A predictive model using gender, the diagnosis-to-ablation time and left atrial function in predicting the recurrence of atrial fibrillation within 1 year after radiofrequency catheter ablation

A predictive model using gender, the diagnosis-to-ablation time and left atrial function in predicting the recurrence of atrial fibrillation within 1 year after radiofrequency catheter ablation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A predictive model using gender, the diagnosis-to-ablation time and left atrial function in predicting the recurrence of atrial fibrillation within 1 year after radiofrequency catheter ablation WenYu Zhang, JunWei Wang, MinZhe Zhu, HaoZe Chen, ZongHong Wu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8687602/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Objective: Radiofrequency catheter ablation (RFCA) serves as a first‑line treatment for symptomatic atrial fibrillation (AF); however, its clinical application is still challenged by a relatively high postoperative recurrence rate. Therefore, numerous studies have focused on identifying factors associated with AF recurrence after RFCA, such as atrial size, gender, diagnosis‑to‑ablation time (DAT), and echocardiographic indices reflecting left atrial function. This study aims to investigate the risk factors for AF recurrence within one year after RFCA and to develop a simple yet effective prediction model based on these factors to guide clinical practice. Methods: This was a single-center retrospective study. We enrolled patients with atrial fibrillation (n=202) who underwent their first catheter radiofrequency ablation in the Third Department of Cardiology at the Second Hospital of Hebei Medical University between January 2019 and January 2022. Clinical characteristics, diagnosis-to-ablation time (DAT), and left atrial functional parameters were compared. A prediction model was constructed using Logistic regression, and its diagnostic performance was evaluated using the receiver operating characteristic (ROC) curve. Finally, a predictive model for atrial fibrillation recurrence was established. Results: (1)A total of 202 symptomatic atrial fibrillation patients who underwent radiofrequency ablation were enrolled, and follow-up data at 1, 3, 6, and 12 months post-procedure were collected. Based on recurrence status, the patients were divided into a sinus rhythm maintenance group (n=136) and a recurrence group (n=66), with an overall postoperative recurrence rate of 32.7%. (2)Univariate analysis showed statistically significant differences between the two groups in terms of gender, diagnosis-to-ablation time (DAT), N-terminal pro-B-type natriuretic peptide (NT‑proBNP), Homocysteine (Hcy), and Left atrial appendage emptying velocity (LAAeV)(P<0.05). (3)Multivariate Logistic regression analysis further indicated that gender, DAT, and LAAeV were independent risk factors for postoperative AF recurrence. The regression equation derived was: Logit(P)=−0.824 + 1.19×gender + 0.015×DAT − 0.016×LAAeV. Goodness-of-fit testing yielded χ²=5.975, and the Hosmer‑Lemeshow test showed P=0.650 (>0.05), indicating a well-fitted model. (4)Receiver operating characteristic (ROC) curve analysis revealed that DAT had the largest area under the curve (AUC=0.665) among the three indicators, while the combined prediction model demonstrated better discriminative ability than DAT alone (AUC=0.722). Conclusions: Gender, DAT, and LAAeV are independent risk factors for early postoperative recurrence in patients with atrial fibrillation. The combined model of these three factors demonstrates good predictive value for early AF recurrence after the procedure. Furthermore, the constructed nomogram can serve as a non‑invasive preoperative assessment tool prior to RFCA, offering enhanced predictive utility. Atrial Fibrillation Postoperative Recurrence Catheter Radiofrequency Ablation Logistic Regression Prediction Model Diagnosis-to-Ablation Time Left Atrial Appendage Peak Emptying Velocity Figures Figure 1 Figure 2 Figure 3 1. Introduction Atrial fibrillation (AF) is one of the most common cardiac arrhythmias, characterized by rapid and disorganized electrical activity in the atria. Its clinical presentation ranges from being asymptomatic to symptoms such as palpitations, chest tightness, shortness of breath, and precordial discomfort. Furthermore, both its morbidity and mortality rates remain persistently high. AF often leads to serious complications like heart failure and cerebral embolism, posing a significant threat to public health and substantially increasing the societal healthcare burden [ 1 ] . Therefore, selecting the most appropriate treatment for patients is of paramount importance. Current treatments for AF primarily include pharmacotherapy, electrical cardioversion, and radiofrequency catheter ablation (RFCA). Among these, RFCA has become a key therapeutic approach for restoring and maintaining sinus rhythm due to its advantages of being minimally invasive, facilitating rapid recovery, and avoiding the long-term side effects associated with antiarrhythmic drugs [ 1 , 2 ] . However, long-term follow-up indicates that the recurrence rate of AF after RFCA remains relatively high [ 3 – 5 ] . Some patients even require multiple procedures, and a considerable proportion still struggle to maintain sinus rhythm over the long term. Consequently, the ability to accurately identify, prior to surgery, the patient population more likely to benefit from ablation and maintain sinus rhythm post-procedure would help improve treatment success rates, avoid ineffective procedures, and thus optimize the clinical benefit-risk ratio of AF radiofrequency ablation [ 6 ] . Studies have shown that atrial fibrillation (AF) recurrence is closely associated with various factors, primarily including age, gender, body mass index (BMI), hyperlipidemia, history of smoking and alcohol consumption, as well as underlying conditions such as hypertension, heart failure (HF), diabetes dellitus (DM), and valvular heart disease (VHD). It is also related to left atrial structure and function. Furthermore, certain laboratory indicators such as high-sensitivity cardiac troponin I (hs-cTnI), N-terminal pro–B-type natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein (hs-CRP), uric acid (UA), and D-dimer have also been confirmed to be associated with recurrence risk [ 4 , 7 – 16 ] . However, there is currently a lack of tools in clinical practice that can accurately and individually assess patients’ recurrence risk, making clinical prediction still challenging. Therefore, this study retrospectively collected clinical data from 202 AF patients who underwent their first catheter radiofrequency ablation at the Second Hospital of Hebei Medical University between January 2019 and January 2022. Multivariate binary logistic regression analysis was used to screen for independent predictors of AF recurrence within one year after the procedure, and based on these predictors, a nomogram prediction model was constructed. This study aims to develop a straightforward and practical scoring tool to assist clinicians in more accurately assessing patients’ recurrence risk and formulating individualized treatment strategies, thereby potentially improving the long-term outcomes and quality of life of AF patients. 2. Methods 2.1. Study Design and Population This was a single‑center retrospective cohort study. Approval was obtained from the hospital ethics committee (Approval No.: 2025 − 942), with a waiver of informed consent. The study population consisted of patients with symptomatic paroxysmal or persistent atrial fibrillation who underwent their first catheter ablation in the Third Department of Cardiovascular Medicine at the Second Hospital of Hebei Medical University between January 1, 2019, and January 1, 2022. No funding was received for this study. The inclusion criteria were as follows: (1) age 18 years or older; (2) a documented diagnosis of atrial fibrillation (AF) confirmed by electrocardiogram and consistent with current international guidelines; (3) scheduled to undergo a first-time catheter ablation procedure; and (4) availability of complete preoperative clinical data, including transthoracic (TTE) and transesophageal echocardiography (TEE). Exclusion criteria included: (1) loss to follow-up within one year after the ablation; (2) a prior history of catheter ablation for AF; (3) valvular heart disease as the primary etiology of AF (i.e., valvular AF); or (4) absence of preoperative imaging data (e.g., echocardiography or cardiac CT) required for assessing left atrial size.he study flowchart is shown in Fig. 1 . Before the procedure, electrocardiography and echocardiography are performed to assess left atrial size, left ventricular size, ejection fraction (EF, %), and diastolic function (E/e′). Transesophageal echocardiography is also completed to rule out left atrial appendage thrombus. Following intraoperative disinfection, the right femoral vein is punctured, and a sheath is inserted via the femoral vein. A coronary sinus (CS) electrode is advanced into the coronary sinus. Transseptal puncture is performed, and contrast medium is injected to confirm the sheath tip is positioned in the left atrium. Heparin is administered through the femoral venous access, and the activated clotting time (ACT) is monitored intraoperatively and maintained between 300–350 seconds, with additional heparin given as needed based on ACT measurements. A Pentaray mapping catheter is introduced via the transseptal sheath, and the procedure is guided by the CARTO® system (Biosense Webster Inc, Diamond Bar, CA, USA). Ablation is performed using a contact force‑sensing saline‑irrigated catheter (ThermoCool SmartTouch® Catheter, Biosense Webster Inc, CA, USA). All patients undergo bilateral pulmonary vein isolation (PVI). The ablation process is carried out under analgesia with remifentanil. For patients with persistent atrial fibrillation, additional box isolation (i.e., additional left atrial posterior wall isolation) is applied. A meta‑analysis showed that the recurrence rate after PVI alone in persistent atrial fibrillation patients was 43% [ 17 ] . Some studies indicate that left atrial box isolation can reduce recurrence in patients with persistent atrial fibrillation [ 18 , 19 ] . Furthermore, when typical atrial flutter occurs during the procedure, additional cavotricuspid isthmus (CTI) ablation is performed. After verifying bidirectional block along the ablation lines, left atrial substrate mapping is conducted in sinus rhythm (if atrial fibrillation persists after linear ablation, electrical cardioversion is performed first) to identify any low‑voltage areas. If low‑voltage zones are present, substrate modification is performed. Postoperatively, provided there are no contraindications, oral anticoagulation (rivaroxaban, dabigatran, or warfarin) and antiarrhythmic drugs (propafenone or amiodarone) are prescribed for three months. 