Preoperative Fragmented QRS as a Predictor of Postoperative Left Ventricular Systolic Dysfunction After Isolated Mitral Valve Replacement

Preoperative Fragmented QRS as a Predictor of Postoperative Left Ventricular Systolic Dysfunction After Isolated Mitral Valve Replacement | 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 Preoperative Fragmented QRS as a Predictor of Postoperative Left Ventricular Systolic Dysfunction After Isolated Mitral Valve Replacement Ismail Balaban, Seda Tanyeri Uzel, Ahmet Karaduman, Zeynep Esra Güner, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8987964/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 16 You are reading this latest preprint version Abstract Background Despite preserved preoperative left ventricular ejection fraction (LVEF), a substantial proportion of patients develop postoperative left ventricular (LV) systolic dysfunction after mitral valve surgery. Myocardial structural abnormalities and limited myocardial reserve may impair postoperative recovery and are not fully captured by conventional functional indices. Fragmented QRS (fQRS) on surface electrocardiography has been proposed as a simple marker of an adverse myocardial substrate. To evaluate the prognostic value of preoperative fragmented QRS for predicting postoperative LV systolic dysfunction in patients undergoing isolated mitral valve replacement and to develop a clinically applicable predictive model. Methods This retrospective analysis included 279 consecutive patients who underwent elective isolated mitral valve replacement, with or without concomitant tricuspid annuloplasty, between 2022 and 2025. Fragmented QRS was assessed on preoperative 12-lead electrocardiography. Postoperative LV systolic dysfunction was defined as an LVEF < 50% at early postoperative echocardiographic follow-up. Multivariable logistic regression analyses, including a backward stepwise approach, were performed to identify independent predictors. Model discrimination and calibration were evaluated, and a nomogram was constructed for individualized risk estimation. Results Fragmented QRS was present in 71 patients (25.4%). Although preoperative LVEF was similar between groups, patients with fQRS had lower postoperative LVEF (p = 0.036) and a higher incidence of postoperative LV systolic dysfunction compared with those without fQRS (45.1% vs. 28.8%; p = 0.012). In the stepwise multivariable model, fQRS independently predicted postoperative LV systolic dysfunction (odds ratio [OR] 2.00, 95% confidence interval [CI] 1.06–3.81; p = 0.033), together with aortic cross-clamp time and chronic kidney disease. The model demonstrated good discrimination (area under the curve 0.78). A nomogram was developed to facilitate individualized risk prediction. Conclusions Preoperative fragmented QRS is independently associated with an increased risk of postoperative left ventricular systolic dysfunction after isolated mitral valve replacement. When integrated with baseline ventricular function and perioperative surgical factors, fQRS may serve as a simple and widely available marker to enhance perioperative risk stratification and support individualized clinical decision-making. Fragmented QRS Mitral valve replacement Postoperative outcomes Left ventricular dysfunction Risk prediction Figures Figure 1 Figure 2 Figure 3 1. BACKGROUND Preservation of left ventricular (LV) systolic function after surgical treatment of mitral valve disease remains a key determinant of both short- and long-term clinical outcomes. Despite substantial advances in surgical techniques, myocardial protection strategies, and perioperative care, a considerable proportion of patients with preoperative left ventricular ejection fraction (LVEF) within guideline-recommended thresholds develop early or late postoperative LV systolic dysfunction after mitral valve surgery ( 1 , 2 ). This apparent dissociation between preserved preoperative systolic function and postoperative ventricular performance suggests that conventional functional parameters may not fully capture the underlying myocardial structural substrate governing postoperative recovery. In chronic mitral valve disease, sustained