The Predictive Value of NT-proBNP for New-Onset Atrial Fibrillation in Patients with Arrhythmia

The Predictive Value of NT-proBNP for New-Onset Atrial Fibrillation in Patients with Arrhythmia | 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 The Predictive Value of NT-proBNP for New-Onset Atrial Fibrillation in Patients with Arrhythmia Ha Khanh Linh Duong, Sang Doan, Vinh Nien Lam, Thanh Vinh Tran, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7803439/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Atrial fibrillation (AF) is a common arrhythmia associated with serious complications. This study aimed to assess the role of NT-proBNP in predicting new-onset AF in patients with non-AF arrhythmia. This was a prospective cohort study of 232 patients followed for a median of 12 months. Data on NT-proBNP, demographics, medical history, presenting symptoms, and paraclinical indices were collected. The results showed that syncope (59.9%) and dizziness (55.6%) were the most common presenting symptoms. Initial NT-proBNP levels were not statistically significant in predicting new-onset AF (HR = 0.9995; p = 0.717). Instead, left atrial (LA) size, a history of diabetes mellitus, and a history of stroke were significant independent predictors. Specifically, a history of stroke increased the risk of AF more than 10-fold, diabetes increased it nearly 6-fold, and each millimeter increase in LA size raised the risk by 27%. The study concluded that NT-proBNP is not an effective prognostic marker for new-onset AF in this population, but left atrial size, diabetes, and a history of stroke are crucial clinical factors to consider in screening and managing AF risk. Atrial fibrillation arrhythmia NT-proBNP prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 I. INTRODUCTION Atrial fibrillation (AF) is one of the most common clinical arrhythmias, characterized by chaotic electrical activity and irregular contraction of the atria [1, 2]. This condition can lead to several serious complications such as stroke, heart failure, and an increased risk of mortality [1, 3]. In particular, the early and accurate prognosis of AF risk in patients with other arrhythmias (excluding AF) presents a significant challenge. Timely identification of risk factors and biomarkers could help to personalize treatment and prevention strategies more effectively. In recent years, considerable research efforts have been dedicated to identifying biomarkers and clinical factors capable of predicting the onset of AF [4-8]. Among these, natriuretic peptides, especially N-terminal pro-B-type natriuretic peptide (NT-proBNP), have garnered significant attention. NT-proBNP is a biomarker released from the ventricles in response to myocardial wall stress and volume overload and is widely used for the diagnosis and prognosis of heart failure [9, 10]. Previous studies have shown that elevated NT-proBNP levels are associated with a higher risk of developing AF in the general population, as well as in patients with heart failure or structural heart disease [4, 11-13]. Proposed mechanisms include the effects of increased ventricular filling pressure, atrial remodeling, inflammation, and myocardial fibrosis, all of which contribute to the pathophysiology of AF. Several studies have demonstrated the independent role of NT-proBNP in predicting new-onset or recurrent AF after interventional procedures [4, 11-13]. However, data on the role of NT-proBNP in predicting AF in the specific population of patients already diagnosed with other arrhythmias (non-AF) remains limited and has not been fully elucidated. This study was conducted to evaluate the role of plasma NT-proBNP levels in predicting the onset of AF in patients with non-AF arrhythmias. This will provide additional scientific evidence to optimize screening, monitoring, and AF risk management strategies in this specific population, thereby helping to improve patient clinical outcomes. II. METHODS 1. Study Design and Population This study was designed as a prospective cohort study conducted at the Arrhythmia Department, Cho Ray Hospital, from December 2024 to June 2026. We followed a group of patients with non-atrial fibrillation (non-AF) arrhythmias to evaluate the incidence of new-onset AF and associated factors over time, with a specific focus on the relationship with baseline NT-proBNP levels. The study population consisted of patients diagnosed with cardiac arrhythmia and treated at the department. Specific inclusion criteria were: Patients aged 18 years or older. Diagnosed with a non-AF arrhythmia upon hospital admission, based on 12-lead electrocardiogram (ECG), Holter ECG, or other cardiac monitoring methods. Had plasma NT-proBNP levels measured at the time of admission. Provided informed consent to participate in the study. Patients were excluded if they had conditions that could significantly influence the study results or complicate follow-up, including: A prior history of AF or an AF diagnosis at the time of admission. Complex or acute cardiac conditions such as severe obstructive hypertrophic cardiomyopathy, severe aortic valve stenosis, or acute myocardial infarction within 7 days. End-stage renal disease (eGFR < 30 ml/min/1.73m²) or undergoing hemodialysis. Refused to participate or had other severe medical conditions (e.g., end-stage cancer, severe liver failure) that precluded complete follow-up. 2. Sample Size The sample size was calculated based on the primary objective of predicting the incidence of AF, using survival analysis. The formula for the minimum sample size in a survival study, based on the hazard ratio (HR), was applied. Based on a study by Staerk et al. (2020) [13], we used an HR for NT-proBNP of 1.73. The prevalence of AF in patients with arrhythmia was referenced from a study by Lindberg et al. [14], which was 36.8%. Substituting these values into the formula, we calculated an initial sample size of 160 patients. Accounting for an estimated 30% patient dropout rate during follow-up, we recruited a minimum of 207 patients. 3. Data Collection and Measurement At the time of hospital admission, baseline clinical information was collected from all patients, including: Demographics and medical history: Age, sex, history of cardiovascular diseases, and comorbidities (hypertension, diabetes mellitus, heart failure, chronic kidney disease, thyroid disease). Clinical examination and basic measurements: Blood pressure, heart rate, body weight, height, and Body Mass Index (BMI). Laboratory and imaging tests: 12-lead ECG to assess rhythm and conduction parameters; echocardiogram to evaluate cardiac function and structure; and basic hematology and biochemistry tests, including renal function (Creatinine, eGFR) and electrolytes. NT-proBNP Measurement: A venous blood sample was drawn at the time of admission, before any intervention that could affect NT-proBNP levels. NT-proBNP concentration was measured using a quantitative immunoassay on the ADVIA Centaur XPT system from Siemens. All assays adhered to the stringent quality control protocols of the accredited laboratory. Primary Outcome: The primary outcome was the occurrence of new-onset AF during the follow-up period. AF was defined as having irregular atrial electrical activity and a loss of P waves, lasting at least 30 seconds, and documented on a 12-lead ECG or Holter ECG. Patients were followed up periodically after hospital discharge via clinical visits and ECG/Holter ECG to detect the outcome. To ensure data reliability, all ECG and Holter ECG recordings were independently interpreted by two experienced cardiologists. Echocardiograms were performed by certified technicians and physicians following international guidelines. 4. Statistical Analysis Data were analyzed using R Studio software. Descriptive statistics: Continuous variables were presented as mean ± standard deviation (SD) for normally distributed data or as median and interquartile range (IQR) for skewed data. Categorical variables were presented as frequencies (n) and percentages (%). Group comparisons: Independent t-tests or Mann-Whitney U tests were used to compare continuous variables between groups, depending on data distribution. Chi-squared tests or Fisher’s exact tests were used for categorical variables. Survival analysis: Kaplan-Meier curves were used to estimate the cumulative incidence of AF over time. Multivariate Cox proportional hazards regression: This was the main method used to identify independent predictors for the incidence of AF, including baseline NT-proBNP levels and other clinical, echocardiographic, and laboratory factors. The statistical significance level was set at p < 0.05. Ethical Considerations: The study was approved by the Institutional Ethics Committee of the University of Medicine and Pharmacy, Ho Chi Minh City. All participating patients provided written informed consent after receiving a detailed explanation of the study. III. RESULTS 1. Baseline Characteristics of the Study Population The study initially collected data from 285 patients. After excluding 41 patients with a pre-existing history of atrial fibrillation (AF) and 12 patients with severe renal impairment (eGFR < 30 ml/min/m²), a total of 232 patients without baseline AF were eligible for participation. The median follow-up duration was 12 months, with an interquartile range (IQR) of 6.75 to 13 months. The study group comprised 124 males (53.4%) and 108 females (46.6%). The mean age for the entire cohort was 63.7 ± 14.0 years, with a median of 67. Notably, females had a significantly higher mean age than males (67.5 ± 14.4 years vs. 60.4 ± 16.7 years). Other anthropometric indices reflected the biological differences between the sexes: males were taller (1.63 ± 0.05 m) and heavier (61.4 ± 10.2 kg) than females (1.55 ± 0.04 m and 53.3 ± 9.19 kg, respectively). The mean BMI for the cohort was 22.5 ± 3.34 kg/m², which falls within the normal range. The mean blood pressure was also stable, with a systolic pressure of 129 ± 19.6 mmHg and a diastolic pressure of 75.1 ± 10.2 mmHg. However, the mean blood pressure tended to be slightly higher in females, which could be related to their higher mean age. (Table 1) Table 1: General characteristics of 232 non-atrial fibrillation patients Total (n=232) Male (n=124) Female (n=108) Mean±SD Median (IQR) Mean±SD Median (IQR) Mean±SD Median (IQR) Age (years) 63.7±14.0 67 (53-76) 60.4±16.7 67 (53-76) 60.4±16.7 70 (58-77) Height (m) 1.59±0.09 1.60 (1.55-1.65) 1.63±0.05 1.60 (1.55-1.65) 1.63±0.05 1.55 (1.50-1.56) Weight (kg) 57.7±10.5 58 (50-65) 61.4±10.2 58 (50-65) 61.4±10.2 53 (48-60) BMI (kg/m²) 22.5±3.34 22.4 (20.4-24.1) 22.8±3.27 22.4 (20.4-24.1) 22.8±3.27 22.1 (20.0-23.6) Systolic BP (mmHg) 129±19.6 130 (120-140) 128±20.5 130 (120-140) 128±20.5 130 (120-140) Diastolic BP (mmHg) 75.1±10.2 74.5 (70-80) 74.2±10.9 74.5 (70-80) 74.2±10.9 80 (70-80) Regarding medical history, the most prevalent cardiovascular risk factors were hypertension (59.0%), dyslipidemia (40.5%), and diabetes mellitus (22.0%). Heart failure (18.0%), coronary artery disease (13.4%), and chronic kidney disease (10.3%) were also common. The presenting symptoms for this patient cohort were highly varied, with syncope (59.9%) and dizziness (55.6%) being the most common, each affecting more than half of the patients. (Figure 1, Table 2) Figure 1: Past medical history and cardiovascular risk factors of 232 non-atrial fibrillation arrhythmia patients participating in the study. Table 2: Presenting symptoms at admission of non-atrial fibrillation arrhythmia patients (n=232) Symptom Frequency (n) Percentage (%) Syncope 139 59.9 Dizziness 129 55.6 Dyspnea (Shortness of breath) 51 21.9 Palpitations 32 13.8 Fatigue 32 13.8 Chest pain 27 11.6 Chest tightness 10 4.31 Lightheadedness/Giddiness/Presyncope 10 4.31 Weakness 7 3.01 Other (difficulty swallowing, hand tremors, slow and difficult speech, abdominal pain, vomiting, hiccups, leg edema, breathlessness, numbness in limbs, pacemaker battery depletion) 12 5.17 2. Laboratory and Paraclinical Findings The distribution of NT-proBNP levels in the study cohort was heterogeneous, with most patients having low concentrations while a smaller subgroup had very high levels. The median NT-proBNP value was 34.5 pmol/L (IQR: 7.2-157). (Figure 2) Figure 2: Distribution of NT-proBNP concentration (pmol/L) in 232 non-atrial fibrillation arrhythmia patients participating in the study Most other laboratory values were within normal limits. The median Creatinine was 0.87 mg/dL and the median eGFR was 83.9 mL/min/1.73m², reflecting an acceptably preserved renal function. Blood lipid levels (Cholesterol, LDL, HDL, Triglyceride) were also within the average range. On echocardiography, the median left atrial (LA) size was 31 mm and the median ejection fraction (EF) was 65%, indicating that the overall cardiac function of the study cohort was preserved. (Table 3) Table 3: Selected laboratory and echocardiographic parameters of the study group N = 232 Median (IQR) Laboratory tests HGB (g/L) 128 (116-138) HCT (%) 38.5 (35.2-41.1) WBC (G/L) 8.20 (6.75-10.2) PLT (G/L) 192 (138-241) INR 1.04 (1.01-1.11) Creatinine (mg/dL) 0.87 (0.75-1.03) eGFR (mL/min/1.73m²) 83.9 (64.3-96.7) Free T4 (pg/mL) 12.3 (11.0-13.4) TSH (mIU/L) 1.41 (0.80-2.23) Cholesterol (mg/dL) 163 (133-191) HDL-Cholesterol (mg/dL) 41 (35-49) LDL-Cholesterol (mg/dL) 96 (72-124) Triglyceride (mg/dL) 136 (94-187) Echocardiography LA (mm) 31 (28-35) EF (%) 65 (57-71) EDV (mL) 102 (85-129) ESV (mL) 36 (28-51) LVEDD (mm) 47 (44-52) LVESD (mm) 30 (27-34) 3. Atrial Fibrillation Status After Follow-up After a median follow-up period of 12 months, 16 cases (6.9%) of new-onset atrial fibrillation developed among the 232 baseline patients. The Kaplan-Meier curve demonstrated a gradual increase in the cumulative incidence of AF over time, reaching nearly 10% after 12 months. The 95% confidence intervals were narrow initially but widened progressively, indicating that the precision of the estimate decreased as the number of remaining patients diminished. The patient count declined significantly over time: from 232 at baseline to just 19 at 17 months, due to events or loss to follow-up. (Figure 3) Figure 3: Kaplan-Meier curve showing cumulative probability of atrial fibrillation over time 4. Role of NT-proBNP and Predictors of Atrial Fibrillation Initial analysis showed that baseline NT-proBNP levels were slightly higher in the group that developed AF (median 36.0 pmol/L, IQR: 7.47-162) compared to the group that did not (median 34.7 pmol/L, IQR: 7.22-157). However, this difference was not statistically significant (p = 0.327) by the Wilcoxon rank sum test. An analysis of Kaplan-Meier curves across NT-proBNP quartiles also revealed no significant difference between the groups (p = 0.3). (Figure 4) Figure 4: Comparison of Kaplan-Meier curves for new-onset atrial fibrillation cases by initial NT-proBNP quartile A multivariate Cox proportional hazards regression model was constructed to identify independent predictors for the incidence of AF. The results revealed that only three factors were statistically associated with AF risk: Left atrial (LA) size: This was the most significant statistical factor (HR = 1.2708, p 1 indicates that each millimeter increase in LA size raises the risk of developing AF. History of diabetes mellitus: This factor was highly statistically significant (HR = 5.9852, p = 0.00631). Patients with a history of diabetes had a nearly 6-fold higher risk of AF compared to those without. History of stroke: This was another powerful risk factor (HR = 10.1755, p = 0.0323), showing that individuals with a prior stroke history had a more than 10-fold higher risk of AF. In contrast, other variables in the model, such as NT-proBNP, age, sex, hypertension, heart failure, eGFR, and other indices, were not statistically significant (p > 0.05) in predicting AF. Although a history of myocardial infarction had a high HR (5.1160), its p-value was 0.0942, failing to reach statistical significance. (Table 4) Table 4: Multivariate Cox regression model assessing the influence of factors on atrial fibrillation risk Variable HR (95% CI) p-value NT-proBNP 0.9995 (0.9968 – 1.002) 0.717 Hs TnI 1.0001 (0.9995 – 1.001) 0.844 Age 1.0199 (0.9804 – 1.061) 0.327 Sex 0.9672 (0.3222 – 2.904) 0.952 Hypertension 0.4142 (0.1154 – 1.486) 0.176 Diabetes Mellitus 5.9852 (1.6574 – 21.614) 0.00631 Heart failure 0.5631 (0.1724 – 1.839) 0.341 LA 1.2708 (1.1652 – 1.386) < 0.001 eGFR 1.0107 (0.9827 – 1.039) 0.458 Sick sinus syndrome 2.6829 (0.8013 – 8.983) 0.109 Dyslipidemia 1.1229 (0.2438 – 5.171) 0.8818 Chronic kidney disease 0.8183 (0.0688 – 9.723) 0.8738 History of stroke 10.1755 (1.2161 – 85.139) 0.0323 History of Myocardial Infarction 5.1160 (0.7564 – 34.600) 0.0942 IV. DISCUSSION This study was conducted with the initial hypothesis that high plasma NT-proBNP levels at hospital admission could be an independent predictor for the onset of atrial fibrillation (AF) in patients with non-AF arrhythmias. The primary objective was to evaluate the role of NT-proBNP in predicting new-onset AF. After excluding cases with baseline AF and severe renal impairment, 232 non-AF patients were included in the analysis and followed for a median duration of 12 months. Although NT-proBNP levels tended to be higher in the group that developed AF, the admission NT-proBNP concentration did not demonstrate a statistically significant, independent predictive role for new-onset AF in the multivariable Cox model (HR = 0.9995; p = 0.717). Instead, left atrial (LA) size, a history of diabetes mellitus, and a history of stroke were the three strongest statistically significant factors for predicting new-onset AF. Specifically, a history of stroke increased the risk of AF by more than 10 times, while diabetes increased it by nearly 6 times, and each millimeter increase in LA size raised the risk by 27%. Our findings differ significantly from many previous studies that have demonstrated a strong predictive role of NT-proBNP for new-onset AF [1, 6, 15-19]. For instance, studies in the general population or in heart failure patients often found an independent association between NT-proBNP and AF risk (Schnabel et al., 2010; Li et al., 2018) [20, 21]. This discrepancy can be explained by several factors. First, our study population consisted of patients with pre-existing non-AF arrhythmias who were admitted with a diverse spectrum of symptoms, with syncope (59.9%) and dizziness (55.6%) being the most common, rather than typical palpitations. This may suggest a more complex underlying pathophysiology, where the baseline arrhythmias were already severe enough to cause hemodynamic disturbances, and NT-proBNP may be reflecting myocardial wall stress from these arrhythmias rather than directly predicting the conversion to AF. Second, the median follow-up period of our study was 12 months, whereas some other studies with longer follow-up durations may have been better able to detect a clearer association. Nevertheless, our results reinforce the role of left atrial (LA) size as a strong and consistent risk factor for AF, which has been widely established in the literature [5, 22, 23]. Left atrial enlargement is a marker of mechanical and electrical atrial remodeling, which is a prerequisite for the development of AF [22]. Similarly, the association between diabetes and AF risk is well-documented, through mechanisms such as systemic inflammation, myocardial fibrosis, and microvascular disease [24-26]. The finding of a history of stroke as a very strong predictor of AF (HR > 10) is particularly noteworthy. This could suggest that patients with a prior stroke may have had undiagnosed paroxysmal AF or other severe underlying risk factors that make them more susceptible to AF. The lack of statistical significance for NT-proBNP in the multivariable model may be due to: The influence of stronger confounding factors: In this patient group, structural factors like LA size, along with comorbidities such as diabetes and stroke, may play a more dominant role in the pathophysiology of AF. NT-proBNP, while a marker of cardiac wall stress, may have been overshadowed by the powerful effects of atrial remodeling and systemic diseases [10, 27]. The non-specificity of NT-proBNP in this cohort: With diverse admission symptoms and multiple comorbidities such as hypertension (59.0%), dyslipidemia (40.5%), heart failure (18.0%), and chronic kidney disease (10.3%), NT-proBNP may be reflecting a more general cardiovascular burden or global dysfunction rather than a specific marker for AF risk in this context. The median NT-proBNP levels in the AF group (36.0 pmol/L) and non-AF group (34.7 pmol/L) were very close, indicating poor discriminatory ability. A low number of events: Only 16 new-onset AF cases were recorded out of 232 patients (a rate of approximately 6.9%) after 12 months of follow-up. This low event count may have reduced the statistical power to detect significant associations, including for NT-proBNP, especially in a complex multivariable analysis. Conversely, the prominence of left atrial size is physiologically sound. LA enlargement is a consequence of chronic elevated atrial filling pressure and a direct marker of atrial remodeling, which increases the risk of developing re-entry circuits and ectopic foci, leading to AF [28, 29]. Similarly, diabetes induces diabetic cardiomyopathy, including atrial fibrosis and endothelial dysfunction, creating a favorable substrate for AF [24, 30-33]. A history of stroke, with