Predictive Value of METS-IR, TYG Index, and TG/HDL Ratio in Atrial Fibrillation Following Coronary Artery Bypass

Predictive Value of METS-IR, TYG Index, and TG/HDL Ratio in Atrial Fibrillation Following Coronary Artery Bypass | 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 Predictive Value of METS-IR, TYG Index, and TG/HDL Ratio in Atrial Fibrillation Following Coronary Artery Bypass Shahin Abbaszadeh, Hossein Montazerghaem, Masoumeh Mahmoudi, Melika Alavitabar, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7679260/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract Background Non-insulin-based markers of insulin resistance such as METS-IR (insulin resistance metabolic score), TyG (triglyceride and glucose) Index, and TG/HDL (triglyceride to high-density lipoprotein) Ratio are associated to several risk factors linked with cardiovascular disease (CVD). However, based on available information, no investigation has specifically focused on the predictive ability of these three markers in the incidence of postoperative atrial fibrillation (PoAF). Therefore, the current study aimed to investigate the predictive impact of METS-IR, TyG Index, and TG/HDL Ratio in the incidence of atrial fibrillation after coronary artery bypass grafting (CABG). Methods In this retrospective study, patients who were treated with isolated CABG from September 2021 to September 2024 were included. Data before, during, and after surgery were recorded. Two groups were created based on the occurance of PoAF among patients. The data obtained for both groups were analyzed using the Chi-square test and logistic regression in SPSS version 24. Results Increased insulin resistance and metabolic syndrome indices were correlated with a higher risk of PoAF (The p-values for TyG, TG/HDL, and METS-IR were 0.02, < 0.001, and 0.005, respectively). Additionally, Female gender, high BMI, shorter height, increased cross-clamp time, higher systolic blood pressure and HbA1C levels were associated with a rise in the prevalence of PoAF. Conclusion Higher values of METS-IR and TG/HDL were connected to elevated prevalence of PoAF. In addition, the current model outperformed a random model, raising hope for utilization in clinical settings. Atrial fibrillation coronary artery bypass insulin resistance cardiovascular disease Figures Figure 1 Introduction Coronary artery disease (CAD) is a crucial condition affecting the expectancy and quality of the people living with it. While there are various intravascular intervention methods, coronary artery bypass grafting (CABG) is still used as a principle treatment ( 1 ). Although surgeries through cardiac-pulmonary bypass (CPB) have minimal mortality rates, the occurrence of postoperative atrial fibrillation (PoAF), kidney and pulmonary complications may still be problematic for patients' recovery ( 2 ). Atrial fibrillation (AF), as a vital clinical condition, accounts for between 10%-65% of cases post-CABG surgery, leading to cerebrovascular accidents, prolonged hospitalization, and increased treatment costs. Furthermore, various risk factors are known, including age, high blood pressure, left atrial dilation, and blood transfusion ( 3 ). Growing evidence suggests that insulin resistance, as a feature diabetes mellitus type 2 alongwith metabolic syndrome, may contribute to the development of CAD ( 4 – 6 ). The Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) The conventional way to estimate insulin sensitivity ( 7 ), which may be inaccurate if the insulin values are imprecise. ( 8 ). In addition, some non-insulin indices of insulin resistance have been proposed as alternatives, such as the triglyceride to high-density lipoprotein ratio(TG/HDL), the triglyceride and glucose index (TyG index), and the insulin resistance metabolic score (IR-METS) ( 9 – 11 ). There are simple biochemical methods available to measure these new indices, making up for the limitations of the conventional methods of measuring insulin resistance. In addition, prior research has demonstrated that non-insulin-based indices correlate to several indicators for CVD, including diabetes, obesity, hypertension, and metabolic syndrome, which can be used as predictive and prognostic factors for CVD ( 12 – 17 ). Previous studies noted the association of diabetes and insulin resistance with post-operative complications and hospitalization length ( 18 ). Additionally, lower HDL cholesterol levels, along with elevated triglyceride and glucose levels, have demonstrated a significant correlation with POAF ( 19 ). Due to the relationship of these three indices with CVD, and lack of data on their association with PoAF, in current study, we explored the predictive role of metabolic parameters IR-METS, TyG Index, and HDL/TG Ratio in the incidence of AF following coronary artery bypass grafting. Method Study design In this retrospective study, 340 patients who underwent isolated CABG surgery at Shahid Mohammadi Hospital between September 2021 and September 2024 were included. Consent forms were obtained from all participating patients, and the study was approved by the Ethics Committee of Hormozgan University of Medical Sciences) IR.HUMS.REC.1403.003). Inclusion criteria consisted of patients who underwent CABG surgery in the above-mentioned center. Exclusion criteria consisted of patients with prior CABG surgery, emergency operations, moderate to severe mitral valve regurgitation, history of AF or known AF attacks before surgery, amiodarone use in the preoperative period, history of renal transplant treatment, and lack of consent to participate. After applying the exclusion and inclusion criteria, patients were categorized into two groups; the first group contained 170 randomly selected cases who did not experience postoperative AF within 72 hours following operation. The second group consists of 170 patients developing POAF within 72 hours after surgery. Non-insulin indices of insulin resistance In the current study, we utilized three indices for measuring insulin resistance: The TyG index was calculated using the natural logarithm transformation of the product of fasting plasma triglycerides and glucose levels, divided by two, with all values expressed in mg/dL. The ratio of triglycerides to HDL cholesterol (TG/HDL-C) was calculated by dividing fasting triglyceride concentration by fasting HDL cholesterol concentration. To estimate the METS-IR, the body mass index (BMI) was multiplied by the natural logarithm of the sum of twice the fasting glucose and triglyceride concentrations, and then divided by the natural logarithm of the HDL cholesterol level ( 20 ). Data collection As for perioperative variables of the study, demographic characteristics, including age, gender, comorbidity, metabolic parameters, and social history, were documented from patients’ records. Total length of the operation was chosen as an intraoperative variable, and postoperative variables included total hospitalization, duration of intensive care unit admission (ICU), postoperative complications such as atrial fibrillation, and bleeding. Statistical analysis Continuous indicators were reported as mean ± standard deviation. The Chi-square test was employed to assess the variables, with P < 0.05 considered statistically significant. Furthermore, logistic regression was employed to assess the predictive value. Data analysis was conducted using SPSS ver.24. Results Table 1 depicts the number of patients in both groups by gender, smoking, hypertension, and diabetes and their correlation with PoAF. Based on the findings of the table below, 181 men (53.3%) and 159 women (46.7%) were included in this study, of whom 175 (51.5%) were smokers, 224 (65.9%) had hypertension, and 159 (46.8%) had diabetes. 