volume overload drives progressive myocardial remodeling characterized by cardiomyocyte hypertrophy, extracellular matrix expansion, and the development of interstitial and replacement fibrosis. Although surgical correction restores valvular competence and alters loading conditions, established myocardial fibrosis may persist and limit reverse remodeling, predisposing patients to postoperative LV dysfunction despite preserved preoperative systolic function ( 3 – 5 ). Consistent with this concept, cardiac magnetic resonance studies using late gadolinium enhancement and T1 mapping have demonstrated that myocardial fibrosis burden is closely associated with postoperative LV functional trajectory and long-term outcomes following valvular surgery ( 6 – 8 ). However, the routine use of CMR is limited by availability, cost, and practical constraints, prompting growing interest in simple and widely accessible surrogate markers of myocardial structural disease. Fragmented QRS (fQRS) on surface electrocardiography reflects heterogeneous ventricular conduction and has been consistently associated with myocardial scar and fibrosis across a broad spectrum of ischemic and non-ischemic heart diseases ( 9 – 12 ). Histopathological and CMR validation studies have shown that fQRS is associated with myocardial fibrosis with high specificity but limited sensitivity ( 13 – 15 ). Accordingly, fQRS is best regarded not as a tool to exclude myocardial fibrosis, but rather as a practical electrocardiographic marker indicating the presence of an adverse myocardial substrate. Beyond its diagnostic implications, fQRS has demonstrated prognostic relevance in various clinical settings, including coronary artery disease, hypertensive heart disease, and non-ischemic cardiomyopathies, where its presence has been associated with increased risks of arrhythmias, heart failure progression, and mortality ( 16 – 18 ). In the context of mitral valve surgery, emerging evidence suggests that preoperative fQRS may identify patients at increased risk of postoperative LV systolic dysfunction, even in the presence of preserved preoperative LVEF ( 19 , 20 ). Nevertheless, most available studies have been conducted in relatively small, valve-specific populations and exhibit heterogeneity in outcome definitions. Moreover, the interaction between pre-existing myocardial structural vulnerability, as reflected by fQRS, and perioperative surgical stress has not been adequately explored in integrative, clinically applicable predictive models. Although current guidelines emphasize timely intervention and multimodality imaging in the management of valvular heart disease, electrocardiographic markers of myocardial fibrosis have not yet been systematically incorporated into perioperative risk stratification algorithms ( 1 , 2 ). Therefore, the present study aimed to evaluate the prognostic significance of preoperative fQRS for predicting postoperative LV systolic dysfunction in patients undergoing isolated mitral valve replacement (MVR) and to develop a clinically applicable predictive model incorporating electrocardiographic, echocardiographic, and surgical variables. The conceptual and predictive framework underlying this study is graphically summarized in the Graphical Abstract . 2. METHODS 2.1. Study Design This study was conducted as a retrospective analysis of prospectively collected clinical, electrocardiographic, and echocardiographic data from patients undergoing elective isolated mitral valve replacement. The primary objective was to assess the prognostic value of preoperative fragmented QRS (fQRS) in predicting postoperative left ventricular (LV) systolic dysfunction and to develop a clinically applicable predictive model. Given the retrospective nature of the analysis, written informed consent was obtained or waived in accordance with institutional ethics committee regulations. The study protocol complied with the principles outlined in the Declaration of Helsinki. 