a high HR, may reflect a patient group with pre-existing cerebrovascular damage often accompanied by severe underlying cardiovascular conditions (including undiagnosed paroxysmal AF) or a high thrombotic risk. The results of this study provide a nuanced understanding of AF predictors in a specific and clinically relevant patient population. While NT-proBNP was not an independent predictor in this cohort, the findings underscore that a careful evaluation of classical clinical factors and left atrial size remains central to AF risk stratification. For patients with non-AF arrhythmias who are admitted to the hospital, particularly with symptoms like syncope and dizziness, meticulous screening for a history of diabetes, stroke, and an echocardiographic assessment of LA size are critically important. Patients with these risk factors should be monitored more aggressively for AF, which may include extended Holter ECG or more advanced cardiac rhythm monitoring devices. This has significant implications for personalizing risk management and stroke prevention strategies related to AF. This study possesses several notable strengths that contribute to its scientific value. First, the prospective cohort design allows us to establish a temporal relationship between predictors and outcomes, significantly minimizing recall bias compared to retrospective designs. Second, the focus on a specific study population—patients with pre-existing non-AF arrhythmias—fills a crucial knowledge gap in a field often overlooked by larger studies. Third, comprehensive data collection, including clinical information, standardized laboratory tests (including NT-proBNP), and detailed echocardiographic indices, was performed. Finally, the median follow-up of 12 months is a reasonable timeframe to observe new-onset AF events, allowing for a dynamic assessment of the disease over time. However, this research also has several limitations that should be acknowledged. The most significant drawback is the small number of events; despite a total of 232 patients, only 16 new-onset AF cases were recorded. This low event count may reduce the statistical power of the analysis, making it difficult to detect statistically significant associations for variables with small or moderate effects, especially in a complex multivariable analysis, which may be the primary reason why NT-proBNP did not achieve statistical significance in our model. Another limitation is the intermittent follow-up and a notable rate of loss to follow-up; the substantial decrease in the number of study participants over time (down to only 19 patients at 17 months) may introduce bias into the survival estimates and reduce the reliability of the results in the later stages of the study. Furthermore, the detection of AF relied solely on ECG and Holter ECG, which may have missed short or asymptomatic episodes of paroxysmal AF. The use of more prolonged monitoring devices like implantable loop recorders could improve detection but was not feasible in the context of this study. Finally, this is a single-center study, which may limit the generalizability of the results to more diverse patient populations or other medical centers with different clinical characteristics and management protocols. V. CONCLUSION Our study concludes that baseline NT-proBNP is not an independent predictor for new-onset AF in patients with non-AF arrhythmias. However, traditional clinical factors play a highly significant role. Specifically, left atrial size, a history of diabetes mellitus, and a history of stroke were identified as strong, independent predictors. These findings underscore the necessity of a comprehensive assessment of classical risk factors for AF screening and management, particularly in high-risk patients. Abbreviations • AF Atrial Fibrillation • BMI Body Mass Index • ECG Electrocardiogram • EF Ejection Fraction • eGFR Estimated Glomerular Filtration Rate • HR Hazard Ratio • IQR Interquartile Range • LA Left Atrial • NT-proBNP N-terminal pro-B-type natriuretic peptide Declarations Ethics approval and consent to participate The study was approved by the Institutional Ethics Committee of the University of Medicine and Pharmacy, Ho Chi Minh City. All participating patients provided written informed consent after receiving a detailed explanation of the study. All methods were carried out in accordance with relevant guidelines and regulations. The study was conducted in accordance with the Declaration of Helsinki. Consent for publication Informed consent obtained from all participants included consent for their anonymized data to be used for research and publication. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to policies protecting participant privacy and confidentiality, but are available from the corresponding author (Ha Khanh Linh Duong, [email protected] ) on reasonable request. Competing Interests The authors declare that they have no competing interests. Funding The authors did not receive support from any organization for the submitted work. No funding was received to assist with the preparation of this manuscript. Authors’ contributions Ha Khanh Linh Duong designed the study, analyzed the data, and wrote the main manuscript. Sang Doan, Vinh Nien Lam, and Thanh Vinh Tran were involved in patient recruitment, data collection, and follow-up. Ngoc Dung Kieu and Tri Thuc Nguyen provided supervision, contributed to the interpretation of the data, and critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript. Acknowledgements We extend our sincere gratitude to all the patients who voluntarily participated in this study, whose cooperation was invaluable to our research. We also wish to thank the Arrhythmia Department at Cho Ray Hospital for their support in patient recruitment and data collection. Our appreciation also goes to the Biochemistry Department for their assistance with the laboratory analyses. Finally, we are profoundly grateful to the University of Medicine and Pharmacy at Ho Chi Minh City for providing the necessary resources, ethical oversight, and academic environment that facilitated this research. References Schnabel RB, et al. 50 year trends in atrial fibrillation prevalence, incidence, risk factors, and mortality in the Framingham Heart Study: a cohort study. Lancet. 2015;386(9989):154–62. Kornej J, et al. Epidemiology of Atrial Fibrillation in the 21st Century: Novel Methods and New Insights. Circ Res. 2020;127(1):4–20. Society HR. Guide to Atrial Fibrillation. USA; 2014. Wang W, et al. Association between NT-proBNP levels and risk of atrial fibrillation: a systematic review and meta-analysis of cohort studies. Heart. 2025;111(3):109–16. Bakir EO et al. The relationship between the left atrium/left ventricle ratio and atrial fibrillation in patients with ischemic stroke without significant left atrial enlargement. Int J Cardiovasc Imaging, 2025. Lancini D, et al. Predictors of New Onset Atrial Fibrillation Burden in the Critically Ill. Cardiology. 2024;149(2):165–73. O'Neal WT, et al. Brachial flow-mediated dilation and incident atrial fibrillation: the multi-ethnic study of atherosclerosis. Arterioscler Thromb Vasc Biol. 2014;34(12):2717–20. Galea R, et al. Inflammation and C-reactive protein in atrial fibrillation: cause or effect? Tex Heart Inst J. 2014;41(5):461–8. Sandefur CC, Jialal I. Atrial Natriuretic Peptide , in StatPearls . 2025: Treasure Island (FL) ineligible companies. Disclosure: Ishwarlal Jialal declares no relevant financial relationships with ineligible companies. Novack ML, Zubair M. Natriuretic Peptide B Type Test , in StatPearls . 2025: Treasure Island (FL) ineligible companies. Disclosure: Muhammad Zubair declares no relevant financial relationships with ineligible companies. Nasab Mehrabi E, Toupchi-Khosroshahi V, Athari SS. Relationship of atrial fibrillation and N terminal pro brain natriuretic peptide in heart failure patients. ESC Heart Fail. 2023;10(6):3250–7. Girerd N, et al. Protein Biomarkers of New-Onset Heart Failure: Insights From the Heart Omics and Ageing Cohort, the Atherosclerosis Risk in Communities Study, and the Framingham Heart Study. Circ Heart Fail. 2023;16(5):e009694. Staerk L, et al. Protein Biomarkers and Risk of Atrial Fibrillation: The FHS. Circ Arrhythm Electrophysiol. 2020;13(2):e007607. Lindberg T, et al. Prevalence and Incidence of Atrial Fibrillation and Other Arrhythmias in the General Older Population: Findings From the Swedish National Study on Aging and Care. Gerontol Geriatr Med. 2019;5:2333721419859687. Ardhianto P, Yuniadi Y. Biomarkers of Atrial Fibrillation: Which One Is a True Marker? Cardiol Res Pract, 2019. 2019: p. 8302326. Borschel CS, et al. Risk prediction of atrial fibrillation in the community combining biomarkers and genetics. Europace. 2021;23(5):674–81. Manjer J, et al. The Malmo Diet and Cancer Study: representativity, cancer incidence and mortality in participants and non-participants. Eur J Cancer Prev. 2001;10(6):489–99. Patton KK, et al. N-terminal pro-B-type natriuretic peptide is a major predictor of the development of atrial fibrillation: the Cardiovascular Health Study. Circulation. 2009;120(18):1768–74. Patton KK, et al. N-terminal pro-B-type natriuretic peptide as a predictor of incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis: the effects of age, sex and ethnicity. Heart. 2013;99(24):1832–6. Schnabel RB, et al. Relations of biomarkers of distinct pathophysiological pathways and atrial fibrillation incidence in the community. Circulation. 2010;121(2):200–7. Li L, et al. Association of N-terminal pro B-type natriuretic peptide (NT-proBNP) change with the risk of atrial fibrillation in the ARIC cohort. Am Heart J. 2018;204:119–27. Suryabanshi A, et al. Left Atrial Enlargement as a Predictor of Atrial Fibrillation in Rheumatic Mitral Valve Disease: An Echocardiography-based Retrospective Cross-sectional Study. J Nepal Health Res Counc. 2024;21(4):593–8. Saadeh R, et al. The relationship of atrial fibrillation with left atrial size in patients with essential hypertension. Sci Rep. 2024;14(1):1250. Ugowe FE, Jackson LR 2nd, and, Thomas KL. Atrial Fibrillation and Diabetes Mellitus: Can We Modify Stroke Risk Through Glycemic Control? Circ Arrhythm Electrophysiol. 2019;12(5):e007351. Fatemi O, et al. Impact of intensive glycemic control on the incidence of atrial fibrillation and associated cardiovascular outcomes in patients with type 2 diabetes mellitus (from the Action to Control Cardiovascular Risk in Diabetes Study). Am J Cardiol. 2014;114(8):1217–22. Dublin S, et al. Diabetes mellitus, glycemic control, and risk of atrial fibrillation. J Gen Intern Med. 2010;25(8):853–8. Cao Z, Jia Y, Zhu B. BNP and NT-proBNP as Diagnostic Biomarkers for Cardiac Dysfunction in Both Clinical and Forensic Medicine. Int J Mol Sci, 2019. 