80 men (23.5%) and 90 female patients (26.5%) experienced PoAF. After the data analysis, smoking (p = 0.91), hypertension (p = 0.36), and diabetes (p = 0.13) did not display statistically significant however, gender differences were significantly associated with PoAF occurrence (p = 0.015). Table 1 Association of categorical patient characteristics with PoAF occurrence Parameter Category Total (Number, %) PoAF occurrence (Number, %) No PoAF (Number, %) p-value Gender Male 181 (53.3%) 80 (23.5%) 101 (29.7%) 0.015 Female 159 (46.7%) 90 (26.5%) 69 (20.3%) Smoking Yes 175 (51.5%) 92 (27.1%) 83 (24.4%) 0.91 No 165 (48.5%) 78 (22.9%) 87 (25.6%) Hypertension Yes 224 (65.9%) 114 (33.5%) 110 (32.4%) 0.36 No 116 (34.1%) 56 (16.5%) 60 (17.6%) Diabetes Yes 159 (46.8%) 74 (21.8%) 85 (25.0%) 0.13 No 181 (53.2%) 96 (28.2%) 85 (25.0%) Table 2 exhibits the association of clinical and laboratory parameters with PoAF status in patients. Considering the results, the height and BMI of patients significantly correlated with the presence of PoAF (p = 0.03 and 0.015, respectively). However age and weight were not statistically correlated to the occurrence of PoAF (p = 0.91 and 0.24, respectively). Based on the findings in the table, left ventricle (LV) size (p = 0.29), LV ejection fraction (EF) (p = 0.32), cross-clamp duration (p = 0.49), systolic blood pressure (p = 0.06), diastolic blood pressure (DBP) (p = 0.82), creatinine levels (p = 0.08), hemoglobin (Hb) (p = 0.49), HbA1c (p = 0.08), fasting blood glucose (FBS) (p = 0.51), and HDL cholesterol (p = 0.29) did not display statistically significant associations with PoAF. Additionally, patients who experienced PoAF had significantly higher triglyceride (TG) levels (136.87 ± 50.39) compared to those without PoAF (98.55 ± 24.73). The analysis revealed a strong association between TG levels and PoAF occurrence (p < 0.001). According to the table below, the TyG index was 8.82 ± 0.47 in patients without PoAF, and 8.91 ± 0.45 in those with PoAF, resulting a significant association between TyG and PoAF status (P = 0.02). The average TG to HDL ratio computed for patients without PoAF was 2.54 ± 0.94, and for the other group was 3.62 ± 1.72, demonstrating a statistically meaningful association between TG/HDL and PoAF (p < 0.001). Furthermore, the median MET-IR determined for the group without PoAF was 42.43 ± 7.60 and, for patients with PoAF was 44.63 ± 6.90, displaying a statistically significant correlation between PoAF and METS-IR as well (p = 0.005). Table 2 Association of quantitative patient characteristics with PoAF Status Parameter PoAF Status Number Mean Standard Deviation P-value Age (Year) No 170 61.86 9.28 0.91 Yes 170 61.78 9.68 Weight (kg) No 170 72.46 8.05 0.24 Yes 170 73.48 7.32 Height (cm) No 170 1.67 0.10 0.03 Yes 170 1.65 0.08 BMI (kg/m²) No 170 26.13 4.46 0.015 Yes 170 27.08 3.50 LV Size (mm) No 170 44.06 2.59 0.29 Yes 170 44.36 2.37 LVEF (%) No 170 47.24 6.29 0.32 Yes 170 46.34 7.04 Cross Clamp Time (min) No 170 64.75 6.14 0.49 Yes 170 62.93 6.27 SBP (mmHg) No 170 139.00 13.04 0.06 Yes 170 133.29 12.94 DBP (mmHg) No 170 87.12 10.46 0.82 Yes 170 83.32 11.65 Cr (mg/dl) No 170 1.01 0.24 0.08 Yes 170 1.02 0.19 Hb (g/dl) No 170 10.81 1.35 0.49 Yes 170 10.68 1.49 Hb A1C (%) No 170 7.04 1.46 0.08 Yes 170 7.26 1.28 FBS (mg/dl) No 170 139.59 60.28 0.51 Yes 170 141.52 74.20 TG (mg/dl) No 170 98.55 24.73 < 0.001 Yes 170 136.87 50.39 HDL (mg/dl) No 170 40.37 7.43 0.29 Yes 170 39.44 6.56 TyG No 170 8.82 0.47 0.02 Yes 170 8.91 0.45 TG/HDL No 170 2.54 0.94 < 0.001 Yes 170 3.62 1.72 METS-IR No 170 42.43 7.60 0.005 Yes 170 44.63 6.90 Logistic Regression Analysis In this study, factors affecting the incidence of PoAF were assessed using logistic regression. The results are presented in two forms: crude analysis and adjusted analysis. The analysis showed that females are significantly more at risk of PoAF. In the crude analysis, the probability of PoAF in women was 68% higher than in men (OR = 1.67; CI95%: 1.07–2.53; p = 0.02). This correlation continued stable after adjusting for other confounding variables, and the likelihood of PoAF in women was still estimated to be 67% higher than in men (OR = 1.67; CI95%:1.02–2.75; p = 0.03). The findings of the univariate model demonstrated that increasing height was significantly associated with a reduced risk of PoAF. It was observed that for every unit rise in height, the risk of PoAF decreased by 94% (OR = 0.06; CI95%: 0.006–0.65; p = 0.02). The findings of the univariate model showed that higher BMI was significantly correlated with an increased risk of PoAF. The results indicate that for each additional unit in BMI, The chance of PoAF elevated by 6% (OR = 1.06; 95% CI: 1.006–1.12; p = 0.03). The results of the univariate model showed that per per-unit increment in HbA1C was related to a 15% rise in the risk of PoAF (OR = 1.15; 95% CI: 0.98–1.35; p = 0.08). This effect was reversed in the adjusted model, which could be due to the confounding effect of other variables. Increased cross-clamp time in the crude model showed that as it raised by one unit, the risk of PoAF also raised by 1% (OR = 1.01; 95% CI: 1.05–0.97; P = 0.49. This association was reversed in the adjusted model; after adjustment, each unit increase in cross-clamp time decreased the risk of PoAF by 7% (OR = 0.93; 95% CI: 0.89–0.97; P = 0.001). This results could be due to the effect of confounding variables. In the crude model, a one-unit increase in systolic blood pressure elevated the odds of PoAF by 2% (OR = 1.91; 95% CI: 1.02–1.04; p = 0.06). This correlation was reversed in the adjusted model, and each unit rise in systolic blood pressure decreased the risk of PoAF by 4% (OR = 0.96; 95% CI: 0.96 − 0.93; p < 0.001). This difference could also be due to the effect of other variables and their confounding role. Lowering diastolic blood pressure, however, did not affect the risk of PoAF (OR = 1.00; CI95%: 1.01–0.97; p = 0.82). Increased TG levels also notably raised the risk of PoAF. In the crude analysis, risk of PoAF increased by 1% with each unit rise in TG levels. (OR = 1.01; 95% CI: 1.007–1.02; p = 0.03). Raised TG/HDL ratio was meaningfully correlated to elevated risk of PoAF. In the crude model, with for each additional unit of TG/HDL ratio, the likelihood of PoAF doubled (OR = 2.20; 95% CI: 1.70–2.84; p < 0.001). This significance was maintained in the adjusted analysis, and with each unit rise in this ratio, the risk of PoAF more than doubled (OR = 2.65; 95% CI: 1.95–2.84; p < 0.001). Increasing METS-IR was significantly correlated with elevated risk of PoAF. Furthermore, the analysis revealed that with each unit rise in METS-IR, the odds of PoAF raised by 0.4% (OR = 1.04; 95% CI: 1.02–1.07; p = 0.006). This correlation was maintained in the adjusted analysis, and with each unit rise in this ratio, the risk of PoAF increased by 1% (OR = 1.01; 95% CI: 0.97–1.02; p = 0.049). Table 3 Confidence interval (CI) and significance level (α ) for the variables Variables Adjusted p-value Adjusted OR (95% CI) Crude p-value Crude OR (95% CI) Age (year) - - 0.93 0.99 (0.97, 1.02) Gender (female vs. male) 0.02 1.68 (1.07, 2.53) 0.03 1.68 (1.02, 2.75) Smoking (yes vs. no) - - 0.32 1.23 (0.80, 1.89) Weight (kg) - - 0.22 1.01 (0.99, 1.04) Height (m) - - 0.02 0.06 (0.006, 0.65) BMI (kg/m²) - - 0.03 1.06 (1.006, 1.12) Hypertension (yes vs. no) - - 0.64 1.11 (0.70, 1.73) Diabetes (yes vs. no) - - 0.23 0.77 (0.50, 1.18) LV Size (mm) - - 0.26 1.05 (0.96, 1.14) LVEF (%) - - 0.21 0.98 (0.94, 1.01) Cross-Clamp Time (min) 0.001 0.93 (0.89, 0.97) 0.49 1.01 (0.97, 1.05) Systolic blood pressure (mmHg) 0.001 < 0.001 0.96 (0.93, 0.96) 0.06 1.02 (1.01, 1.04) Diastolic blood pressure (mmHg) - - - 0.82 1.00 (0.97, 1.01) Cr (mg/dL) - - 0.57 1.32 (0.50, 3.42) Hb (mg/dL) - - 0.39 0.93 (0.80, 1.08) HbA1C (%) < 0.001 0.65 (0.52, 0.80) 0.08 1.15 (0.98, 1.35) FBS (mg/dL) - - 0.79 0.99 (0.99, 1.004) TG (mg/dL) - - < 0.001 1.01 (1.007, 1.02) HDL (mg/dL) - - 0.21 0.98 (0.95, 1.01) TyG - - 0.10 1.47 (2.35, 0.92) TG/HDL < 0.001 2.65 (2.84, 1.95) < 0.001 2.20 (2.84, 1.70) METS-IR 0.049 1.01 (0.97, 1.02) 0.006 1.04 (1.02, 1.07) AUC – Area Under the Curve The AUC value for the adjusted model was 0.73 (CI95%: 0.79 − 0.68; p < 0.001). This value indicates that the model exhibits satisfactory predictive potential for PoAF. The value of 0.65 for the area under the curve indicates that the model performed significantly better than a random limit and could be useful in identifying patients at risk. Discussion The prevalence of AF among the general public is 0.4%-1%, reaching 8% in those over 80 years of age ( 21 ). Older age results in cardiac fibrosis and atrial dilation, increasing the incidence of PoAF. ( 22 ). In a study by Kotfis et al. in cases without metabolic syndrome, the incidence of PoAF was 25% in the age range of 18–35 years and grew to 27.4% in the age range of 56–78 years, but in cases with metabolic syndrome, this rate increased to 50%, and the incidence of infection after surgery in the same age range was 9.9% ( 22 ). The length of stay was affected by age in cases with PoAF by 10.7%, which was statistically significant. It can be argued that, in addition to the increase in the rate of atrial fibrosis in older age, metabolic syndrome, also more frequent in advanced age, is effective in increasing the incidence of PoAF. Moreover to avoidable causes, other factors such as surgical resection, pericarditis, pericardial injury, atrial expansion associated with perioperative volume excess, left ventricular dysfunction,, electrolyte and blood transfusion disorders, cardioplegia administration methods, and insufficiency of the atrial anti-inflammatory system can activate this system ( 23 ). This context can be explained by the fact that use of anti-inflammatory agents use along with statins and corticosteroids could reduce the incidence of PoAF ( 24 , 25 ). Studies have shown that PoAF incidence is notably higher in the female gender, and women were estimated to be 67% more at risk of PoAF than men. This finding clearly contradicts the results of Matthew et al. that men have a higher risk of PoAF ( 25 ). Additionally, geographical region and ethnicity are noteworthy in the development of PoAF, with regional incidence reported variously ( 26 ). This difference may