2.2. Study Population Consecutive patients who underwent elective isolated mitral valve replacement with either mechanical or bioprosthetic prostheses between 2022 and 2025 were screened for eligibility. Inclusion criteria were as follows: (i) availability of a standard 12-lead surface electrocardiogram (ECG) obtained in the preoperative period; (ii) assessment of LV systolic function by transthoracic echocardiography both before surgery and during the early postoperative period; (iii) preserved preoperative left ventricular ejection fraction (LVEF ≥ 50%). Patients who underwent mitral valve repair, concomitant aortic or other left-sided valvular surgery, prior cardiac surgery, or non-elective procedures were excluded; concomitant tricuspid annuloplasty was not considered an exclusion criterion. Additional exclusion criteria included bundle branch block, permanent pacemaker or implantable cardioverter-defibrillator rhythm, congenital intraventricular conduction abnormalities, significant ECG artifacts, inadequate echocardiographic image quality, or missing essential clinical data. These criteria were applied to ensure reliable assessment of fQRS and accurate evaluation of LV systolic function. 2.3. Clinical and Perioperative Variables Demographic characteristics, cardiovascular risk factors, and comorbid conditions (including hypertension, diabetes mellitus, chronic kidney disease, and history of atrial fibrillation) were recorded using standardized data collection forms. Chronic kidney disease was defined in accordance with internationally accepted guidelines ( 21 ). Surgical variables included mitral valve prosthesis type (mechanical or bioprosthetic), the presence of concomitant tricuspid annuloplasty, and aortic cross-clamp duration. Cross-clamp time was incorporated into the analyses as a surrogate marker of perioperative myocardial ischemic burden. 2.4. Electrocardiographic Assessment Preoperative ECGs were obtained using standard settings (paper speed 25 mm/s, calibration 10 mm/mV) and analyzed from 12-lead surface recordings. Fragmented QRS was defined as the presence of notching, multiple R waves, or additional R′ deflections within the QRS complex in the absence of bundle branch block, observed in at least two contiguous leads ( 10 – 12 ). All ECGs were independently evaluated by two experienced investigators who were blinded to clinical, surgical, and echocardiographic data. In cases of disagreement, ECGs were re-reviewed jointly and a consensus decision was reached. 2.5. Echocardiographic Assessment Transthoracic echocardiography was performed in the preoperative period and during early postoperative follow-up in accordance with current echocardiographic guidelines ( 22 , 23 ). Left ventricular ejection fraction was primarily calculated using the biplane Simpson method. Left atrial diameter, left ventricular end-diastolic and end-systolic dimensions, and other standard echocardiographic parameters were recorded concurrently. Postoperative echocardiographic assessment was performed within the first 7 days after surgery. All echocardiographic measurements were analyzed independently of electrocardiographic findings and clinical data, and assessments were conducted under blinded conditions to minimize measurement bias. 2.6. Outcome Definition The primary endpoint of the study was the development of postoperative LV systolic dysfunction. Postoperative LV systolic dysfunction was defined as a reduction in LVEF to < 50% at postoperative follow-up. This threshold was selected in accordance with commonly used criteria in the literature for defining clinically relevant LV systolic impairment after mitral valve surgery. 2.7. Statistical Analysis The distribution of continuous variables was assessed using visual (histograms and Q–Q plots) and analytical methods. Continuous variables are presented as mean ± standard deviation if normally distributed, or as median with interquartile range (IQR) if non-normally distributed. Categorical variables are expressed as counts and percentages. Between-group comparisons were performed using the independent samples t test or the Mann–Whitney U test for continuous variables, as appropriate, and the Pearson chi-square test or Fisher’s exact test for categorical variables. To identify potential determinants of postoperative LV systolic dysfunction, univariable analyses were initially performed. Variables