20(8). Seckin O, Unlu S, Yalcin MR. The hidden role of left atrial strain: insights into end-organ damage in dipper and nondipper hypertension. J Hum Hypertens. 2025;39(6):425–31. Parajuli P, Alahmadi MH, Ahmed AA. Left Atrial Enlargement , in StatPearls . 2025: Treasure Island (FL) ineligible companies. Disclosure: Mohamed Alahmadi declares no relevant financial relationships with ineligible companies. Disclosure: Andaleeb Ahmed declares no relevant financial relationships with ineligible companies. Leopoulou M, et al. Diabetes mellitus and atrial fibrillation-from pathophysiology to treatment. World J Diabetes. 2023;14(5):512–27. Seyed Ahmadi S, et al. Risk of atrial fibrillation in persons with type 2 diabetes and the excess risk in relation to glycaemic control and renal function: a Swedish cohort study. Cardiovasc Diabetol. 2020;19(1):9. Wang A, et al. Atrial Fibrillation and Diabetes Mellitus: JACC Review Topic of the Week. J Am Coll Cardiol. 2019;74(8):1107–15. Aune D, et al. Diabetes mellitus, blood glucose and the risk of atrial fibrillation: A systematic review and meta-analysis of cohort studies. J Diabetes Complications. 2018;32(5):501–11. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7803439","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551085628,"identity":"c3618a01-79b1-4533-9e5b-24b7dbba9fc0","order_by":0,"name":"Ha Khanh Linh Duong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBACCQkGBmaGAgkGA2b+7x8+AEXY2InSYgDUws5gxjgDpIWZOC0MDAb8DGbMPCAhQlokZzc/e1xgYJFnzsyQ9tjm1zZ5PmYGxg8fc3BrkZY5Zm48w0Ci2LKZ4bhxbt9twzZmBmbJmdtwa5GTSDCT5jGQSNxwmLFBOrfnNiNQCxszL14t6d+gWpgZpC17btsT1CItkQOzhY1NmuHH7USCWiRn5JRBtfAwG/Y23E5uY2ZsxusXiRvp26R5KuoSN5w/w/jgx5/btvPbmw9++IhHCypgbAOTDcSqB4E/pCgeBaNgFIyCkQIA3SVHV4jewy8AAAAASUVORK5CYII=","orcid":"","institution":"Cho Ray Hospital","correspondingAuthor":true,"prefix":"","firstName":"Ha","middleName":"Khanh Linh","lastName":"Duong","suffix":""},{"id":551085629,"identity":"aeca2383-9009-419d-8a7f-0ef2283af656","order_by":1,"name":"Sang Doan","email":"","orcid":"","institution":"Eye Hospital of Ho Chi Minh City","correspondingAuthor":false,"prefix":"","firstName":"Sang","middleName":"","lastName":"Doan","suffix":""},{"id":551085630,"identity":"f0008a34-578b-4bd5-a850-827a5b91e76b","order_by":2,"name":"Vinh Nien Lam","email":"","orcid":"","institution":"University of Medicine and Pharmacy at Ho Chi Minh City","correspondingAuthor":false,"prefix":"","firstName":"Vinh","middleName":"Nien","lastName":"Lam","suffix":""},{"id":551085631,"identity":"b286520a-51bc-4fa3-8143-a02c670644de","order_by":3,"name":"Thanh Vinh Tran","email":"","orcid":"","institution":"Cho Ray Hospital","correspondingAuthor":false,"prefix":"","firstName":"Thanh","middleName":"Vinh","lastName":"Tran","suffix":""},{"id":551085632,"identity":"f97892a4-c98a-4971-99cc-a2e321223970","order_by":4,"name":"Ngoc Dung Kieu","email":"","orcid":"","institution":"Cho Ray Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ngoc","middleName":"Dung","lastName":"Kieu","suffix":""},{"id":551085633,"identity":"5c0369b7-18ff-47b4-81fa-3a4e09520363","order_by":5,"name":"Tri Thuc Nguyen","email":"","orcid":"","institution":"Cho Ray Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tri","middleName":"Thuc","lastName":"Nguyen","suffix":""}],"badges":[],"createdAt":"2025-10-08 02:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7803439/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7803439/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97141719,"identity":"68ab7aa4-c1f8-4a2c-8a6e-2c46cf8eb649","added_by":"auto","created_at":"2025-12-01 10:06:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":406282,"visible":true,"origin":"","legend":"","description":"","filename":"ThePredictiveValueofNTproBNPforNewOnsetAtrialFibrillationinPatientswithArrhythmia.docx","url":"https://assets-eu.researchsquare.com/files/rs-7803439/v1/8cf4c93e1f46d0a7960a9530.docx"},{"id":97115563,"identity":"da48e0e4-5959-479b-b9a0-c9f7eeb92651","added_by":"auto","created_at":"2025-12-01 07:05:04","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7293,"visible":true,"origin":"","legend":"","description":"","filename":"3eeb7f56cfa34cc799c9d44565fe4748.json","url":"https://assets-eu.researchsquare.com/files/rs-7803439/v1/6d8753f258e221f632792013.json"},{"id":97142912,"identity":"f5e78544-5627-4d06-af3a-e44c384f4f47","added_by":"auto","created_at":"2025-12-01 10:08:06","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":94220,"visible":true,"origin":"","legend":"","description":"","filename":"3eeb7f56cfa34cc799c9d44565fe47481enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7803439/v1/5ced327a824c84423676563e.xml"},{"id":97142816,"identity":"17379743-060e-48e8-9c71-cebb16315248","added_by":"auto","created_at":"2025-12-01 10:07:58","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":21532,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7803439/v1/03da595014646c09b9b4fd92.png"},{"id":97115568,"identity":"e54d7408-ee3e-435e-904e-f6b6725b2317","added_by":"auto","created_at":"2025-12-01 07:05:05","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27563,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7803439/v1/c1b06b1c8823cea07d67008b.png"},{"id":97115569,"identity":"a213844f-abdc-4c23-b2b9-fd74a2cf880a","added_by":"auto","created_at":"2025-12-01 07:05:05","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11549,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7803439/v1/fb463bd2f9ec3050dea17b2e.png"},{"id":97115567,"identity":"e3e905e4-e9f7-4fe9-b82c-78fd9905820f","added_by":"auto","created_at":"2025-12-01 07:05:05","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14741,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7803439/v1/588c2b463289aa54d52009fe.png"},{"id":97115570,"identity":"858776cc-189f-454d-9687-2cd7d6011f8d","added_by":"auto","created_at":"2025-12-01 07:05:05","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91680,"visible":true,"origin":"","legend":"","description":"","filename":"3eeb7f56cfa34cc799c9d44565fe47481structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7803439/v1/af5dd62b3f5c3c920131b248.xml"},{"id":97115566,"identity":"69db8259-b732-484a-bcb2-63064f01898e","added_by":"auto","created_at":"2025-12-01 07:05:05","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100535,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7803439/v1/eb8144aaea375cde9a9628c2.html"},{"id":97115565,"identity":"5dd188c0-3f24-44f1-a704-8463efdea18f","added_by":"auto","created_at":"2025-12-01 07:05:05","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86627,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePast medical history and cardiovascular risk factors of 232 non-atrial fibrillation arrhythmia patients participating in the study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7803439/v1/f725d83cc9d44c9d922ab679.jpeg"},{"id":97115558,"identity":"68588e64-e48b-4a3a-83af-bb876cd9e358","added_by":"auto","created_at":"2025-12-01 07:05:04","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of NT-proBNP concentration (pmol/L) in 232 non-atrial fibrillation arrhythmia patients participating in the study\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7803439/v1/69302d21abbe80240942d3ae.jpeg"},{"id":97115560,"identity":"73a81f96-137c-425d-b573-317c5604ae76","added_by":"auto","created_at":"2025-12-01 07:05:04","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curve showing cumulative probability of atrial fibrillation over time\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7803439/v1/797d022f429b0528d23214d6.jpeg"},{"id":97142262,"identity":"c1b0017a-4c23-4bbb-b116-3eff23edaa62","added_by":"auto","created_at":"2025-12-01 10:07:28","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":65237,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of Kaplan-Meier curves for new-onset atrial fibrillation cases by initial NT-proBNP quartile\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7803439/v1/4ffdbbea95466c977cbecd22.jpeg"},{"id":97818545,"identity":"3a584532-a42d-4e0f-ae73-0f327d90eb7f","added_by":"auto","created_at":"2025-12-09 17:23:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1546098,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7803439/v1/109ffb9c-45f6-4f6b-9611-dfe0130163aa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Predictive Value of NT-proBNP for New-Onset Atrial Fibrillation in Patients with Arrhythmia","fulltext":[{"header":"I. INTRODUCTION","content":"\u003cp\u003eAtrial fibrillation (AF) is one of the most common clinical arrhythmias, characterized by chaotic electrical activity and irregular contraction of the atria [1, 2]. This condition can lead to several serious complications such as stroke, heart failure, and an increased risk of mortality [1, 3]. In particular, the early and accurate prognosis of AF risk in patients with other arrhythmias (excluding AF) presents a significant challenge. Timely identification of risk factors and biomarkers could help to personalize treatment and prevention strategies more effectively.\u003c/p\u003e\n\u003cp\u003eIn recent years, considerable research efforts have been dedicated to identifying biomarkers and clinical factors capable of predicting the onset of AF [4-8]. Among these, natriuretic peptides, especially N-terminal pro-B-type natriuretic peptide (NT-proBNP), have garnered significant attention. NT-proBNP is a biomarker released from the ventricles in response to myocardial wall stress and volume overload and is widely used for the diagnosis and prognosis of heart failure [9, 10].\u003c/p\u003e\n\u003cp\u003ePrevious studies have shown that elevated NT-proBNP levels are associated with a higher risk of developing AF in the general population, as well as in patients with heart failure or structural heart disease [4, 11-13]. Proposed mechanisms include the effects of increased ventricular filling pressure, atrial remodeling, inflammation, and myocardial fibrosis, all of which contribute to the pathophysiology of AF. Several studies have demonstrated the independent role of NT-proBNP in predicting new-onset or recurrent AF after interventional procedures [4, 11-13]. However, data on the role of NT-proBNP in predicting AF in the specific population of patients already diagnosed with other arrhythmias (non-AF) remains limited and has not been fully elucidated.\u003c/p\u003e\n\u003cp\u003eThis study was conducted to evaluate the role of plasma NT-proBNP levels in predicting the onset of AF in patients with non-AF arrhythmias. This will provide additional scientific evidence to optimize screening, monitoring, and AF risk management strategies in this specific population, thereby helping to improve patient clinical outcomes.