be associated with the prevalence of metabolic syndrome and also implies that whites are more susceptible to PoAF. However, conducting a large meta-analysis and cohort study, examining associated factors, is crucial. This study demonstrated a positive correlation between the height and BMI of the patients and PoAF. BMI is a measure of total body fat and does not reflect fat distribution or metabolic dysfunction. Past studies show that being obese, measured by higher BMIs, counts as an independent risk factor. ( 27 , 28 ).Despite, insulin resistance being directly related to abdominal obesity, it may occur without obesity in some cases, nevertheless, left atrial dimensions gradually increase with increasing BMI, therefore, as Ducceschi et al. published, among 150 cases with BMI above 30 kg/m², the rate of PoAF and left atrial dilation were higher ( 29 ). Inflammatory mediators were elevated in the atrial biopsy of patients with PoAF, which can show the part of inflammation in the manifestation of PoAF ( 30 ). Evidence suggests that hormones such as leptin, adiponectin, resistin, and also cytokines (TNF-ɑ, IL-6,IL-8) are secreted from adipose tissue, contributing to elevating the level of systemic inflammatory response, and therefore the occurrence of atrial fibrillation ( 31 ). In the study by Ucar et al. it was reported that high waist circumference and high BMI were considerably related to PoAF ( 30 ). The mean waist circumference (WC) in cases with PoAF was 90.1 cm ± 12.6, while in cases without PoAF, it was 81.6 cm ± 14. However, while waist circumference values ​​were higher in patients with PoAF having adverse effects such as infection, bleeding, stroke, and mortality, data revealed that the association with PoAF was only significant in cases of stroke and infection ( 28 ). Girerd et al. demonstrated that cases with higher waist circumference and higher CRP levels are more likely to experience PoAF ( 32 ). However, there have even been reports that obesity does not affect PoAF( 27 ). The heterogeneity between results may be due to the variety in fat distribution and the level of accumulated cardio toxic metabolites. The rate of PoAF was elevated in shorter patients with higher BMI. Also, increased height reduced the chances of PoAF significantly, and with each unit of rise in height, the risk of PoAF decreased by 94%. This, if assuming an inverse relationship between BMI and height, confirms the findings of Ducceschi et al. ( 29 ) and Ucar et al. ( 30 ). Regarding the laboratory parameters, Hb A1C, and non-insulin indices of insulin resistance such as TG/HDL-C, TyG, and METS-IR were higher in patients with PoAF. These results are also aligned with the findings of Kotfis et al. and many other studies indicating that the incidence of PoAF is increased in the case of metabolic syndrome and insulin resistance ( 22 , 33 – 39 ). Previous studies indicate that the TyG index can help assess the prognosis and screening of AF ( 40 , 41 ). However, findings of current study revealed that although TyG was in association with PoAF it could not predict the chances of PoAF. Luo et al. suggested that these insulin resistance indices also show potential to be considered as prognostic factors for late recurrent AF after radiofrequency catheter ablation treatment in AF patients ( 42 ). Elevated hemoglobin values exhibited an association with increased AF incidence, although not statistically significant. Fasting glucose levels in patients with PoAF were higher than in the control group, although this difference was not statistically significant. The risk of PoAF was significantly elevated along with higher TG/HDL levels, such that with each unit of increase in TG/HDL ratio, the odds of PoAF doubled. This verifies the results of studies that have identified metabolic syndrome and increased insulin resistance as factors that increase the incidence of PoAF. Increasing cross-clamp time was significantly correlated with an increase in the likelihood of PoAF, and with each unit of increase in cross-clamp time, the risk of AF increases by 1%. This also confirms the findings of Bannister et al. ( 43 ). Roffman et al. also stated that the incidence of arrhythmias increased with increasing the quantity of grafts or length of the procedure ( 44 ). According to Bannister et al. increased duration of surgery disrupts the mechanism of glucose transport into the cell and consequently increases glucose levels in the bloodstream, resulting in acidosis in the blood. When the patient is getting warm to prevent hypothermia after surgery, the insulin response improves, but hyperglycemia persists for an additional 1–2 hours. Additionally, thyroid hormone metabolism is affected, and triiodothyronine (T3) levels decrease( 43 ). Almassi et al. demonstrated that hospital mortality was 2 times higher (3% vs. 6%), while 6-month mortality was 4.7% vs. 9% in cases with PoAF compared to those without PoAF ( 45 ). The concept that PoAF alone increases mortality is inadequate; however, PoAF could increase postoperative complications. In patients with metabolic syndrome, PoAF increases the length of hospital stay by 31%. It has been further shown that metabolic syndrome increases the incidence of infection and stroke by 109 times, with stroke being more common in patients with metabolic syndrome who develop PoAF. Several studies report an incidence of 15%–40% for AF within 1–5 days post-operation ( 46 ), while some studies have reported an incidence of 10%–65% ( 47 , 48 ). The clinical significance of this complication depends on the underlying cause. 30% of PoAF cases resolve spontaneously within the first 2 hours. Moreover, 25%–80% of PoAF cases have been found to resolve within 1 day with digoxin administration( 49 ). Mathew et al found that PoAF is influenced by male gender, advanced age, hypertension, history of AF, COPD, valvular disease, heart failure, digoxin use before surgery, and lack of preoperative beta-blocker use. The prevalence of PoAF has been found to be higher in patients where the operation involves pulmonary valve insertion or bicaval cannulation ( 25 , 50 ). PoAF was also more common in patients with higher SBP and DBP, although no meaningful difference was observed between the groups. Findings of Bell et al. ( 24 ) and Patti et al. ( 51 ) are in line with our study. Bell et al. reported that 60% of PoAF cases were in patients with hypertension ( 24 ). Patti et al. identified hypertension as a standalone predictor of postoperative atrial fibrillation and in their study, there was a notable rise in the prevalence of AF (27%) in individuals with hypertension ( 51 ). In-hospital mortality was twice as high in patients with hypertension; therefore, controlling blood pressure may prevent the occurrence of AF. Prior researches have exhibited that diabetes, metabolic syndrome, obesity, and advanced age have a positive effect on PoAF. However, a significant negative association was seen between PoAF and beta-blocker use. While ACE inhibitor and statins use were not associated with PoAF. Matthew et al. reported a correlation between statins or ACE inhibitor use with PoAF and showed that the PoAF can significantly increase the ICU stay, the length of hospitalization and the occurrence of stroke( 25 ). Of all cases, 88% of PoAF cases develop within 1–5 days after surgery, and 98% of these cases resolve within 1–3 days. With decreased LVEF, the prevalence of PoAF increases. Our study has several limitations. First, our data are limited to patients hospitalized after isolated CABG surgery at Shahid Mohammadi Hospital, which could influence the applicability of our findings. In addition, our study design may be subject to collection and selection bias due to using clinical records; therefore, more studies should be conducted. Additional studies with a larger sample and multi-center data, investigating confounding factors impacting the effect of the determiner parameters with longer follow-up time, would be of great value. Conclusion The study imply that higher values of METS-IR and TG/HDL increase the incidence of PoAF. Female gender, high BMI, shorter height could elevate the risk of developing PoAF. Also, increased cross-clamp time is correlated to a rise in the prevalence of PoAF. Analyzing the area under the curve indicates that the model performed significantly better than a random limit and could be useful in identifying patients at risk. Declarations Consent for publication Consent for publication was informed and obtained from all participants. Competing interests The authors declare no conflict of interest. Funding This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution Conceptualization: S.A, H.M; Data curation: A.S.A, A.M.B; Formal analysis: M.M; Funding acquisition: S.A, H.M; Investigation: A.A; Methodology: A.A, S.A; Project administration: H.M; Resources: S.A, A.A; Supervision: S.A, H.M; Validation: K.A; Visualization: K.A; Writing - original draft: K.A, A.M.B; Writing - review & Editing: A.S.A, A.A Acknowledgement We thank our counselors and staff at the Shahid Mohammadi Hospital in Bandar Abbas. Data Availability The data sets used during the current study are applicable from the corresponding author upon reasonable request. Ethics approval The study was approved by the research council and ethics committee (IR.HUMS.REC.1403.003) of Hormozgan University of Medical Sciences. Furthermore, the current study was conducted in accordance with the principles of the Declaration of Helsinki, and informed consent was obtained from all participants. References Maisel WH, Rawn JD, Stevenson WG. 