considered clinically relevant and/or those demonstrating statistical significance in univariable analyses (p < 0.10) were entered into multivariable logistic regression models. A backward stepwise variable selection approach was applied to reduce model complexity and minimize the risk of overfitting, while preserving clinical interpretability. Odds ratios (ORs) with 95% confidence intervals (CIs) were reported. Model discrimination was assessed using receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) was calculated. The discriminative performance of the full model and the more parsimonious stepwise model was compared. Model calibration was evaluated using calibration plots comparing observed and predicted event probabilities across risk strata. Based on the final multivariable model, a nomogram was constructed to enable individualized risk estimation of postoperative LV systolic dysfunction at the patient level. All statistical analyses were performed using appropriate statistical software (R Foundation for Statistical Computing, Vienna, Austria), and a two-sided p value < 0.05 was considered statistically significant. 3. RESULTS 3.1. Study population and baseline characteristics A total of 279 patients who underwent elective mitral valve replacement, with or without concomitant tricuspid annuloplasty, were included in the study. Fragmented QRS (fQRS) on preoperative electrocardiography was identified in 71 patients (25.4%), whereas 208 patients (74.6%) had no evidence of fQRS. Baseline demographic, clinical, laboratory, echocardiographic, and operative characteristics according to fQRS status are summarized in Table 1 . Patients with fQRS were significantly older than those without fQRS (p = 0.019) and exhibited a markedly higher prevalence of preoperative atrial fibrillation (p < 0.001). A history of smoking was also more frequent in the fQRS-positive group (p = 0.040). Chronic kidney disease tended to be more common among patients with fQRS; however, this difference did not reach statistical significance (p = 0.083). Table 1 Baseline and Operative Characteristics According to fQRS Status Variable All (n = 279) fQRS negative (n = 208) fQRS positive (n = 71) p value Demographic & Clinical Age, (years) 57.4 (12.7) 56.3 (12.8) 60.4 (12.1) 0.019 Gender, (male) n (%) 120 (43) 85 (40.9) 35 (49.3) 0.215 BMI, (kg/m²) 28.2 (5.22) 28.2 (5.48) 28.1 (4.43) 0.943 HT, n (%) 112 (40.1) 84 (40.4) 28 (39.4) 0.888 DM, n (%) 61 (21.9) 42 (20.2) 19 (26.8) 0.248 HL, n (%) 23 (8.2) 18 (8.7) 5 (7.0) 0.670 Smoker, n (%) 50 (17.9) 43 (20.7) 7 (9.9) 0.040 Stroke, n (%) 31 (11.1) 20 (9.6) 11 (15.5) 0.174 PAD, n (%) 3 (1.1) 2 (1.9) 1 (1.4) 1.000 COPD, n (%) 37 (13.3) 26 (12.5) 11 (15.5) 0.521 CKD, n (%) 22 (7.9) 13 (6.3) 9 (12.7) 0.083 Preop AF, n (%) 109 (39.1) 69 (33.2) 40 (56.3) < 0.001 Laboratory findings Hgb, (g/dL) 12.7 (1.88) 12.7 (1.88) 12.7 (1.91) 0.846 WBC, (×10⁹/L) 7.35 (6.14–8.86) 7.27 (6.18–8.73) 7.51 (6.04–9.21) 0.864 Monocyte, (×10⁹/L) 0.59 (0.47–0.72) 0.57 (0.46–0.70) 0.67 (0.54–0.80) 0.010 Creatinine, (mg/dL) 0.84 (0.73–1.05) 0.83 (0.73–1.02) 0.89 (0.74–1.13) 0.136 CRP, (mg/L) 3.89 (1.81–9.98) 3.92 (1.81–9.68) 3.74 (1.84–12.4) 0.838 Albumin, (g/L) 40.3 (5.28) 40.1 (5.4) 40.7 (4.9) 0.408 Uric acid, (mg/dL) 6.03 (1.77) 5.9 (1.6) 6.4 (2.1) 0.054 Preoperative echocardiography Preop EF, (%) 63.5 (2.67) 63.5 (2.7) 63.4 (2.6) 0.725 LA diameter, (cm) 4.84 (0.87) 4.75 (0.8) 5.10 (1.0) 0.007 LVEDD, (cm) 5.19 (0.60) 5.15 (0.55) 5.30 (0.73) 0.080 LVESD, (cm) 3.40 (0.64) 3.37 (0.58) 3.48 (0.77) 0.228 Etiology (Rheumatic / Degenerative / Endocarditis), n (%) 91 / 160 / 18 75 / 120 / 13 26 / 40 / 5 0.815 Operative characteristics Bioprosthesis, n (%) 51 (18.3) 32 (15.4) 19 (26.8) 0.032 Mechanical valve, n (%) 228 (81.7) 176 (84.6) 52 (73.2) 0.032 Tricuspid ring, n (%) 82 (29.4) 53 (25.5) 29 (40.8) 0.014 Maze, n (%) 28 (10.0) 19 (9.1) 9 (12.7) 0.391 Cross-clamp time, (min) 189 (144–224) 185 (147–224) 193 (140–235) 0.622 Bypass time, (min) 224 (179–259) 220 (182–259) 228 (175–270) 0.607 Abbreviations: AF, atrial fibrillation; BMI, body mass index; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein; DM, diabetes mellitus; EF, ejection