2.2. Data Collection Baseline clinical data were collected for these patients, including age, gender, BMI, hypertension, diabetes, heart failure, history of stroke/TIA, CHADS₂-VASc score, and medication history (antiarrhythmic drugs). Left atrial functional parameters, such as left atrial size, left ventricular ejection fraction, and left atrial appendage emptying velocity, were obtained by reviewing echocardiography reports (transthoracic or transesophageal). The catheter ablation approach and the use of intraoperative electrical cardioversion were documented. Patient electrocardiograms, 24-hour Holter monitoring results, and outpatient follow-up records were retrieved from the clinic system to assess AF recurrence. Recurrence was defined as the occurrence of atrial fibrillation, atrial flutter, or atrial tachycardia lasting more than 30 seconds within one year after ablation, with a 3-month blanking period post-procedure. All data were double-checked and entered to ensure accuracy. 2.3. Outcome Definition The primary outcome was AF recurrence within one year after the procedure, defined as the occurrence of a tachyarrhythmia (including documented atrial fibrillation, atrial flutter, or atrial tachycardia) lasting longer than 30 seconds after a 3-month blanking period following the ablation procedure. 2.4. Statistical Analysis Continuous variables were described using mean ± standard deviation (x̄ ± s) or interquartile range (IQR). Categorical variables were presented as counts and percentages (%). For comparisons of continuous variables between groups, independent samples t-tests or Mann-Whitney U non-parametric tests were used, as appropriate. Comparisons of categorical variables were performed using the chi-square test or Fisher's exact probability test. Based on relevant literature, we initially screened a series of predictor variables derived from guidelines, reviews, and existing prediction models. The following variables were assessed: gender, age, body mass index (BMI), diabetes mellitus (DM), hypercholesterolemia, coronary heart disease (CHD), pulmonary arterial hypertension (PAH), stroke, left ventricular dysfunction, CHA₂DS₂-VASc score [ 20 ] , type of AF (paroxysmal vs. persistent), use of antiarrhythmic drugs (AAD), DAT, intraoperative use of electrical cardioversion or ibutilide, as well as relevant serological markers and echocardiographic parameters. A P-value of less than 0.05 was considered statistically significant. Univariate analysis was conducted using the methods described above. Subsequently, candidate factors were entered into a multivariate logistic regression analysis. Stepwise regression was employed to identify independent predictors of recurrence and to construct the prediction model. The performance of the model was evaluated by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC) along with its 95% confidence interval (95%CI). The goodness-of-fit of the model was assessed using the Hosmer-Lemeshow test [ 21 ] . Statistical analyses were performed using R software (version 4.2.3). The "pROC 1.18.4" package was used for plotting the ROC curves. The "rms 6.7.1" package was utilized for constructing and calibrating the nomogram. The "MASS 7.3.60" package was applied for effect transformation to improve model fit. A P-value < 0.05 was considered statistically significant (two-tailed). 3. Results 3.1. Baseline Characteristics and Univariate Analysis A total of 202 patients (113 [55.9%] male, 89 [44.1%] female) were included in the study. All patients were successfully converted to sinus rhythm during the index procedure. According to the 12-month follow-up results, patients were categorized into two groups: a recurrence group (n = 66, 32.7%) and a sinus rhythm maintenance group (n = 136, 67.3%). The baseline characteristics of patients, including clinical demographics, type of atrial fibrillation, CHA₂DS₂-VASc score, diagnosis-to-ablation time (DAT), procedural details (use of electrical cardioversion or ibutilide), and echocardiographic parameters (e.g., left atrial appendage peak emptying velocity [LAAeV]), were compared between the sinus rhythm maintenance group and the recurrence group. As summarized in Table 1 , significant intergroup differences were observed in the proportion of females, DAT, and the levels of NT-proBNP, homocysteine, and LAAeV (all P 0.05). Table 1 Baseline Characteristics Sinus Rhythm Group (n = 136) Recurrence Group (n = 66) P Value Female, n (%) 48(35.3) 41(62.1) < 0.001 Age (Mean ± SD) 63.4 ± 9.4 63.0 ± 9.4 0.811 BMI (IQR) 25.4(23.4–27.9) 25.7(24.2,28.4) 0.394 Hypertension, n (%) 84(61.8) 46(69.7) 0.270 DM, n(%) 19(14.0) 11(16.7) 0.613 Hyperlipidemia, n (%) 8(5.8) 7(10.6) 0.360 CHD, n(%) 38(28.0) 17(25.8) 0.744 PAH, n(%) 1(0.8) 0(0.00) 1.000 Stroke, n (%) 18(13.24) 8(12.12) 0.824 Left Ventricular Dysfunction, n (%) 52(38.24) 24(36.36) 0.797 CHA₂DS₂-VASc Score (IQR) 3(2–4) 3(2–4) 0.342 Type of Atrial Fibrillation 0.661 Paroxysmal, n (%) 93(68.4) 42(63.6) - Persistent, n (%) 23(16.9) 11(16.7) - Unknown, n (%) 20(14.7) 13(19.7) - ATD(%) 91(66.9) 47(71.2) 0.538 DAT(IQR) 1(0.3–10) 12(1–36) < 0.001 Intraoperative Electrical Cardioversion Applied (%) 36(26.5) 14(21.2) 0.417 Intraoperative Ibutilide Applied (%) 15(11.0) 6(9.1) 0.672 NT-proBNP(IQR) 497(144.8-1164.3) 1088.5(592.0-2004.0) < 0.001 hsCRP(IQR) 1.7(0.9–3.7) 1.4(0.8–3.1) 0.493 Alb(IQR) 40.0(38.0-41.7) 39.8(38.3–42.3) 0.582 UA(IQR) 336.5(273.8-393.8) 317.0(267.0-377.3) 0.226 LDL( \(\:\overline{x}\pm\:s\) ) 2.316 ± 0.74322 2.3598 ± 0.61042 0.678 hcy(IQR) 13.3(10.7–17.6) 11.4(9.8–14.5) 0.008 INR(IQR) 1.04(1.0-1.1) 1.05(1.0-1.1) 0.547 LA(IQR) 37(34–41) 38(36–42) 0.091 LVEF(IQR) 63.1(58.3–66.0) 62.5(59.5–69.4) 0.382 E’e(IQR) 13.0(10.0-16.3) 12.0(10.0-16.3) 0.868 E/A(IQR) 0.8(0.7–1.2) 0.9(0.8–1.5) 0.101 LAAeV(IQR) 46.5(32.0-66.8) 37.0(27.8–53.0) 0.016 3.2. Multivariate Binary Logistic Regression Analysis Next, we performed a multivariate binary logistic regression analysis. The variables showing statistically significant differences in Table 1 —female gender, DAT, NT-proBNP, homocysteine, and LAAeV (P < 0.05)—were included as independent variables. Using stepwise regression to sequentially remove statistically insignificant variables, the results indicated that female gender, DAT, and LAAeV were independent factors influencing early AF recurrence after RFCA. Specifically, a higher DAT value (P = 0.024, OR 1.015, 95% CI = 1.003–1.029) and being female (P < 0.001, OR 3.287, 95% CI = 1.753–6.310) were identified as independent risk factors for postoperative recurrence, with corresponding regression coefficients of 0.015 and 1.19, respectively. Conversely, a higher echocardiographic LAAeV value (P = 0.048, OR 0.984, 95% CI = 0.969–0.999) was an independent protective factor against postoperative recurrence, with a regression coefficient of -0.016 ( Table 2 ). Subsequently, a prediction model for early AF recurrence after RFCA was constructed. The formula is: Logit(P) = − 0.824 + 1.19 × gender + 0.015 × DAT − 0.016 × LAAeV. The model fit was assessed as χ² = 5.975, and the Hosmer-Lemeshow (HL) test yielded a value of 0.650 (> 0.05), indicating good model calibration. Table 2 Model Parameters from the Multivariable Logistic Regression Analysis Estimate SE Z P Odds Ratio 95% CI Lower Upper (Intercept) -0.824 0.445 -1.851 0.064 0.439 0.181 1.042 DAT 0.015 0.007 2.261 0.024 1.015 1.003 1.029 LAAeV -0.016 0.008 -1.976 0.048 0.984 0.969 0.999 Gender 1.19 0.326 3.654 < 0.001 3.287 1.753 6.310 3.3. ROC Curves of the Three Influencing Factors Based on the multivariate logistic regression analysis, receiver operating characteristic (ROC) curve analysis was performed for the factors influencing postoperative recurrence. The results showed that for gender (male assigned as 1, female as 2), the optimal cutoff value was 2.0, with an AUC of 0.634, sensitivity of 62.1%, and specificity of 64.7%. For DAT, the optimal cutoff value was 10.0, with an AUC of 0.665, sensitivity of 57.6%, and specificity of 74.3%. For the echocardiographic parameter LAAeV, the optimal cutoff value was 62.0, with an AUC of 0.605, sensitivity of 30.1%, and specificity of 89.4%. Additionally, the optimal cutoff value for the model was calculated to be 0.34, with an AUC of 0.722, sensitivity of 69.7%, and specificity of 66.2% (Table 3 ). Table 3 Performance of Single Factors and the Prediction Model in Predicting AF Recurrence as Assessed by ROC Curves Variable AUC Optimal Cut-off Value Sensitivity Specificity Youden's Index Gender 0.634 2.0 0.621 0.647 0.268 DAT(month) 0.665 10.0 0.576 0.743 0.318 LAAeV(cm/s) 0.605 62.0 0.301 0.894 0.195 Model 0.722 0.34 0.697 0.662 0.359 Among the three variables—DAT, LAAeV, and gender—DAT had the largest AUC (Table 3 , Fig. 2 (A)). ROC curve analysis was used to compare the diagnostic performance of the best individual predictor (DAT) with that of the prediction model (Table 3 , Fig. 2 (B)). The results indicated that the prediction model outperformed DAT in terms of predictive performance (AUC: 0.722 vs. 0.665). 