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Ucar HI, Tok M, Atalar E, Dogan OF, Oc M, Farsak B, et al. Predictive significance of plasma levels of interleukin-6 and high-sensitivity C-reactive protein in atrial fibrillation after coronary artery bypass surgery. Heart Surg Forum. 2007;10(2):E131–5. Aizawa K, Shoemaker JK, Overend TJ, Petrella RJ. Metabolic syndrome, endothelial function and lifestyle modification. Diab Vasc Dis Res. 2009;6(3):181–9. Girerd N, Pibarot P, Fournier D, Daleau P, Voisine P, O'Hara G, et al. Middle-aged men with increased waist circumference and elevated C-reactive protein level are at higher risk for postoperative atrial fibrillation following coronary artery bypass grafting surgery. Eur Heart J. 2009;30(10):1270–8. Zacharias A, Schwann TA, Riordan CJ, Durham SJ, Shah AS, Habib RH. Obesity and risk of new-onset atrial fibrillation after cardiac surgery. Circulation. 2005;112(21):3247–55. Giaccardi M, Macchi C, Colella A, Polcaro P, Zipoli R, Cecchi F, et al. Postacute rehabilitation after coronary surgery: the effect of preoperative physical activity on the incidence of paroxysmal atrial fibrillation. Am J Phys Med Rehabil. 2011;90(4):308–15. Bramer S, van Straten AH, Soliman Hamad MA, Berreklouw E, van den Broek KC, Maessen JG. Body mass index predicts new-onset atrial fibrillation after cardiac surgery. Eur J Cardiothorac Surg. 2011;40(5):1185–90. ERRATUM. Crit Care Med. 2005;33(7):1678. Steinberg JS, Zelenkofske S, Wong SC, Gelernt M, Sciacca R, Menchavez E. Value of the P-wave signal-averaged ECG for predicting atrial fibrillation after cardiac surgery. Circulation. 1993;88(6):2618–22. Korantzopoulos P, Kolettis TM, Galaris D, Goudevenos JA. The role of oxidative stress in the pathogenesis and perpetuation of atrial fibrillation. Int J Cardiol. 2007;115(2):135–43. Bramer S, van Straten AH, Soliman Hamad MA, Berreklouw E, Martens EJ, Maessen JG. The impact of new-onset postoperative atrial fibrillation on mortality after coronary artery bypass grafting. Ann Thorac Surg. 2010;90(2):443–9. Sun Y, Ji H, Sun W, An X, Lian F. Triglyceride glucose (TyG) index: A promising biomarker for diagnosis and treatment of different diseases. Eur J Intern Med. 2025;131:3–14. Nayak SS, Kuriyakose D, Polisetty LD, Patil AA, Ameen D, Bonu R, et al. Diagnostic and prognostic value of triglyceride glucose index: a comprehensive evaluation of meta-analysis. Cardiovasc Diabetol. 2024;23(1):310. Luo Y, Luo D, Yang G, Huang W, Tang Y, Xu B, et al. The effect of non-insulin-based insulin resistance indices on the prediction of recurrence in patients with atrial fibrillation undergoing radiofrequency catheter ablation. Cardiovasc Diabetol. 2024;23(1):291. Bannister CF, Finlayson DC. The Endocrine System: Effects of Cardiopulmonary Bypass. In: Mora CT, Guyton RA, Finlayson DC, Rigatti RL, editors. Cardiopulmonary Bypass: Principles and Techniques of Extracorporeal Circulation. New York, NY: Springer New York; 1995. pp. 180–95. Roffman JA, Fieldman A. Digoxin and propranolol in the prophylaxis of supraventricular tachydysrhythmias after coronary artery bypass surgery. Ann Thorac Surg. 1981;31(6):496–501. Almassi GH, Schowalter T, Nicolosi AC, Aggarwal A, Moritz TE, Henderson WG, et al. Atrial fibrillation after cardiac surgery: a major morbid event? Ann Surg. 1997;226(4):501–11. discussion 11 – 3. Ferreira AF, Moreira FAS, R R, Pinho JCMJA. Postoperative Atrial Fibrillation After Coronary Artery Bypass Grafting Surgery. Rev Port Cir Cardiotorac Vasc. 2017;24(3–4):129. Dupont E, Ko Y, Rothery S, Coppen SR, Baghai M, Haw M, Severs NJ. The gap-junctional protein connexin40 is elevated in patients susceptible to postoperative atrial fibrillation. Circulation. 2001;103(6):842–9. Karaca M, Demirbas MI, Biceroglu S, Cevik A, Cetin Y, Arpaz M, Yilmaz H. Prediction of early postoperative atrial fibrillation after cardiac surgery: is it possible? Cardiovasc J Afr. 2012;23(1):34–6. Cochrane AD, Siddins M, Rosenfeldt FL, Salamonsen R, McConaghy L, Marasco S, Davis BB. A comparison of amiodarone and digoxin for treatment of supraventricular arrhythmias after cardiac surgery. Eur J Cardiothorac Surg. 1994;8(4):194–8. Aranki SF, Shaw DP, Adams DH, Rizzo RJ, Couper GS, VanderVliet M, et al. Predictors of atrial fibrillation after coronary artery surgery. Current trends and impact on hospital resources. Circulation. 1996;94(3):390–7. Patti G, Chello M, Candura D, Pasceri V, D'Ambrosio A, Covino E, Di Sciascio G. Randomized trial of atorvastatin for reduction of postoperative atrial fibrillation in patients undergoing cardiac surgery: results of the ARMYDA-3 (Atorvastatin for Reduction of MYocardial Dysrhythmia After cardiac surgery) study. Circulation. 2006;114(14):1455–61. Additional Declarations No competing interests reported. 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12:43:26","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":149306,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7679260/v1/c7a152a0e2d568aeb6cfaae3.html"},{"id":97703787,"identity":"c2aa905a-b2b9-4958-a74d-f0980ca7a519","added_by":"auto","created_at":"2025-12-08 12:43:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87260,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity chart vs. specificity of the study model\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7679260/v1/2ae3c4b72a72bc4ad7cfdb38.png"},{"id":97902376,"identity":"3d1d7668-8c08-4b34-9080-8230f072e852","added_by":"auto","created_at":"2025-12-10 15:51:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":985799,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7679260/v1/a3815611-0be9-4a61-9e0a-1d95381bb9c9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Value of METS-IR, TYG Index, and TG/HDL Ratio in Atrial Fibrillation Following Coronary Artery Bypass","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronary artery disease (CAD) is a crucial condition affecting the expectancy and quality of the people living with it. While there are various intravascular intervention methods, coronary artery bypass grafting (CABG) is still used as a principle treatment (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Although surgeries through cardiac-pulmonary bypass (CPB) have minimal mortality rates, the occurrence of postoperative atrial fibrillation (PoAF), kidney and pulmonary complications may still be problematic for patients' recovery (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Atrial fibrillation (AF), as a vital clinical condition, accounts for between 10%-65% of cases post-CABG surgery, leading to cerebrovascular accidents, prolonged hospitalization, and increased treatment costs. Furthermore, various risk factors are known, including age, high blood pressure, left atrial dilation, and blood transfusion (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGrowing evidence suggests that insulin resistance, as a feature diabetes mellitus type 2 alongwith metabolic syndrome, may contribute to the development of CAD (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) The conventional way to estimate insulin sensitivity (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), which may be inaccurate if the insulin values are imprecise. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In addition, some non-insulin indices of insulin resistance have been proposed as alternatives, such as the triglyceride to high-density lipoprotein ratio(TG/HDL), the triglyceride and glucose index (TyG index), and the insulin resistance metabolic score (IR-METS) (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). There are simple biochemical methods available to measure these new indices, making up for the limitations of the conventional methods of measuring insulin resistance. In addition, prior research has demonstrated that non-insulin-based indices correlate to several indicators for CVD, including diabetes, obesity, hypertension, and metabolic syndrome, which can be used as predictive and prognostic factors for CVD (\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrevious studies noted the association of diabetes and insulin resistance with post-operative complications and hospitalization length (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Additionally, lower HDL cholesterol levels, along with elevated triglyceride and glucose levels, have demonstrated a significant correlation with POAF (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Due to the relationship of these three indices with CVD, and lack of data on their association with PoAF, in current study, we explored the predictive role of metabolic parameters IR-METS, TyG Index, and HDL/TG Ratio in the incidence of AF following coronary artery bypass grafting.