fraction; fQRS, fragmented QRS; Hgb, hemoglobin; HL, hyperlipidemia; HT, hypertension; LA, left atrium; LVEDD, left ventricular end-diastolic diameter; LVESD, left ventricular end-systolic diameter; PAD, peripheral arterial disease; WBC, white blood cell count. Preoperative left ventricular ejection fraction (LVEF) was similar between groups (p = 0.725). Likewise, no significant differences were observed in left ventricular dimensions, major laboratory parameters, or cardiopulmonary bypass and aortic cross-clamp durations ( Table 1 ). Concomitant tricuspid annuloplasty was more frequently performed in patients with fQRS compared with those without fQRS (40.8% vs. 25.5%, p = 0.014). 3.2. Postoperative left ventricular systolic function Despite comparable preoperative LVEF values, postoperative echocardiographic findings differed significantly according to fQRS status ( Table 2 ). Patients with fQRS exhibited a significantly lower postoperative LVEF compared with those without fQRS (p = 0.036). Accordingly, the incidence of postoperative left ventricular systolic dysfunction—defined as an LVEF <50%—was significantly higher in the fQRS-positive group than in the fQRS-negative group (45.1% vs. 28.8%; p = 0.012). Table 2. Postoperative Echocardiographic and Clinical Outcomes According to fQRS Status Variable All (n=279) fQRS negative (n=208) fQRS positive (n=71) p value Postoperative echocardiography Postop EF, (%) 55.8 (9.91) 56.5 (9.47) 53.7 (10.9) 0.036 Postop EF <50, n (%) 92 (33) 60 (28.8) 32 (45.1) 0.012 Δ LVEF (%) 5 (0–15) 5 (0–15) 10 (0–15) 0.038 Postop LA, (cm) 4.67 (0.76) 4.60 (0.68) 4.87 (0.94) 0.010 Postop LVEDD, (cm) 4.92 (0.51) 4.89 (0.48) 5.01 (0.57) 0.070 Postop LVESD, (cm) 3.27 (0.59) 3.24 (0.58) 3.34 (0.65) 0.260 Postoperative rhythm & outcomes Postop AF, n (%) 124 (44.4) 84 (40.4) 40 (56.3) 0.020 Postop complication, n (%) 68 (24.4) 50 (24.0) 18 (25.4) 0.824 Postop stroke, n (%) 19 (6.8) 15 (7.2) 4 (5.6) 0.649 Postop dialysis, n (%) 15 (5.4) 10 (4.8) 5 (7.0) 0.471 Postop pacemaker, n (%) 2 (0.7) 2 (1.0) 0 (0) 1.000 Postop tamponade, n (%) 30 (10.8) 21 (10.1) 9 (12.7) 0.545 Postop revision, n (%) 36 (12.9) 25 (12.0) 11 (15.5) 0.451 In-hospital mortality, n (%) 28 (9.7) 19 (9.1) 8 (11.3) 0.600 30-day mortality, n (%) 26 (9.3) 18 (8.7) 8 (11.3) 0.513 Abbreviations: AF, atrial fibrillation; EF, ejection fraction; fQRS, fragmented QRS; LA, left atrium; LVEDD, left ventricular end-diastolic diameter; LVESD, left ventricular end-systolic diameter; LVEF, left ventricular ejection fraction. In addition, the change in LVEF from the preoperative to postoperative assessment (ΔLVEF) was significantly more pronounced in patients with fQRS, indicating a greater postoperative deterioration in LV systolic function (p = 0.038). 3.3. Postoperative rhythm disturbances and early clinical outcomes Postoperative atrial fibrillation occurred significantly more frequently in patients with fQRS than in those without fQRS (56.3% vs. 40.4%; p = 0.020). In contrast, the incidence of other early postoperative complications—including stroke, need for dialysis, permanent pacemaker implantation, cardiac tamponade, surgical revision, in-hospital mortality, and 30-day mortality—did not differ significantly between the two groups (all p > 0.05) ( Table 2 ). 3.4. Multivariable logistic regression analyses To identify independent determinants of postoperative left ventricular systolic dysfunction (LVEF < 50%), a two-step multivariable logistic regression approach was applied. In the first step, a full model including age, aortic cross-clamp time, preoperative LVEF, body mass index, sex, presence of fragmented QRS (fQRS), hypertension, diabetes mellitus, and chronic kidney disease was constructed ( Table 3 ). In this model, aortic cross-clamp time and preoperative LVEF were independently associated with postoperative left ventricular systolic dysfunction. Each 1-minute increase in aortic cross-clamp time was associated with a significantly higher likelihood of postoperative LVEF < 50% (OR 1.014, 95% CI 1.010–1.020; p < 0.001), whereas higher preoperative LVEF exerted a protective effect (OR 0.868, 95% CI 0.782–0.962; p = 0.007). The presence of fQRS, hypertension, and