Elevated Frontal QRS-T Angle as a Predictor of Cardiovascular Risk in Graves’ Disease: A Comparative Study

Elevated Frontal QRS-T Angle as a Predictor of Cardiovascular Risk in Graves’ Disease: A Comparative Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Elevated Frontal QRS-T Angle as a Predictor of Cardiovascular Risk in Graves’ Disease: A Comparative Study Zhen Wang, Jia Xu, Ting-ting Fan, A-juan Gong, Meng-li Li, Nin-jun Zhu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5352051/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Graves' disease (GD) is linked to cardiovascular complications, but reliable non-invasive predictors are limited. This study aimed to assess whether the frontal QRS-T (fQRS-T) angle could predict mortality and cardiovascular outcomes in GD patients. Methods Initially, we conducted a retrospective analysis of electrocardiogram (ECG) parameters from 291 GD patients compared with 96 healthy controls. GD patients were then categorized based on the presence of hyperthyroid heart disease. Using logistic regression, we identified predictors of cardiovascular outcomes. Subsequently, a retrospective cohort study followed 41 patients with an fQRS-T angle ≥90° and 82 matched controls over five years, assessing all-cause mortality and ventricular fibrillation (VF) using Kaplan-Meier analysis. Results Initial analysis showed that a significantly higher proportion of GD patients had an fQRS-T angle ≥90° compared to healthy controls (p< 0.001). Among GD patients, those with hyperthyroid heart disease were more likely to have an fQRS-T angle ≥90° (p < 0.001). Multivariate logistic regression analysis revealed that age, fQRS-T angle, thyroxine (T4), and platelet were independent predictors of hyperthyroid heart disease. In the cohort study, 41 GD patients with an fQRS-T angle ≥90° were selected as the exposure group, and 82 patients without a widened angle were selected as controls. Kaplan-Meier analysis demonstrated a significant difference in event-free survival between the groups, as assessed by log-rank test(P=0.001). Conclusion An increased fQRS-T angle is significantly associated with higher mortality and cardiovascular risk in GD patients. Measuring the fQRS-T angle could enhance risk stratification and guide clinical management in this population Frontal QRS-T Graves’Disease Hyperthyroid heart disease Hyperthyroidism Figures Figure 1 Figure 2 Figure 3 Introduction Hyperthyroidism, characterized by excessive synthesis and secretion of thyroid hormones, is primarily caused by GD. Elevated thyroid hormone levels exert various hemodynamic effects, leading to cardiac arrhythmias, congestive heart failure, and increased cardiovascular mortality, even in patients without pre-existing heart disease. [ 1 ] These cardiovascular impacts often present subtly but can escalate rapidly, underscoring the need for early identification of patients at heightened risk. [ 2 – 7 ] Recent developments in ECG have highlighted the potential of specific markers, such as the fQRS-T angle, in predicting cardiovascular outcomes. The fQRS-T angle, which represents the disparity between ventricular depolarization and repolarization, has been shown to be a robust predictor of arrhythmias and mortality in various patient populations. [ 8 – 20 ] However, its prognostic value specifically in GD patients remains unclear. This study aims to determine whether an elevated fQRS-T angle can serve as a reliable biomarker for identifying GD patients at increased risk of ventricular arrhythmias or mortality. By focusing on early risk stratification, we hope to refine predictive models for cardiac complications in GD and enhance the ability to identify high-risk individuals, ultimately contributing to improved clinical decision-making for this vulnerable patient group. Materials and Methods Study Design In this retrospective cohort study, we investigated the prognostic role of the frontal QRS-T angle in patients with Graves' disease (GD) treated at The Second Affiliated Hospital of Anhui Medical University, Hefei, China. Initially, we collected ECG parameters from 291 patients diagnosed with GD and 96 healthy controls between January 2018 and December 2023. This allowed us to compare ECG parameters between GD patients and healthy controls. Subsequently, we classified the 291 GD patients into two groups based on the presence of hyperthyroid heart disease. We then performed multivariate logistic regression analysis on the GD cohort to identify independent predictors of hyperthyroid heart disease, focusing on the frontal QRS-T angle as a key variable. Additionally, to explore whether a large fQRS-T angle (≥ 90°) could predict mortality, we selected a subgroup of 41 GD patients with a large fQRS-T angle who had visited the hospital at least five years prior as the exposure group. Originally, we attempted to use propensity score matching to identify suitable controls but did not achieve satisfactory results. Instead, we employed a random number table to select 82 patients without a large fQRS-T angle from the remaining GD patients, ensuring they met follow-up requirements. This selection formed our control group for further analysis. The study protocol was approved by the Ethics Committee of The Second Affiliated Hospital of Anhui Medical University (Approval No. YX2024-115). All patients provided written informed consent. Participants We included 291 patients diagnosed with Graves' disease, who were admitted to The Second Affiliated Hospital of Anhui Medical University during the defined recruitment period. The inclusion criteria were patients aged 18 years or older with a confirmed diagnosis of GD based on clinical symptoms and biochemical markers, including elevated levels of free T4 and triiodothyronine (T3), with suppressed thyroid-stimulating hormone (TSH). Patients were excluded if they had a history of pulmonary embolism, coronary artery disease, significant valvular heart disease, congenital heart disease, or other severe systemic diseases that could confound ECG interpretation. Additionally, a control group of 96 healthy individuals from the same geographic region was included to provide baseline ECG comparisons. For the subgroup analysis, 41 GD patients with large frontal QRS-T angles (≥ 90°) were selected as the exposure group, and 82 patients without large angles were randomly selected from the remaining patients as the control group. Procedures Data collection involved a detailed assessment of each patient's medical history, physical examination findings, and laboratory results. ECGs were recorded using a standardized 12-lead system (MedEx MECG-300, Beijing, China) at baseline for all participants. The frontal QRS-T angle was calculated as the absolute difference between the QRS axis and T-wave axis. If the resulting angle exceeded 180°, it was recalculated by subtracting the value from 360°. A frontal QRS-T angle greater than 90° was classified as abnormal. Other ECG parameters collected included heart rate, QRS duration, QT interval, corrected QT interval (QTc), Tpeak-to-Tend (TpTe) interval, and left ventricular hypertrophy, assessed using the Sokolow-Lyon index. According to Bazett’s formula, QTc = QT/ RR^0.5. The TpTe interval was measured from the peak to the end of the T-wave, corrected TpTe = TpTe / RR^0.5. [ 21 ] Laboratory assessments, including levels of T3, T4, TSH, and routine blood tests, were conducted upon admission. All ECG measurements were conducted by two experienced cardiologists who were blinded to the clinical status of the participants to reduce bias. Outcomes The primary outcome of this study was a composite of all-cause mortality or ventricular fibrillation during the five-year follow-up. The secondary outcomes included differences in baseline ECG parameters between GD patients and healthy controls, as well as the association of the fQRS-T angle with hyperthyroid heart disease. We also examined whether an elevated fQRS-T angle could predict increased mortality risk within the GD patient cohort. Statistic Data were analyzed using SPSS 25.0 (IBM Corp, Armonk, NY, USA) software. The χ-square test was used to analyze nonparametric data. All variables underwent normality testing via the Kolmogorov-Smirnov test, and variance homogeneity was assessed using Levene's test before performing significance tests. For normally distributed data with homogeneous variances, comparisons between independent groups were made using the t-test. For non-normally distributed parameters, comparisons between independent groups were made using Mann-Whitney U test. Multivariate logistic regression was employed to assess independent predictors of hyperthyroid heart disease, specifically focusing on the fQRS-T angle and other relevant variables. Kaplan-Meier survival curves were generated to estimate event-free survival, and differences between groups were assessed using the log-rank or Breslow test. Subject to a sufficient number of events, we aim to conduct a multivariate Cox proportional hazards models to identify predictors of all-cause mortality and VF. Missing data were handled using multiple imputation techniques. Statistical significance was set at a p-value of less than 0.05 for all analyses. Results Comparison of Baseline Clinical Characteristics in GD and Healthy Controls This research encompassed 291 patients with GD and 96 healthy controls. We analyzed and compared demographic, clinical, and laboratory information across both groups. The comparison revealed no notable differences in terms of gender distribution between the patients with GD and the healthy control group (χ2 = 0.000, p = 0.999). However, The Body Mass Index (BMI) of patients with GD was notably lower compared to healthy controls. (t = 3.086, p = 0.002), the age of patients with GD was notably higher compared to healthy controls (Z = 2.232, p = 0.026). Heart rate was significantly elevated in patients with GD compared to the control group. (Z = 9.269, p < 0.001). QTc interval was greater in patients with GD (Z = 5.219, p 90° was observed more frequently in patients with GD (χ2 = 15.279, p < 0.001). Left ventricular hypertrophy (RV5 + SV1) was significantly more prevalent among the GD group(Z = 5.544, p < 0.001). However, the incidence of delayed QRS transition at lead V4 or later showed no significant difference (χ2 = 2.251, p = 0.134). The TpTe interval, when corrected for heart rate, was significantly longer in patients with GD (Z = 2.496, p = 0.013). All other findings are summarized in Table 1 . Table 1 Baseline Characteristics and ECG Parameters: GD Patients vs. Healthy Controls Healthy Graves’disease X 2 t,or Z value p -Value N = 96 N = 291 Age(years) 38(33–52) 47(32–57) 2.232 0.026 Female,n(%) 66(68.75%) 200(68.73%) 0.000 0.999 Body Mass Index (BMI) 23.42(20.94–25.65) 21.99(20.02–24.34) 3.086 0.002 WBC count (x10 9 /L) 6.01(4.76–6.94) 5.05(4.16–6.20) 3.942 < 0.001 Neutrophil count (x10 9 /L) 3.33(2.70–4.27) 2.49(1.82–3.37) 5.547 < 0.001 Lymphocyte count (x10 9 /L) 1.9(1.49–2.31) 1.79(1.34–2.26) 1.64 0.101 Neutrophil/Lymphocyte ratio 1.68(1.42–2.23) 1.34(0.97-2.00) 3.898 < 0.001 Hemoglobin, g/L 133.5(126.25–146) 