Ratio (INR), mean (SD) 1.06 (0.29) 1.07 (0.31) 0.583 Red blood cell (RBC) count, 10^12/L mean (SD) 4.34 (0.69) 4.32 (0.65) 0.704 Troponin, ng/L mean (SD) 43.33 (115.35) 37.57 (108.32) 0.384 Creatinine, µmoI/L mean (SD) 87.13 (33.02) 99.41 (78.01) 0.001 Myoglobin, ng/ml mean (SD) 44.47 (41.40) 51.16 (67.74) 0.051 Creatine kinase MB Form (CK-MB), ng/ml mean (SD) 2.47 (2.42) 2.19 (1.26) 0.012 Total bilirubin (TBIL), mg/dl mean (SD) 15.25 (18.03) 14.35 (8.52) 0.262 Direct bilirubin (DBIL), mg/dl mean (SD) 5.73 (14.16) 5.24 (5.20) 0.417 Indirect bilirubin (IBIL), mg/dl mean (SD) 9.53 (5.70) 9.12 (4.65) 0.175 Prothrombin time (PT), mean (SD) 11.89 (3.22) 12.00 (3.36) 0.554 Glomerular Filtration Rate (GFR), mean (SD) 73.58 (19.99) 69.86 (20.43) 0.002 Hemoglobin, mean (SD) 130.21 (19.76) 129.17 (18.17) 0.352 Platelet count (PC), seconds mean (SD) 158.10 (55.73) 164.35 (61.23) 0.074 History of hypertension, n (%) 213 (41.9) 379 (58.7) < 0.001 History of diabetes, n (%) 106 (20.9) 99 (15.3) 0.018 Incidence of complications Of all patients, 40 cases of acute kidney injury (11 in BAV and 29 in TAV, OR 0.52 [0.23, 1.09]), 134 cases of new-onset permanent pacemaker implantations (67 in BAV and 67 in TAV, OR 1.29 [0.87, 1.92]), 129 cases of paravalvular leak (43 in BAV and 86 in TAV, OR 1.29 [0.82, 2.02]), 30 cases of device failure (10 in BAV and 20 in TAV, OR 0.54 [0.22, 1.23]) were recorded (Table 2 ). Of these cute kidney injury cases, 24 were stage 1 (8 in BAV and 16 in TAV), 10 were stage 2 (2 in BAV and 8 in TAV), and 6 were stage 3 (1 in BAV and 5 in TAV). Table 2 Incidence of post-TAVR complications in BAV and TAV patients by valve type. The OR values presented in the table have been adjusted for other factors using logistic regression. Complications No. of cases in BAV No. of case in TAV OR [95% CI] Acute kidney injury 11/508 29/646 0.52 [0.23, 1.09] Permanent pacemaker implantation 67/508 67/646 1.29 [0.87, 1.92] Paravalvular leak 43/508 86/646 1.29 [0.82, 2.02] Device failure 10/508 20/646 0.54 [0.22, 1.23] Main analysis Valve type and complication incidence Among all patients, in the crude model, the odds ratio (OR) of acute kidney injury were 51% lower in BAV patients compared with TAV patients (OR, 0.49; 95%CI, 0.23–0.96; p = 0.045). Other three complications showed no significant association with valve type. Specifically, the OR was 1.31 (95%CI, 0.91–1.88; p = 0.139) for permanent pacemaker implantation, 1.13 (95%CI, 0.74–1.70; p = 0.564) for paravalvular leak, and 0.63 (95%CI, 0.28–1.33; p = 0.236) for device failure. After progressively adding sociodemographic characteristics, health status of heart characteristics and health status of other organs as potential confounders, the full multiple logistic regression showed that all complications, including acute kidney injury, new-onset permanent pacemaker implantation, paravalvular leak and device failure, showed no statistically significant association with valve type. Specifically, the OR was 0.52 (95%CI, 0.23–1.09) for acute kidney injury, 1.29 (95%CI, 0.87–1.92) for permanent pacemaker implantation, 1.29 (95%CI, 0.82–2.02) for paravalvular leak, and 0.54 (95%CI, 0.22–1.23) for device failure (Fig. 2 ). Hierarchical Multiple Regression Models Analysis from the hierarchical multiple regression models suggests a potential significant correlation between the incidence of acute kidney injury and the health status of organs other than the heart, as half of the examined scenarios (three out of six) demonstrated significance. Interestingly, the need for a permanent pacemaker implantation was statistically linked to both sociodemographic characteristics and the health status of other organs. Furthermore, the health condition of the heart appeared to influence the occurrence of paravalvular leak, with significance shown in three out of six scenarios. Importantly, only the sociodemographic variables displayed a clear correlation with device failures ( Sup Table S2 ). Additional analysis: Risk factors for complications Given that the valve type did not exhibit a statistically significant relationship with complications, it was omitted from the full model to further delve into specific risk factors for each complication. The subsequent results are detailed below: Acute kidney injury: Both TBIL and ALB significantly influenced the incidence of AKI. A unit increase in TBIL was associated with a 2% increase in the odds of AKI (OR: 1.02; 95%CI: 1.00-1.04), whereas a unit increase in ALB was linked to a 12% decrease in its odds (OR: 0.88; 95%CI: 0.80–0.95). Permanent pacemaker implementation: Significant associations were observed with age, NYHA scores, TBIL, and ALB. For each unit increase in age and ALB, the odds of requiring a permanent pacemaker rose by 5% (Age - OR: 1.05; 95%CI: 1.02–1.08; ALB - OR: 1.05; 95%CI: 1.00-1.11). Conversely, every unit increase in TBIL led to a 5% decrease in these odds (OR: 0.95; 95%CI: 0.92–0.98). Additionally, patients with high NYHA scores had 54% lower odds of requiring a permanent pacemaker compared to those with low scores (OR: 0.46; 95%CI: 0.28–0.77). Paravalvular leak: A significant correlation was found with PC. Specifically, for each unit increase in PC, the odds of a paravalvular leak reduced nearly by 1% (OR: 0.99; 95%CI: 0.99-1.00). Device failure: Age emerged as a pivotal factor. Each unit increase in age led to a 6% decrease in the odds of device failure (OR: 0.94; 95%CI: 0.91–0.98) (Table 3 ). Table 3 Associations between clinical parameters and post-TAVR complications: odds ratios and 95% confidence intervals. Acute kidney injury Permanent pacemaker implantation Paravalvular leak Device failure Coefficients P value Coefficients P value Coefficients P value Coefficients P value BMI, mean (SD) 1.04 [0.99, 1.09] 0.125 0.99 [0.96, 1.02] 0.677 1.00 [0.97, 1.03] 0.795 0.99 [0.94, 1.05] 0.849 Female, n (%) 1.20 [0.53, 2.63] 0.657 1.18 [0.75, 1.83] 0.477 0.71 [0.43, 1.17] 0.182 1.42 [0.55, 3.62] 0.464 Age, mean (SD) 1.04 [0.99, 1.09] 0.147 1.05 [1.02, 1.08] 0.002 1.02 [0.98, 1.05] 0.348 0.94 [0.91, 0.98] 0.003 NYHA high level, n (%) 2.01 [0.58, 12.72] 0.349 0.46 [0.28, 0.77] 0.003 2.37 [0.96, 7.20] 0.088 0.70 [0.27, 2.19] 0.496 ASL/ALT, mean (SD) 1.05 [0.70, 1.28] 0.718 1.03 [0.79, 1.22] 0.775 1.17 [0.82, 1.66] 0.374 0.80 [0.34, 1.20] 0.564 Albumin (ALB), g/L mean (SD) 0.88 [0.80, 0.95] 0.002 1.05 [1.00, 1.11] 0.037 1.01 [0.96, 1.07] 0.725 0.99 [0.89, 1.09] 0.837 white blood cell (WBC) count, HP mean (SD) 1.02 [0.85, 1.20] 0.856 0.95 [0.84, 1.07] 0.406 1.05 [0.93, 1.17] 0.422 1.17 [0.94, 1.42] 0.143 Cholesterol, mmol/L mean (SD) 1.17 [0.80, 1.67] 0.396 0.97 [0.79, 1.19] 0.781 0.97 [0.77, 1.21] 0.807 0.87 [0.55, 1.31] 0.513 Triglyceride, mmol/L mean (SD) 0.77 [0.39, 1.36] 0.425 1.01 [0.74, 1.33] 0.953 1.01 [0.69, 1.41] 0.97 0.84 [0.40, 1.53] 0.616 International Normalized Ratio (INR), mean (SD) 0.77 [0.02, 266.22] 0.915 0.57 [0.03, 17.73] 0.71 1.83 [0.09, 90.29] 0.722 0.01 [0.00, 0.42] 0.036 Red blood cell (RBC) count, 10^12/L mean (SD) 0.56 [0.20, 1.28] 0.214 1.03 [0.63, 1.61] 0.896 1.22 [0.77, 1.90] 0.384 0.38 [0.09, 1.23] 0.159 Troponin, ng/L mean (SD) 1.00 [1.00, 1.00] 0.477 1.00 [1.00, 1.00] 0.935 1.00 [1.00, 1.01] 0.064 0.99 [0.98, 1.00] 0.338 Myoglobin, ng/ml mean (SD) 1.00 [0.99, 1.00] 0.42 1.00 [1.00, 1.01] 0.199 0.99 [0.99, 1.00] 0.102 1.00 [0.99, 1.01] 0.928 Creatine kinase MB Form (CK-MB), ng/ml mean (SD) 1.00 [0.84, 1.19] 0.993 1.03 [0.91, 1.14] 0.651 1.08 [0.92, 1.26] 0.313 1.18 [0.88, 1.50] 0.197 Prothrombin time (PT), mean (SD) 1.03 [0.61, 1.43] 0.89 1.16 [0.85, 1.49] 0.278 0.96 [0.66, 1.26] 0.774 1.49 [1.08, 2.47] 0.032 Glomerular Filtration Rate (GFR), mean (SD) 1.01 [0.99, 1.03] 0.435 1.00 [0.99, 1.01] 0.976 1.01 [1.00, 1.02] 0.101 0.99 [0.97, 1.02] 0.548 Hemoglobin, mean (SD) 1.01 [0.98, 1.04] 0.667 1.00 [0.98, 1.01] 0.686 0.98 [0.97, 1.00] 0.066 1.04 [0.99, 1.09] 0.133 Platelet count (PC), seconds mean (SD) 1.00 [0.99, 1.01] 0.945 1.00 [1.00, 1.00] 0.907 0.99 [0.99, 1.00] 0.002 1.00 [0.99, 1.00] 0.29 Total bilirubin (TBIL), mg/dl mean (SD) 1.02 [1.00, 1.04] 0.034 0.95 [0.92, 0.98] 0.004 1.00 [0.98, 1.01] 0.785 1.00 [0.96, 1.01] 0.965 History of hypertension, n (%) 0.95 [0.47, 1.95] 0.894 1.06 [0.71, 1.58] 0.777 0.77 [0.51, 1.17] 0.222 1.87 [0.81, 4.56] 0.15 History of diabetes, n (%) 1.62 [0.69, 3.53] 0.247 1.29 [0.79, 2.04] 0.291 1.09 [0.59, 1.93] 0.779 0.56 [0.13, 1.71] 0.368 Discussion BAV is a low prevalence congenital disease with a prevalence of approximately 1% of the population, of which 1/3 − 1/4 are