chronic kidney disease demonstrated borderline statistical significance in the full model. Table 3. Results of the full multivariable logistic regression model for postoperative left ventricular systolic dysfunction (LVEF <50%). Dependent variable: Postoperative LVEF <50% Variable OR 95% CI β (Estimate) p value Aortic cross-clamp time (min) 1.014 1.010–1.020 0.014 <0.001 Age (years) 1.017 0.992–1.044 0.017 0.180 Preoperative LVEF (%) 0.868 0.782–0.962 −0.142 0.007 Body mass index (kg/m²) 0.993 0.932–1.056 −0.007 0.816 Male sex 1.63 0.90–2.96 0.487 0.109 Fragmented QRS 1.90 0.99–3.64 0.642 0.052 Hypertension 0.55 0.28–1.06 −0.605 0.077 Diabetes mellitus 1.00 0.47–2.12 0.005 0.990 Chronic kidney disease 2.70 0.96–7.82 0.995 0.062 Table footnote: Odds ratios (ORs) were calculated as the exponentiated logistic regression coefficients [exp(β)]. The 95% confidence intervals were obtained using the profile likelihood method. For continuous variables, ORs represent the change in odds per 1-unit increase in the respective variable. A p value <0.05 was considered statistically significant. In the second step, backward stepwise variable selection was applied to enhance clinical applicability and derive a parsimonious model ( Table 4 ). In the final backward stepwise model, aortic cross-clamp time, preoperative LVEF, presence of fQRS, chronic kidney disease, sex, and hypertension were retained as predictors of postoperative left ventricular systolic dysfunction. Sex and hypertension demonstrated borderline statistical significance but were retained to preserve clinical interpretability and model stability. Among these variables, the presence of fQRS was associated with an approximately twofold increase in the risk of postoperative LVEF < 50% (OR 2.00, 95% CI 1.06–3.81; p = 0.033), while chronic kidney disease emerged as one of the strongest predictors (OR 3.06, 95% CI 1.11–8.56; p = 0.030). Regression coefficients and 95% confidence intervals from the stepwise model are illustrated in Figure 1 . Table 4. Backward Stepwise Multivariable Logistic Regression Analysis for Postoperative Left Ventricular Systolic Dysfunction (LVEF <50%) Dependent variable: Postoperative LVEF <50% Variable OR 95% CI p value Aortic cross-clamp time (min) 1.015 1.010–1.020 <0.001 Preoperative LVEF (%) 0.860 0.776–0.953 0.004 Male sex 1.63 0.91–2.92 0.102 Fragmented QRS 2.00 1.06–3.81 0.033 Hypertension 0.58 0.31–1.07 0.086 Chronic kidney disease 3.06 1.11–8.56 0.030 Table footnote: Odds ratios (ORs) were calculated as the exponentiated logistic regression coefficients [exp(β)]. The 95% confidence intervals were obtained using the profile likelihood method. For continuous variables, ORs represent the change in odds per 1-unit increase in the respective variable. A p value <0.05 was considered statistically significant. 3.5. Model performance: discrimination and calibration Model performance was assessed in terms of both discrimination and calibration. The discriminatory ability of the full multivariable logistic regression model, evaluated using receiver operating characteristic (ROC) curve analysis, yielded an area under the curve (AUC) of 0.79. In the simplified stepwise model, the AUC was 0.78, indicating that discrimination was largely preserved despite model simplification and supporting the clinical applicability of the parsimonious model ( Figure 2 ). Model calibration was evaluated by comparing predicted probabilities with observed event rates using calibration plots. The calibration curve of the stepwise model closely followed the reference 45-degree line, particularly across low- and intermediate-risk ranges. The absence of a systematic deviation at higher predicted risk levels further supports the reliability of the simplified model for absolute risk estimation ( Figure 3 ). 