124(113–136) 5.367 < 0.001 Platelet count (x10 9 /L) 232.5(201.25-275.75) 192.5(144–240) 5.466 < 0.001 Glutamic pyruvic transaminase(U/L) 18(13–29) 27(19–43) 5.605 < 0.001 Glutamic oxaloacetic transaminase(U/L) 21(18–27) 25(20-37.5) 3.661 0.003 Creatinine(umol/L) 54(46–64) 40(30–53) 7.19 < 0.001 Urea nitrogen(mmol/L) 4.76(3.99–5.59) 5.35(4.5–6.58) 3.947 0.001 Total cholesterol(mmol/L) 5.21(4.44–5.85) 3.24(2.73–3.86) 12.239 < 0.001 Triglyceride(mmol/L) 1.11(0.80–1.54) 0.87(0.66–1.15) 4.496 < 0.001 Low density lipoprotein cholesterol (LDL-c)(mmol/L) 3.12(2.65–3.6) 1.82(1.49–2.21) 11.658 < 0.001 Triiodothyronine(T3) (nmol/L) 1.74(1.64–1.95) 4.85(3.21–6.45) 12.187 < 0.001 Thyroxine(T4) (nmol/L) 104.65(97.08-119.15) 227.2(155.2-294.9) 11.307 < 0.001 Thyroid-stimulating hormone(mIU/L) 2.32(1.60–3.16) 0.005(0.004–0.006) 13.735 < 0.001 Heart rate(bpm) 73(68–79) 91(79–102) 9.269 < 0.001 QT interval (msec) 368(352.5–386) 348(328–370) 5.219 < 0.001 QTc interval (msec) 408(392.25–424.5) 421(405–436) 4.194 90 o ,n 0 48 15.279 < 0.001 P-wave amplitude in lead II(mV) 0.09(0.07–0.12) 0.13(0.09–0.17) 6.492 < 0.001 P-wave duration(msec) 100(92–106) 98(90–106) 1.57 0.116 QRS duration(msec) 89(84–96) 88(80–92) 2.203 0.028 Left ventricular hypertrophy(mV) 1.99(1.65–2.37) 2.54(1.95–3.25) 5.544 < 0.001 Delayed QRS transition at lead V4 or later,n 8 44 2.251 0.134 Tpeak-to-Tend(TpTe) (msec) 110(96–118) 102(90–116) 2.494 0.013 TpTe/(60/heart rate) 1/2 (msec) 120.50(104.13-131.83) 125.95(109.91-139.68) 2.496 0.013 Data are presented as median [25th–75th percentile] OR Mean ± Standart Deviation. Comparison of Baseline Clinical Characteristics Between Non-Hyperthyroid Heart Disease and Hyperthyroid Heart Disease in Patients With GD Patients with GD were divided into two categories based on whether they exhibited hyperthyroid heart disease or not. No significant differences were detected in left ventricular hypertrophy, and corrected TpTe interval between GD patients with and without hyperthyroid heart disease (Z = 0.724, p = 0.469; Z = 1.362, p = 0.173, respectively). However, age (Z = -7.245, p < 0.001), creatinine levels (Z = 4.52, p < 0.001) and delayed QRS transition (χ2 = 5.826, p < 0.016), were higher in the hyperthyroid heart disease group. Platelet counts (Z = 5.448, p 90° was more prevalent among patients with hyperthyroid heart disease (χ2 = 13.49, p < 0.001). (Table 2 ). Table 2 Baseline Characteristics and ECG Parameters in GD Patients, Stratified by Hyperthyroid Heart Disease GD only GD with hyperthyroid heart disease X 2 t,or Z value p -Value N = 156 N = 135 Age(years) 37(29–51) 56(45.25-66) 7.245 < 0.001 Female,n(%) 105(67.30%) 95(70.37%) 0.058 0.809 WBC count (x10 9 /L) 5.0(4.15–6.23) 5.16(4.24–6.20) 0.846 0.397 Neutrophil count (x10 9 /L) 2.43(1.73–3.08) 2.74(2.00-3.68) 2.749 0.006 Lymphocyte count (x10 9 /L) 1.94(1.59–2.44) 1.62(1.27–2.18) 3.727 < 0.001 Hemoglobin, g/L 126.22 ± 18.19 124.56 ± 17.81 0.473 0.637 Platelet count (x10 9 /L) 221(176–255) 169.5(124.25–217) 5.448 < 0.001 Glutamic pyruvic transaminase(U/L) 28(20–43) 26(18-42.75) 1.356 0.175 Glutamic oxaloacetic transaminase(U/L) 24(19–31) 26(20–40) 1.824 0.068 Creatinine(umol/L) 34(27–46) 45.5(35-60.75) 4.52 < 0.001 Urea nitrogen(mmol/L) 5.11(4.49–6.03) 5.61(4.70–7.26) 3.162 0.002 Total cholesterol(mmol/L) 3.36(2.96–3.88) 3.13(2.52–3.81) 2.152 0.031 Low density lipoprotein cholesterol(mmol/L) 1.91(1.60–2.23) 1.79(1.38–2.24) 1.884 0.06 Triiodothyronine(T3)(nmol/L) 5.36(3.82–7.01) 4.52(2.43–6.41) 3.545 < 0.001 Thyroxine(T4)(nmol/L) 246.88 ± 82.69 202.53 ± 89.00) 4.492 < 0.001 Thyroid-stimulating hormone(mIU/L) 0.004(0.004–0.006) 0.005(0.005–0.006) 5.425 90 o ,n 12 36 13.49 < 0.001 QRS duration(msec) 88(82–92) 88(80-99.5) 0.063 0.95 Left ventricular hypertrophy(mV) 2.52(2.05–2.94) 2.70(1.89–3.36) 0.724 0.469 Delayed QRS transition at lead V4 or later,n 15 29 5.826 0.016 Tpeak-to-Tend(TpTe) (msec) 104(94–116) 100(84–116) 1.696 0.09 TpTe/(60/heart rate) 1/2 (msec) 128.57(113.95-140.13) 122.58(105.97-138.41) 1.362 0.173 Duration of hyperthyroidism(years) 1(0.17-3) 2(0.25-10) 2.912 0.004 Regression analysis of risk factors for hyperthyroid heart disease in patients with GD We conducted both univariate and multivariate logistic regression analyses to identify predictors of hyperthyroid heart disease. Univariate analysis indicated significant associations with age（p<0.001）, fQRS-T angle（p <0.001）, platelet count(p<0.001), lymphocyte count（p=0.035）, creatinine（p<0.001）, urea nitrogen（p<0.001）, and T4 levels（p<0.001）. Subsequent multivariate analysis confirmed age(p<0.001), fQRS-T angle(p<0.001), T4 levels(p=0.003), and platelet count(p=0.001) as independent predictors (see Table 3). Table 3 Logistic Regression Analysis of Predictors for Hyperthyroid Heart Disease in GD Patients B SE Wald P Exp(B) 95%CI for B,Lower Upper Univariate analysis fQRS-T angle 1.892 0.392 23.259 < 0.001 6.635 3.075 14.318 platelet count 0.010 0.002 23.353 < 0.001 0.990 0.986 0.994 LDL-c 0.221 0.194 1.300 0.254 0.801 0.548 1.173 age 0.062 0.009 46.941 < 0.001 1.064 1.045 1.083 Delayed QRS transition at lead V4 or later 0.945 0.343 7.582 0.006 2.572 1.313 5.038 Duration of hyperthyroidism 0.063 0.019 10.858 0.001 1.064 1.026 1.105 Neutrophil 0.123 0.064 3.743 0.053 1.131 0.998 1.282 Lymphocyte 0.347 0.165 4.432 0.035 0.707 0.511 0.976 Creatinine 0.030 0.007 17.446 < 0.001 1.031 1.016 1.046 urea nitrogen 0.242 0.068 12.796 < 0.001 1.274 1.116 1.456 Total cholesterol 0.178 0.134 1.785 0.182 0.837 0.644 1.087 T3 0.141 0.044 10.289 0.001 0.869 0.797 0.947 T4 0.006 0.002 17.899 < 0.001 0.994 0.991 0.997 TSH 0.037 0.029 1.565 0.211 1.037 0.979 1.098 Multivariate analysis age 0.049 0.010 25.010 < 0.001 1.051 1.030 1.071 platelet count 0.007 0.002 10.201 0.001 0.993 0.988 0.997 fQRS-T angle 1.944 0.463 17.604 < 0.001 6.989 2.818 17.331 T4 0.005 0.002 8.738 0.003 0.995 0.991 0.998 We performed a ROC curve analysis to evaluate the diagnostic utility of age, platelet count, and fQRS-T angle combined. The optimal Youden index was 0.485, with a sensitivity of 65% and a specificity of 83%, yielding an AUC of 0.80 (refer to Fig. 2 for details). Baseline Comparisons and ECG Parameters in Cohort Study There were no notable differences in terms of gender and age distribution between the patients with fQRS-T > 90° and fQRS-T 90° and fQRS-T < 90° group (Z = 1.625, p = 0.105; Z = 1.365, p = 0.172, respectively). However, creatinine levels (Z = 3.617, p 90° group (Table 4 ). Table 4 Baseline Characteristics and ECG Parameters in GD Patients, Stratified by fQRS-T Angle > 90° GD with fQRS-T angle > 90 o GD with fQRS-T angle < 90 o X 2 t,or Z value p -Value N = 41 N = 82 Age(years) 55(45.5–67) 56(41.75-66) 0.301 0.764 Female, n(%) 30(73.17%) 63(76.83%) 0.028 0.868 Hyperthyroid heart disease, n 35 64 0.099 0.753 WBC count (x10 9 /L) 5.29(4.50–7.03) 5.08(4.24–6.10) 1.339 0.181 Neutrophil count (x10 9 /L) 3.24(2.00-4.02) 2.53(1.76–3.27) 2.339 0.019 Lymphocyte count (x10 9 /L) 1.44(1.12–2.19) 1.72(1.32–2.28) 1.403 0.161 Hemoglobin, g/L 128.07 ± 22.95 122.47 ± 15.59 1.410 0.164 Platelet count (x10 9 /L) 186(109–253) 178(141-222.25) 0.174 0.862 Glutamic pyruvic transaminase(U/L) 24(18-42.5) 28(18-47.09) 0.263 0.793 Glutamic oxaloacetic transaminase(U/L) 30(20.5–48.5) 25(20-40.5) 0.897 0.370 Creatinine(umol/L) 55(39-79.5) 40(29.75–47.5) 3.617 < 0.001 Urea nitrogen(mmol/L) 5.81(4.44–8.42) 5.61(4.69–6.58) 1.03 0.303 Total cholesterol(mmol/L) 3.35(2.39–4.20) 3.25(2.73–3.56) 0.118 0.906 Triglyceride(mmol/L) 0.96(0.69–1.17) 0.86(0.65–1.06) 1.047 0.295 Low density lipoprotein cholesterol(mmol/L) 1.92(1.29–2.37) 1.92(1.57–2.01) 0.151 0.880 Triiodothyronine(T3)(nmol/L) 3.49(1.92–5.39) 5.15(2.90–6.26) 2.409 0.016 Thyroxine(T4)(nmol/L) 189.20(104.35–269.40) 224.87(142.93-277.98) 1.959 0.05 Thyroid-stimulating hormone(mIU/L) 0.005(0.005–0.0125) 0.005(0.004–0.006) 2.272 0.023 Heart rate 93(81–126) 88.5(76–101) 1.358 0.175 QRS duration(msec) 86(79–103) 86(80-92.5) 0.298 0.765 Left ventricular hypertrophy(mV) 2.50(1.58–3.11) 2.73(1.97–3.34) 1.625 0.105 Delayed QRS transition at lead V4 or later,n 12 9 4.372 0.037 Tpeak-to-Tend(TpTe) (msec) 92(80–120) 103(90-118.5) 1.801 0.072 TpTe/(60/heart rate)1/2(msec) 117.92(101.42-142.61) 126.22 (111.36-142.25) 1.365 0.172 QT interval (msec) 350.85(347.42–366) 350.85(343.5–367) 0.090 0.928 QTc interval (msec) 421.07(421-426.5) 421.07(409.75-430.25) 0.616 0.538 Duration of hyperthyroidism(years) 4(0.67–13.5) 1(0.167–5.25) 2.259 0.024 Death or ventricular fibrillation, n 5 0 6.606 0.010 Five-year Follow-Up Outcomes Within the 5-year period, out of 41 patients in the fQRS-T > 90° group, 5 experienced death or ventricular fibrillation—4 died and 1 was resuscitated following ventricular fibrillation. There were no deaths or cases of ventricular fibrillation in the fQRS-T < 90° group. Given the low incidence of outcomes and the small cohort size, a comprehensive multivariate analysis was not feasible. Subgroup analyses for factors like gender, presence of hyperthyroid heart disease, and delayed QRS transition did not reveal statistically significant differences in outcomes (Table 5 ). The Kaplan-Meier curves, supported by log rank and Breslow test results, further validate these findings (P = 0.001; see Fig. 3 for details). Table 5 Comparison of Categorical Variables Between Patients with and Without Death or Ventricular Fibrillation Number of death or ventricular fibrillation Number of all patients Chi-square value P Delayed QRS transition at lead V4 or later Yes:1 21 Fisher test 1.000 No:4 102 Sex Female:5 93 0.524 0.469 Male:0 30 Hyperthyroid heart disease Yes:4 99 Fisher test 1.000 No:1 24 The ROC curve analysis showed that the cutoff for predicting hyperthyroid heart disease was 0.485(Youden index, sensitivity 65%, specificity 83%), and the area under the ROC curve was 0.80 [CI, 0.75–0.85]. CI, confidence interval; ROC, receiver operating characteristic. Discussion This study aimed to evaluate the prognostic role of the frontal QRS-T angle in patients with GD and its association with cardiovascular risk, particularly mortality and ventricular fibrillation. Our main findings indicate that an elevated frontal QRS-T angle (≥ 90°) is associated with increased mortality and cardiovascular risk in patients with GD. Furthermore, hyperthyroid heart disease was more common in GD patients with a larger fQRS-T angle, suggesting the potential utility of this marker in stratifying cardiovascular risk among these patients. Our results align with previous studies that have shown a large fQRS-T angle to be a marker of increased cardiovascular risk across different patient populations, including those with acute pulmonary embolism and schizophrenia. [ 12 , 13 ] The association between a widened fQRS-T angle and increased arrhythmia and mortality risk has been well documented in multiple contexts, but its specific role in hyperthyroid populations, especially GD, was not comprehensively explored until now. [ 22 , 23 ] Our findings extend the current understanding by demonstrating that the fQRS-T angle is not only a marker of arrhythmia but also a potentially valuable predictor of mortality in GD