\u003c/p\u003e"},{"header":"II. METHODS","content":"\u003cp\u003e\u003cstrong\u003e1. Study Design and Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was designed as a prospective cohort study conducted at the Arrhythmia Department, Cho Ray Hospital, from December 2024 to June 2026. We followed a group of patients with non-atrial fibrillation (non-AF) arrhythmias to evaluate the incidence of new-onset AF and associated factors over time, with a specific focus on the relationship with baseline NT-proBNP levels.\u003c/p\u003e\n\u003cp\u003eThe study population consisted of patients diagnosed with cardiac arrhythmia and treated at the department. Specific inclusion criteria were:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003ePatients aged 18 years or older.\u003c/li\u003e\n \u003cli\u003eDiagnosed with a non-AF arrhythmia upon hospital admission, based on 12-lead electrocardiogram (ECG), Holter ECG, or other cardiac monitoring methods.\u003c/li\u003e\n \u003cli\u003eHad plasma NT-proBNP levels measured at the time of admission.\u003c/li\u003e\n \u003cli\u003eProvided informed consent to participate in the study.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePatients were excluded if they had conditions that could significantly influence the study results or complicate follow-up, including:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eA prior history of AF or an AF diagnosis at the time of admission.\u003c/li\u003e\n \u003cli\u003eComplex or acute cardiac conditions such as severe obstructive hypertrophic cardiomyopathy, severe aortic valve stenosis, or acute myocardial infarction within 7 days.\u003c/li\u003e\n \u003cli\u003eEnd-stage renal disease (eGFR \u0026lt; 30 ml/min/1.73m²) or undergoing hemodialysis.\u003c/li\u003e\n \u003cli\u003eRefused to participate or had other severe medical conditions (e.g., end-stage cancer, severe liver failure) that precluded complete follow-up.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e2. Sample Size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample size was calculated based on the primary objective of predicting the incidence of AF, using survival analysis. The formula for the minimum sample size in a survival study, based on the hazard ratio (HR), was applied. Based on a study by Staerk et al. (2020) [13], we used an HR for NT-proBNP of 1.73. The prevalence of AF in patients with arrhythmia was referenced from a study by Lindberg et al. [14], which was 36.8%.\u003c/p\u003e\n\u003cp\u003eSubstituting these values into the formula, we calculated an initial sample size of 160 patients. Accounting for an estimated 30% patient dropout rate during follow-up, we recruited a minimum of 207 patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Data Collection and Measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the time of hospital admission, baseline clinical information was collected from all patients, including:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eDemographics and medical history: Age, sex, history of cardiovascular diseases, and comorbidities (hypertension, diabetes mellitus, heart failure, chronic kidney disease, thyroid disease).\u003c/li\u003e\n \u003cli\u003eClinical examination and basic measurements: Blood pressure, heart rate, body weight, height, and Body Mass Index (BMI).\u003c/li\u003e\n \u003cli\u003eLaboratory and imaging tests: 12-lead ECG to assess rhythm and conduction parameters; echocardiogram to evaluate cardiac function and structure; and basic hematology and biochemistry tests, including renal function (Creatinine, eGFR) and electrolytes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eNT-proBNP Measurement:\u003c/strong\u003e A venous blood sample was drawn at the time of admission, before any intervention that could affect NT-proBNP levels. NT-proBNP concentration was measured using a quantitative immunoassay on the ADVIA Centaur XPT system from Siemens. All assays adhered to the stringent quality control protocols of the accredited laboratory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary Outcome:\u003c/strong\u003e The primary outcome was the occurrence of new-onset AF during the follow-up period. AF was defined as having irregular atrial electrical activity and a loss of P waves, lasting at least 30 seconds, and documented on a 12-lead ECG or Holter ECG. Patients were followed up periodically after hospital discharge via clinical visits and ECG/Holter ECG to detect the outcome. To ensure data reliability, all ECG and Holter ECG recordings were independently interpreted by two experienced cardiologists. Echocardiograms were performed by certified technicians and physicians following international guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were analyzed using R Studio software.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eDescriptive statistics: Continuous variables were presented as mean ± standard deviation (SD) for normally distributed data or as median and interquartile range (IQR) for skewed data. Categorical variables were presented as frequencies (n) and percentages (%).\u003c/li\u003e\n \u003cli\u003eGroup comparisons: Independent t-tests or Mann-Whitney U tests were used to compare continuous variables between groups, depending on data distribution. Chi-squared tests or Fisher’s exact tests were used for categorical variables.\u003c/li\u003e\n \u003cli\u003eSurvival analysis: Kaplan-Meier curves were used to estimate the cumulative incidence of AF over time.\u003c/li\u003e\n \u003cli\u003eMultivariate Cox proportional hazards regression: This was the main method used to identify independent predictors for the incidence of AF, including baseline NT-proBNP levels and other clinical, echocardiographic, and laboratory factors. The statistical significance level was set at p \u0026lt; 0.05.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations:\u003c/strong\u003e The study was approved by the Institutional Ethics Committee of the University of Medicine and Pharmacy, Ho Chi Minh City. All participating patients provided written informed consent after receiving a detailed explanation of the study.\u003c/p\u003e"},{"header":"III. RESULTS","content":"\u003cp\u003e\u003cstrong\u003e1. Baseline Characteristics of the Study Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study initially collected data from 285 patients. After excluding 41 patients with a pre-existing history of atrial fibrillation (AF) and 12 patients with severe renal impairment (eGFR \u0026lt; 30 ml/min/m\u0026sup2;), a total of 232 patients without baseline AF were eligible for participation. The median follow-up duration was 12 months, with an interquartile range (IQR) of 6.75 to 13 months.\u003c/p\u003e\n\u003cp\u003eThe study group comprised 124 males (53.4%) and 108 females (46.6%). The mean age for the entire cohort was 63.7 \u0026plusmn; 14.0 years, with a median of 67. Notably, females had a significantly higher mean age than males (67.5 \u0026plusmn; 14.4 years vs. 60.4 \u0026plusmn; 16.7 years). Other anthropometric indices reflected the biological differences between the sexes: males were taller (1.63 \u0026plusmn; 0.05 m) and heavier (61.4 \u0026plusmn; 10.2 kg) than females (1.55 \u0026plusmn; 0.04 m and 53.3 \u0026plusmn; 9.19 kg, respectively). The mean BMI for the cohort was 22.5 \u0026plusmn; 3.34 kg/m\u0026sup2;, which falls within the normal range. The mean blood pressure was also stable, with a systolic pressure of 129 \u0026plusmn; 19.6 mmHg and a diastolic pressure of 75.1 \u0026plusmn; 10.2 mmHg. However, the mean blood pressure tended to be slightly higher in females, which could be related to their higher mean age. (Table 1)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: General characteristics of 232 non-atrial fibrillation patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"692\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (n=232)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale (n=124)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale (n=108) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eMean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eMean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eMean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e63.7\u0026plusmn;14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e67 (53-76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e60.4\u0026plusmn;16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e67 (53-76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e60.4\u0026plusmn;16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e70 (58-77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1.59\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.60 (1.55-1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.63\u0026plusmn;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1.60 (1.55-1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.63\u0026plusmn;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.55 (1.50-1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e57.7\u0026plusmn;10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e58 (50-65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e61.4\u0026plusmn;10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e58 (50-65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e61.4\u0026plusmn;10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e53 (48-60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e22.5\u0026plusmn;3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e22.4 (20.4-24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e22.8\u0026plusmn;3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e22.4 (20.4-24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e22.8\u0026plusmn;3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e22.1 (20.0-23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSystolic BP (mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e129\u0026plusmn;19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e130 (120-140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e128\u0026plusmn;20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e130 (120-140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e128\u0026plusmn;20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e130 (120-140)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiastolic BP (mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e75.1\u0026plusmn;10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e74.5 (70-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e74.2\u0026plusmn;10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e74.5 (70-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e74.2\u0026plusmn;10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e80 (70-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eRegarding medical history, the most prevalent cardiovascular risk factors were hypertension (59.0%), dyslipidemia (40.5%), and diabetes mellitus (22.0%). Heart failure (18.0%), coronary artery disease (13.4%), and chronic kidney disease (10.3%) were also common. The presenting symptoms for this patient cohort were highly varied, with syncope (59.9%) and dizziness (55.6%) being the most common, each affecting more than half of the patients. (Figure 1, Table 2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1: Past medical history and cardiovascular risk factors of 232 non-atrial fibrillation arrhythmia patients participating in the study.