3.4. Construction of a Clinical Nomogram Prediction Model We developed a nomogram prediction model based on three factors—gender, DAT, and LAAeV—to estimate the risk of AF recurrence within one year after RFCA. As illustrated in the nomogram (Fig. 3 ), the assigned points increase with a higher DAT value, a lower LAAeV value, and when the patient is female. Correspondingly, a higher total score indicates an elevated risk of recurrence. 4. Discussion Catheter radiofrequency ablation for atrial fibrillation has been widely recognized as an effective treatment for symptomatic atrial fibrillation. Multicenter randomized clinical trials have demonstrated that surgical intervention yields more definitive outcomes compared to pharmacotherapy [ 22 ] . The《2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation》suggests that RFCA can serve as a first-line treatment to improve outcomes in selected symptomatic AF patients [ 23 ] . However, a subset of patients still faces a considerable risk of recurrence post-procedure, particularly in studies involving persistent AF patients, where recurrence rates range from 20% to 40% [ 20 ] , especially during the early phase after treatment [ 2 ] . The results of this study show a recurrence rate of 32.7% within one year after RFCA in AF patients. Some scholars have suggested that atrial remodeling may be a key factor contributing to AF recurrence [ 16 ] . This study aimed to construct a prediction model that incorporates multiple factors, including gender, diagnosis-to-ablation time (DAT), and left atrial function, to assess the likelihood of AF recurrence after RFCA. Our ultimate goal is to develop a non-invasive assessment tool to provide more precise and personalized treatment recommendations for patients with atrial fibrillation. In this prediction model, the time from diagnosis to ablation (DAT) carries the greatest weight, second only to the model itself. Although, as a post-hoc analysis of a randomized trial, this study has limitations such as potential inaccuracies in the timing of diagnosis, multiple clinical studies have confirmed the critical role of DAT. The CAPLA trial [ 24 ] demonstrated that patients with a longer DAT (≥ 67 months) had a significantly higher recurrence rate; however, all groups exhibited extremely low post-procedure atrial fibrillation burden and improved quality of life. This studyfurther introduced the concept of the "AF progression point," suggesting that the timing for intervention should be calculated from this point rather than the date of diagnosis [ 24 , 25 ] . Research by Yves De Greef et al. [ 26 ] indicated that the relationship between DAT and recurrence rate plateaus after exceeding 36 months, emphasizing the importance of early intervention within 3 years of diagnosis, particularly for patients with persistent AF. A systematic review also noted that a DAT of ≤ 1 year is significantly associated with a reduced recurrence risk [ 27 ] . A Study has shown that a longer disease course can exacerbate atrial fibrosis and remodeling, leading to left atrial enlargement and dysfunction, thereby increasing the recurrence risk [ 28 ] . Performing catheter ablation early in the disease course to intervene in atrial remodeling can not only improve procedural success rates but also effectively slow the progression from paroxysmal to persistent AF [ 29 ] . Thus, clinicians should pay close attention to the time interval from the initial diagnosis of AF to the performance of radiofrequency ablation. Based on the findings of this study, DAT is an independent risk factor for recurrence after AF ablation and holds the greatest weight within the predictive model. Therefore, early ablation to intervene in atrial remodeling not only improves procedural success rates but also delays AF progression. Clinicians should attach great importance to the DAT indicator, and this study confirms it as an independent risk factor for postoperative recurrence with the greatest weight in the model. The left atrial appendage (LAA), a muscular structure extending anteriorly and inferiorly from the main body of the left atrium, increases left atrial compliance by modulating its volume and pressure to a certain extent, thereby enhancing hemodynamic function. LAAeV reflects both LAA function (including contractility, fibrosis, and stunning) [ 30 ] and is associated with the contraction velocity of the left atrial wall [ 31 ] . Therefore, LAAeV represents the integrated function of the left atrium [ 32 ] and has been validated by several studies as a reliable predictor of AF recurrence [ 33 ] . In a prospective study by Fukushima et al. [ 34 ] , LAAeV was identified as a protective factor and an independent predictor of recurrence after RFCA. While some studies suggest that left atrial diameter (LAD) is a significant influencing factor for postoperative AF recurrence [ 35 ] , LAAeV appears to be a more accurate indicator for assessing left atrial function compared to LAD [ 36 ] . This study showed no statistically significant association between LAD and recurrence, a finding that may be attributed to the sample size and potential selection bias, as our center tends to select patients with smaller LAD for RFCA, resulting in a relatively low proportion of patients with larger LAD in this cohort. In this study, LAAeV emerged as a protective factor and an independent predictor of recurrence after RFCA. Thus, preoperative echocardiographic assessment of LAAeV by clinicians may help identify patients who are more likely to benefit from the procedure. Epidemiological studies indicate that the prevalence of atrial fibrillation (AF) in male patients is approximately 1.5 times that in females. Although recurrence rates following AF treatment vary by gender, female patients exhibit a more pronounced tendency for recurrence [ 37 ] . Concurrently, female AF patients often experience more severe symptoms and face a higher risk of thromboembolism [ 38 ] . This study reviewed data from hospitalized patients at our center over the past three years. Logistic regression analysis revealed that female gender may be a significant factor influencing early postoperative recurrence in AF patients, a finding consistent with previous research [ 39 ] . This discrepancy may be attributed to several factors. Current catheter ablation therapy for symptomatic AF primarily relies on pulmonary vein isolation [ 1 ] , which mainly targets the elimination of common AF triggers. However, the presence of non-pulmonary vein triggers might explain the differences in postoperative recurrence rates between genderes [ 37 ] . Therefore, during the initial radiofrequency ablation procedure, clinicians should employ more sensitive detection methods to identify non-pulmonary vein triggers, thereby improving the success rate of the first ablation—particularly for female AF patients. These findings provide new perspectives and insights for further optimizing predictive models and treatment strategies. For patients predicted to have a lower risk of recurrence, a more proactive approach in selecting radiofrequency ablation therapy should be considered to achieve rhythm control. However, as this study is single-centered with a relatively small sample size, and the data were derived from patients who consented to undergo RFCA, there exists a certain degree of selection bias. Therefore, further validation through multicenter, large-scale studies with external cohorts remains necessary. 5. Conclusion (1) gender, diagnosis-to-ablation time (DAT), and preoperative baseline left atrial appendage emptying velocity (LAAeV) may be important factors influencing early (within 1 year) recurrence after catheter ablation in patients with atrial fibrillation. (2) A model combining these three factors can effectively predict early postoperative AF recurrence. (3) The nomogram we developed can also serve as a non‑invasive preoperative assessment tool prior to radiofrequency catheter ablation (RFCA), providing valuable reference information for clinicians. Declarations Acknowledgements We would like to thank our colleagues in the Electrocardiography Department and Cardiac Ultrasound Department of our hospital for their assistance during data collection. We are also grateful to the experts at the Statistics Center for their guidance on data analysis. Author Contributions WenYu Zhang: Conceptualization, Methodology, Data Curation, Writing – Original Draft Preparation, Visualization, Software. JunWei Wang: Resources, Data Curation, Writing – Review & Editing. MinZhe Zhu, HaoZe Chen, ZongHong Wu: Validation, Writing – Review & Editing. Jie Hao: Conceptualization, Design, Supervision, Final Approval Funding This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of Competing Interest All authors declare no competing interests. Data Availability Statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. Ethical Approval Statement This study involved human participants and was approved by the Ethics Committee of The Second Hospital of Hebei Medical University (Approval No.: 2025-R942). 