patients, thereby highlighting the importance of including this parameter in risk assessments for hyperthyroid populations. The underlying mechanisms by which patients with GD exhibit increased fQRS-T angles remain unclear. Possible explanations include: firstly, excess thyroid hormone may induce initial and terminal repolarization sequences whose drastically different orientations contribute to a widened QRS-T angle. [ 24 ] These abnormalities of repolarization occur at an early stage of GD, prior to the development of left ventricular hypertrophy, cardiac chamber enlargement, and diastolic dysfunction. Secondly, excess thyroid hormone leads to pathological structural changes in the heart, thereby altering ventricular repolarization. Thirdly, thyroid hormones induce pathophysiological changes in ionic channel mechanisms within specific myocardial regions, subsequently altering the regional sequence of ventricular repolarization. Fourthly, thyroid hormones potentially regulate multiple proteins, including potassium channels. [ 25 ] Previous studies have shown that drugs blocking potassium channels significantly affect the spatial heterogeneity of ventricular repolarization. [ 26 ] Fifth, hyperthyroidism may also amplify the influence of catecholamines, thereby lead to more arrhythmias. [ 27 ] Therefore, unlike other cardiovascular conditions, GD uniquely affects the cardiac electrophysiology through the systemic influence of excess thyroid hormones, which may explain the particularly pronounced changes in the fQRS-T angle observed in our study. This specific response underscores the potential utility of the fQRS-T angle as a sensitive and robust marker for early detection of cardiovascular abnormalities in GD. Clinically, our findings suggest that routine ECG monitoring, particularly focusing on the fQRS-T angle, could enhance risk stratification in GD patients, allowing for better identification of those at high risk of adverse cardiovascular events. The identification of a widened fQRS-T angle in these patients could prompt more intensive cardiovascular monitoring and management strategies. For instance, these patients may benefit from closer follow-up intervals, early referrals to cardiologists, and the use of advanced imaging techniques like echocardiography to monitor cardiac structure and function in detail. In high-risk individuals, medications such as beta-blockers could be considered to manage heart rate and reduce the risk of arrhythmias. Furthermore, early identification of increased fQRS-T angles could lead to interventions aimed at modifying other cardiovascular risk factors, such as hypertension and hyperlipidemia, which could ultimately improve outcomes. The integration of fQRS-T angle measurement into routine clinical practice for GD patients may also help in individualized risk assessment, enabling healthcare providers to classify patients into different risk categories. This stratification could help prioritize the allocation of healthcare resources and tailor patient-specific management plans. For instance, patients identified with a high-risk ECG profile could be offered more intensive lifestyle modification counseling, including smoking cessation, exercise, and dietary changes, which are known to improve cardiovascular outcomes. Additionally, it could foster early interventions in those who are likely to develop hyperthyroid heart disease, thus potentially mitigating the progression to more severe cardiac complications. Our study findings are consistent with prior literature exploring various predictors of prognosis in GD patients. Arjola Bano et al demonstrated that higher levels of TSH receptor antibodies (TRAb) are associated with greater disease severity and increased risk of relapse, especially in younger patients. [ 28 ] TRAb levels provide valuable insights into autoimmune activity and disease severity in GD, but do not directly reflect cardiovascular risk. In contrast, our study extends these findings by showing that the fQRS-T angle, an ECG-derived parameter, can provide crucial information regarding the electrical stability of the heart and directly predict cardiovascular outcomes, offering a different dimension in risk stratification for GD patients. Similarly, the study by Nami Suzuki et al focused on age and sex as determinants of GD prognosis, with younger patients and males showing more severe disease and higher risks of adverse outcomes. [ 29 ] While age and sex are important demographic factors in predicting disease progression, they lack the specificity required for direct cardiovascular assessment. Our findings contribute additional value by identifying the fQRS-T angle as a specific cardiovascular risk marker that can be easily measured and monitored. Unlike demographic factors, the fQRS-T angle provides an individualized assessment of the electrical activity of the heart, offering a more nuanced understanding of cardiovascular risks in GD patients. The comparison with these prior studies underscores the novelty of our approach in utilizing an ECG parameter to directly quantify cardiovascular risk in GD patients. Whereas previous markers such as TRAb levels and demographic factors provide valuable insights into the overall severity and prognosis of GD, the fQRS-T angle specifically addresses cardiovascular vulnerability, which is critical for managing and mitigating potential complications such as arrhythmias and sudden cardiac death. By integrating this parameter into routine practice, alongside other known risk markers, clinicians can adopt a more holistic approach to managing GD patients, ultimately improving outcomes and reducing the incidence of severe cardiovascular events. This study has several strengths, including a well-defined cohort of GD patients with comprehensive ECG and laboratory data, and a long follow-up period that allowed us to assess mortality and VF as significant outcomes. However, there are also limitations that must be considered. First, the retrospective nature of this study introduces potential biases, including selection bias and residual confounding. The reliance on medical records for data collection could have led to misclassification or incomplete information. Additionally, although we attempted to use propensity score matching to create a control group, the lack of satisfactory matching results led us to use random sampling, which may have introduced some imbalance between groups. Loss to follow-up was minimal, but the use of multiple imputation to handle missing data may still have influenced the results. Another limitation is the relatively small sample size of the subgroup with an elevated fQRS-T angle. This limited our ability to conduct more detailed subgroup analyses or adjust for additional confounders in the survival analysis. Future studies with larger sample sizes should aim to validate these findings and provide a more nuanced understanding of the mechanisms underlying the observed associations. There are some potential controversies raised by this study. The use of a single ECG marker, such as the fQRS-T angle, as a predictor of mortality has been questioned by some researchers due to concerns about its sensitivity and specificity in predicting adverse outcomes. However, our findings indicate that the fQRS-T angle is a relatively simple, non-invasive, and valuable marker that could be effectively integrated into routine clinical practice for identifying high-risk GD patients. Nevertheless, additional prospective studies are needed to confirm its predictive value and to determine whether targeted interventions based on fQRS-T angle measurements could improve outcomes. In conclusion, this study demonstrates that an elevated frontal QRS-T angle is significantly associated with increased cardiovascular risk in patients with Graves' disease, including mortality and ventricular fibrillation. Given the ease of measuring the fQRS-T angle and its potential utility in predicting adverse outcomes, integrating this marker into routine clinical assessments could improve risk stratification and patient management. Future research should focus on larger-scale prospective studies to validate these findings and explore targeted intervention strategies for patients identified as high risk based on their fQRS-T angle. These efforts could ultimately contribute to improved clinical outcomes and potentially influence guidelines for the management of patients with hyperthyroidism. Declarations Acknowledgements No acknowledgements are declared by the authors. Author contributions ZW and XCW contributed to conception and design of the study, and acquisition, analysis, and interpretation of data; JX, AJG, MLL, NJZ, TTF contributed to acquisition and interpretation of data. All authors contributed to revision of the manuscript, approved the final version, and had a final responsibility for the decision to submit for publication. Funding This study was supported by grants from Health Research Project in Anhui Province (AHWJ2022b020). Conflict of interest The authors declare no competing interests. References Lee, S.Y. and E.N. Pearce, Hyperthyroidism: A Review. Jama, 2023. 330 (15): p. 1472-1483. Peng, C.C., et al., MACE and Hyperthyroidism Treated With Medication, Radioactive Iodine, or Thyroidectomy. JAMA Netw Open, 2024. 7 (3): p. e240904. Kim, H.J. and D.S.A. McLeod, Subclinical Hyperthyroidism and Cardiovascular Disease. Thyroid, 2024. Ruan, W., et al., Thyroid function effect on cardiac structure, cardiac function, and disease risk: Evidence of causal associations in European ancestry. Heart Rhythm, 2024. Huang, P.S., et al., Higher Risk of Incident Hyperthyroidism in Patients With Atrial Fibrillation. J Clin Endocrinol Metab, 2023. 109 (1): p. 92-99. Olanrewaju, O.A., et al., Thyroid and Its Ripple Effect: Impact on Cardiac Structure, Function, and Outcomes. Cureus, 2024. 16 (1): p. e51574. Chaker, L., et al., Hyperthyroidism. Lancet, 2024. 403 (10428): p. 768-780. Abus, S., et al., Evaluation of frontal QRS-T angle values in electrocardiography in patients with chronic rhinosinusitis. BMC Cardiovasc Disord, 2023. 23 (1): p. 160. Kapıcı, O.B., et al., Comparison of frontal QRS-T angle of patients with nasal septal deviation with healthy controls. BMC Cardiovasc Disord, 2023. 23 (1): p. 415. Tereshchenko, L.G., et al., Competing risks of monomorphic vs. non-monomorphic ventricular arrhythmias in primary prevention implantable cardioverter-defibrillator recipients: Global Electrical Heterogeneity and Clinical Outcomes (GEHCO) study. Europace, 2024. 26 (6). Colluoglu, T., et al., Combined use of frontal plane QRS-T angle and platelet-to-lymphocyte ratio in the risk prediction of ischemic cardiomyopathy in STEMI. Ann Noninvasive Electrocardiol, 2024. 29 (1): p. e13106. Tekin, A., et al., Comparison of frontal QRS-T angle in patients with schizophrenia and healthy volunteers. J Psychiatr Res, 2022. 149 : p. 76-82. Algül, E., et al., Frontal QRS - T angle is associated with severity and prognosis of acute pulmonary embolism. J Electrocardiol, 2023. 