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Presenting symptoms at admission of non-atrial fibrillation arrhythmia patients (n=232)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSymptom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003eSyncope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e59.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003eDizziness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e55.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003eDyspnea (Shortness of breath)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003ePalpitations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003eFatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003eChest pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003eChest tightness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003eLightheadedness/Giddiness/Presyncope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003eWeakness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003eOther (difficulty swallowing, hand tremors, slow and difficult speech, abdominal pain, vomiting, hiccups, leg edema, breathlessness, numbness in limbs, pacemaker battery depletion)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e2. Laboratory and Paraclinical Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe distribution of NT-proBNP levels in the study cohort was heterogeneous, with most patients having low concentrations while a smaller subgroup had very high levels. The median NT-proBNP value was 34.5 pmol/L (IQR: 7.2-157). (Figure 2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2: Distribution of NT-proBNP concentration (pmol/L) in 232 non-atrial fibrillation arrhythmia patients participating in the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMost other laboratory values were within normal limits. The median Creatinine was 0.87 mg/dL and the median eGFR was 83.9 mL/min/1.73m\u0026sup2;, reflecting an acceptably preserved renal function. Blood lipid levels (Cholesterol, LDL, HDL, Triglyceride) were also within the average range. On echocardiography, the median left atrial (LA) size was 31 mm and the median ejection fraction (EF) was 65%, indicating that the overall cardiac function of the study cohort was preserved. (Table 3)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Selected laboratory and echocardiographic parameters of the study group\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"376\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN = 232\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory tests\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eHGB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e128 (116-138)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eHCT (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e38.5 (35.2-41.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eWBC (G/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e8.20 (6.75-10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003ePLT (G/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e192 (138-241)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.04 (1.01-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.87 (0.75-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eeGFR (mL/min/1.73m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e83.9 (64.3-96.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eFree T4 (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e12.3 (11.0-13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eTSH (mIU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.41 (0.80-2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eCholesterol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e163 (133-191)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eHDL-Cholesterol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e41 (35-49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eLDL-Cholesterol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e96 (72-124)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eTriglyceride (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e136 (94-187)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEchocardiography\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eLA (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e31 (28-35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eEF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e65 (57-71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eEDV (mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e102 (85-129)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eESV (mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e36 (28-51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eLVEDD (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e47 (44-52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 230px;\"\u003e\n \u003cp\u003eLVESD (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e30 (27-34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3. Atrial Fibrillation Status After Follow-up\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter a median follow-up period of 12 months, 16 cases (6.9%) of new-onset atrial fibrillation developed among the 232 baseline patients.\u003c/p\u003e\n\u003cp\u003eThe Kaplan-Meier curve demonstrated a gradual increase in the cumulative incidence of AF over time, reaching nearly 10% after 12 months. The 95% confidence intervals were narrow initially but widened progressively, indicating that the precision of the estimate decreased as the number of remaining patients diminished. The patient count declined significantly over time: from 232 at baseline to just 19 at 17 months, due to events or loss to follow-up. (Figure 3)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3: Kaplan-Meier curve showing cumulative probability of atrial fibrillation over time\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Role of NT-proBNP and Predictors of Atrial Fibrillation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInitial analysis showed that baseline NT-proBNP levels were slightly higher in the group that developed AF (median 36.0 pmol/L, IQR: 7.47-162) compared to the group that did not (median 34.7 pmol/L, IQR: 7.22-157). However, this difference was not statistically significant (p = 0.327) by the Wilcoxon rank sum test. An analysis of Kaplan-Meier curves across NT-proBNP quartiles also revealed no significant difference between the groups (p = 0.3). (Figure 4)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4: Comparison of Kaplan-Meier curves for new-onset atrial fibrillation cases by initial NT-proBNP quartile\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multivariate Cox proportional hazards regression model was constructed to identify independent predictors for the incidence of AF. The results revealed that only three factors were statistically associated with AF risk:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eLeft atrial (LA) size: This was the most significant statistical factor (HR = 1.2708, p \u0026lt; 0.001). An HR \u0026gt; 1 indicates that each millimeter increase in LA size raises the risk of developing AF.\u003c/li\u003e\n \u003cli\u003eHistory of diabetes mellitus: This factor was highly statistically significant (HR = 5.9852, p = 0.00631). Patients with a history of diabetes had a nearly 6-fold higher risk of AF compared to those without.\u003c/li\u003e\n \u003cli\u003eHistory of stroke: This was another powerful risk factor (HR = 10.1755, p = 0.0323), showing that individuals with a prior stroke history had a more than 10-fold higher risk of AF.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn contrast, other variables in the model, such as NT-proBNP, age, sex, hypertension, heart failure, eGFR, and other indices, were not statistically significant (p \u0026gt; 0.05) in predicting AF. Although a history of myocardial infarction had a high HR (5.1160), its p-value was 0.0942, failing to reach statistical significance. (Table 4)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Multivariate Cox regression model assessing the influence of factors on atrial fibrillation risk\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNT-proBNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9995 (0.9968 \u0026ndash; 1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHs TnI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0001 (0.9995 \u0026ndash; 1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0199 (0.9804 \u0026ndash; 1.061)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9672 (0.3222 \u0026ndash; 2.904)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4142 (0.1154 \u0026ndash; 1.486)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.9852 (1.6574 \u0026ndash; 21.614)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00631\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHeart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5631 (0.1724 \u0026ndash; 1.839)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.2708 (1.1652 \u0026ndash; 1.386)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0107 (0.9827 \u0026ndash; 1.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSick sinus syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.6829 (0.8013 \u0026ndash; 8.983)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1229 (0.2438 \u0026ndash; 5.171)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8183 (0.0688 \u0026ndash; 9.723)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHistory of stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1755 (1.2161 \u0026ndash; 85.139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHistory of Myocardial Infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.1160 (0.7564 \u0026ndash; 34.600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0942\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"IV. DISCUSSION","content":"\u003cp\u003eThis study was conducted with the initial hypothesis that high plasma NT-proBNP levels at hospital admission could be an independent predictor for the onset of atrial fibrillation (AF) in patients with non-AF arrhythmias. The primary objective was to evaluate the role of NT-proBNP in predicting new-onset AF. After excluding cases with baseline AF and severe renal impairment, 232 non-AF patients were included in the analysis and followed for a median duration of 12 months.