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Int J Cardiol. 2013;164:82–7. https://doi.org/10.1016/j.ijcard.2011.06.094 . Zink MD, Chua W, Zeemering S, Di Biase L, Antoni BDL, David C, et al. Predictors of recurrence of atrial fibrillation within the first 3 months after ablation. EP Europace. 2020;22:1337–44. https://doi.org/10.1093/europace/euaa132 . Ma X, Zhang X, Guo W. Factors to Predict Recurrence of Atrial Fibrillation in Patients with Hypertension. Clin Cardiol. 2009;32:264–8. https://doi.org/10.1002/clc.20449 . Vizzardi E, Curnis A, Latini MG, Salghetti F, Rocco E, Lupi L, et al. Risk factors for atrial fibrillation recurrence: a literature review. J Cardiovasc Med. 2014;15:235–53. https://doi.org/10.2459/JCM.0b013e328358554b . Marrouche N. Left Atrial Fibrosis and Recurrent Arrhythmia—Reply. JAMA. 2014;311:2335. https://doi.org/10.1001/jama.2014.5187 . Clarnette JA, Brooks AG, Mahajan R, Elliott AD, Twomey DJ, Pathak RK et al. Outcomes of persistent and long-standing persistent atrial fibrillation ablation: a systematic review and meta-analysis. EP Europace. 2018;20 FI_3:f366–76. https://doi.org/10.1093/europace/eux297 Ahn J, Shin DG, Han SJ, Lim HE. Does isolation of the left atrial posterior wall using cryoballoon ablation improve clinical outcomes in patients with persistent atrial fibrillation? A prospective randomized controlled trial. EP Europace. 2022;24:1093–101. https://doi.org/10.1093/europace/euac005 . Aryana A, Allen SL, Pujara DK, Bowers MR, O’Neill PG, Yamauchi Y, et al. Concomitant Pulmonary Vein and Posterior Wall Isolation Using Cryoballoon With Adjunct Radiofrequency in Persistent Atrial Fibrillation. JACC: Clin Electrophysiol. 2021;7:187–96. https://doi.org/10.1016/j.jacep.2020.08.016 . Calkins H, Kuck KH, Cappato R, Brugada J, Camm AJ, Chen S-A, et al. 2012 HRS/EHRA/ECAS expert consensus statement on catheter and surgical ablation of atrial fibrillation: recommendations for patient selection, procedural techniques, patient management and follow-up, definitions, endpoints, and research trial design. J Interv Card Electrophysiol. 2012;33:171–257. https://doi.org/10.1007/s10840-012-9672-7 . Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration. Ann Intern Med. 2015;162:W1–73. https://doi.org/10.7326/M14-0698 . Morillo CA, Verma A, Connolly SJ, Kuck KH, Nair GM, Champagne J, et al. Radiofrequency Ablation vs Antiarrhythmic Drugs as First-Line Treatment of Paroxysmal Atrial Fibrillation (RAAFT-2): A Randomized Trial. JAMA. 2014;311:692. https://doi.org/10.1001/jama.2014.467 . Joglar JA, Chung MK, Armbruster AL, Benjamin EJ, Chyou JY, Cronin EM, et al. 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2024;149. https://doi.org/10.1161/CIR.0000000000001193 . Crowley R, Lim MW, Chieng D, Segan L, William J, Morton JB, et al. Diagnosis to Ablation in Persistent AF. JACC: Clin Electrophysiol. 2024;10:1689–99. https://doi.org/10.1016/j.jacep.2024.05.031 . Crowley R, Chieng D, Segan L, William J, Sugumar H, Prabhu S, et al. Persistent Atrial Fibrillation Phenotypes and Ablation Outcomes. JACC: Clin Electrophysiol. 2025;11:10–8. https://doi.org/10.1016/j.jacep.2024.09.018 . De Greef Y, Bogaerts K, Sofianos D, Buysschaert I. Impact of Diagnosis-to-Ablation Time on AF Recurrence. JACC: Clin Electrophysiol. 2023;9:2263–72. https://doi.org/10.1016/j.jacep.2023.07.008 . Lycke M, Kyriakopoulou M, El Haddad M, Wielandts J-Y, Hilfiker G, Almorad A, et al. Predictors of recurrence after durable pulmonary vein isolation for paroxysmal atrial fibrillation. EP Europace. 2021;23:861–7. https://doi.org/10.1093/europace/euaa383 . Strisciuglio T, El Haddad M, Debonnaire P, De Pooter J, Demolder A, Wolf M, et al. Paroxysmal atrial fibrillation with high vs. low arrhythmia burden: atrial remodelling and ablation outcome. EP Europace. 2020;22:1189–96. https://doi.org/10.1093/europace/euaa071 . Hof I, Chilukuri K, Arbab-Zadeh A, Scherr D, Dalal D, Nazarian S, et al. Does Left Atrial Volume and Pulmonary Venous Anatomy Predict the Outcome of Catheter Ablation of Atrial Fibrillation? Cardiovasc electrophysiol. 2009;20:1005–10. https://doi.org/10.1111/j.1540-8167.2009.01504.x . He Y, Zhang B, Zhu F, Hu Z, Zhong J, Zhu W. Transesophageal echocardiography measures left atrial appendage volume and function and predicts recurrence of paroxysmal atrial fibrillation after radiofrequency catheter ablation. Echocardiography. 2018;35:985–90. https://doi.org/10.1111/echo.13856 . Yoshida N, Okamoto M, Beppu S. Validation of Transthoracic Tissue Doppler Assessment of Left Atrial Appendage Function. J Am Soc Echocardiogr. 2007;20:521–6. https://doi.org/10.1016/j.echo.2006.10.010 . Kanda T, Masuda M, Sunaga A, Fujita M, Iida O, Okamoto S, et al. Low left atrial appendage flow velocity predicts recurrence of atrial fibrillation after catheter ablation of persistent atrial fibrillation. J Cardiol. 2015;66:377–81. https://doi.org/10.1016/j.jjcc.2015.04.009 . Han S, Liu M, Jia R, Cen Z, Guo R, Liu G, et al. Left atrial appendage function and structure predictors of recurrent atrial fibrillation after catheter ablation: A meta-analysis of observational studies. Front Cardiovasc Med. 2022;9:1009494. https://doi.org/10.3389/fcvm.2022.1009494 . Fukushima K, Fukushima N, Ejima K, Kato K, Sato Y, Uematsu S, et al. Left Atrial Appendage Flow Velocity and Time from P-Wave Onset to Tissue Doppler–Derived A’ Predict Atrial Fibrillation Recurrence after Radiofrequency Catheter Ablation. Echocardiography. 2015;32:1101–8. https://doi.org/10.1111/echo.12823 . Wang Q, Zhuo C, Shang Y, Zhao J, Chen N, Lv N, et al. U-Shaped Relationship Between Left Atrium Size on Echocardiography and 1-Year Recurrence of Atrial Fibrillation After Radiofrequency Catheter Ablation - Prognostic Value Study. Circ J. 2019;83:1463–71. https://doi.org/10.1253/circj.CJ-19-0167 . Tian X, Zhang X-J, Yuan Y-F, Li C-Y, Zhou L-X, Gao B-L. Morphological and functional parameters of left atrial appendage play a greater role in atrial fibrillation relapse after radiofrequency ablation. Sci Rep. 2020;10:8072. https://doi.org/10.1038/s41598-020-65056-3 . Park YJ, Park J-W, Yu HT, Kim T-H, Uhm J-S, Joung B, et al. Sex difference in atrial fibrillation recurrence after catheter ablation and antiarrhythmic drugs. Heart. 2023;109:519–26. https://doi.org/10.1136/heartjnl-2021-320601 . Kannel WB, Wolf PA, Benjamin EJ, Levy D. Prevalence, incidence, prognosis, and predisposing conditions for atrial fibrillation: population-based estimates 11Reprints are not available. Am J Cardiol. 1998;82. https://doi.org/10.1016/S0002-9149(98)00583-9 . :2N-9N. Patel D, Mohanty P, Di Biase L, Sanchez JE, Shaheen MH, Burkhardt JD, et al. Outcomes and complications of catheter ablation for atrial fibrillation in females. Heart Rhythm. 2010;7:167–72. https://doi.org/10.1016/j.hrthm.2009.10.025 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8687602","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614575489,"identity":"44f759f6-2411-4ecf-85e7-77b1dcfc6eea","order_by":0,"name":"WenYu Zhang","email":"","orcid":"","institution":"Third Department of Cardiovascular Medicine, The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"WenYu","middleName":"","lastName":"Zhang","suffix":""},{"id":614575490,"identity":"0bb1c642-dec4-45f8-ab10-da8a74ee9e37","order_by":1,"name":"JunWei Wang","email":"","orcid":"","institution":"Third Department of Cardiovascular Medicine, The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"JunWei","middleName":"","lastName":"Wang","suffix":""},{"id":614575491,"identity":"5f0d7185-e091-4e59-bfa9-8e504087114d","order_by":2,"name":"MinZhe Zhu","email":"","orcid":"","institution":"Third Department of Cardiovascular Medicine, The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"MinZhe","middleName":"","lastName":"Zhu","suffix":""},{"id":614575492,"identity":"224acf8e-1e03-4c3e-bc9d-74c26051ea5e","order_by":3,"name":"HaoZe Chen","email":"","orcid":"","institution":"Third Department of Cardiovascular Medicine, The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"HaoZe","middleName":"","lastName":"Chen","suffix":""},{"id":614575493,"identity":"1f7c19a9-e948-49fd-9b35-4d2070770b43","order_by":4,"name":"ZongHong Wu","email":"","orcid":"","institution":"Third Department of Cardiovascular Medicine, The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"ZongHong","middleName":"","lastName":"Wu","suffix":""},{"id":614575494,"identity":"2c05f6ea-bcd9-4b76-a14c-de67622cd113","order_by":5,"name":"Jie Hao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACAxBOALGONzY+/ECaljOHm40liNUCATfS2wR4iNFizn74QMGDim1yfDcftjFIMNjJ6TYQ0GLZk5ZgkHDmtrHk7cS2BwUMycZmBwg57ECOgUFi2+3EDbcT2w0kGA4kbiOo5fwboJZ/t+s33DzYJsFDlJYbIFsabicY3GAkWsszoF+O3TaceSYRGMgGxPjlfPIxwx81t+X5jh9/+PBDhZ0cQS1AwIaIG6RowguYHxCnbhSMglEwCkYsAADOi0sjytNWKgAAAABJRU5ErkJggg==","orcid":"","institution":"Third Department of Cardiovascular Medicine, The Second Hospital of Hebei Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jie","middleName":"","lastName":"Hao","suffix":""}],"badges":[],"createdAt":"2026-01-24 14:54:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8687602/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8687602/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106070245,"identity":"f921f2ac-fe36-42c7-9881-93c7744d976b","added_by":"auto","created_at":"2026-04-03 06:26:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63885,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant inclusion, exclusion, and group allocation\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8687602/v1/311d9f9338f2958cb4d8e428.jpg"},{"id":106070246,"identity":"4a9ab8e9-70b1-4662-84ff-dadbb169018a","added_by":"auto","created_at":"2026-04-03 06:26:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90906,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves comparing individual factors and the prediction model in predicting AF recurrence.