79 : p. 8-12. Han, X., et al., Prognostic significance of QRS distortion and frontal QRS-T angle in patients with ST-elevation myocardial infarction. Int J Cardiol, 2021. 345 : p. 1-6. Sweda, R., et al., Diagnostic and prognostic values of the QRS-T angle in patients with suspected acute decompensated heart failure. ESC Heart Fail, 2020. 7 (4): p. 1817-1829. Jensen, C.J., et al., QRS-T angle in patients with Hypertrophic Cardiomyopathy - A comparison with Cardiac Magnetic Resonance Imaging. Int J Med Sci, 2021. 18 (3): p. 821-825. Zhang, Y.T., et al., Relationship Between Index of Cardiac Electrophysiological Balance, Frontal QRS-T Angle and Retinopathy in People with Type 2 Diabetes. Diabetes Metab Syndr Obes, 2023. 16 : p. 861-871. Kılıçaslan, F., A. Tan, and Z. Tanriverdi, Evaluation of Frontal QRS-T Angle in Children With ADHD and Healthy Controls. J Atten Disord, 2024: p. 10870547241288353. Gunduz, R., et al., Frontal QRS/T angle can predict mortality in COVID-19 patients. Am J Emerg Med, 2022. 58 : p. 66-72. Aro, A.L., et al., Electrical risk score beyond the left ventricular ejection fraction: prediction of sudden cardiac death in the Oregon Sudden Unexpected Death Study and the Atherosclerosis Risk in Communities Study. Eur Heart J, 2017. 38 (40): p. 3017-3025. Tse, G. and B.P. Yan, Traditional and novel electrocardiographic conduction and repolarization markers of sudden cardiac death. Europace, 2017. 19 (5): p. 712-721. Aydin, A. and K. Gayretli Yayla, The assessment of Tp-e interval and Tp-e/QT ratio in patients with hyperthyroidism before and after thyroid surgery. Int J Clin Pract, 2021. 75 (12): p. e14937. Akkuş, G., et al., Comparison of 24-Hour Electrocardiogram Parameters in Patients with Graves' Disease Before and After Anti-Thyroid Therapy. Endocr Metab Immune Disord Drug Targets, 2021. 21 (1): p. 183-191. Rautaharju, P.M., et al., Heart rate, gender differences, and presence versus absence of diagnostic ST elevation as determinants of spatial QRS|T angle widening in acute coronary syndrome. Am J Cardiol, 2011. 107 (12): p. 1744-50. Jabbar, A., et al., Thyroid hormones and cardiovascular disease. Nat Rev Cardiol, 2017. 14 (1): p. 39-55. Corino, V.D.A., et al., Assessment of spatial heterogeneity of ventricular repolarization after multi-channel blocker drugs in healthy subjects. Comput Methods Programs Biomed, 2020. 189 : p. 105291. Silva, J.E. and S.D. Bianco, Thyroid-adrenergic interactions: physiological and clinical implications. Thyroid, 2008. 18 (2): p. 157-65. Bano, A., et al., Age May Influence the Impact of TRAbs on Thyroid Function and Relapse-Risk in Patients With Graves Disease. J Clin Endocrinol Metab, 2019. 104 (5): p. 1378-1385. Suzuki, N., et al., Does Age or Sex Relate to Severity or Treatment Prognosis in Graves' Disease? Thyroid, 2021. 31 (9): p. 1409-1415. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5352051","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":371643471,"identity":"832b5dbd-75df-4b96-a26c-d1e60511b829","order_by":0,"name":"Zhen Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Wang","suffix":""},{"id":371643472,"identity":"216a3a7a-fef8-43cd-abef-4d8cac1c178b","order_by":1,"name":"Jia Xu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Xu","suffix":""},{"id":371643473,"identity":"43f332b2-b013-4d5a-9815-5a2b5dd7eb8b","order_by":2,"name":"Ting-ting Fan","email":"","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ting-ting","middleName":"","lastName":"Fan","suffix":""},{"id":371643474,"identity":"9d23a3ba-ad05-4466-b99b-0bbc6b9c031f","order_by":3,"name":"A-juan Gong","email":"","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"A-juan","middleName":"","lastName":"Gong","suffix":""},{"id":371643476,"identity":"189b89c8-9f6c-42bd-bf92-a26be78ff92d","order_by":4,"name":"Meng-li Li","email":"","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meng-li","middleName":"","lastName":"Li","suffix":""},{"id":371643478,"identity":"7a4ab1c0-af0e-47dd-a515-f249bc14d563","order_by":5,"name":"Nin-jun Zhu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Nin-jun","middleName":"","lastName":"Zhu","suffix":""},{"id":371643479,"identity":"9900ec79-032d-4b79-9e21-d4b9d3e258ea","order_by":6,"name":"Xiao-chen Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnUlEQVRIiWNgGAWjYBACA+YDBgwMFRJy/MRrYUsAajljYSzZQJIWxraKxA1EazFnY9744Oc8CcYNDMwPH90gRotlG1uxYe82CWZzBjZj4xyiHHa/x0yacZsEm2UDD5s0cVqO8QC1zJHgMThAmpYGCQlStAD90nNMwkCymWi/HAOG2I+auvp+9uaHj4nSggDMpCkfBaNgFIyCUYAPAABWeiqkeMKgwAAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiao-chen","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-10-29 07:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5352051/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5352051/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69436807,"identity":"0eb72cb0-0250-41c1-b90b-3dc74528f5fb","added_by":"auto","created_at":"2024-11-20 10:39:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49886,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram showing cohort study design and patient selection\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5352051/v1/7f3979507dc1c49d1aa0622f.png"},{"id":69435670,"identity":"f2dc9ba2-a065-415a-8e3d-648be4ea5a77","added_by":"auto","created_at":"2024-11-20 10:31:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65256,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve Analysis for Predicting Hyperthyroid Heart Disease Using Age, Platelet Count, and Frontal QRS-T Angle\u003c/p\u003e\n\u003cp\u003eThe ROC curve analysis showed that the cutoff for predicting hyperthyroid heart disease was 0.485(Youden index, sensitivity 65%, specificity 83%), and the area under the ROC curve was 0.80 [CI, 0.75–0.85]. CI, confidence interval; ROC, receiver operating characteristic.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5352051/v1/246209391c32670f77aede0e.png"},{"id":69435668,"identity":"9c552305-542e-44b8-8120-b6ae2f7e1904","added_by":"auto","created_at":"2024-11-20 10:31:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":52671,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival Comparison Between Groups with fQRS-T Angles \u0026gt;90° and \u0026lt;90°\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5352051/v1/3d3ccbc3d8a32757b0ebe894.png"},{"id":69438705,"identity":"ddd7207e-a650-4fd7-829e-2a4dc556546a","added_by":"auto","created_at":"2024-11-20 10:55:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1104500,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5352051/v1/01afae29-2844-4f49-a35f-71fd7de0bf84.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eElevated Frontal QRS-T Angle as a Predictor of Cardiovascular Risk in Graves’ Disease: A Comparative Study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHyperthyroidism, characterized by excessive synthesis and secretion of thyroid hormones, is primarily caused by GD. Elevated thyroid hormone levels exert various hemodynamic effects, leading to cardiac arrhythmias, congestive heart failure, and increased cardiovascular mortality, even in patients without pre-existing heart disease.\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e These cardiovascular impacts often present subtly but can escalate rapidly, underscoring the need for early identification of patients at heightened risk.\u003csup\u003e[\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eRecent developments in ECG have highlighted the potential of specific markers, such as the fQRS-T angle, in predicting cardiovascular outcomes. The fQRS-T angle, which represents the disparity between ventricular depolarization and repolarization, has been shown to be a robust predictor of arrhythmias and mortality in various patient populations.\u003csup\u003e[\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e However, its prognostic value specifically in GD patients remains unclear.\u003c/p\u003e \u003cp\u003eThis study aims to determine whether an elevated fQRS-T angle can serve as a reliable biomarker for identifying GD patients at increased risk of ventricular arrhythmias or mortality. By focusing on early risk stratification, we hope to refine predictive models for cardiac complications in GD and enhance the ability to identify high-risk individuals, ultimately contributing to improved clinical decision-making for this vulnerable patient group.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eIn this retrospective cohort study, we investigated the prognostic role of the frontal QRS-T angle in patients with Graves' disease (GD) treated at The Second Affiliated Hospital of Anhui Medical University, Hefei, China. Initially, we collected ECG parameters from 291 patients diagnosed with GD and 96 healthy controls between January 2018 and December 2023. This allowed us to compare ECG parameters between GD patients and healthy controls.\u003c/p\u003e \u003cp\u003eSubsequently, we classified the 291 GD patients into two groups based on the presence of hyperthyroid heart disease. We then performed multivariate logistic regression analysis on the GD cohort to identify independent predictors of hyperthyroid heart disease, focusing on the frontal QRS-T angle as a key variable.\u003c/p\u003e \u003cp\u003eAdditionally, to explore whether a large fQRS-T angle (\u0026ge;\u0026thinsp;90\u0026deg;) could predict mortality, we selected a subgroup of 41 GD patients with a large fQRS-T angle who had visited the hospital at least five years prior as the exposure group. Originally, we attempted to use propensity score matching to identify suitable controls but did not achieve satisfactory results. Instead, we employed a random number table to select 82 patients without a large fQRS-T angle from the remaining GD patients, ensuring they met follow-up requirements. This selection formed our control group for further analysis. The study protocol was approved by the Ethics Committee of The Second Affiliated Hospital of Anhui Medical University (Approval No. YX2024-115). All patients provided written informed consent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eWe included 291 patients diagnosed with Graves' disease, who were admitted to The Second Affiliated Hospital of Anhui Medical University during the defined recruitment period. The inclusion criteria were patients aged 18 years or older with a confirmed diagnosis of GD based on clinical symptoms and biochemical markers, including elevated levels of free T4 and triiodothyronine (T3), with suppressed thyroid-stimulating hormone (TSH). Patients were excluded if they had a history of pulmonary embolism, coronary artery disease, significant valvular heart disease, congenital heart disease, or other severe systemic diseases that could confound ECG interpretation. Additionally, a control group of 96 healthy individuals from the same geographic region was included to provide baseline ECG comparisons. For the subgroup analysis, 41 GD patients with large frontal QRS-T angles (\u0026ge;\u0026thinsp;90\u0026deg;) were selected as the exposure group, and 82 patients without large angles were randomly selected from the remaining patients as the control group.