\u003c/p\u003e\n\u003cp\u003eAlthough NT-proBNP levels tended to be higher in the group that developed AF, the admission NT-proBNP concentration did not demonstrate a statistically significant, independent predictive role for new-onset AF in the multivariable Cox model (HR = 0.9995; p = 0.717). Instead, left atrial (LA) size, a history of diabetes mellitus, and a history of stroke were the three strongest statistically significant factors for predicting new-onset AF. Specifically, a history of stroke increased the risk of AF by more than 10 times, while diabetes increased it by nearly 6 times, and each millimeter increase in LA size raised the risk by 27%.\u003c/p\u003e\n\u003cp\u003eOur findings differ significantly from many previous studies that have demonstrated a strong predictive role of NT-proBNP for new-onset AF [1, 6, 15-19]. For instance, studies in the general population or in heart failure patients often found an independent association between NT-proBNP and AF risk (Schnabel et al., 2010; Li et al., 2018) [20, 21]. This discrepancy can be explained by several factors.\u003c/p\u003e\n\u003cp\u003eFirst, our study population consisted of patients with pre-existing non-AF arrhythmias who were admitted with a diverse spectrum of symptoms, with syncope (59.9%) and dizziness (55.6%) being the most common, rather than typical palpitations. This may suggest a more complex underlying pathophysiology, where the baseline arrhythmias were already severe enough to cause hemodynamic disturbances, and NT-proBNP may be reflecting myocardial wall stress from these arrhythmias rather than directly predicting the conversion to AF.\u003c/p\u003e\n\u003cp\u003eSecond, the median follow-up period of our study was 12 months, whereas some other studies with longer follow-up durations may have been better able to detect a clearer association.\u003c/p\u003e\n\u003cp\u003eNevertheless, our results reinforce the role of left atrial (LA) size as a strong and consistent risk factor for AF, which has been widely established in the literature [5, 22, 23]. Left atrial enlargement is a marker of mechanical and electrical atrial remodeling, which is a prerequisite for the development of AF [22]. Similarly, the association between diabetes and AF risk is well-documented, through mechanisms such as systemic inflammation, myocardial fibrosis, and microvascular disease [24-26]. The finding of a history of stroke as a very strong predictor of AF (HR \u0026gt; 10) is particularly noteworthy. This could suggest that patients with a prior stroke may have had undiagnosed paroxysmal AF or other severe underlying risk factors that make them more susceptible to AF.\u003c/p\u003e\n\u003cp\u003eThe lack of statistical significance for NT-proBNP in the multivariable model may be due to:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eThe influence of stronger confounding factors: In this patient group, structural factors like LA size, along with comorbidities such as diabetes and stroke, may play a more dominant role in the pathophysiology of AF. NT-proBNP, while a marker of cardiac wall stress, may have been overshadowed by the powerful effects of atrial remodeling and systemic diseases [10, 27].\u003c/li\u003e\n \u003cli\u003eThe non-specificity of NT-proBNP in this cohort: With diverse admission symptoms and multiple comorbidities such as hypertension (59.0%), dyslipidemia (40.5%), heart failure (18.0%), and chronic kidney disease (10.3%), NT-proBNP may be reflecting a more general cardiovascular burden or global dysfunction rather than a specific marker for AF risk in this context. The median NT-proBNP levels in the AF group (36.0 pmol/L) and non-AF group (34.7 pmol/L) were very close, indicating poor discriminatory ability.\u003c/li\u003e\n \u003cli\u003eA low number of events: Only 16 new-onset AF cases were recorded out of 232 patients (a rate of approximately 6.9%) after 12 months of follow-up. This low event count may have reduced the statistical power to detect significant associations, including for NT-proBNP, especially in a complex multivariable analysis.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eConversely, the prominence of left atrial size is physiologically sound. LA enlargement is a consequence of chronic elevated atrial filling pressure and a direct marker of atrial remodeling, which increases the risk of developing re-entry circuits and ectopic foci, leading to AF [28, 29]. Similarly, diabetes induces diabetic cardiomyopathy, including atrial fibrosis and endothelial dysfunction, creating a favorable substrate for AF [24, 30-33]. A history of stroke, with a high HR, may reflect a patient group with pre-existing cerebrovascular damage often accompanied by severe underlying cardiovascular conditions (including undiagnosed paroxysmal AF) or a high thrombotic risk.\u003c/p\u003e\n\u003cp\u003eThe results of this study provide a nuanced understanding of AF predictors in a specific and clinically relevant patient population. While NT-proBNP was not an independent predictor in this cohort, the findings underscore that a careful evaluation of classical clinical factors and left atrial size remains central to AF risk stratification.\u003c/p\u003e\n\u003cp\u003eFor patients with non-AF arrhythmias who are admitted to the hospital, particularly with symptoms like syncope and dizziness, meticulous screening for a history of diabetes, stroke, and an echocardiographic assessment of LA size are critically important. Patients with these risk factors should be monitored more aggressively for AF, which may include extended Holter ECG or more advanced cardiac rhythm monitoring devices. This has significant implications for personalizing risk management and stroke prevention strategies related to AF.\u003c/p\u003e\n\u003cp\u003eThis study possesses several notable strengths that contribute to its scientific value. First, the prospective cohort design allows us to establish a temporal relationship between predictors and outcomes, significantly minimizing recall bias compared to retrospective designs. Second, the focus on a specific study population—patients with pre-existing non-AF arrhythmias—fills a crucial knowledge gap in a field often overlooked by larger studies. Third, comprehensive data collection, including clinical information, standardized laboratory tests (including NT-proBNP), and detailed echocardiographic indices, was performed. Finally, the median follow-up of 12 months is a reasonable timeframe to observe new-onset AF events, allowing for a dynamic assessment of the disease over time.\u003c/p\u003e\n\u003cp\u003eHowever, this research also has several limitations that should be acknowledged. The most significant drawback is the small number of events; despite a total of 232 patients, only 16 new-onset AF cases were recorded. This low event count may reduce the statistical power of the analysis, making it difficult to detect statistically significant associations for variables with small or moderate effects, especially in a complex multivariable analysis, which may be the primary reason why NT-proBNP did not achieve statistical significance in our model. Another limitation is the intermittent follow-up and a notable rate of loss to follow-up; the substantial decrease in the number of study participants over time (down to only 19 patients at 17 months) may introduce bias into the survival estimates and reduce the reliability of the results in the later stages of the study. Furthermore, the detection of AF relied solely on ECG and Holter ECG, which may have missed short or asymptomatic episodes of paroxysmal AF. The use of more prolonged monitoring devices like implantable loop recorders could improve detection but was not feasible in the context of this study. Finally, this is a single-center study, which may limit the generalizability of the results to more diverse patient populations or other medical centers with different clinical characteristics and management protocols.\u003c/p\u003e"},{"header":"V. CONCLUSION","content":"\u003cp\u003eOur study concludes that baseline NT-proBNP is not an independent predictor for new-onset AF in patients with non-AF arrhythmias. However, traditional clinical factors play a highly significant role. Specifically, left atrial size, a history of diabetes mellitus, and a history of stroke were identified as strong, independent predictors. These findings underscore the necessity of a comprehensive assessment of classical risk factors for AF screening and management, particularly in high-risk patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eAF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAtrial Fibrillation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eECG\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eElectrocardiogram\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eEF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEjection Fraction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eeGFR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEstimated Glomerular Filtration Rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eHR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHazard Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eIQR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterquartile Range\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eLA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLeft Atrial\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eNT-proBNP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eN-terminal pro-B-type natriuretic peptide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Institutional Ethics Committee of the University of Medicine and Pharmacy, Ho Chi Minh City. All participating patients provided written informed consent after receiving a detailed explanation of the study. All methods were carried out in accordance with relevant guidelines and regulations. The study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent obtained from all participants included consent for their anonymized data to be used for research and publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to policies protecting participant privacy and confidentiality, but are available from the corresponding author (Ha Khanh Linh Duong, [email protected]) on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive support from any organization for the submitted work. No funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHa Khanh Linh Duong designed the study, analyzed the data, and wrote the main manuscript. Sang Doan, Vinh Nien Lam, and Thanh Vinh Tran were involved in patient recruitment, data collection, and follow-up. Ngoc Dung Kieu and Tri Thuc Nguyen provided supervision, contributed to the interpretation of the data, and critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our sincere gratitude to all the patients who voluntarily participated in this study, whose cooperation was invaluable to our research. We also wish to thank the Arrhythmia Department at Cho Ray Hospital for their support in patient recruitment and data collection. Our appreciation also goes to the Biochemistry Department for their assistance with the laboratory analyses. Finally, we are profoundly grateful to the University of Medicine and Pharmacy at Ho Chi Minh City for providing the necessary resources, ethical oversight, and academic environment that facilitated this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSchnabel RB, et al. 50 year trends in atrial fibrillation prevalence, incidence, risk factors, and mortality in the Framingham Heart Study: a cohort study. Lancet. 