3.6. Nomogram development Based on the final stepwise logistic regression model, a nomogram was developed to facilitate individualized prediction of postoperative left ventricular systolic dysfunction (LVEF < 50%) ( Supplementary Figure 1 ). In the nomogram, each variable is assigned a score proportional to its corresponding regression coefficient, and the sum of these scores is used to estimate an individual patient’s risk. Aortic cross-clamp time, preoperative LVEF, presence of fragmented QRS, and chronic kidney disease constituted the major contributors to the total risk score. This visual tool provides an integrated representation of the relative impact of independent predictors identified in the multivariable model and may support potential clinical risk stratification. 4. DISCUSSION In this study, preoperative fragmented QRS (fQRS) was independently associated with an increased risk of postoperative left ventricular (LV) systolic dysfunction in patients undergoing mitral valve replacement, with or without concomitant tricuspid annuloplasty. This association persisted after adjustment for baseline LV systolic function and perioperative surgical stress and remained robust in a simplified multivariable model. Collectively, these findings suggest that postoperative LV functional recovery following mitral valve surgery is influenced not only by the hemodynamic effects of valve correction but also by pre-existing myocardial structural and electrical characteristics. Clinically, it is well recognized that a subset of patients develop postoperative deterioration or incomplete recovery of LV systolic function despite preserved preoperative ejection fraction and technically successful surgery. The present findings are consistent with this observation and support the concept that conventional functional indices alone may not fully capture latent myocardial vulnerability that becomes clinically relevant after surgical intervention (1,3–5). 4.1. Myocardial substrate and postoperative LV functional recovery Chronic mitral valve disease is associated with sustained volume overload, leading to progressive myocardial remodeling characterized by cardiomyocyte hypertrophy, extracellular matrix expansion, and the development of interstitial and replacement fibrosis. Although mitral valve replacement restores valvular competence and abruptly alters loading conditions, established myocardial fibrosis may persist and limit reverse remodeling. Consequently, postoperative LV systolic dysfunction may occur even in patients with preserved preoperative ejection fraction, underscoring the importance of the underlying myocardial substrate in shaping postoperative functional trajectories (4,5). Cardiac magnetic resonance (CMR) studies using late gadolinium enhancement and quantitative tissue characterization techniques have consistently demonstrated that myocardial fibrosis burden is closely associated with impaired postoperative LV recovery and adverse outcomes after mitral valve surgery (6–8). However, despite its high diagnostic accuracy, routine use of CMR in the perioperative evaluation of valvular heart disease remains constrained by availability, cost, and logistical considerations, limiting its widespread application in everyday clinical practice. 4.2. Fragmented QRS as a surrogate marker of myocardial vulnerability Fragmented QRS (fQRS) on surface electrocardiography reflects heterogeneous ventricular conduction resulting from discontinuous myocardial activation. Experimental, histopathological, and cardiac magnetic resonance–based studies have demonstrated a close association between fQRS and the presence of myocardial scar and fibrosis across a wide spectrum of ischemic and non-ischemic heart diseases (9–12,14,15). Importantly, fragmented QRS should not be interpreted as a direct surrogate for imaging-based quantification of myocardial fibrosis. Instead, it represents a pragmatic and widely available electrocardiographic phenotype identifying patients with an adverse myocardial substrate characterized by electrical heterogeneity and reduced myocardial reserve (12,15). 