\u003c/p\u003e\n\u003ch3\u003eProcedures\u003c/h3\u003e\n\u003cp\u003eData collection involved a detailed assessment of each patient's medical history, physical examination findings, and laboratory results. ECGs were recorded using a standardized 12-lead system (MedEx MECG-300, Beijing, China) at baseline for all participants. The frontal QRS-T angle was calculated as the absolute difference between the QRS axis and T-wave axis. If the resulting angle exceeded 180\u0026deg;, it was recalculated by subtracting the value from 360\u0026deg;. A frontal QRS-T angle greater than 90\u0026deg; was classified as abnormal.\u003c/p\u003e \u003cp\u003eOther ECG parameters collected included heart rate, QRS duration, QT interval, corrected QT interval (QTc), Tpeak-to-Tend (TpTe) interval, and left ventricular hypertrophy, assessed using the Sokolow-Lyon index. According to Bazett\u0026rsquo;s formula, QTc\u0026thinsp;=\u0026thinsp;QT/ RR^0.5. The TpTe interval was measured from the peak to the end of the T-wave, corrected TpTe\u0026thinsp;=\u0026thinsp;TpTe / RR^0.5.\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003eLaboratory assessments, including levels of T3, T4, TSH, and routine blood tests, were conducted upon admission. All ECG measurements were conducted by two experienced cardiologists who were blinded to the clinical status of the participants to reduce bias.\u003c/p\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003cp\u003eThe primary outcome of this study was a composite of all-cause mortality or ventricular fibrillation during the five-year follow-up. The secondary outcomes included differences in baseline ECG parameters between GD patients and healthy controls, as well as the association of the fQRS-T angle with hyperthyroid heart disease. We also examined whether an elevated fQRS-T angle could predict increased mortality risk within the GD patient cohort.\u003c/p\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003cp\u003eData were analyzed using SPSS 25.0 (IBM Corp, Armonk, NY, USA) software. The χ-square test was used to analyze nonparametric data. All variables underwent normality testing via the Kolmogorov-Smirnov test, and variance homogeneity was assessed using Levene's test before performing significance tests. For normally distributed data with homogeneous variances, comparisons between independent groups were made using the t-test. For non-normally distributed parameters, comparisons between independent groups were made using Mann-Whitney U test. Multivariate logistic regression was employed to assess independent predictors of hyperthyroid heart disease, specifically focusing on the fQRS-T angle and other relevant variables. Kaplan-Meier survival curves were generated to estimate event-free survival, and differences between groups were assessed using the log-rank or Breslow test. Subject to a sufficient number of events, we aim to conduct a multivariate Cox proportional hazards models to identify predictors of all-cause mortality and VF. Missing data were handled using multiple imputation techniques. Statistical significance was set at a p-value of less than 0.05 for all analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eComparison of Baseline Clinical Characteristics in GD and Healthy Controls\u003c/h2\u003e \u003cp\u003eThis research encompassed 291 patients with GD and 96 healthy controls. We analyzed and compared demographic, clinical, and laboratory information across both groups. The comparison revealed no notable differences in terms of gender distribution between the patients with GD and the healthy control group (χ2\u0026thinsp;=\u0026thinsp;0.000, p\u0026thinsp;=\u0026thinsp;0.999). However, The Body Mass Index (BMI) of patients with GD was notably lower compared to healthy controls. (t\u0026thinsp;=\u0026thinsp;3.086, p\u0026thinsp;=\u0026thinsp;0.002), the age of patients with GD was notably higher compared to healthy controls (Z\u0026thinsp;=\u0026thinsp;2.232, p\u0026thinsp;=\u0026thinsp;0.026). Heart rate was significantly elevated in patients with GD compared to the control group. (Z\u0026thinsp;=\u0026thinsp;9.269, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). QTc interval was greater in patients with GD (Z\u0026thinsp;=\u0026thinsp;5.219, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The fQRS-T angle\u0026thinsp;\u0026gt;\u0026thinsp;90\u0026deg; was observed more frequently in patients with GD (χ2\u0026thinsp;=\u0026thinsp;15.279, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Left ventricular hypertrophy (RV5\u0026thinsp;+\u0026thinsp;SV1) was significantly more prevalent among the GD group(Z\u0026thinsp;=\u0026thinsp;5.544, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the incidence of delayed QRS transition at lead V4 or later showed no significant difference (χ2\u0026thinsp;=\u0026thinsp;2.251, p\u0026thinsp;=\u0026thinsp;0.134). The TpTe interval, when corrected for heart rate, was significantly longer in patients with GD (Z\u0026thinsp;=\u0026thinsp;2.496, p\u0026thinsp;=\u0026thinsp;0.013). All other findings are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics and ECG Parameters: GD Patients vs. Healthy Controls\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGraves\u0026rsquo;disease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e t,or Z value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(33\u0026ndash;52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(32\u0026ndash;57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66(68.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200(68.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody Mass Index (BMI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.42(20.94\u0026ndash;25.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.99(20.02\u0026ndash;24.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.01(4.76\u0026ndash;6.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.05(4.16\u0026ndash;6.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.33(2.70\u0026ndash;4.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.49(1.82\u0026ndash;3.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9(1.49\u0026ndash;2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79(1.34\u0026ndash;2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil/Lymphocyte ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.68(1.42\u0026ndash;2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34(0.97-2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133.5(126.25\u0026ndash;146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124(113\u0026ndash;136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232.5(201.25-275.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192.5(144\u0026ndash;240)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamic pyruvic transaminase(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(13\u0026ndash;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(19\u0026ndash;43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamic oxaloacetic transaminase(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(18\u0026ndash;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(20-37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54(46\u0026ndash;64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(30\u0026ndash;53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea nitrogen(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.76(3.99\u0026ndash;5.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.35(4.5\u0026ndash;6.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.21(4.44\u0026ndash;5.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.24(2.73\u0026ndash;3.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11(0.80\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87(0.66\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow density lipoprotein cholesterol (LDL-c)(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.12(2.65\u0026ndash;3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.82(1.49\u0026ndash;2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriiodothyronine(T3) (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.74(1.64\u0026ndash;1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.85(3.21\u0026ndash;6.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroxine(T4) (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.65(97.08-119.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e227.2(155.2-294.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid-stimulating hormone(mIU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.32(1.60\u0026ndash;3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005(0.004\u0026ndash;0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate(bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73(68\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91(79\u0026ndash;102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQT interval (msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e368(352.5\u0026ndash;386)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e348(328\u0026ndash;370)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQTc interval (msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e408(392.25\u0026ndash;424.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e421(405\u0026ndash;436)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrontal QRS-T angle\u0026thinsp;\u0026gt;\u0026thinsp;90 \u003csup\u003eo\u003c/sup\u003e,n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-wave amplitude in lead II(mV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09(0.07\u0026ndash;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13(0.09\u0026ndash;0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-wave duration(msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100(92\u0026ndash;106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98(90\u0026ndash;106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQRS duration(msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89(84\u0026ndash;96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88(80\u0026ndash;92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft ventricular hypertrophy(mV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.99(1.65\u0026ndash;2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.54(1.95\u0026ndash;3.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelayed QRS transition at lead V4 or later,n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTpeak-to-Tend(TpTe) (msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110(96\u0026ndash;118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102(90\u0026ndash;116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTpTe/(60/heart rate)\u003csup\u003e1/2\u003c/sup\u003e(msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120.50(104.13-131.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125.95(109.91-139.