2015;386(9989):154\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKornej J, et al. Epidemiology of Atrial Fibrillation in the 21st Century: Novel Methods and New Insights. Circ Res. 2020;127(1):4\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSociety HR. Guide to Atrial Fibrillation. USA; 2014.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang W, et al. Association between NT-proBNP levels and risk of atrial fibrillation: a systematic review and meta-analysis of cohort studies. Heart. 2025;111(3):109\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBakir EO et al. The relationship between the left atrium/left ventricle ratio and atrial fibrillation in patients with ischemic stroke without significant left atrial enlargement. Int J Cardiovasc Imaging, 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLancini D, et al. Predictors of New Onset Atrial Fibrillation Burden in the Critically Ill. Cardiology. 2024;149(2):165\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO'Neal WT, et al. Brachial flow-mediated dilation and incident atrial fibrillation: the multi-ethnic study of atherosclerosis. Arterioscler Thromb Vasc Biol. 2014;34(12):2717\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGalea R, et al. Inflammation and C-reactive protein in atrial fibrillation: cause or effect? Tex Heart Inst J. 2014;41(5):461\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSandefur CC, Jialal I. \u003cem\u003eAtrial Natriuretic Peptide\u003c/em\u003e, in \u003cem\u003eStatPearls\u003c/em\u003e. 2025: Treasure Island (FL) ineligible companies. Disclosure: Ishwarlal Jialal declares no relevant financial relationships with ineligible companies.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNovack ML, Zubair M. \u003cem\u003eNatriuretic Peptide B Type Test\u003c/em\u003e, in \u003cem\u003eStatPearls\u003c/em\u003e. 2025: Treasure Island (FL) ineligible companies. Disclosure: Muhammad Zubair declares no relevant financial relationships with ineligible companies.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNasab Mehrabi E, Toupchi-Khosroshahi V, Athari SS. Relationship of atrial fibrillation and N terminal pro brain natriuretic peptide in heart failure patients. ESC Heart Fail. 2023;10(6):3250\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGirerd N, et al. Protein Biomarkers of New-Onset Heart Failure: Insights From the Heart Omics and Ageing Cohort, the Atherosclerosis Risk in Communities Study, and the Framingham Heart Study. Circ Heart Fail. 2023;16(5):e009694.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStaerk L, et al. Protein Biomarkers and Risk of Atrial Fibrillation: The FHS. Circ Arrhythm Electrophysiol. 2020;13(2):e007607.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLindberg T, et al. Prevalence and Incidence of Atrial Fibrillation and Other Arrhythmias in the General Older Population: Findings From the Swedish National Study on Aging and Care. Gerontol Geriatr Med. 2019;5:2333721419859687.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArdhianto P, Yuniadi Y. \u003cem\u003eBiomarkers of Atrial Fibrillation: Which One Is a True Marker?\u003c/em\u003e Cardiol Res Pract, 2019. 2019: p. 8302326.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBorschel CS, et al. Risk prediction of atrial fibrillation in the community combining biomarkers and genetics. Europace. 2021;23(5):674\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eManjer J, et al. The Malmo Diet and Cancer Study: representativity, cancer incidence and mortality in participants and non-participants. Eur J Cancer Prev. 2001;10(6):489\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatton KK, et al. N-terminal pro-B-type natriuretic peptide is a major predictor of the development of atrial fibrillation: the Cardiovascular Health Study. Circulation. 2009;120(18):1768\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatton KK, et al. N-terminal pro-B-type natriuretic peptide as a predictor of incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis: the effects of age, sex and ethnicity. Heart. 2013;99(24):1832\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchnabel RB, et al. Relations of biomarkers of distinct pathophysiological pathways and atrial fibrillation incidence in the community. Circulation. 2010;121(2):200\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi L, et al. Association of N-terminal pro B-type natriuretic peptide (NT-proBNP) change with the risk of atrial fibrillation in the ARIC cohort. Am Heart J. 2018;204:119\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuryabanshi A, et al. Left Atrial Enlargement as a Predictor of Atrial Fibrillation in Rheumatic Mitral Valve Disease: An Echocardiography-based Retrospective Cross-sectional Study. J Nepal Health Res Counc. 2024;21(4):593\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaadeh R, et al. The relationship of atrial fibrillation with left atrial size in patients with essential hypertension. Sci Rep. 2024;14(1):1250.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUgowe FE, Jackson LR 2nd, and, Thomas KL. Atrial Fibrillation and Diabetes Mellitus: Can We Modify Stroke Risk Through Glycemic Control? Circ Arrhythm Electrophysiol. 2019;12(5):e007351.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFatemi O, et al. Impact of intensive glycemic control on the incidence of atrial fibrillation and associated cardiovascular outcomes in patients with type 2 diabetes mellitus (from the Action to Control Cardiovascular Risk in Diabetes Study). Am J Cardiol. 2014;114(8):1217\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDublin S, et al. Diabetes mellitus, glycemic control, and risk of atrial fibrillation. J Gen Intern Med. 2010;25(8):853\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCao Z, Jia Y, Zhu B. BNP and NT-proBNP as Diagnostic Biomarkers for Cardiac Dysfunction in Both Clinical and Forensic Medicine. Int J Mol Sci, 2019. 20(8).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeckin O, Unlu S, Yalcin MR. The hidden role of left atrial strain: insights into end-organ damage in dipper and nondipper hypertension. J Hum Hypertens. 2025;39(6):425\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParajuli P, Alahmadi MH, Ahmed AA. \u003cem\u003eLeft Atrial Enlargement\u003c/em\u003e, in \u003cem\u003eStatPearls\u003c/em\u003e. 2025: Treasure Island (FL) ineligible companies. Disclosure: Mohamed Alahmadi declares no relevant financial relationships with ineligible companies. Disclosure: Andaleeb Ahmed declares no relevant financial relationships with ineligible companies.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeopoulou M, et al. Diabetes mellitus and atrial fibrillation-from pathophysiology to treatment. World J Diabetes. 2023;14(5):512\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeyed Ahmadi S, et al. Risk of atrial fibrillation in persons with type 2 diabetes and the excess risk in relation to glycaemic control and renal function: a Swedish cohort study. Cardiovasc Diabetol. 2020;19(1):9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang A, et al. Atrial Fibrillation and Diabetes Mellitus: JACC Review Topic of the Week. J Am Coll Cardiol. 2019;74(8):1107\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAune D, et al. Diabetes mellitus, blood glucose and the risk of atrial fibrillation: A systematic review and meta-analysis of cohort studies. J Diabetes Complications. 2018;32(5):501\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Atrial fibrillation, arrhythmia, NT-proBNP, prediction","lastPublishedDoi":"10.21203/rs.3.rs-7803439/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7803439/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAtrial fibrillation (AF) is a common arrhythmia associated with serious complications. This study aimed to assess the role of NT-proBNP in predicting new-onset AF in patients with non-AF arrhythmia. This was a prospective cohort study of 232 patients followed for a median of 12 months. Data on NT-proBNP, demographics, medical history, presenting symptoms, and paraclinical indices were collected. The results showed that syncope (59.9%) and dizziness (55.6%) were the most common presenting symptoms. Initial NT-proBNP levels were not statistically significant in predicting new-onset AF (HR\u0026thinsp;=\u0026thinsp;0.9995; p\u0026thinsp;=\u0026thinsp;0.717). Instead, left atrial (LA) size, a history of diabetes mellitus, and a history of stroke were significant independent predictors. Specifically, a history of stroke increased the risk of AF more than 10-fold, diabetes increased it nearly 6-fold, and each millimeter increase in LA size raised the risk by 27%. The study concluded that NT-proBNP is not an effective prognostic marker for new-onset AF in this population, but left atrial size, diabetes, and a history of stroke are crucial clinical factors to consider in screening and managing AF risk.\u003c/p\u003e","manuscriptTitle":"The Predictive Value of NT-proBNP for New-Onset Atrial Fibrillation in Patients with Arrhythmia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 07:05:00","doi":"10.21203/rs.3.rs-7803439/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c42b14fa-153d-405b-8a4c-80b8017983ab","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-03T11:24:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 07:05:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7803439","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7803439","identity":"rs-7803439","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}