female [ 16 ] . Due to the high medical costs (about 300,000 RMB), TAVR is a relatively rare option for valvular heart disease patients in China compared to SAVR. To the best of our knowledge, this is the first time a study has been conducted in China on a large TAVR population cohort, specifically in patients with BAV. The results of this study are of great clinical importance for physicians to understand the prognosis of TAVR in patients with BAV and the risk factors influencing the complications of TAVR. In this large-scale of TAVR- undergone population, we found that patients with BAV diagnosis were not at increased risk of complications. Based on the anatomical structure, in contrast to the TAV, the BAV has an asymmetrical leaflet shape and an oval sinus orifice, which may obstruct blood flow through the valve opening, resulting in increased flow velocity and eddy flow, and ultimately, valve thickening and asymmetric calcification [ 17 ] . In the past, some research found that BAV is prone to accelerated aortic valve calcification due to both genetic mechanisms and anatomical causes, therefore patients with BAV should undergo earlier than TAV [ 18 , 19 ] . Other studies have pointed out that the outcome of TAVR surgery in BAV patients is influenced by the overall calcium burden and the presence of calcified raphe, which can prevent optimal device expansion [ 20 ] . For example, Mentias found that BAV patients were more likely to place pacemakers than TAV patients (12.2% vs. 7.6%) [ 21 ] . In fact, BAV patients have long been considered unsuitable for TAVR and have often been excluded from randomized clinical trials [ 22 ] . In this study, within all complications, the ORs for valve type (BAV or TAV) are not significant in adjusted models. Notably, the OR in the crude model of acute kidney injury was nearly significant, but the significance disappeared in the subsequent regression model, indicating that the OR of the model was truly confounded by the risk factors, which also suggested that the results of our adjusted model are plausible. Our study recommends that TAVR should be performed on BAV patients when the surgeon judges the patient to be suitable for that. Acute kidney injury is a common postoperative complication of TAVR. In our study, we found that patients with high TBIL and low ALB are more likely to gain acute kidney injury than others. High TBIL means that patients may have abnormal liver function, which is thought to increase the risk of kidney damage [ 23 ] . Albumin represents the nutritional status of the body. Patients with low albumin may have a reduction in plasma osmotic pressure, which leads to an imbalance of fluid in the body between blood vessels and tissues, increasing on the kidney and resulting in acute kidney injury. Permanent pacemakers are used more often in elderly patients and in patients with poorer cardiac function score outcomes. This may be related to the poorer cardiac function, which should be given more attention for TAVR, in these patients. TBIL and albumin have also been shown to be associated with permanent pacemaker implantation, and further studies are needed to clarify their association and interpretation. Paravalvular leak is another common complication after TAVR. Our findings suggested that patients with lower platelet counts (PC) were at higher risk for moderate or severe paravalvular leaks. However, their relationship is just on the border between significant and insignificant, so more research is needed to further explore whether there is a relationship between paravalvular leak and PC or not. Device failure was defined as intraoperative death and intraoperative conversion to open-heart surgery. 30 patients (2.60%) suffered device failure, including 2 deaths (0.17%) in this study. The model showed that patients with younger age were under higher risk of device failure, which is contrary to general knowledge. This result may be related to the surgeon's surgical selection strategy and further study is also needed. It is obviously that BAV patients in this study may be influenced by the better baseline characteristics of BAV patients, including lower age (70.26 vs. 72.12, p < 0.001), lower proportions of high-NYHA scores(81.7% vs. 92.6, p < 0.001), and lower proportions of hypertensions (41.9% vs. 58.7%, p < 0.001), which may lead to a lesser degree of valve calcification and aortic wall lesions in this group of patients, ultimately leading to a comparable risk of complications in BAV and TAV patients. This indicates that the surgical decisions made by physicians were associated with valve type among patients: a more stringent screening criteria would be implemented for BAV patients. This tendency to treat BAV patients differently from TAV patients may finally affect the occurrence of complications. Limitations Our study has several limitations. Firstly, the absence of ultrasound imaging data from our patients restricted our ability to examine information pertaining to dynamic valve and blood flow. Additionally, we did not explore long-term complications and their risk factors, which clearly warrants further research. Lastly, although our study sample was relatively large, it was limited to a single-center study. We plan to conduct multi-center research in the future to further validate our conclusions. Conclusion We suggested that TAVR is appropriate in the BAV population. Patients with BAV diagnosis do not have an increased risk of complications. Furthermore, we recommend that physicians should be concerned about the possibility of acute kidney injury in patients who have abnormal liver function, while patients with cardiac insufficiency are at risk of permanent pacemaker implantation, and patients with anemia are prone to paravalvular leak and should be given more intraoperative attention. Abbreviations AKI, acute kidney injury; ALB, albumin; ALT, alanine aminotransferase; ANOVA, analysis of variance; AR, aortic regurgitation; AS, aortic stenosis; ASL, aspartate aminotransferase; BAV, bicuspid aortic valves; BMI, body mass index; CI, confidence interval; CK-MB, creatine kinase-myoglobin binding; DBIL, direct bilirubin; EMR, electronic medical record; GFR, glomerular filtration rate; IBIL, indirect bilirubin; INR, international normalized ratio; LIS, laboratory information system; NYHA, New York Heart Association; OR, odds ratio; PC, platelet count; PPI, permanent pacemaker implantation; PT, prothrombin time; RBC, red blood cell; RCTs, randomized controlled trials; SAVR, surgical aortic valve replacement; TAV, tricuspid aortic valves; TAVR, transcatheter aortic valve replacement; TBIL, total bilirubin; VARC-3, Valvular Academic Research Consortium-3; VHD, valvular heart disease; WBC, white blood cell count Declarations Funding This work was supported by the projects of the Department of Science and Technology of Sichuan Province (grant number 2021YFS0091 to Xiaoyan Yang and grant number 2023YFS0200 to Xiaobo Zhou). Conflict of interest The authors declare that they have no conflict of interest. Ethics approval and consent for participate This study was approved by Ethics Committee on Biomedical Research, West China Hospital of Sichuan University (reference number: 2021.856). Due to the retrospective nature of the data used in this study, the requirement for informed consent was waived by the Ethics Committee. Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. References GOLDBARG S H, ELMARIAH S, MILLER M A, et al. Insights into degenerative aortic valve disease [J]. Journal of the American College of Cardiology, 2007, 50(13): 1205–13. HARRIS A W, BACH D S. Mixed Aortic Valve Disease and Strain: Unraveling the Myocardial Response [J]. JACC Cardiovascular imaging, 2021, 14(7): 1335–7. COFFEY S, ROBERTS-THOMSON R, BROWN A, et al. Global epidemiology of valvular heart disease [J]. Nature reviews Cardiology, 2021, 18(12): 853–64. BONOW R O, CARABELLO B A, CHATTERJEE K, et al. 2008 focused update incorporated into the ACC/AHA 2006 guidelines for the management of patients with valvular heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to revise the 1998 guidelines for the management of patients with valvular heart disease). Endorsed by the Society of Cardiovascular Anesthesiologists, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons [J]. Journal of the American College of Cardiology, 2008, 52(13): e1-142. REARDON M J, VAN MIEGHEM N M, POPMA J J, et al. Surgical or Transcatheter Aortic-Valve Replacement in Intermediate-Risk Patients [J]. The New England journal of medicine, 2017, 376(14): 1321–31. LEON M B, SMITH C R, MACK M J, et al. Transcatheter or Surgical Aortic-Valve Replacement in Intermediate-Risk Patients [J]. The New England journal of medicine, 2016, 374(17): 1609–20. ATHAPPAN G, PATVARDHAN E, TUZCU E M, et al. Incidence, predictors, and outcomes of aortic regurgitation after transcatheter aortic valve replacement: meta-analysis and systematic review of literature [J]. Journal of the American College of Cardiology, 2013, 61(15): 1585–95. GéNéREUX P, HEAD S J, HAHN R, et al. Paravalvular leak after transcatheter aortic valve replacement: the new Achilles' heel? A comprehensive review of the literature [J]. Journal of the American College of Cardiology, 2013, 61(11): 1125–36. WANG J, DENG W, LV Q, et al. Aortic Dilatation in Patients With Bicuspid Aortic Valve [J]. Frontiers in physiology, 2021, 12: 615175. TCHETCHE D, DE BIASE C, VAN GILS L, et al. Bicuspid Aortic Valve Anatomy and Relationship With Devices: The BAVARD Multicenter Registry [J]. Circulation Cardiovascular interventions, 2019, 12(1): e007107. KONG W K, REGEER M V, NG A C, et al. Sex Differences in Phenotypes of Bicuspid Aortic Valve and Aortopathy: Insights From a Large Multicenter, International Registry [J]. Circulation Cardiovascular imaging, 2017, 10(3). FORREST J K, KAPLE R K, RAMLAWI B, et al. Transcatheter Aortic Valve Replacement in Bicuspid Versus Tricuspid Aortic Valves From the STS/ACC TVT Registry [J]. JACC Cardiovascular interventions, 2020, 13(15): 1749–59. MAKKAR R R, YOON S H, LEON M B, et al. Association Between Transcatheter Aortic Valve Replacement for Bicuspid vs Tricuspid Aortic Stenosis and Mortality or Stroke [J]. Jama, 2019, 321(22): 2193–202. YOON S H, BLEIZIFFER S, DE BACKER O, et al. Outcomes in Transcatheter Aortic Valve Replacement for Bicuspid Versus Tricuspid Aortic Valve Stenosis [J]. Journal of the American College of Cardiology, 2017, 69(21): 2579–89. PEDERSEN A B, MIKKELSEN E M, CRONIN-FENTON D, et al. Missing data and multiple imputation in clinical epidemiological research [J]. Clinical epidemiology, 2017, 9: 157–66. MASRI A, KALAHASTI V, SVENSSON L G, et al. Aortic Cross-Sectional Area/Height Ratio and Outcomes in Patients With Bicuspid Aortic Valve and a Dilated Ascending Aorta [J]. Circulation Cardiovascular imaging, 2017, 10(6): e006249. ZHANG X, PUEHLER T, FRANK D, et al. TAVR for All? The Surgical Perspective [J]. Journal of cardiovascular development and disease, 2022, 9(7). EVANGELISTA MASIP A, GALIAN-GAY L, GUALA A, et al. Unraveling Bicuspid Aortic Valve Enigmas by Multimodality Imaging: Clinical Implications [J]. Journal of clinical medicine, 2022, 11(2). TESSLER I, GOUDOT G, ALBUISSON J, et al. Is Bicuspid Aortic Valve Morphology Genetically Determined? A Family-Based Study [J]. The American journal of cardiology, 2022, 163: 85–90. YOON S H, KIM W K, DHOBLE A, et al. Bicuspid Aortic Valve Morphology and Outcomes After Transcatheter Aortic Valve Replacement [J]. Journal of the American College of Cardiology, 2020, 76(9): 1018–30. MENTIAS A, SARRAZIN M V, DESAI M Y, et al. Transcatheter Versus Surgical Aortic Valve Replacement in Patients With Bicuspid Aortic Valve Stenosis [J]. Journal of the American College of Cardiology, 2020, 75(19): 2518–9. WAKSMAN R, CRAIG P E, TORGUSON R, et al. Transcatheter Aortic Valve Replacement in Low-Risk Patients With Symptomatic Severe Bicuspid Aortic Valve Stenosis [J]. JACC Cardiovascular interventions, 2020, 13(9): 1019–27. BETROSIAN A P, AGARWAL B, DOUZINAS E E. Acute renal dysfunction in liver diseases [J]. World journal of gastroenterology, 2007, 13(42): 5552–9. Additional Declarations No competing interests reported. Supplementary Files supplementary.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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University","correspondingAuthor":false,"prefix":"","firstName":"Jiawei","middleName":"","lastName":"Luo","suffix":""},{"id":345362100,"identity":"18e395ab-6cd2-4824-9f6f-0945e87e1f1f","order_by":2,"name":"Xuan Huang","email":"","orcid":"","institution":"West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Huang","suffix":""},{"id":345362108,"identity":"fdda4c8c-40ce-4fb5-a8a6-b72835f572ff","order_by":3,"name":"Lulu Liu","email":"","orcid":"","institution":"West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Lulu","middleName":"","lastName":"Liu","suffix":""},{"id":345362111,"identity":"684415f6-9a03-424e-8b9c-4236ac12501b","order_by":4,"name":"Kehan Li","email":"","orcid":"","institution":"Sichuan 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University","correspondingAuthor":false,"prefix":"","firstName":"Yingqiang","middleName":"","lastName":"Guo","suffix":""},{"id":345362124,"identity":"d026743e-8412-453f-a905-345d7aef6dbf","order_by":8,"name":"Xiaoyan Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYNACAzDJ+ADCSyBeC7MBCVoggE2CKC0Gx88efs1TYCcHZByr/FFzmIGfPceA4ecOPFrO5KVZzjBINgYxbvMcO8wg2fPGgLH3DB4tB3LMDD4YMCduADJuMzYcZjC4kWPAzNiGR8v5N2YGCQb1iRuAjMKfQC32BLXcyDF+8MHgcOKGGzlmDLwgWyQIaJG88caMcYbBcWMgw1ia51g6j8SZZwUHe/Fo4TufY/yZ50+1HJBh+PFHjbUcf3vyxgc/8WhROACNDiADDHhAxAHcGhgY5BsYmD9AGaNgFIyCUTAKsAMAnAxUgAPAyZMAAAAASUVORK5CYII=","orcid":"","institution":"West China Hospital, Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-07-24 07:27:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4793214/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4793214/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63888930,"identity":"07afcfa2-cf11-4f55-b0b1-dcf29e0c3072","added_by":"auto","created_at":"2024-09-03 11:47:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23782,"visible":true,"origin":"","legend":"","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4793214/v1/24455abac478a4dbdf0be060.png"},{"id":63888932,"identity":"2cf13932-d2a5-4366-a30a-7e5a280a565f","added_by":"auto","created_at":"2024-09-03 11:47:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":55127,"visible":true,"origin":"","legend":"","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4793214/v1/519a4bebbd981b6d6fcce0de.png"},{"id":67461239,"identity":"9829eee4-5715-421f-8e8e-82292e1440f0","added_by":"auto","created_at":"2024-10-25 09:39:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":764792,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4793214/v1/d6d4df4d-ce0e-4826-9cc6-4451ed1e699d.pdf"},{"id":63890533,"identity":"ba35b06c-794c-45f9-a3ce-910e49b2d439","added_by":"auto","created_at":"2024-09-03 12:03:40","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":48565,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4793214/v1/4fc01b05860179ff311b6cce.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of in-hospital complication rates after transcatheter aortic valve replacement in patients with bicuspid versus tricuspid aortic valves: a retrospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAortic stenosis (AS) and aortic regurgitation (AR) are most common cause of valvular heart disease (VHD) in the Western world. These conditions has affected more than 41\u0026nbsp;million people, leading to significant premature mortality since 2000 and reducing quality of life \u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Historically, the primary treatment for VHD was the high-risk open-heart procedure known as surgical aortic valve replacement (SAVR)\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, However, transcatheter aortic valve replacement (TAVR) has recently emerged as a viable interventional treatment modality. It is now a well-established therapy, particularly for inoperable and high-risk patients with clinical VHD \u003csup\u003e[\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the general population, the aortic valve typically comprises three nearly identical, semilunar-shaped aortic cusps, termed the tricuspid aortic valve (TAV). In contrast, some individuals possess only two leaflets of unequal size, referred to as a bicuspid aortic valve (BAV) \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. BAV is the most prevalent congenital heart valve anomaly, present in approximately 0.5\u0026ndash;2% of the global population\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Notably, in our data, the ratio of BAV to TAV is approximately 0.79, indicating a significant proportion of BAV cases. The distinct valve anatomy in BAV patients can lead to an \"annulus-valve\" mismatch, complicating the selection of an appropriate TAVR system size for physicians. Additionally, certain BAV patients