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8687602/v1/aa06d6d841cf9ba3ef16f5ca.jpg"},{"id":106070247,"identity":"06a5b36a-371f-4319-b6b0-bff761477fa3","added_by":"auto","created_at":"2026-04-03 06:26:52","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42262,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting the risk of atrial fibrillation (AF) recurrence after radiofrequency catheter ablation (RFCA) based on gender, diagnosis-to-ablation time (DAT), and left atrial appendage emptying velocity (LAAeV)\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8687602/v1/475635a22a92a582b7c38fce.jpg"},{"id":106095076,"identity":"1f52bda3-35b4-4243-b976-04bb4040bf45","added_by":"auto","created_at":"2026-04-03 11:44:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":976830,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8687602/v1/7ca55052-7c07-4ac8-9a77-933a606d9f7d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A predictive model using gender, the diagnosis-to-ablation time and left atrial function in predicting the recurrence of atrial fibrillation within 1 year after radiofrequency catheter ablation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAtrial fibrillation (AF) is one of the most common cardiac arrhythmias, characterized by rapid and disorganized electrical activity in the atria. Its clinical presentation ranges from being asymptomatic to symptoms such as palpitations, chest tightness, shortness of breath, and precordial discomfort. Furthermore, both its morbidity and mortality rates remain persistently high. AF often leads to serious complications like heart failure and cerebral embolism, posing a significant threat to public health and substantially increasing the societal healthcare burden\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Therefore, selecting the most appropriate treatment for patients is of paramount importance.\u003c/p\u003e \u003cp\u003eCurrent treatments for AF primarily include pharmacotherapy, electrical cardioversion, and radiofrequency catheter ablation (RFCA). Among these, RFCA has become a key therapeutic approach for restoring and maintaining sinus rhythm due to its advantages of being minimally invasive, facilitating rapid recovery, and avoiding the long-term side effects associated with antiarrhythmic drugs\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. However, long-term follow-up indicates that the recurrence rate of AF after RFCA remains relatively high\u003csup\u003e[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Some patients even require multiple procedures, and a considerable proportion still struggle to maintain sinus rhythm over the long term. Consequently, the ability to accurately identify, prior to surgery, the patient population more likely to benefit from ablation and maintain sinus rhythm post-procedure would help improve treatment success rates, avoid ineffective procedures, and thus optimize the clinical benefit-risk ratio of AF radiofrequency ablation\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStudies have shown that atrial fibrillation (AF) recurrence is closely associated with various factors, primarily including age, gender, body mass index (BMI), hyperlipidemia, history of smoking and alcohol consumption, as well as underlying conditions such as hypertension, heart failure (HF), diabetes dellitus (DM), and valvular heart disease (VHD). It is also related to left atrial structure and function. Furthermore, certain laboratory indicators such as high-sensitivity cardiac troponin I (hs-cTnI), N-terminal pro\u0026ndash;B-type natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein (hs-CRP), uric acid (UA), and D-dimer have also been confirmed to be associated with recurrence risk\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. However, there is currently a lack of tools in clinical practice that can accurately and individually assess patients\u0026rsquo; recurrence risk, making clinical prediction still challenging.\u003c/p\u003e \u003cp\u003eTherefore, this study retrospectively collected clinical data from 202 AF patients who underwent their first catheter radiofrequency ablation at the Second Hospital of Hebei Medical University between January 2019 and January 2022. Multivariate binary logistic regression analysis was used to screen for independent predictors of AF recurrence within one year after the procedure, and based on these predictors, a nomogram prediction model was constructed. This study aims to develop a straightforward and practical scoring tool to assist clinicians in more accurately assessing patients\u0026rsquo; recurrence risk and formulating individualized treatment strategies, thereby potentially improving the long-term outcomes and quality of life of AF patients.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design and Population\u003c/h2\u003e \u003cp\u003eThis was a single‑center retrospective cohort study. Approval was obtained from the hospital ethics committee (Approval No.: 2025\u0026thinsp;\u0026minus;\u0026thinsp;942), with a waiver of informed consent. The study population consisted of patients with symptomatic paroxysmal or persistent atrial fibrillation who underwent their first catheter ablation in the Third Department of Cardiovascular Medicine at the Second Hospital of Hebei Medical University between January 1, 2019, and January 1, 2022. No funding was received for this study.\u003c/p\u003e \u003cp\u003e The inclusion criteria were as follows: (1) age 18 years or older; (2) a documented diagnosis of atrial fibrillation (AF) confirmed by electrocardiogram and consistent with current international guidelines; (3) scheduled to undergo a first-time catheter ablation procedure; and (4) availability of complete preoperative clinical data, including transthoracic (TTE) and transesophageal echocardiography (TEE). Exclusion criteria included: (1) loss to follow-up within one year after the ablation; (2) a prior history of catheter ablation for AF; (3) valvular heart disease as the primary etiology of AF (i.e., valvular AF); or (4) absence of preoperative imaging data (e.g., echocardiography or cardiac CT) required for assessing left atrial size.he study flowchart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBefore the procedure, electrocardiography and echocardiography are performed to assess left atrial size, left ventricular size, ejection fraction (EF, %), and diastolic function (E/e\u0026prime;). Transesophageal echocardiography is also completed to rule out left atrial appendage thrombus. Following intraoperative disinfection, the right femoral vein is punctured, and a sheath is inserted via the femoral vein. A coronary sinus (CS) electrode is advanced into the coronary sinus. Transseptal puncture is performed, and contrast medium is injected to confirm the sheath tip is positioned in the left atrium. Heparin is administered through the femoral venous access, and the activated clotting time (ACT) is monitored intraoperatively and maintained between 300\u0026ndash;350 seconds, with additional heparin given as needed based on ACT measurements. A Pentaray mapping catheter is introduced via the transseptal sheath, and the procedure is guided by the CARTO\u0026reg; system (Biosense Webster Inc, Diamond Bar, CA, USA). Ablation is performed using a contact force‑sensing saline‑irrigated catheter (ThermoCool SmartTouch\u0026reg; Catheter, Biosense Webster Inc, CA, USA). All patients undergo bilateral pulmonary vein isolation (PVI). The ablation process is carried out under analgesia with remifentanil. For patients with persistent atrial fibrillation, additional box isolation (i.e., additional left atrial posterior wall isolation) is applied. A meta‑analysis showed that the recurrence rate after PVI alone in persistent atrial fibrillation patients was 43%\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Some studies indicate that left atrial box isolation can reduce recurrence in patients with persistent atrial fibrillation\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Furthermore, when typical atrial flutter occurs during the procedure, additional cavotricuspid isthmus (CTI) ablation is performed. After verifying bidirectional block along the ablation lines, left atrial substrate mapping is conducted in sinus rhythm (if atrial fibrillation persists after linear ablation, electrical cardioversion is performed first) to identify any low‑voltage areas. If low‑voltage zones are present, substrate modification is performed. Postoperatively, provided there are no contraindications, oral anticoagulation (rivaroxaban, dabigatran, or warfarin) and antiarrhythmic drugs (propafenone or amiodarone) are prescribed for three months.