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eIn this retrospective study, 340 patients who underwent isolated CABG surgery at Shahid Mohammadi Hospital between September 2021 and September 2024 were included. Consent forms were obtained from all participating patients, and the study was approved by the Ethics Committee of Hormozgan University of Medical Sciences) IR.HUMS.REC.1403.003). Inclusion criteria consisted of patients who underwent CABG surgery in the above-mentioned center. Exclusion criteria consisted of patients with prior CABG surgery, emergency operations, moderate to severe mitral valve regurgitation, history of AF or known AF attacks before surgery, amiodarone use in the preoperative period, history of renal transplant treatment, and lack of consent to participate. After applying the exclusion and inclusion criteria, patients were categorized into two groups; the first group contained 170 randomly selected cases who did not experience postoperative AF within 72 hours following operation. The second group consists of 170 patients developing POAF within 72 hours after surgery.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eNon-insulin indices of insulin resistance\u003c/h3\u003e\n\u003cp\u003eIn the current study, we utilized three indices for measuring insulin resistance: The TyG index was calculated using the natural logarithm transformation of the product of fasting plasma triglycerides and glucose levels, divided by two, with all values expressed in mg/dL. The ratio of triglycerides to HDL cholesterol (TG/HDL-C) was calculated by dividing fasting triglyceride concentration by fasting HDL cholesterol concentration. To estimate the METS-IR, the body mass index (BMI) was multiplied by the natural logarithm of the sum of twice the fasting glucose and triglyceride concentrations, and then divided by the natural logarithm of the HDL cholesterol level (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eAs for perioperative variables of the study, demographic characteristics, including age, gender, comorbidity, metabolic parameters, and social history, were documented from patients\u0026rsquo; records. Total length of the operation was chosen as an intraoperative variable, and postoperative variables included total hospitalization, duration of intensive care unit admission (ICU), postoperative complications such as atrial fibrillation, and bleeding.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous indicators were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. The Chi-square test was employed to assess the variables, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. Furthermore, logistic regression was employed to assess the predictive value. Data analysis was conducted using SPSS ver.24.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the number of patients in both groups by gender, smoking, hypertension, and diabetes and their correlation with PoAF. Based on the findings of the table below, 181 men (53.3%) and 159 women (46.7%) were included in this study, of whom 175 (51.5%) were smokers, 224 (65.9%) had hypertension, and 159 (46.8%) had diabetes. 80 men (23.5%) and 90 female patients (26.5%) experienced PoAF. After the data analysis, smoking (p\u0026thinsp;=\u0026thinsp;0.91), hypertension (p\u0026thinsp;=\u0026thinsp;0.36), and diabetes (p\u0026thinsp;=\u0026thinsp;0.13) did not display statistically significant however, gender differences were significantly associated with PoAF occurrence (p\u0026thinsp;=\u0026thinsp;0.015).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of categorical patient characteristics with PoAF occurrence\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal (Number, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePoAF occurrence (Number, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo PoAF\u003c/p\u003e\u003cp\u003e(Number, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e181 (53.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80 (23.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e101 (29.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e159 (46.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90 (26.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e69 (20.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e175 (51.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e92 (27.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e83 (24.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e165 (48.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78 (22.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e87 (25.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e224 (65.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e114 (33.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e110 (32.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116 (34.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56 (16.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60 (17.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e159 (46.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e74 (21.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e85 (25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e181 (53.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96 (28.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e85 (25.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e exhibits the association of clinical and laboratory parameters with PoAF status in patients. Considering the results, the height and BMI of patients significantly correlated with the presence of PoAF (p\u0026thinsp;=\u0026thinsp;0.03 and 0.015, respectively). However age and weight were not statistically correlated to the occurrence of PoAF (p\u0026thinsp;=\u0026thinsp;0.91 and 0.24, respectively). Based on the findings in the table, left ventricle (LV) size (p\u0026thinsp;=\u0026thinsp;0.29), LV ejection fraction (EF) (p\u0026thinsp;=\u0026thinsp;0.32), cross-clamp duration (p\u0026thinsp;=\u0026thinsp;0.49), systolic blood pressure (p\u0026thinsp;=\u0026thinsp;0.06), diastolic blood pressure (DBP) (p\u0026thinsp;=\u0026thinsp;0.82), creatinine levels (p\u0026thinsp;=\u0026thinsp;0.08), hemoglobin (Hb) (p\u0026thinsp;=\u0026thinsp;0.49), HbA1c (p\u0026thinsp;=\u0026thinsp;0.08), fasting blood glucose (FBS) (p\u0026thinsp;=\u0026thinsp;0.51), and HDL cholesterol (p\u0026thinsp;=\u0026thinsp;0.29) did not display statistically significant associations with PoAF. Additionally, patients who experienced PoAF had significantly higher triglyceride (TG) levels (136.87\u0026thinsp;\u0026plusmn;\u0026thinsp;50.39) compared to those without PoAF (98.55\u0026thinsp;\u0026plusmn;\u0026thinsp;24.73). The analysis revealed a strong association between TG levels and PoAF occurrence (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eAccording to the table below, the TyG index was 8.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47 in patients without PoAF, and 8.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45 in those with PoAF, resulting a significant association between TyG and PoAF status (P\u0026thinsp;=\u0026thinsp;0.02). The average TG to HDL ratio computed for patients without PoAF was 2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94, and for the other group was 3.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72, demonstrating a statistically meaningful association between TG/HDL and PoAF (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, the median MET-IR determined for the group without PoAF was 42.43\u0026thinsp;\u0026plusmn;\u0026thinsp;7.60 and, for patients with PoAF was 44.63\u0026thinsp;\u0026plusmn;\u0026thinsp;6.90, displaying a statistically significant correlation between PoAF and METS-IR as well (p\u0026thinsp;=\u0026thinsp;0.005).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of quantitative patient characteristics with PoAF Status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoAF Status\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAge (Year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWeight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e72.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHeight (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLV Size (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLVEF (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCross Clamp Time (min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e139.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e133.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e83.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCr (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHb (g/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHb A1C (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFBS (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e139.