may exhibit associated aortic abnormalities, such as a dilated ascending aorta, potentially resulting in macrovascular complications \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Although some randomized controlled trials (RCTs) have investigated the post-TAVR complications in BAV patients, the findings have been inconsistent \u003csup\u003e[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Over recent years, numerous researchers have advocated for the broader adoption of TAVR in BAV patients. However, given the novelty of TAVR and its associated high costs, many relevant studies have been constrained by small sample sizes. Based on an early-stage literature review by our team, while the number of studies on TAVR in BAV patients has been on the rise from 2014 to 2022, over 60% of these investigations included fewer than 100 BAV patients, and 85% had fewer than 500 BAV participants (\u003cb\u003eSup Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWest China Hospital, the preeminent general hospital in southwest China, was among the pioneers in introducing TAVR procedures in the country. Its comprehensive electronic medical record (EMR) system, encompassing both the Hospital Information System (HIS) and the Laboratory Information System (LIS), has chronicled medical histories of over 7\u0026nbsp;million patients annually since 2000. This wealth of data, which includes detailed valve information, sociodemographic profiles, socioeconomic statuses, and a substantial record of TAVR cases, offers a unique research opportunity.\u003c/p\u003e \u003cp\u003eIn this study, our objective is to evaluate the post-TAVR complication risks in VHD patients with BAV in comparison to those with TAV. We also delve into the specific risk factors associated with each complication.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe HIS and LIS recorded data on 2393 middle-aged patients who underwent Transcatheter Aortic Valve Replacement (TAVR) at West China Hospital from 2010 to 2022. The hospital's EMR system automatically logged patient information during their stay, including sociodemographic details such as age and sex, health-related metrics like Body Mass Index (BMI) and New York Heart Association (NYHA) score, laboratory indices including white blood cell count (WBC) and direct bilirubin (DBIL), as well as pre-existing conditions like hypertension and diabetes. Patients who lacked a definitive aortic valve type or were missing more than 75% of their laboratory tests were excluded from the study. Ultimately, 1154 patients were included in the research. The detailed patient selection process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eValve types\u003c/h2\u003e \u003cp\u003eValve data was extracted from medical records, discharge summaries, in-hospital examination results, and surgical reports. Cases with discrepancies across different records underwent further verification for study eligibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eComplications\u003c/h2\u003e \u003cp\u003eThis study monitored four complications: acute kidney injury (AKI), new-onset permanent pacemaker implantation, paravalvular leak, and device failure. TAVR complications were defined according to the Valvular Academic Research Consortium-3 (VARC-3) guidelines. Acute kidney injury was defined as increase in serum creatinine\u0026thinsp;\u0026ge;\u0026thinsp;150\u0026ndash;200% within 7 days compared with baseline or increase of \u0026ge;\u0026thinsp;0.3mg/dL (\u0026ge;\u0026thinsp;26.4 \u0026micro;mol/L) within 48 hour of the index procedure. Permanent pacemaker implantation (PPI) was defined as a new permanent pacemaker implanted during hospitalization after TAVR; paravalvular leak was defined as any degree of paravalvular leak that occurs during hospitalization; device failure was defined as patients undertook an unsuccessfully TAVR with intraoperative conversion to open-heart surgery or in-hospital death.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eData on sociodemographic characteristics such as age and sex, health-related factors like BMI and NYHA score, laboratory indices (including WBC, DBIL, among others), and underlying diseases (specifically hypertension and diabetes) were extracted from the EMR. BMI was determined using height (in centimeters) and weight (in kilograms) recorded at admission. NYHA scores of III or IV were categorized as high-level.\u003c/p\u003e \u003cp\u003eIn our main analysis, all factors, with the exception of valve type (BAV vs TAV) that might influence the incidence of complications, were treated as potential confounders, as suggested by prior literature. The covariates were classified into three categories: (1) Sociodemographic features, encompassing age, sex, and BMI. (2) Cardiac health indicators, such as hypertension, NYHA score, troponin, and myoglobin. (3) General health metrics of other organs, which included measures like hemoglobin, platelet count (PC), red blood cell (RBC) count, white blood cell (WBC) count, total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), Aspartate aminotransferase/alanine aminotransferase ratio (ASL/ALT ratio), albumin (ALB), glomerular filtration rate (GFR), creatine kinase-myoglobin binding (CK-MB), cholesterol, triglyceride, international normalized ratio (INR), prothrombin time (PT), and medical histories pertaining to hypertension and diabetes.\u003c/p\u003e \u003cp\u003eIn addition, based on the results of our main analysis, we pooled all the cases together, regardless of their valve type, to test the risk factors associated with the four complications mentioned above.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMissing data\u003c/h2\u003e \u003cp\u003eCovariates with a missing rate exceeding 25% were considered inappropriate for imputation and were thus excluded from the study. We utilized the MICE package in R for multiple imputation, a popular method for addressing data gaps due to its ability to provide unbiased and consistent estimates of missing values \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. After the imputation process, we observed no significant changes in the distributional characteristics (Refer to \u003cb\u003eSup Table S3\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe incidence rate of complications was determined using a 95% confidence interval (CI). A multivariable logistic model was deployed to evaluate the influence of various risk factors on the four observed complications.\u003c/p\u003e \u003cp\u003eFor our main analysis, we delved into the association between valve type and the aforementioned complications. We began with a basic model, centered solely around the valve type. Subsequently, the model was systematically expanded by introducing different groups of covariates: sociodemographic attributes, heart health metrics, and health indicators of other organs. Utilizing hierarchical multiple regression, we thoroughly assessed the effects of integrating these covariate groups in diverse sequences. Every possible sequence of introducing these covariate groups was meticulously explored. With the inclusion of each new group, we conducted Analysis of Variance (ANOVA) tests to discern statistical variations before and after their addition. Through this methodical approach, we derived six unique nested models, encompassing our foundational statistical model.\u003c/p\u003e \u003cp\u003eIn the additional analysis, building on the outcomes from the main analysis, we constructed separate multivariable logistic regression models for each complication. This allowed us to discern the specific risk factors for each complication.\u003c/p\u003e \u003cp\u003eStatistical significance was pegged at a two-tailed \u003cem\u003eP\u003c/em\u003e-value of less than 0.05. All computations and analyses were executed using R software, version 3.6.1.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 1239 persons were excluded according to the exclusion criteria, leaving 1154 eligible patients for further analysis, among which, 508 patients with diagnosis of BAV and 646 with TAV. The male-to-female ratio was approximately 1.51, and median age was 71.90 years. Compared with TAV patients, we found that, the proportion or means were statistical lower in BAV patients: Age (70.26 vs. 72.12), NYHA high level (81.7% vs. 92.6%), and higher in BAV patients: GFR (73.58 vs. 69.86), female proportion (43.5% vs. 36.8%), ALB (42.13vs. 40.98), Triglyceride (1.37 vs. 1.27), CK-MB (2.47 vs. 2.19). We also found that BAV patients had a higher proportion of diabetes (20.9% vs. 15.3%) and lower proportion of hypertension (41.9% vs. 58.7%) (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 characteristics and clinical parameters of BAV and TAV patients.