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Collection\u003c/h2\u003e \u003cp\u003eBaseline clinical data were collected for these patients, including age, gender, BMI, hypertension, diabetes, heart failure, history of stroke/TIA, CHADS₂-VASc score, and medication history (antiarrhythmic drugs). Left atrial functional parameters, such as left atrial size, left ventricular ejection fraction, and left atrial appendage emptying velocity, were obtained by reviewing echocardiography reports (transthoracic or transesophageal). The catheter ablation approach and the use of intraoperative electrical cardioversion were documented. Patient electrocardiograms, 24-hour Holter monitoring results, and outpatient follow-up records were retrieved from the clinic system to assess AF recurrence. Recurrence was defined as the occurrence of atrial fibrillation, atrial flutter, or atrial tachycardia lasting more than 30 seconds within one year after ablation, with a 3-month blanking period post-procedure. All data were double-checked and entered to ensure accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Outcome Definition\u003c/h2\u003e \u003cp\u003eThe primary outcome was AF recurrence within one year after the procedure, defined as the occurrence of a tachyarrhythmia (including documented atrial fibrillation, atrial flutter, or atrial tachycardia) lasting longer than 30 seconds after a 3-month blanking period following the ablation procedure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were described using mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̄ \u0026plusmn; s) or interquartile range (IQR). Categorical variables were presented as counts and percentages (%). For comparisons of continuous variables between groups, independent samples t-tests or Mann-Whitney U non-parametric tests were used, as appropriate. Comparisons of categorical variables were performed using the chi-square test or Fisher's exact probability test.\u003c/p\u003e \u003cp\u003e Based on relevant literature, we initially screened a series of predictor variables derived from guidelines, reviews, and existing prediction models. The following variables were assessed: gender, age, body mass index (BMI), diabetes mellitus (DM), hypercholesterolemia, coronary heart disease (CHD), pulmonary arterial hypertension (PAH), stroke, left ventricular dysfunction, CHA₂DS₂-VASc score\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, type of AF (paroxysmal vs. persistent), use of antiarrhythmic drugs (AAD), DAT, intraoperative use of electrical cardioversion or ibutilide, as well as relevant serological markers and echocardiographic parameters. A P-value of less than 0.05 was considered statistically significant. Univariate analysis was conducted using the methods described above.\u003c/p\u003e \u003cp\u003eSubsequently, candidate factors were entered into a multivariate logistic regression analysis. Stepwise regression was employed to identify independent predictors of recurrence and to construct the prediction model. The performance of the model was evaluated by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC) along with its 95% confidence interval (95%CI). The goodness-of-fit of the model was assessed using the Hosmer-Lemeshow test \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed using R software (version 4.2.3). The \"pROC 1.18.4\" package was used for plotting the ROC curves. The \"rms 6.7.1\" package was utilized for constructing and calibrating the nomogram. The \"MASS 7.3.60\" package was applied for effect transformation to improve model fit. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant (two-tailed).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Baseline Characteristics and Univariate Analysis\u003c/h2\u003e \u003cp\u003eA total of 202 patients (113 [55.9%] male, 89 [44.1%] female) were included in the study. All patients were successfully converted to sinus rhythm during the index procedure. According to the 12-month follow-up results, patients were categorized into two groups: a recurrence group (n\u0026thinsp;=\u0026thinsp;66, 32.7%) and a sinus rhythm maintenance group (n\u0026thinsp;=\u0026thinsp;136, 67.3%).\u003c/p\u003e \u003cp\u003eThe baseline characteristics of patients, including clinical demographics, type of atrial fibrillation, CHA₂DS₂-VASc score, diagnosis-to-ablation time (DAT), procedural details (use of electrical cardioversion or ibutilide), and echocardiographic parameters (e.g., left atrial appendage peak emptying velocity [LAAeV]), were compared between the sinus rhythm maintenance group and the recurrence group. As summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, significant intergroup differences were observed in the proportion of females, DAT, and the levels of NT-proBNP, homocysteine, and LAAeV (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No statistically significant differences were found for the remaining variables (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSinus Rhythm Group (n\u0026thinsp;=\u0026thinsp;136)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecurrence Group (n\u0026thinsp;=\u0026thinsp;66)\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\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003eAge (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.4(23.4\u0026ndash;27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.7(24.2,28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84(61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(69.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHD, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAH, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(13.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(12.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Ventricular Dysfunction, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52(38.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(36.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHA₂DS₂-VASc Score (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(2\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(2\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of Atrial Fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParoxysmal, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93(68.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42(63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersistent, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATD(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91(66.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(71.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAT(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.3\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(1\u0026ndash;36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003eIntraoperative Electrical Cardioversion Applied (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36(26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntraoperative Ibutilide Applied (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT-proBNP(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e497(144.8-1164.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1088.5(592.0-2004.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003ehsCRP(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7(0.9\u0026ndash;3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4(0.8\u0026ndash;3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.0(38.0-41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.8(38.3\u0026ndash;42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336.5(273.8-393.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e317.0(267.0-377.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\overline{x}\\pm\\:s\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.316\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3598\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehcy(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.3(10.7\u0026ndash;17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.4(9.8\u0026ndash;14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04(1.0-1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05(1.0-1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(34\u0026ndash;41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(36\u0026ndash;42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.1(58.3\u0026ndash;66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5(59.5\u0026ndash;69.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u0026rsquo;e(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.0(10.0-16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.0(10.0-16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE/A(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8(0.7\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9(0.8\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAAeV(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.5(32.0-66.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.0(27.8\u0026ndash;53.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\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=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Multivariate Binary Logistic Regression Analysis\u003c/h2\u003e \u003cp\u003eNext, we performed a multivariate binary logistic regression analysis. The variables showing statistically significant differences in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026mdash;female gender, DAT, NT-proBNP, homocysteine, and LAAeV (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u0026mdash;were included as independent variables. Using stepwise regression to sequentially remove statistically insignificant variables, the results indicated that female gender, DAT, and LAAeV were independent factors influencing early AF recurrence after RFCA. Specifically, a higher DAT value (P\u0026thinsp;=\u0026thinsp;0.024, OR 1.015, 95% CI\u0026thinsp;=\u0026thinsp;1.003\u0026ndash;1.029) and being female (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR 3.287, 95% CI\u0026thinsp;=\u0026thinsp;1.753\u0026ndash;6.310) were identified as independent risk factors for postoperative recurrence, with corresponding regression coefficients of 0.015 and 1.19, respectively. Conversely, a higher echocardiographic LAAeV value (P\u0026thinsp;=\u0026thinsp;0.048, OR 0.984, 95% CI\u0026thinsp;=\u0026thinsp;0.969\u0026ndash;0.999) was an independent protective factor against postoperative recurrence, with a regression coefficient of -0.016 ( Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubsequently, a prediction model for early AF recurrence after RFCA was constructed. The formula is: Logit(P)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.824\u0026thinsp;+\u0026thinsp;1.19 \u0026times; gender\u0026thinsp;+\u0026thinsp;0.015 \u0026times; DAT\u0026thinsp;\u0026minus;\u0026thinsp;0.016 \u0026times; LAAeV. The model fit was assessed as χ\u0026sup2; = 5.975, and the Hosmer-Lemeshow (HL) test yielded a value of 0.650 (\u0026gt;\u0026thinsp;0.05), indicating good model calibration.