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are presented as median [25th\u0026ndash;75th percentile] OR Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standart Deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eComparison of Baseline Clinical Characteristics Between Non-Hyperthyroid Heart Disease and Hyperthyroid Heart Disease in Patients With GD\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePatients with GD were divided into two categories based on whether they exhibited hyperthyroid heart disease or not. No significant differences were detected in left ventricular hypertrophy, and corrected TpTe interval between GD patients with and without hyperthyroid heart disease (Z\u0026thinsp;=\u0026thinsp;0.724, p\u0026thinsp;=\u0026thinsp;0.469; Z\u0026thinsp;=\u0026thinsp;1.362, p\u0026thinsp;=\u0026thinsp;0.173, respectively). However, age (Z = -7.245, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), creatinine levels (Z\u0026thinsp;=\u0026thinsp;4.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and delayed QRS transition (χ2\u0026thinsp;=\u0026thinsp;5.826, p\u0026thinsp;\u0026lt;\u0026thinsp;0.016), were higher in the hyperthyroid heart disease group. Platelet counts (Z\u0026thinsp;=\u0026thinsp;5.448, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and total cholesterol levels (Z\u0026thinsp;=\u0026thinsp;2.152, p\u0026thinsp;=\u0026thinsp;0.031) were lower in this group. A frontal QRS-T angle\u0026thinsp;\u0026gt;\u0026thinsp;90\u0026deg; was more prevalent among patients with hyperthyroid heart disease (χ2\u0026thinsp;=\u0026thinsp;13.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics and ECG Parameters in GD Patients, Stratified by Hyperthyroid Heart Disease\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGD only\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGD with hyperthyroid heart disease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e t,or Z value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(29\u0026ndash;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(45.25-66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105(67.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95(70.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0(4.15\u0026ndash;6.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.16(4.24\u0026ndash;6.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.43(1.73\u0026ndash;3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.74(2.00-3.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.94(1.59\u0026ndash;2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62(1.27\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.22\u0026thinsp;\u0026plusmn;\u0026thinsp;18.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124.56\u0026thinsp;\u0026plusmn;\u0026thinsp;17.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221(176\u0026ndash;255)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169.5(124.25\u0026ndash;217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamic pyruvic transaminase(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(20\u0026ndash;43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(18-42.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamic oxaloacetic transaminase(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(19\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(20\u0026ndash;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(27\u0026ndash;46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.5(35-60.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea nitrogen(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.11(4.49\u0026ndash;6.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.61(4.70\u0026ndash;7.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.36(2.96\u0026ndash;3.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.13(2.52\u0026ndash;3.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow density lipoprotein cholesterol(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.91(1.60\u0026ndash;2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79(1.38\u0026ndash;2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriiodothyronine(T3)(nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.36(3.82\u0026ndash;7.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.52(2.43\u0026ndash;6.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroxine(T4)(nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e246.88\u0026thinsp;\u0026plusmn;\u0026thinsp;82.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202.53\u0026thinsp;\u0026plusmn;\u0026thinsp;89.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid-stimulating hormone(mIU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004(0.004\u0026ndash;0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005(0.005\u0026ndash;0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrontal QRS-T angle\u0026thinsp;\u0026gt;\u0026thinsp;90 \u003csup\u003eo\u003c/sup\u003e,n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQRS duration(msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88(82\u0026ndash;92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88(80-99.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft ventricular hypertrophy(mV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.52(2.05\u0026ndash;2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.70(1.89\u0026ndash;3.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelayed QRS transition at lead V4 or later,n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTpeak-to-Tend(TpTe) (msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104(94\u0026ndash;116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100(84\u0026ndash;116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTpTe/(60/heart rate)\u003csup\u003e1/2\u003c/sup\u003e(msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128.57(113.95-140.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.58(105.97-138.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of hyperthyroidism(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.17-3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(0.25-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003cp\u003e\u003cstrong\u003eRegression analysis of risk factors for hyperthyroid heart disease in patients with GD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted both univariate and multivariate logistic regression analyses to identify predictors of hyperthyroid heart disease. Univariate analysis indicated significant associations with age（p\u0026lt;0.001）, fQRS-T angle（p \u0026lt;0.001）, platelet count(p\u0026lt;0.001), lymphocyte count（p=0.035）, creatinine（p\u0026lt;0.001）, urea nitrogen（p\u0026lt;0.001）, and T4 levels（p\u0026lt;0.001）. Subsequent multivariate analysis confirmed age(p\u0026lt;0.001), fQRS-T angle(p\u0026lt;0.001), T4 levels(p=0.003), and platelet count(p=0.001) as independent predictors (see Table 3).\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\u003eLogistic Regression Analysis of Predictors for Hyperthyroid Heart Disease in GD Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExp(B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI for B,Lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efQRS-T angle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eplatelet count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelayed QRS transition at lead V4 or later\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of hyperthyroidism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eurea nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eplatelet count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efQRS-T angle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.998\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\u003eWe performed a ROC curve analysis to evaluate the diagnostic utility of age, platelet count, and fQRS-T angle combined. The optimal Youden index was 0.485, with a sensitivity of 65% and a specificity of 83%, yielding an AUC of 0.80 (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for details).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBaseline Comparisons and ECG Parameters in Cohort Study\u003c/h3\u003e\n\u003cp\u003eThere were no notable differences in terms of gender and age distribution between the patients with fQRS-T\u0026thinsp;\u0026gt;\u0026thinsp;90\u0026deg; and fQRS-T\u0026thinsp;\u0026lt;\u0026thinsp;90\u0026deg; group (χ2\u0026thinsp;=\u0026thinsp;0.028, p\u0026thinsp;=\u0026thinsp;0.868; Z\u0026thinsp;=\u0026thinsp;0.301, p\u0026thinsp;=\u0026thinsp;0.764, respectively). No significant differences were detected in left ventricular hypertrophy, and corrected TpTe interval between GD with fQRS-T\u0026thinsp;\u0026gt;\u0026thinsp;90\u0026deg; and fQRS-T\u0026thinsp;\u0026lt;\u0026thinsp;90\u0026deg; group (Z\u0026thinsp;=\u0026thinsp;1.625, p\u0026thinsp;=\u0026thinsp;0.105; Z\u0026thinsp;=\u0026thinsp;1.365, p\u0026thinsp;=\u0026thinsp;0.172, respectively). However, creatinine levels (Z\u0026thinsp;=\u0026thinsp;3.617, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and delayed QRS transition (χ2\u0026thinsp;=\u0026thinsp;4.372, p\u0026thinsp;=\u0026thinsp;0.037), were higher in fQRS-T\u0026thinsp;\u0026gt;\u0026thinsp;90\u0026deg; group (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics and ECG Parameters in GD Patients, Stratified by fQRS-T Angle\u0026thinsp;\u0026gt;\u0026thinsp;90\u0026deg;\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGD with fQRS-T angle\u0026thinsp;\u0026gt;\u0026thinsp;90 \u003csup\u003eo\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGD with fQRS-T angle\u0026thinsp;\u0026lt;\u0026thinsp;90 \u003csup\u003eo\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e t,or Z value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(45.5\u0026ndash;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(41.75-66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(73.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63(76.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperthyroid heart disease, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.29(4.50\u0026ndash;7.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.08(4.24\u0026ndash;6.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.24(2.00-4.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.53(1.76\u0026ndash;3.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44(1.12\u0026ndash;2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.72(1.32\u0026ndash;2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128.07\u0026thinsp;\u0026plusmn;\u0026thinsp;22.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.47\u0026thinsp;\u0026plusmn;\u0026thinsp;15.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186(109\u0026ndash;253)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178(141-222.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamic pyruvic transaminase(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(18-42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(18-47.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamic oxaloacetic transaminase(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(20.5\u0026ndash;48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(20-40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(39-79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(29.75\u0026ndash;47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea nitrogen(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.81(4.44\u0026ndash;8.