\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients with BAV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatients with TAV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll patients, \u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e646\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\u003eFemale, \u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge,\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.26 (9.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.12 (7.96)\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\u003eBMI,\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.74 (7.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.65 (7.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNYHA high level,\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e415 (81.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e598 (92.6)\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\u003eASL/ALT,\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.44 (1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (ALB), g/L\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.13 (4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.98 (4.27)\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\u003ewhite blood cell (WBC) count, HP\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.15 (1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.32 (2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol, mmol/L\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.20 (1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.11 (1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride, mmol/L\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37 (0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternational Normalized Ratio (INR),\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07 (0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed blood cell (RBC) count, 10^12/L\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.34 (0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.32 (0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTroponin, ng/L\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.33 (115.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.57 (108.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, \u0026micro;moI/L\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.13 (33.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.41 (78.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyoglobin, ng/ml\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.47 (41.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.16 (67.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine kinase MB Form (CK-MB), ng/ml\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.47 (2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.19 (1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin (TBIL), mg/dl\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.25 (18.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.35 (8.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect bilirubin (DBIL), mg/dl\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.73 (14.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.24 (5.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect bilirubin (IBIL), mg/dl\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.53 (5.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.12 (4.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProthrombin time (PT),\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.89 (3.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.00 (3.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerular Filtration Rate (GFR),\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.58 (19.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.86 (20.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin,\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130.21 (19.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129.17 (18.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count (PC), seconds\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158.10 (55.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164.35 (61.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of hypertension,\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213 (41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e379 (58.7)\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\u003eHistory of diabetes,\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\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 \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIncidence of complications\u003c/h2\u003e \u003cp\u003eOf all patients, 40 cases of acute kidney injury (11 in BAV and 29 in TAV, OR 0.52 [0.23, 1.09]), 134 cases of new-onset permanent pacemaker implantations (67 in BAV and 67 in TAV, OR 1.29 [0.87, 1.92]), 129 cases of paravalvular leak (43 in BAV and 86 in TAV, OR 1.29 [0.82, 2.02]), 30 cases of device failure (10 in BAV and 20 in TAV, OR 0.54 [0.22, 1.23]) were recorded (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Of these cute kidney injury cases, 24 were stage 1 (8 in BAV and 16 in TAV), 10 were stage 2 (2 in BAV and 8 in TAV), and 6 were stage 3 (1 in BAV and 5 in TAV).\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\u003eIncidence of post-TAVR complications in BAV and TAV patients by valve type. The OR values presented in the table have been adjusted for other factors using logistic regression.\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\u003eComplications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of cases\u003c/p\u003e \u003cp\u003e in BAV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. of case \u003c/p\u003e \u003cp\u003ein TAV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute kidney injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11/508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29/646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52 [0.23, 1.09]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePermanent pacemaker implantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67/508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67/646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.29 [0.87, 1.92]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParavalvular leak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43/508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86/646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.29 [0.82, 2.02]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevice failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10/508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20/646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54 [0.22, 1.23]\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 \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMain analysis\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eValve type and complication incidence\u003c/h2\u003e \u003cp\u003eAmong all patients, in the crude model, the odds ratio (OR) of acute kidney injury were 51% lower in BAV patients compared with TAV patients (OR, 0.49; 95%CI, 0.23\u0026ndash;0.96; p\u0026thinsp;=\u0026thinsp;0.045). Other three complications showed no significant association with valve type. Specifically, the OR was 1.31 (95%CI, 0.91\u0026ndash;1.88; p\u0026thinsp;=\u0026thinsp;0.139) for permanent pacemaker implantation, 1.13 (95%CI, 0.74\u0026ndash;1.70; p\u0026thinsp;=\u0026thinsp;0.564) for paravalvular leak, and 0.63 (95%CI, 0.28\u0026ndash;1.33; p\u0026thinsp;=\u0026thinsp;0.236) for device failure. After progressively adding sociodemographic characteristics, health status of heart characteristics and health status of other organs as potential confounders, the full multiple logistic regression showed that all complications, including acute kidney injury, new-onset permanent pacemaker implantation, paravalvular leak and device failure, showed no statistically significant association with valve type. Specifically, the OR was 0.52 (95%CI, 0.23\u0026ndash;1.09) for acute kidney injury, 1.29 (95%CI, 0.87\u0026ndash;1.92) for permanent pacemaker implantation, 1.29 (95%CI, 0.82\u0026ndash;2.02) for paravalvular leak, and 0.54 (95%CI, 0.22\u0026ndash;1.23) for device failure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eHierarchical Multiple Regression Models\u003c/h2\u003e \u003cp\u003eAnalysis from the hierarchical multiple regression models suggests a potential significant correlation between the incidence of acute kidney injury and the health status of organs other than the heart, as half of the examined scenarios (three out of six) demonstrated significance. Interestingly, the need for a permanent pacemaker implantation was statistically linked to both sociodemographic characteristics and the health status of other organs. Furthermore, the health condition of the heart appeared to influence the occurrence of paravalvular leak, with significance shown in three out of six scenarios. Importantly, only the sociodemographic variables displayed a clear correlation with device failures (\u003cb\u003eSup Table S2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAdditional analysis: Risk factors for complications\u003c/h2\u003e \u003cp\u003eGiven that the valve type did not exhibit a statistically significant relationship with complications, it was omitted from the full model to further delve into specific risk factors for each complication. The subsequent results are detailed below:\u003c/p\u003e \u003cp\u003eAcute kidney injury: Both TBIL and ALB significantly influenced the incidence of AKI. A unit increase in TBIL was associated with a 2% increase in the odds of AKI (OR: 1.02; 95%CI: 1.00-1.04), whereas a unit increase in ALB was linked to a 12% decrease in its odds (OR: 0.88; 95%CI: 0.80\u0026ndash;0.95).\u003c/p\u003e \u003cp\u003ePermanent pacemaker implementation: Significant associations were observed with age, NYHA scores, TBIL, and ALB. For each unit increase in age and ALB, the odds of requiring a permanent pacemaker rose by 5% (Age - OR: 1.05; 95%CI: 1.02\u0026ndash;1.08; ALB - OR: 1.05; 95%CI: 1.00-1.11). Conversely, every unit increase in TBIL led to a 5% decrease in these odds (OR: 0.95; 95%CI: 0.92\u0026ndash;0.98). Additionally, patients with high NYHA scores had 54% lower odds of requiring a permanent pacemaker compared to those with low scores (OR: 0.46; 95%CI: 0.28\u0026ndash;0.77).\u003c/p\u003e \u003cp\u003eParavalvular leak: A significant correlation was found with PC. Specifically, for each unit increase in PC, the odds of a paravalvular leak reduced nearly by 1% (OR: 0.99; 95%CI: 0.99-1.00).\u003c/p\u003e \u003cp\u003eDevice failure: Age emerged as a pivotal factor. Each unit increase in age led to a 6% decrease in the odds of device failure (OR: 0.94; 95%CI: 0.91\u0026ndash;0.98) (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\u003eAssociations between clinical parameters and post-TAVR complications: odds ratios and 95% confidence intervals.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAcute kidney injury\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ePermanent pacemaker implantation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eParavalvular leak\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eDevice failure\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI,\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 [0.99, 1.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 [0.96, 1.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 [0.97, 1.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.99 [0.94, 1.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, \u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20 [0.53, 2.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18 [0.75, 1.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.71 [0.43, 1.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.42 [0.55, 3.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge,\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 [0.99, 1.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05 [1.02, 1.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.02 [0.98, 1.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.94 [0.91, 0.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNYHA high level,\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.01 [0.58, 12.72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46 [0.28, 0.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.37 [0.96, 7.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.70 [0.27, 2.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASL/ALT,\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 [0.70, 1.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03 [0.79, 1.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.17 [0.82, 1.66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.80 [0.34, 1.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (ALB), g/L\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88 [0.80, 0.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05 [1.00, 1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01 [0.96, 1.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.99 [0.89, 1.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewhite blood cell (WBC) count, HP\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 [0.85, 1.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95 [0.84, 1.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.05 [0.93, 1.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.17 [0.94, 1.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol, mmol/L\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17 [0.80, 1.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97 [0.79, 1.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97 [0.77, 1.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.87 [0.55, 1.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride, mmol/L\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77 [0.39, 1.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01 [0.74, 1.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01 [0.69, 1.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.84 [0.40, 1.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternational Normalized Ratio (INR),\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77 [0.02, 266.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57 [0.03, 17.73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.83 [0.09, 90.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.01 [0.00, 0.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed blood cell (RBC) count, 10^12/L\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56 [0.20, 1.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03 [0.63, 1.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.22 [0.77, 1.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.38 [0.09, 1.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTroponin, ng/L\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 [1.00, 1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.99 [0.98, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyoglobin, ng/ml\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [0.99, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 [1.00, 1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 [0.99, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 [0.99, 1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine kinase MB Form (CK-MB), ng/ml\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [0.84, 1.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03 [0.91, 1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.08 [0.92, 1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.18 [0.88, 1.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProthrombin time (PT),\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 [0.61, 1.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16 [0.85, 1.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.96 [0.66, 1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.49 [1.08, 2.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerular Filtration Rate (GFR),\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 [0.99, 1.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 [0.99, 1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01 [1.00, 1.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.99 [0.97, 1.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin,\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 [0.98, 1.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 [0.98, 1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98 [0.97, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.04 [0.99, 1.