\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\u003eModel Parameters from the Multivariable Logistic Regression Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAAeV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.310\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=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. ROC Curves of the Three Influencing Factors\u003c/h2\u003e \u003cp\u003eBased on the multivariate logistic regression analysis, receiver operating characteristic (ROC) curve analysis was performed for the factors influencing postoperative recurrence. The results showed that for gender (male assigned as 1, female as 2), the optimal cutoff value was 2.0, with an AUC of 0.634, sensitivity of 62.1%, and specificity of 64.7%. For DAT, the optimal cutoff value was 10.0, with an AUC of 0.665, sensitivity of 57.6%, and specificity of 74.3%. For the echocardiographic parameter LAAeV, the optimal cutoff value was 62.0, with an AUC of 0.605, sensitivity of 30.1%, and specificity of 89.4%. Additionally, the optimal cutoff value for the model was calculated to be 0.34, with an AUC of 0.722, sensitivity of 69.7%, and specificity of 66.2% (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\u003ePerformance of Single Factors and the Prediction Model in Predicting AF Recurrence as Assessed by ROC Curves\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOptimal Cut-off Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYouden's Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAT(month)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAAeV(cm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the three variables\u0026mdash;DAT, LAAeV, and gender\u0026mdash;DAT had the largest AUC (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(A)). ROC curve analysis was used to compare the diagnostic performance of the best individual predictor (DAT) with that of the prediction model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(B)). The results indicated that the prediction model outperformed DAT in terms of predictive performance (AUC: 0.722 vs. 0.665).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Construction of a Clinical Nomogram Prediction Model\u003c/h2\u003e \u003cp\u003eWe developed a nomogram prediction model based on three factors\u0026mdash;gender, DAT, and LAAeV\u0026mdash;to estimate the risk of AF recurrence within one year after RFCA. As illustrated in the nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the assigned points increase with a higher DAT value, a lower LAAeV value, and when the patient is female. Correspondingly, a higher total score indicates an elevated risk of recurrence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCatheter radiofrequency ablation for atrial fibrillation has been widely recognized as an effective treatment for symptomatic atrial fibrillation. Multicenter randomized clinical trials have demonstrated that surgical intervention yields more definitive outcomes compared to pharmacotherapy\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The《2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation》suggests that RFCA can serve as a first-line treatment to improve outcomes in selected symptomatic AF patients\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. However, a subset of patients still faces a considerable risk of recurrence post-procedure, particularly in studies involving persistent AF patients, where recurrence rates range from 20% to 40%\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, especially during the early phase after treatment\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The results of this study show a recurrence rate of 32.7% within one year after RFCA in AF patients. Some scholars have suggested that atrial remodeling may be a key factor contributing to AF recurrence\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. This study aimed to construct a prediction model that incorporates multiple factors, including gender, diagnosis-to-ablation time (DAT), and left atrial function, to assess the likelihood of AF recurrence after RFCA. Our ultimate goal is to develop a non-invasive assessment tool to provide more precise and personalized treatment recommendations for patients with atrial fibrillation.\u003c/p\u003e \u003cp\u003eIn this prediction model, the time from diagnosis to ablation (DAT) carries the greatest weight, second only to the model itself. Although, as a post-hoc analysis of a randomized trial, this study has limitations such as potential inaccuracies in the timing of diagnosis, multiple clinical studies have confirmed the critical role of DAT. The CAPLA trial\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e demonstrated that patients with a longer DAT (\u0026ge;\u0026thinsp;67 months) had a significantly higher recurrence rate; however, all groups exhibited extremely low post-procedure atrial fibrillation burden and improved quality of life. This studyfurther introduced the concept of the \"AF progression point,\" suggesting that the timing for intervention should be calculated from this point rather than the date of diagnosis\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Research by Yves De Greef et al.\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e indicated that the relationship between DAT and recurrence rate plateaus after exceeding 36 months, emphasizing the importance of early intervention within 3 years of diagnosis, particularly for patients with persistent AF. A systematic review also noted that a DAT of \u0026le;\u0026thinsp;1 year is significantly associated with a reduced recurrence risk\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. A Study has shown that a longer disease course can exacerbate atrial fibrosis and remodeling, leading to left atrial enlargement and dysfunction, thereby increasing the recurrence risk\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Performing catheter ablation early in the disease course to intervene in atrial remodeling can not only improve procedural success rates but also effectively slow the progression from paroxysmal to persistent AF\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Thus, clinicians should pay close attention to the time interval from the initial diagnosis of AF to the performance of radiofrequency ablation. Based on the findings of this study, DAT is an independent risk factor for recurrence after AF ablation and holds the greatest weight within the predictive model. Therefore, early ablation to intervene in atrial remodeling not only improves procedural success rates but also delays AF progression. Clinicians should attach great importance to the DAT indicator, and this study confirms it as an independent risk factor for postoperative recurrence with the greatest weight in the model.\u003c/p\u003e \u003cp\u003eThe left atrial appendage (LAA), a muscular structure extending anteriorly and inferiorly from the main body of the left atrium, increases left atrial compliance by modulating its volume and pressure to a certain extent, thereby enhancing hemodynamic function. LAAeV reflects both LAA function (including contractility, fibrosis, and stunning) \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003eand is associated with the contraction velocity of the left atrial wall\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Therefore, LAAeV represents the integrated function of the left atrium\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e and has been validated by several studies as a reliable predictor of AF recurrence\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. In a prospective study by Fukushima et al.\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, LAAeV was identified as a protective factor and an independent predictor of recurrence after RFCA. While some studies suggest that left atrial diameter (LAD) is a significant influencing factor for postoperative AF recurrence\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, LAAeV appears to be a more accurate indicator for assessing left atrial function compared to LAD\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. This study showed no statistically significant association between LAD and recurrence, a finding that may be attributed to the sample size and potential selection bias, as our center tends to select patients with smaller LAD for RFCA, resulting in a relatively low proportion of patients with larger LAD in this cohort. In this study, LAAeV emerged as a protective factor and an independent predictor of recurrence after RFCA. Thus, preoperative echocardiographic assessment of LAAeV by clinicians may help identify patients who are more likely to benefit from the procedure.