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.61(4.69\u0026ndash;6.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.35(2.39\u0026ndash;4.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.25(2.73\u0026ndash;3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96(0.69\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86(0.65\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow density lipoprotein cholesterol(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.92(1.29\u0026ndash;2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.92(1.57\u0026ndash;2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriiodothyronine(T3)(nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.49(1.92\u0026ndash;5.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.15(2.90\u0026ndash;6.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroxine(T4)(nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189.20(104.35\u0026ndash;269.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e224.87(142.93-277.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid-stimulating hormone(mIU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005(0.005\u0026ndash;0.0125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005(0.004\u0026ndash;0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93(81\u0026ndash;126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.5(76\u0026ndash;101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQRS duration(msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86(79\u0026ndash;103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86(80-92.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft ventricular hypertrophy(mV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.50(1.58\u0026ndash;3.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.73(1.97\u0026ndash;3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelayed QRS transition at lead V4 or later,n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTpeak-to-Tend(TpTe) (msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92(80\u0026ndash;120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103(90-118.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTpTe/(60/heart rate)1/2(msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117.92(101.42-142.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126.22 (111.36-142.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQT interval (msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e350.85(347.42\u0026ndash;366)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350.85(343.5\u0026ndash;367)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQTc interval (msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e421.07(421-426.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e421.07(409.75-430.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of hyperthyroidism(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(0.67\u0026ndash;13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.167\u0026ndash;5.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath or ventricular fibrillation, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eFive-year Follow-Up Outcomes\u003c/h3\u003e\n\u003cp\u003eWithin the 5-year period, out of 41 patients in the fQRS-T\u0026thinsp;\u0026gt;\u0026thinsp;90\u0026deg; group, 5 experienced death or ventricular fibrillation\u0026mdash;4 died and 1 was resuscitated following ventricular fibrillation. There were no deaths or cases of ventricular fibrillation in the fQRS-T\u0026thinsp;\u0026lt;\u0026thinsp;90\u0026deg; group. Given the low incidence of outcomes and the small cohort size, a comprehensive multivariate analysis was not feasible. Subgroup analyses for factors like gender, presence of hyperthyroid heart disease, and delayed QRS transition did not reveal statistically significant differences in outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The Kaplan-Meier curves, supported by log rank and Breslow test results, further validate these findings (P\u0026thinsp;=\u0026thinsp;0.001; see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for details).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Categorical Variables Between Patients with and Without Death or Ventricular Fibrillation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of death or ventricular fibrillation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of all patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChi-square value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\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\u003eDelayed QRS transition at lead V4 or later\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFisher test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale:5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale:0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHyperthyroid heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFisher test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe ROC curve analysis showed that the cutoff for predicting hyperthyroid heart disease was 0.485(Youden index, sensitivity 65%, specificity 83%), and the area under the ROC curve was 0.80 [CI, 0.75\u0026ndash;0.85]. CI, confidence interval; ROC, receiver operating characteristic.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to evaluate the prognostic role of the frontal QRS-T angle in patients with GD and its association with cardiovascular risk, particularly mortality and ventricular fibrillation. Our main findings indicate that an elevated frontal QRS-T angle (\u0026ge;\u0026thinsp;90\u0026deg;) is associated with increased mortality and cardiovascular risk in patients with GD. Furthermore, hyperthyroid heart disease was more common in GD patients with a larger fQRS-T angle, suggesting the potential utility of this marker in stratifying cardiovascular risk among these patients.\u003c/p\u003e \u003cp\u003eOur results align with previous studies that have shown a large fQRS-T angle to be a marker of increased cardiovascular risk across different patient populations, including those with acute pulmonary embolism and schizophrenia.\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e The association between a widened fQRS-T angle and increased arrhythmia and mortality risk has been well documented in multiple contexts, but its specific role in hyperthyroid populations, especially GD, was not comprehensively explored until now.\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e Our findings extend the current understanding by demonstrating that the fQRS-T angle is not only a marker of arrhythmia but also a potentially valuable predictor of mortality in GD patients, thereby highlighting the importance of including this parameter in risk assessments for hyperthyroid populations.\u003c/p\u003e \u003cp\u003eThe underlying mechanisms by which patients with GD exhibit increased fQRS-T angles remain unclear. Possible explanations include: firstly, excess thyroid hormone may induce initial and terminal repolarization sequences whose drastically different orientations contribute to a widened QRS-T angle.\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e These abnormalities of repolarization occur at an early stage of GD, prior to the development of left ventricular hypertrophy, cardiac chamber enlargement, and diastolic dysfunction. Secondly, excess thyroid hormone leads to pathological structural changes in the heart, thereby altering ventricular repolarization. Thirdly, thyroid hormones induce pathophysiological changes in ionic channel mechanisms within specific myocardial regions, subsequently altering the regional sequence of ventricular repolarization. Fourthly, thyroid hormones potentially regulate multiple proteins, including potassium channels.\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e Previous studies have shown that drugs blocking potassium channels significantly affect the spatial heterogeneity of ventricular repolarization.\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003eFifth, hyperthyroidism may also amplify the influence of catecholamines, thereby lead to more arrhythmias.\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e Therefore, unlike other cardiovascular conditions, GD uniquely affects the cardiac electrophysiology through the systemic influence of excess thyroid hormones, which may explain the particularly pronounced changes in the fQRS-T angle observed in our study. This specific response underscores the potential utility of the fQRS-T angle as a sensitive and robust marker for early detection of cardiovascular abnormalities in GD.\u003c/p\u003e \u003cp\u003eClinically, our findings suggest that routine ECG monitoring, particularly focusing on the fQRS-T angle, could enhance risk stratification in GD patients, allowing for better identification of those at high risk of adverse cardiovascular events. The identification of a widened fQRS-T angle in these patients could prompt more intensive cardiovascular monitoring and management strategies. For instance, these patients may benefit from closer follow-up intervals, early referrals to cardiologists, and the use of advanced imaging techniques like echocardiography to monitor cardiac structure and function in detail. In high-risk individuals, medications such as beta-blockers could be considered to manage heart rate and reduce the risk of arrhythmias. Furthermore, early identification of increased fQRS-T angles could lead to interventions aimed at modifying other cardiovascular risk factors, such as hypertension and hyperlipidemia, which could ultimately improve outcomes.\u003c/p\u003e \u003cp\u003eThe integration of fQRS-T angle measurement into routine clinical practice for GD patients may also help in individualized risk assessment, enabling healthcare providers to classify patients into different risk categories. This stratification could help prioritize the allocation of healthcare resources and tailor patient-specific management plans. For instance, patients identified with a high-risk ECG profile could be offered more intensive lifestyle modification counseling, including smoking cessation, exercise, and dietary changes, which are known to improve cardiovascular outcomes. Additionally, it could foster early interventions in those who are likely to develop hyperthyroid heart disease, thus potentially mitigating the progression to more severe cardiac complications.\u003c/p\u003e \u003cp\u003eOur study findings are consistent with prior literature exploring various predictors of prognosis in GD patients. Arjola Bano et al demonstrated that higher levels of TSH receptor antibodies (TRAb) are associated with greater disease severity and increased risk of relapse, especially in younger patients.\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e TRAb levels provide valuable insights into autoimmune activity and disease severity in GD, but do not directly reflect cardiovascular risk. In contrast, our study extends these findings by showing that the fQRS-T angle, an ECG-derived parameter, can provide crucial information regarding the electrical stability of the heart and directly predict cardiovascular outcomes, offering a different dimension in risk stratification for GD patients.