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count (PC), seconds\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [0.99, 1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 [0.99, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 [0.99, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin (TBIL), mg/dl\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 [1.00, 1.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95 [0.92, 0.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 [0.98, 1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 [0.96, 1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of hypertension,\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 [0.47, 1.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06 [0.71, 1.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.77 [0.51, 1.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.87 [0.81, 4.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of diabetes,\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62 [0.69, 3.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29 [0.79, 2.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.09 [0.59, 1.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.56 [0.13, 1.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.368\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"},{"header":"Discussion","content":"\u003cp\u003eBAV is a low prevalence congenital disease with a prevalence of approximately 1% of the population, of which 1/3\u0026thinsp;\u0026minus;\u0026thinsp;1/4 are female \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Due to the high medical costs (about 300,000 RMB), TAVR is a relatively rare option for valvular heart disease patients in China compared to SAVR. To the best of our knowledge, this is the first time a study has been conducted in China on a large TAVR population cohort, specifically in patients with BAV. The results of this study are of great clinical importance for physicians to understand the prognosis of TAVR in patients with BAV and the risk factors influencing the complications of TAVR.\u003c/p\u003e \u003cp\u003eIn this large-scale of TAVR- undergone population, we found that patients with BAV diagnosis were not at increased risk of complications. Based on the anatomical structure, in contrast to the TAV, the BAV has an asymmetrical leaflet shape and an oval sinus orifice, which may obstruct blood flow through the valve opening, resulting in increased flow velocity and eddy flow, and ultimately, valve thickening and asymmetric calcification \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. In the past, some research found that BAV is prone to accelerated aortic valve calcification due to both genetic mechanisms and anatomical causes, therefore patients with BAV should undergo earlier than TAV \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Other studies have pointed out that the outcome of TAVR surgery in BAV patients is influenced by the overall calcium burden and the presence of calcified raphe, which can prevent optimal device expansion \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. For example, Mentias found that BAV patients were more likely to place pacemakers than TAV patients (12.2% vs. 7.6%) \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In fact, BAV patients have long been considered unsuitable for TAVR and have often been excluded from randomized clinical trials \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. In this study, within all complications, the ORs for valve type (BAV or TAV) are not significant in adjusted models. Notably, the OR in the crude model of acute kidney injury was nearly significant, but the significance disappeared in the subsequent regression model, indicating that the OR of the model was truly confounded by the risk factors, which also suggested that the results of our adjusted model are plausible. Our study recommends that TAVR should be performed on BAV patients when the surgeon judges the patient to be suitable for that.\u003c/p\u003e \u003cp\u003eAcute kidney injury is a common postoperative complication of TAVR. In our study, we found that patients with high TBIL and low ALB are more likely to gain acute kidney injury than others. High TBIL means that patients may have abnormal liver function, which is thought to increase the risk of kidney damage \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Albumin represents the nutritional status of the body. Patients with low albumin may have a reduction in plasma osmotic pressure, which leads to an imbalance of fluid in the body between blood vessels and tissues, increasing on the kidney and resulting in acute kidney injury.\u003c/p\u003e \u003cp\u003ePermanent pacemakers are used more often in elderly patients and in patients with poorer cardiac function score outcomes. This may be related to the poorer cardiac function, which should be given more attention for TAVR, in these patients. TBIL and albumin have also been shown to be associated with permanent pacemaker implantation, and further studies are needed to clarify their association and interpretation.\u003c/p\u003e \u003cp\u003eParavalvular leak is another common complication after TAVR. Our findings suggested that patients with lower platelet counts (PC) were at higher risk for moderate or severe paravalvular leaks. However, their relationship is just on the border between significant and insignificant, so more research is needed to further explore whether there is a relationship between paravalvular leak and PC or not.\u003c/p\u003e \u003cp\u003eDevice failure was defined as intraoperative death and intraoperative conversion to open-heart surgery. 30 patients (2.60%) suffered device failure, including 2 deaths (0.17%) in this study. The model showed that patients with younger age were under higher risk of device failure, which is contrary to general knowledge. This result may be related to the surgeon's surgical selection strategy and further study is also needed.\u003c/p\u003e \u003cp\u003eIt is obviously that BAV patients in this study may be influenced by the better baseline characteristics of BAV patients, including lower age (70.26 vs. 72.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lower proportions of high-NYHA scores(81.7% vs. 92.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and lower proportions of hypertensions (41.9% vs. 58.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which may lead to a lesser degree of valve calcification and aortic wall lesions in this group of patients, ultimately leading to a comparable risk of complications in BAV and TAV patients. This indicates that the surgical decisions made by physicians were associated with valve type among patients: a more stringent screening criteria would be implemented for BAV patients. This tendency to treat BAV patients differently from TAV patients may finally affect the occurrence of complications.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eOur study has several limitations. Firstly, the absence of ultrasound imaging data from our patients restricted our ability to examine information pertaining to dynamic valve and blood flow. Additionally, we did not explore long-term complications and their risk factors, which clearly warrants further research. Lastly, although our study sample was relatively large, it was limited to a single-center study. We plan to conduct multi-center research in the future to further validate our conclusions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe suggested that TAVR is appropriate in the BAV population. Patients with BAV diagnosis do not have an increased risk of complications. Furthermore, we recommend that physicians should be concerned about the possibility of acute kidney injury in patients who have abnormal liver function, while patients with cardiac insufficiency are at risk of permanent pacemaker implantation, and patients with anemia are prone to paravalvular leak and should be given more intraoperative attention.