\u003c/p\u003e \u003cp\u003eEpidemiological studies indicate that the prevalence of atrial fibrillation (AF) in male patients is approximately 1.5 times that in females. Although recurrence rates following AF treatment vary by gender, female patients exhibit a more pronounced tendency for recurrence\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Concurrently, female AF patients often experience more severe symptoms and face a higher risk of thromboembolism\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. This study reviewed data from hospitalized patients at our center over the past three years. Logistic regression analysis revealed that female gender may be a significant factor influencing early postoperative recurrence in AF patients, a finding consistent with previous research\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis discrepancy may be attributed to several factors. Current catheter ablation therapy for symptomatic AF primarily relies on pulmonary vein isolation\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, which mainly targets the elimination of common AF triggers. However, the presence of non-pulmonary vein triggers might explain the differences in postoperative recurrence rates between genderes\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Therefore, during the initial radiofrequency ablation procedure, clinicians should employ more sensitive detection methods to identify non-pulmonary vein triggers, thereby improving the success rate of the first ablation\u0026mdash;particularly for female AF patients.\u003c/p\u003e \u003cp\u003eThese findings provide new perspectives and insights for further optimizing predictive models and treatment strategies. For patients predicted to have a lower risk of recurrence, a more proactive approach in selecting radiofrequency ablation therapy should be considered to achieve rhythm control. However, as this study is single-centered with a relatively small sample size, and the data were derived from patients who consented to undergo RFCA, there exists a certain degree of selection bias. Therefore, further validation through multicenter, large-scale studies with external cohorts remains necessary.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003e(1) gender, diagnosis-to-ablation time (DAT), and preoperative baseline left atrial appendage emptying velocity (LAAeV) may be important factors influencing early (within 1 year) recurrence after catheter ablation in patients with atrial fibrillation.\u003c/p\u003e \u003cp\u003e(2) A model combining these three factors can effectively predict early postoperative AF recurrence.\u003c/p\u003e \u003cp\u003e(3) The nomogram we developed can also serve as a non‑invasive preoperative assessment tool prior to radiofrequency catheter ablation (RFCA), providing valuable reference information for clinicians.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe would like to thank our colleagues in the Electrocardiography Department and Cardiac Ultrasound Department of our hospital for their assistance during data collection. We are also grateful to the experts at the Statistics Center for their guidance on data analysis.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eWenYu Zhang: Conceptualization, Methodology, Data Curation, Writing \u0026ndash; Original Draft Preparation, Visualization, Software.\u003c/p\u003e\n\u003cp\u003eJunWei Wang: Resources, Data Curation, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eMinZhe Zhu, HaoZe Chen, ZongHong Wu: Validation, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eJie Hao: Conceptualization, Design, Supervision, Final Approval\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eDeclaration of Competing Interest\u003c/p\u003e\n\u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eEthical Approval Statement\u003c/p\u003e\n\u003cp\u003eThis study involved human participants and was approved by the Ethics Committee of The Second Hospital of Hebei Medical University (Approval No.: 2025-R942).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVan Gelder IC, Rienstra M, Bunting KV, Casado-Arroyo R, Caso V, Crijns HJGM, et al. 2024 ESC Guidelines for the management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS). 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Heart. 2023;109:519\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/heartjnl-2021-320601\u003c/span\u003e\u003cspan address=\"10.1136/heartjnl-2021-320601\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKannel WB, Wolf PA, Benjamin EJ, Levy D. Prevalence, incidence, prognosis, and predisposing conditions for atrial fibrillation: population-based estimates 11Reprints are not available. Am J Cardiol. 1998;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0002-9149(98)00583-9\u003c/span\u003e\u003cspan address=\"10.1016/S0002-9149(98)00583-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. :2N-9N.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel D, Mohanty P, Di Biase L, Sanchez JE, Shaheen MH, Burkhardt JD, et al. 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Heart Rhythm. 2010;7:167\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.hrthm.2009.10.025\u003c/span\u003e\u003cspan address=\"10.1016/j.hrthm.2009.10.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Atrial Fibrillation, Postoperative Recurrence, Catheter Radiofrequency Ablation, Logistic Regression, Prediction Model, Diagnosis-to-Ablation Time, Left Atrial Appendage Peak Emptying Velocity","lastPublishedDoi":"10.21203/rs.3.rs-8687602/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8687602/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e Radiofrequency catheter ablation (RFCA) serves as a first‑line treatment for symptomatic atrial fibrillation (AF); however, its clinical application is still challenged by a relatively high postoperative recurrence rate. Therefore, numerous studies have focused on identifying factors associated with AF recurrence after RFCA, such as atrial size, gender, diagnosis‑to‑ablation time (DAT), and echocardiographic indices reflecting left atrial function. This study aims to investigate the risk factors for AF recurrence within one year after RFCA and to develop a simple yet effective prediction model based on these factors to guide clinical practice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This was a single-center retrospective study. We enrolled patients with atrial fibrillation (n=202) who underwent their first catheter radiofrequency ablation in the Third Department of Cardiology at the Second Hospital of Hebei Medical University between January 2019 and January 2022. Clinical characteristics, diagnosis-to-ablation time (DAT), and left atrial functional parameters were compared. A prediction model was constructed using Logistic regression, and its diagnostic performance was evaluated using the receiver operating characteristic (ROC) curve. Finally, a predictive model for atrial fibrillation recurrence was established.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e (1)A total of 202 symptomatic atrial fibrillation patients who underwent radiofrequency ablation were enrolled, and follow-up data at 1, 3, 6, and 12 months post-procedure were collected. Based on recurrence status, the patients were divided into a sinus rhythm maintenance group (n=136) and a recurrence group (n=66), with an overall postoperative recurrence rate of 32.7%. (2)Univariate analysis showed statistically significant differences between the two groups in terms of gender, diagnosis-to-ablation time (DAT), N-terminal pro-B-type natriuretic peptide (NT‑proBNP), Homocysteine (Hcy), and Left atrial appendage emptying velocity (LAAeV)(P\u0026lt;0.05). (3)Multivariate Logistic regression analysis further indicated that gender, DAT, and LAAeV were independent risk factors for postoperative AF recurrence. The regression equation derived was: Logit(P)=−0.824 + 1.19×gender + 0.015×DAT − 0.016×LAAeV. Goodness-of-fit testing yielded χ²=5.975, and the Hosmer‑Lemeshow test showed P=0.650 (\u0026gt;0.05), indicating a well-fitted model. (4)Receiver operating characteristic (ROC) curve analysis revealed that DAT had the largest area under the curve (AUC=0.665) among the three indicators, while the combined prediction model demonstrated better discriminative ability than DAT alone (AUC=0.722).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Gender, DAT, and LAAeV are independent risk factors for early postoperative recurrence in patients with atrial fibrillation. The combined model of these three factors demonstrates good predictive value for early AF recurrence after the procedure. Furthermore, the constructed nomogram can serve as a non‑invasive preoperative assessment tool prior to RFCA, offering enhanced predictive utility.\u003c/p\u003e","manuscriptTitle":"A predictive model using gender, the diagnosis-to-ablation time and left atrial function in predicting the recurrence of atrial fibrillation within 1 year after radiofrequency catheter ablation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 06:26:43","doi":"10.21203/rs.3.rs-8687602/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-30T12:22:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-06T09:28:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-06T09:22:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cardiothoracic Surgery","date":"2026-01-24T14:42:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"702092ef-285e-45a1-9bde-e74ff6106e80","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-03T06:26:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 06:26:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8687602","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8687602","identity":"rs-8687602","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}