\u003c/p\u003e \u003cp\u003eSimilarly, the study by Nami Suzuki et al focused on age and sex as determinants of GD prognosis, with younger patients and males showing more severe disease and higher risks of adverse outcomes.\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e While age and sex are important demographic factors in predicting disease progression, they lack the specificity required for direct cardiovascular assessment. Our findings contribute additional value by identifying the fQRS-T angle as a specific cardiovascular risk marker that can be easily measured and monitored. Unlike demographic factors, the fQRS-T angle provides an individualized assessment of the electrical activity of the heart, offering a more nuanced understanding of cardiovascular risks in GD patients.\u003c/p\u003e \u003cp\u003eThe comparison with these prior studies underscores the novelty of our approach in utilizing an ECG parameter to directly quantify cardiovascular risk in GD patients. Whereas previous markers such as TRAb levels and demographic factors provide valuable insights into the overall severity and prognosis of GD, the fQRS-T angle specifically addresses cardiovascular vulnerability, which is critical for managing and mitigating potential complications such as arrhythmias and sudden cardiac death. By integrating this parameter into routine practice, alongside other known risk markers, clinicians can adopt a more holistic approach to managing GD patients, ultimately improving outcomes and reducing the incidence of severe cardiovascular events.\u003c/p\u003e \u003cp\u003eThis study has several strengths, including a well-defined cohort of GD patients with comprehensive ECG and laboratory data, and a long follow-up period that allowed us to assess mortality and VF as significant outcomes. However, there are also limitations that must be considered. First, the retrospective nature of this study introduces potential biases, including selection bias and residual confounding. The reliance on medical records for data collection could have led to misclassification or incomplete information. Additionally, although we attempted to use propensity score matching to create a control group, the lack of satisfactory matching results led us to use random sampling, which may have introduced some imbalance between groups. Loss to follow-up was minimal, but the use of multiple imputation to handle missing data may still have influenced the results. Another limitation is the relatively small sample size of the subgroup with an elevated fQRS-T angle. This limited our ability to conduct more detailed subgroup analyses or adjust for additional confounders in the survival analysis. Future studies with larger sample sizes should aim to validate these findings and provide a more nuanced understanding of the mechanisms underlying the observed associations.\u003c/p\u003e \u003cp\u003eThere are some potential controversies raised by this study. The use of a single ECG marker, such as the fQRS-T angle, as a predictor of mortality has been questioned by some researchers due to concerns about its sensitivity and specificity in predicting adverse outcomes. However, our findings indicate that the fQRS-T angle is a relatively simple, non-invasive, and valuable marker that could be effectively integrated into routine clinical practice for identifying high-risk GD patients. Nevertheless, additional prospective studies are needed to confirm its predictive value and to determine whether targeted interventions based on fQRS-T angle measurements could improve outcomes.\u003c/p\u003e \u003cp\u003eIn conclusion, this study demonstrates that an elevated frontal QRS-T angle is significantly associated with increased cardiovascular risk in patients with Graves' disease, including mortality and ventricular fibrillation. Given the ease of measuring the fQRS-T angle and its potential utility in predicting adverse outcomes, integrating this marker into routine clinical assessments could improve risk stratification and patient management. Future research should focus on larger-scale prospective studies to validate these findings and explore targeted intervention strategies for patients identified as high risk based on their fQRS-T angle. These efforts could ultimately contribute to improved clinical outcomes and potentially influence guidelines for the management of patients with hyperthyroidism.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo acknowledgements are declared by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZW and XCW contributed to conception and design of the study, and acquisition, analysis, and interpretation of data; JX, AJG, MLL, NJZ, TTF contributed to acquisition and interpretation of data. All authors contributed to revision of the manuscript, approved the final version, and had a final responsibility for the decision to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis study was supported by grants from Health Research Project in Anhui Province (AHWJ2022b020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLee, S.Y. and E.N. 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Tan, and Z. Tanriverdi, \u003cem\u003eEvaluation of Frontal QRS-T Angle in Children With ADHD and Healthy Controls.\u003c/em\u003e J Atten Disord, 2024: p. 10870547241288353.\u003c/li\u003e\n\u003cli\u003eGunduz, R., et al., \u003cem\u003eFrontal QRS/T angle can predict mortality in COVID-19 patients.\u003c/em\u003e Am J Emerg Med, 2022. \u003cstrong\u003e58\u003c/strong\u003e: p. 66-72.\u003c/li\u003e\n\u003cli\u003eAro, A.L., et al., \u003cem\u003eElectrical risk score beyond the left ventricular ejection fraction: prediction of sudden cardiac death in the Oregon Sudden Unexpected Death Study and the Atherosclerosis Risk in Communities Study.\u003c/em\u003e Eur Heart J, 2017. \u003cstrong\u003e38\u003c/strong\u003e(40): p. 3017-3025.\u003c/li\u003e\n\u003cli\u003eTse, G. and B.P. 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Gayretli Yayla, \u003cem\u003eThe assessment of Tp-e interval and Tp-e/QT ratio in patients with hyperthyroidism before and after thyroid surgery.\u003c/em\u003e Int J Clin Pract, 2021. \u003cstrong\u003e75\u003c/strong\u003e(12): p. e14937.\u003c/li\u003e\n\u003cli\u003eAkkuş, G., et al., \u003cem\u003eComparison of 24-Hour Electrocardiogram Parameters in Patients with Graves\u0026apos; Disease Before and After Anti-Thyroid Therapy.\u003c/em\u003e Endocr Metab Immune Disord Drug Targets, 2021. \u003cstrong\u003e21\u003c/strong\u003e(1): p. 183-191.\u003c/li\u003e\n\u003cli\u003eRautaharju, P.M., et al., \u003cem\u003eHeart rate, gender differences, and presence versus absence of diagnostic ST elevation as determinants of spatial QRS|T angle widening in acute coronary syndrome.\u003c/em\u003e Am J Cardiol, 2011. \u003cstrong\u003e107\u003c/strong\u003e(12): p. 1744-50.\u003c/li\u003e\n\u003cli\u003eJabbar, A., et al., \u003cem\u003eThyroid hormones and cardiovascular disease.\u003c/em\u003e Nat Rev Cardiol, 2017. \u003cstrong\u003e14\u003c/strong\u003e(1): p. 39-55.\u003c/li\u003e\n\u003cli\u003eCorino, V.D.A., et al., \u003cem\u003eAssessment of spatial heterogeneity of ventricular repolarization after multi-channel blocker drugs in healthy subjects.\u003c/em\u003e Comput Methods Programs Biomed, 2020. \u003cstrong\u003e189\u003c/strong\u003e: p. 105291.\u003c/li\u003e\n\u003cli\u003eSilva, J.E. and S.D. Bianco, \u003cem\u003eThyroid-adrenergic interactions: physiological and clinical implications.\u003c/em\u003e Thyroid, 2008. \u003cstrong\u003e18\u003c/strong\u003e(2): p. 157-65.\u003c/li\u003e\n\u003cli\u003eBano, A., et al., \u003cem\u003eAge May Influence the Impact of TRAbs on Thyroid Function and Relapse-Risk in Patients With Graves Disease.\u003c/em\u003e J Clin Endocrinol Metab, 2019. \u003cstrong\u003e104\u003c/strong\u003e(5): p. 1378-1385.\u003c/li\u003e\n\u003cli\u003eSuzuki, N., et al., \u003cem\u003eDoes Age or Sex Relate to Severity or Treatment Prognosis in Graves\u0026apos; Disease?\u003c/em\u003e Thyroid, 2021. \u003cstrong\u003e31\u003c/strong\u003e(9): p. 1409-1415.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Frontal QRS-T, Graves’Disease, Hyperthyroid heart disease, Hyperthyroidism","lastPublishedDoi":"10.21203/rs.3.rs-5352051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5352051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGraves' disease (GD) is linked to cardiovascular complications, but reliable non-invasive predictors are limited. This study aimed to assess whether the frontal QRS-T (fQRS-T) angle could predict mortality and cardiovascular outcomes in GD patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInitially, we conducted a retrospective analysis of electrocardiogram (ECG) parameters from 291 GD patients compared with 96 healthy controls. GD patients were then categorized based on the presence of hyperthyroid heart disease. Using logistic regression, we identified predictors of cardiovascular outcomes. Subsequently, a retrospective cohort study followed 41 patients with an fQRS-T angle ≥90° and 82 matched controls over five years, assessing all-cause mortality and ventricular fibrillation (VF) using Kaplan-Meier analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInitial analysis showed that a significantly higher proportion of GD patients had an fQRS-T angle ≥90° compared to healthy controls (p\u0026lt; 0.001). Among GD patients, those with hyperthyroid heart disease were more likely to have an fQRS-T angle ≥90° (p \u0026lt; 0.001). Multivariate logistic regression analysis revealed that age, fQRS-T angle, thyroxine (T4), and platelet were independent predictors of hyperthyroid heart disease. In the cohort study, 41 GD patients with an fQRS-T angle ≥90° were selected as the exposure group, and 82 patients without a widened angle were selected as controls. Kaplan-Meier analysis demonstrated a significant difference in event-free survival between the groups, as assessed by log-rank test(P=0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn increased fQRS-T angle is significantly associated with higher mortality and cardiovascular risk in GD patients. Measuring the fQRS-T angle could enhance risk stratification and guide clinical management in this population\u003c/p\u003e","manuscriptTitle":"Elevated Frontal QRS-T Angle as a Predictor of Cardiovascular Risk in Graves’ Disease: A Comparative Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 10:30:57","doi":"10.21203/rs.3.rs-5352051/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dcb99149-79ff-40c8-9f5b-a49aed9cf5e6","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-20T10:31:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-20 10:30:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5352051","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5352051","identity":"rs-5352051","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}