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAKI, acute kidney injury; ALB, albumin; ALT, alanine aminotransferase; ANOVA, analysis of variance; AR, aortic regurgitation; AS, aortic stenosis; ASL, aspartate aminotransferase; BAV, bicuspid aortic valves; BMI, body mass index; CI, confidence interval; CK-MB, creatine kinase-myoglobin binding; DBIL, direct bilirubin; EMR, electronic medical record; GFR, glomerular filtration rate; IBIL, indirect bilirubin; INR, international normalized ratio; LIS, laboratory information system; NYHA, New York Heart Association; OR, odds ratio; PC, platelet count; PPI, permanent pacemaker implantation; PT, prothrombin time; RBC, red blood cell; RCTs, randomized controlled trials; SAVR, surgical aortic valve replacement; TAV, tricuspid aortic valves; TAVR, transcatheter aortic valve replacement; TBIL, total bilirubin; VARC-3, Valvular Academic Research Consortium-3; VHD, valvular heart disease; WBC, white blood cell count\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the projects of the Department of Science and Technology of Sichuan Province (grant number 2021YFS0091 to Xiaoyan Yang and grant number 2023YFS0200 to Xiaobo Zhou).\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent for participate\u003c/p\u003e\n\u003cp\u003eThis study was approved by Ethics Committee on Biomedical Research, West China Hospital of Sichuan University (reference number: 2021.856).\u0026nbsp;Due to the retrospective nature of the data used in this study, the requirement for informed consent was waived by the Ethics Committee.\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGOLDBARG S H, ELMARIAH S, MILLER M A, et al. Insights into degenerative aortic valve disease [J]. Journal of the American College of Cardiology, 2007, 50(13): 1205\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHARRIS A W, BACH D S. Mixed Aortic Valve Disease and Strain: Unraveling the Myocardial Response [J]. JACC Cardiovascular imaging, 2021, 14(7): 1335\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCOFFEY S, ROBERTS-THOMSON R, BROWN A, et al. Global epidemiology of valvular heart disease [J]. Nature reviews Cardiology, 2021, 18(12): 853\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBONOW R O, CARABELLO B A, CHATTERJEE K, et al. 2008 focused update incorporated into the ACC/AHA 2006 guidelines for the management of patients with valvular heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to revise the 1998 guidelines for the management of patients with valvular heart disease). Endorsed by the Society of Cardiovascular Anesthesiologists, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons [J]. Journal of the American College of Cardiology, 2008, 52(13): e1-142.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eREARDON M J, VAN MIEGHEM N M, POPMA J J, et al. Surgical or Transcatheter Aortic-Valve Replacement in Intermediate-Risk Patients [J]. The New England journal of medicine, 2017, 376(14): 1321\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLEON M B, SMITH C R, MACK M J, et al. Transcatheter or Surgical Aortic-Valve Replacement in Intermediate-Risk Patients [J]. The New England journal of medicine, 2016, 374(17): 1609\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eATHAPPAN G, PATVARDHAN E, TUZCU E M, et al. Incidence, predictors, and outcomes of aortic regurgitation after transcatheter aortic valve replacement: meta-analysis and systematic review of literature [J]. Journal of the American College of Cardiology, 2013, 61(15): 1585\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026eacute;N\u0026eacute;REUX P, HEAD S J, HAHN R, et al. Paravalvular leak after transcatheter aortic valve replacement: the new Achilles' heel? A comprehensive review of the literature [J]. Journal of the American College of Cardiology, 2013, 61(11): 1125\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWANG J, DENG W, LV Q, et al. Aortic Dilatation in Patients With Bicuspid Aortic Valve [J]. Frontiers in physiology, 2021, 12: 615175.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTCHETCHE D, DE BIASE C, VAN GILS L, et al. Bicuspid Aortic Valve Anatomy and Relationship With Devices: The BAVARD Multicenter Registry [J]. Circulation Cardiovascular interventions, 2019, 12(1): e007107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKONG W K, REGEER M V, NG A C, et al. Sex Differences in Phenotypes of Bicuspid Aortic Valve and Aortopathy: Insights From a Large Multicenter, International Registry [J]. Circulation Cardiovascular imaging, 2017, 10(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFORREST J K, KAPLE R K, RAMLAWI B, et al. Transcatheter Aortic Valve Replacement in Bicuspid Versus Tricuspid Aortic Valves From the STS/ACC TVT Registry [J]. JACC Cardiovascular interventions, 2020, 13(15): 1749\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMAKKAR R R, YOON S H, LEON M B, et al. Association Between Transcatheter Aortic Valve Replacement for Bicuspid vs Tricuspid Aortic Stenosis and Mortality or Stroke [J]. Jama, 2019, 321(22): 2193\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYOON S H, BLEIZIFFER S, DE BACKER O, et al. Outcomes in Transcatheter Aortic Valve Replacement for Bicuspid Versus Tricuspid Aortic Valve Stenosis [J]. Journal of the American College of Cardiology, 2017, 69(21): 2579\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePEDERSEN A B, MIKKELSEN E M, CRONIN-FENTON D, et al. Missing data and multiple imputation in clinical epidemiological research [J]. 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World journal of gastroenterology, 2007, 13(42): 5552\u0026ndash;9.\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 aortic valve replacement (TAVR), Bicuspid aortic valves (BAV), Tricuspid aortic valves (TAV), Complications, Cohort study","lastPublishedDoi":"10.21203/rs.3.rs-4793214/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4793214/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eTranscatheter aortic valve replacement (TAVR) has become a popular alternative to surgical aortic valve replacement (SAVR) for patients with valvular heart disease (VHD), particularly for those with aortic anomalies.\u003c/p\u003e\u003ch2\u003eObjectives:\u003c/h2\u003e \u003cp\u003eThe study aimed to compare the risks of post-TAVR complications between patients with bicuspid and tricuspid aortic valves and to identify associated risk factors.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThe association between complications and valve type (bicuspid or tricuspid) was assessed. The study also explored various combinations of factors to understand their impact on complications. Separate analyses were conducted to identify specific risk factors for each complication.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eOut of the 1154 eligible patients, 508 had bicuspid aortic valves (BAV) and 646 had tricuspid aortic valves (TAV). The study identified 40 cases of acute kidney injury, 134 instances of new-onset permanent pacemaker implantations, 129 occurrences of paravalvular leak, and 30 device failures. The comprehensive logistic regression revealed no statistically significant association between complications and valve type (ORs: 0.52 (95%CI, 0.23\u0026ndash;1.09) for acute kidney injury, 1.29 (95%CI, 0.87\u0026ndash;1.92) for permanent pacemaker implantation, 1.29 (95%CI, 0.82\u0026ndash;2.02) for paravalvular leak, and 0.54 (95%CI, 0.22\u0026ndash;1.23) for device failure). Total bilirubin (TBIL), albumin (ALB), age, and New York Heart Association (NYHA) scores, among other factors, were associated with specific post-TAVR complications.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThe incidence of acute kidney injury, new permanent pacemaker implantations, paravalvular leaks, and device failures did not differ significantly between patients with BAV and TAV following TAVR. Specific risk factors for these complications were identified, highlighting the importance of careful clinical monitoring in post-TAVR management.\u003c/p\u003e","manuscriptTitle":"Comparison of in-hospital complication rates after transcatheter aortic valve replacement in patients with bicuspid versus tricuspid aortic valves: a retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-03 11:47:35","doi":"10.21203/rs.3.rs-4793214/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":"132c2299-73a6-4640-a14f-733c6b7c3ff0","owner":[],"postedDate":"September 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":36608178,"name":"Health sciences/Risk factors"},{"id":36608180,"name":"Health sciences/Medical research/Epidemiology"},{"id":36608182,"name":"Health sciences/Diseases/Cardiovascular diseases/Valvular disease"}],"tags":[],"updatedAt":"2024-10-25T09:38:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-03 11:47:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4793214","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4793214","identity":"rs-4793214","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}