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e141.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e74.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTG (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e98.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e136.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e50.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHDL (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTyG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTG/HDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMETS-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eLogistic Regression Analysis\u003c/h2\u003e\u003cp\u003eIn this study, factors affecting the incidence of PoAF were assessed using logistic regression. The results are presented in two forms: crude analysis and adjusted analysis. The analysis showed that females are significantly more at risk of PoAF. In the crude analysis, the probability of PoAF in women was 68% higher than in men (OR\u0026thinsp;=\u0026thinsp;1.67; CI95%: 1.07\u0026ndash;2.53; p\u0026thinsp;=\u0026thinsp;0.02). This correlation continued stable after adjusting for other confounding variables, and the likelihood of PoAF in women was still estimated to be 67% higher than in men (OR\u0026thinsp;=\u0026thinsp;1.67; CI95%:1.02\u0026ndash;2.75; p\u0026thinsp;=\u0026thinsp;0.03). The findings of the univariate model demonstrated that increasing height was significantly associated with a reduced risk of PoAF. It was observed that for every unit rise in height, the risk of PoAF decreased by 94% (OR\u0026thinsp;=\u0026thinsp;0.06; CI95%: 0.006\u0026ndash;0.65; p\u0026thinsp;=\u0026thinsp;0.02). The findings of the univariate model showed that higher BMI was significantly correlated with an increased risk of PoAF. The results indicate that for each additional unit in BMI, The chance of PoAF elevated by 6% (OR\u0026thinsp;=\u0026thinsp;1.06; 95% CI: 1.006\u0026ndash;1.12; p\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e\u003cp\u003eThe results of the univariate model showed that per per-unit increment in HbA1C was related to a 15% rise in the risk of PoAF (OR\u0026thinsp;=\u0026thinsp;1.15; 95% CI: 0.98\u0026ndash;1.35; p\u0026thinsp;=\u0026thinsp;0.08). This effect was reversed in the adjusted model, which could be due to the confounding effect of other variables. Increased cross-clamp time in the crude model showed that as it raised by one unit, the risk of PoAF also raised by 1% (OR\u0026thinsp;=\u0026thinsp;1.01; 95% CI: 1.05\u0026ndash;0.97; P\u0026thinsp;=\u0026thinsp;0.49. This association was reversed in the adjusted model; after adjustment, each unit increase in cross-clamp time decreased the risk of PoAF by 7% (OR\u0026thinsp;=\u0026thinsp;0.93; 95% CI: 0.89\u0026ndash;0.97; P\u0026thinsp;=\u0026thinsp;0.001). This results could be due to the effect of confounding variables. In the crude model, a one-unit increase in systolic blood pressure elevated the odds of PoAF by 2% (OR\u0026thinsp;=\u0026thinsp;1.91; 95% CI: 1.02\u0026ndash;1.04; p\u0026thinsp;=\u0026thinsp;0.06). This correlation was reversed in the adjusted model, and each unit rise in systolic blood pressure decreased the risk of PoAF by 4% (OR\u0026thinsp;=\u0026thinsp;0.96; 95% CI: 0.96\u0026thinsp;\u0026minus;\u0026thinsp;0.93; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This difference could also be due to the effect of other variables and their confounding role. Lowering diastolic blood pressure, however, did not affect the risk of PoAF (OR\u0026thinsp;=\u0026thinsp;1.00; CI95%: 1.01\u0026ndash;0.97; p\u0026thinsp;=\u0026thinsp;0.82). Increased TG levels also notably raised the risk of PoAF. In the crude analysis, risk of PoAF increased by 1% with each unit rise in TG levels. (OR\u0026thinsp;=\u0026thinsp;1.01; 95% CI: 1.007\u0026ndash;1.02; p\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e\u003cp\u003eRaised TG/HDL ratio was meaningfully correlated to elevated risk of PoAF. In the crude model, with for each additional unit of TG/HDL ratio, the likelihood of PoAF doubled (OR\u0026thinsp;=\u0026thinsp;2.20; 95% CI: 1.70\u0026ndash;2.84; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This significance was maintained in the adjusted analysis, and with each unit rise in this ratio, the risk of PoAF more than doubled (OR\u0026thinsp;=\u0026thinsp;2.65; 95% CI: 1.95\u0026ndash;2.84; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Increasing METS-IR was significantly correlated with elevated risk of PoAF. Furthermore, the analysis revealed that with each unit rise in METS-IR, the odds of PoAF raised by 0.4% (OR\u0026thinsp;=\u0026thinsp;1.04; 95% CI: 1.02\u0026ndash;1.07; p\u0026thinsp;=\u0026thinsp;0.006). This correlation was maintained in the adjusted analysis, and with each unit rise in this ratio, the risk of PoAF increased by 1% (OR\u0026thinsp;=\u0026thinsp;1.01; 95% CI: 0.97\u0026ndash;1.02; p\u0026thinsp;=\u0026thinsp;0.049).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eConfidence interval (CI) and significance level (α\u003cb\u003e)\u003c/b\u003e for the variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdjusted p-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCrude p-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCrude OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.99 (0.97, 1.02)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (female vs. male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.68 (1.07, 2.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.68 (1.02, 2.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking (yes vs. no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.23 (0.80, 1.89)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.01 (0.99, 1.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.06 (0.006, 0.65)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.06 (1.006, 1.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension (yes vs. no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.11 (0.70, 1.73)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes (yes vs. no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.77 (0.50, 1.18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLV Size (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.05 (0.96, 1.14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLVEF (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.98 (0.94, 1.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-Clamp Time (min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93 (0.89, 0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.01 (0.97, 1.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic blood pressure (mmHg) 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.96 (0.93, 0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.02 (1.01, 1.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiastolic blood pressure (mmHg) -\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00 (0.97, 1.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.32 (0.50, 3.42)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHb (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.93 (0.80, 1.08)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1C (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.65 (0.52, 0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.15 (0.98, 1.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFBS (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.99 (0.99, 1.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.01 (1.007, 1.02)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.98 (0.95, 1.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.47 (2.35, 0.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG/HDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.65 (2.84, 1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.20 (2.84, 1.70)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMETS-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.01 (0.97, 1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.04 (1.02, 1.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAUC – Area Under the Curve\u003c/h3\u003e\n\u003cp\u003eThe AUC value for the adjusted model was 0.73 (CI95%: 0.79\u0026thinsp;\u0026minus;\u0026thinsp;0.68; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This value indicates that the model exhibits satisfactory predictive potential for PoAF. The value of 0.65 for the area under the curve indicates that the model performed significantly better than a random limit and could be useful in identifying patients at risk.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe prevalence of AF among the general public is 0.4%-1%, reaching 8% in those over 80 years of age (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Older age results in cardiac fibrosis and atrial dilation, increasing the incidence of PoAF. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In a study by Kotfis et al. in cases without metabolic syndrome, the incidence of PoAF was 25% in the age range of 18\u0026ndash;35 years and grew to 27.4% in the age range of 56\u0026ndash;78 years, but in cases with metabolic syndrome, this rate increased to 50%, and the incidence of infection after surgery in the same age range was 9.9% (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The length of stay was affected by age in cases with PoAF by 10.7%, which was statistically significant. It can be argued that, in addition to the increase in the rate of atrial fibrosis in older age, metabolic syndrome, also more frequent in advanced age, is effective in increasing the incidence of PoAF. Moreover to avoidable causes, other factors such as surgical resection, pericarditis, pericardial injury, atrial expansion associated with perioperative volume excess, left ventricular dysfunction,, electrolyte and blood transfusion disorders, cardioplegia administration methods, and insufficiency of the atrial anti-inflammatory system can activate this system (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This context can be explained by the fact that use of anti-inflammatory agents use along with statins and corticosteroids could reduce the incidence of PoAF (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Studies have shown that PoAF incidence is notably higher in the female gender, and women were estimated to be 67% more at risk of PoAF than men. This finding clearly contradicts the results of Matthew et al. that men have a higher risk of PoAF (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Additionally, geographical region and ethnicity are noteworthy in the development of PoAF, with regional incidence reported variously (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). This difference may be associated with the prevalence of metabolic syndrome and also implies that whites are more susceptible to PoAF. However, conducting a large meta-analysis and cohort study, examining associated factors, is crucial.\u003c/p\u003e\u003cp\u003eThis study demonstrated a positive correlation between the height and BMI of the patients and PoAF. BMI is a measure of total body fat and does not reflect fat distribution or metabolic dysfunction. Past studies show that being obese, measured by higher BMIs, counts as an independent risk factor. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).Despite, insulin resistance being directly related to abdominal obesity, it may occur without obesity in some cases, nevertheless, left atrial dimensions gradually increase with increasing BMI, therefore, as Ducceschi et al. published, among 150 cases with BMI above 30 kg/m\u0026sup2;, the rate of PoAF and left atrial dilation were higher (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Inflammatory mediators were elevated in the atrial biopsy of patients with PoAF, which can show the part of inflammation in the manifestation of PoAF (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Evidence suggests that hormones such as leptin, adiponectin, resistin, and also cytokines (TNF-ɑ, IL-6,IL-8) are secreted from adipose tissue, contributing to elevating the level of systemic inflammatory response, and therefore the occurrence of atrial fibrillation (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). In the study by Ucar et al. it was reported that high waist circumference and high BMI were considerably related to PoAF (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The mean waist circumference (WC) in cases with PoAF was 90.1 cm\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6, while in cases without PoAF, it was 81.6 cm\u0026thinsp;\u0026plusmn;\u0026thinsp;14. However, while waist circumference values ​​were higher in patients with PoAF having adverse effects such as infection, bleeding, stroke, and mortality, data revealed that the association with PoAF was only significant in cases of stroke and infection (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Girerd et al. demonstrated that cases with higher waist circumference and higher CRP levels are more likely to experience PoAF (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). However, there have even been reports that obesity does not affect PoAF(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The heterogeneity between results may be due to the variety in fat distribution and the level of accumulated cardio toxic metabolites. The rate of PoAF was elevated in shorter patients with higher BMI. Also, increased height reduced the chances of PoAF significantly, and with each unit of rise in height, the risk of PoAF decreased by 94%. This, if assuming an inverse relationship between BMI and height, confirms the findings of Ducceschi et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) and Ucar et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRegarding the laboratory parameters, Hb A1C, and non-insulin indices of insulin resistance such as TG/HDL-C, TyG, and METS-IR were higher in patients with PoAF. These results are also aligned with the findings of Kotfis et al. and many other studies indicating that the incidence of PoAF is increased in the case of metabolic syndrome and insulin resistance (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34 CR35 CR36 CR37 CR38\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Previous studies indicate that the TyG index can help assess the prognosis and screening of AF (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). However, findings of current study revealed that although TyG was in association with PoAF it could not predict the chances of PoAF. Luo et al. suggested that these insulin resistance indices also show potential to be considered as prognostic factors for late recurrent AF after radiofrequency catheter ablation treatment in AF patients (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Elevated hemoglobin values exhibited an association with increased AF incidence, although not statistically significant. Fasting glucose levels in patients with PoAF were higher than in the control group, although this difference was not statistically significant. The risk of PoAF was significantly elevated along with higher TG/HDL levels, such that with each unit of increase in TG/HDL ratio, the odds of PoAF doubled. This verifies the results of studies that have identified metabolic syndrome and increased insulin resistance as factors that increase the incidence of PoAF.\u003c/p\u003e\u003cp\u003eIncreasing cross-clamp time was significantly correlated with an increase in the likelihood of PoAF, and with each unit of increase in cross-clamp time, the risk of AF increases by 1%. This also confirms the findings of Bannister et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Roffman et al. also stated that the incidence of arrhythmias increased with increasing the quantity of grafts or length of the procedure (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). According to Bannister et al. increased duration of surgery disrupts the mechanism of glucose transport into the cell and consequently increases glucose levels in the bloodstream, resulting in acidosis in the blood. When the patient is getting warm to prevent hypothermia after surgery, the insulin response improves, but hyperglycemia persists for an additional 1\u0026ndash;2 hours. Additionally, thyroid hormone metabolism is affected, and triiodothyronine (T3) levels decrease(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Almassi et al. demonstrated that hospital mortality was 2 times higher (3% vs. 6%), while 6-month mortality was 4.7% vs. 9% in cases with PoAF compared to those without PoAF (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). The concept that PoAF alone increases mortality is inadequate; however, PoAF could increase postoperative complications. In patients with metabolic syndrome, PoAF increases the length of hospital stay by 31%. It has been further shown that metabolic syndrome increases the incidence of infection and stroke by 109 times, with stroke being more common in patients with metabolic syndrome who develop PoAF.\u003c/p\u003e\u003cp\u003eSeveral studies report an incidence of 15%\u0026ndash;40% for AF within 1\u0026ndash;5 days post-operation (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), while some studies have reported an incidence of 10%\u0026ndash;65% (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). The clinical significance of this complication depends on the underlying cause. 30% of PoAF cases resolve spontaneously within the first 2 hours. Moreover, 25%\u0026ndash;80% of PoAF cases have been found to resolve within 1 day with digoxin administration(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Mathew et al found that PoAF is influenced by male gender, advanced age, hypertension, history of AF, COPD, valvular disease, heart failure, digoxin use before surgery, and lack of preoperative beta-blocker use. The prevalence of PoAF has been found to be higher in patients where the operation involves pulmonary valve insertion or bicaval cannulation (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). PoAF was also more common in patients with higher SBP and DBP, although no meaningful difference was observed between the groups. Findings of Bell et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) and Patti et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) are in line with our study. Bell et al. reported that 60% of PoAF cases were in patients with hypertension (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Patti et al. identified hypertension as a standalone predictor of postoperative atrial fibrillation and in their study, there was a notable rise in the prevalence of AF (27%) in individuals with hypertension (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). In-hospital mortality was twice as high in patients with hypertension; therefore, controlling blood pressure may prevent the occurrence of AF. Prior researches have exhibited that diabetes, metabolic syndrome, obesity, and advanced age have a positive effect on PoAF. However, a significant negative association was seen between PoAF and beta-blocker use. While ACE inhibitor and statins use were not associated with PoAF. Matthew et al. reported a correlation between statins or ACE inhibitor use with PoAF and showed that the PoAF can significantly increase the ICU stay, the length of hospitalization and the occurrence of stroke(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Of all cases, 88% of PoAF cases develop within 1\u0026ndash;5 days after surgery, and 98% of these cases resolve within 1\u0026ndash;3 days. With decreased LVEF, the prevalence of PoAF increases.\u003c/p\u003e\u003cp\u003eOur study has several limitations. First, our data are limited to patients hospitalized after isolated CABG surgery at Shahid Mohammadi Hospital, which could influence the applicability of our findings. In addition, our study design may be subject to collection and selection bias due to using clinical records; therefore, more studies should be conducted. Additional studies with a larger sample and multi-center data, investigating confounding factors impacting the effect of the determiner parameters with longer follow-up time, would be of great value.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study imply that higher values of METS-IR and TG/HDL increase the incidence of PoAF. Female gender, high BMI, shorter height could elevate the risk of developing PoAF. Also, increased cross-clamp time is correlated to a rise in the prevalence of PoAF. Analyzing the area under the curve indicates that the model performed significantly better than a random limit and could be useful in identifying patients at risk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConsent for publication\u003c/h2\u003e\u003cp\u003e Consent for publication was informed and obtained from all participants.\u003c/p\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: S.A, H.M; Data curation: A.S.A, A.M.B; Formal analysis: M.M; Funding acquisition: S.A, H.M; Investigation: A.A; Methodology: A.A, S.A; Project administration: H.M; Resources: S.A, A.A; Supervision: S.A, H.M; Validation: K.A; Visualization: K.A; Writing - original draft: K.A, A.M.B; Writing - review \u0026amp; Editing: A.S.A, A.A\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank our counselors and staff at the Shahid Mohammadi Hospital in Bandar Abbas.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data sets used during the current study are applicable from the corresponding author upon reasonable request.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEthics approval\u003c/h2\u003e\u003cp\u003e The study was approved by the research council and ethics committee (IR.HUMS.REC.1403.003) of Hormozgan University of Medical Sciences. Furthermore, the current study was conducted in accordance with the principles of the Declaration of Helsinki, and informed consent was obtained from all participants.\u003c/p\u003e\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMaisel WH, Rawn JD, Stevenson WG. Atrial fibrillation after cardiac surgery. Ann Intern Med. 2001;135(12):1061\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCox JL. A perspective of postoperative atrial fibrillation in cardiac operations. Ann Thorac Surg. 1993;56(3):405\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCui X, Xu C, Chen C, Su Y, Li J, He X, Wang D. New-Onset Post-Operative Atrial Fibrillation in Patients Undergoing Coronary Artery Bypass Grafting Surgery - A Retrospective Case-Control Study. Braz J Cardiovasc Surg. 2023;38(1):149\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBergman RN, Finegood DT, Ader M. 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Circulation. 2006;114(14):1455\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Atrial fibrillation, coronary artery bypass, insulin resistance, cardiovascular disease","lastPublishedDoi":"10.21203/rs.3.rs-7679260/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7679260/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eNon-insulin-based markers of insulin resistance such as METS-IR (insulin resistance metabolic score), TyG (triglyceride and glucose) Index, and TG/HDL (triglyceride to high-density lipoprotein) Ratio are associated to several risk factors linked with cardiovascular disease (CVD). However, based on available information, no investigation has specifically focused on the predictive ability of these three markers in the incidence of postoperative atrial fibrillation (PoAF). Therefore, the current study aimed to investigate the predictive impact of METS-IR, TyG Index, and TG/HDL Ratio in the incidence of atrial fibrillation after coronary artery bypass grafting (CABG).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this retrospective study, patients who were treated with isolated CABG from September 2021 to September 2024 were included. Data before, during, and after surgery were recorded. Two groups were created based on the occurance of PoAF among patients. The data obtained for both groups were analyzed using the Chi-square test and logistic regression in SPSS version 24.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIncreased insulin resistance and metabolic syndrome indices were correlated with a higher risk of PoAF (The p-values for TyG, TG/HDL, and METS-IR were 0.02, \u0026lt;\u0026thinsp;0.001, and 0.005, respectively). Additionally, Female gender, high BMI, shorter height, increased cross-clamp time, higher systolic blood pressure and HbA1C levels were associated with a rise in the prevalence of PoAF.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eHigher values of METS-IR and TG/HDL were connected to elevated prevalence of PoAF. In addition, the current model outperformed a random model, raising hope for utilization in clinical settings.\u003c/p\u003e","manuscriptTitle":"Predictive Value of METS-IR, TYG Index, and TG/HDL Ratio in Atrial Fibrillation Following Coronary Artery Bypass","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 12:43:22","doi":"10.21203/rs.3.rs-7679260/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-18T11:43:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-11T11:14:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218683322874393169337404848274245919039","date":"2025-12-10T15:50:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-10T14:14:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-08T21:32:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333035340299096967622791405546462918721","date":"2025-12-07T12:05:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80639705125120902570440566069325530730","date":"2025-12-07T09:21:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-05T18:37:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-05T17:50:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314855677738099226643152474919723017845","date":"2025-12-05T16:34:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275614071189200398351606584082691773221","date":"2025-12-05T12:21:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170805629052513982814444527541633557177","date":"2025-12-05T11:59:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-05T11:52:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-01T21:05:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-10T04:05:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-07T12:58:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-11-03T06:20:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a8297e8a-8dcb-46b4-8fc6-61af7b8060c8","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T12:43:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 12:43:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7679260","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7679260","identity":"rs-7679260","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}