4.3. Interaction with perioperative stress and surgical factors A key observation of this study is the interaction between pre-existing myocardial vulnerability, as reflected by fragmented QRS (fQRS), and perioperative ischemic burden, represented by aortic cross-clamp duration. While cross-clamp time consistently emerged as a determinant of postoperative left ventricular (LV) systolic dysfunction, the persistence of an independent association with fQRS suggests that baseline myocardial susceptibility modulates tolerance to surgical ischemia–reperfusion injury rather than acting as an isolated determinant of postoperative outcome. This substrate–stress interaction aligns with contemporary cardiac surgery literature emphasizing that postoperative ventricular performance is shaped by the combined effects of intrinsic myocardial health and procedural stress, rather than by either factor alone (24–26). In addition to ischemic burden, concomitant surgical factors warrant consideration. In the present cohort, tricuspid annuloplasty was more frequently performed in patients with fQRS, likely reflecting more advanced atrial remodeling, long-standing volume overload, and right-sided involvement rather than a direct effect on LV systolic performance. Given that tricuspid annuloplasty itself is not expected to impair LV systolic function, it should be regarded as a marker of more advanced underlying disease rather than a confounder of the observed association between fQRS and postoperative LV dysfunction. 4.4. Relation to previous studies Evidence linking fQRS to postoperative LV systolic dysfunction in valvular heart disease is limited but increasingly consistent. Prior studies have demonstrated that preoperative fQRS predicts postoperative LV dysfunction in patients undergoing surgery for aortic regurgitation or mitral valve repair (19,20). The present study extends these observations by focusing specifically on mitral valve replacement and by integrating fQRS into a multivariable framework that accounts for baseline LV function and perioperative ischemic burden, thereby providing a clinically applicable approach to postoperative risk stratification. 4.5. Predictive modeling and clinical applicability Beyond identifying associations, the present study provides a clinically applicable predictive framework by integrating fragmented QRS with baseline ventricular function and perioperative surgical factors. The parsimonious model preserved discriminatory performance despite simplification and demonstrated acceptable calibration, supporting its internal robustness. The use of a clinically relevant LVEF threshold and the development of a nomogram further facilitate individualized risk estimation in routine practice. From a practical perspective, identification of fragmented QRS on standard preoperative electrocardiography offers incremental insight into myocardial vulnerability in patients undergoing mitral valve surgery. In particular, fQRS may help identify patients who remain at increased risk for postoperative left ventricular systolic dysfunction despite preserved preoperative ejection fraction, thereby informing perioperative myocardial protection strategies, closer early postoperative echocardiographic surveillance, and more individualized follow-up. Given its limited sensitivity for myocardial fibrosis, fQRS should not be interpreted in isolation but rather integrated within a comprehensive clinical, echocardiographic, and procedural risk assessment. 4.6. Limitations Several limitations should be considered when interpreting these findings. First, the retrospective, single-center design limits causal inference and may affect the generalizability of the results. Second, myocardial fibrosis was not directly quantified using cardiac magnetic resonance imaging or histopathology; instead, fragmented QRS was used as a surrogate marker of myocardial structural disease, which may be associated with misclassification and limited sensitivity for diffuse fibrosis. In addition, the analysis focused on early postoperative left ventricular systolic dysfunction defined by an LVEF threshold and did not capture more subtle changes in myocardial mechanics or long-term left ventricular remodeling trajectories. Although concomitant tricuspid annuloplasty was included as a perioperative variable, residual confounding related to disease severity and right-sided involvement cannot be fully excluded. Finally, external validation of the predictive model in independent cohorts is required before broader clinical application. 5. CONCLUSIONS Preoperative fragmented QRS is independently associated with an increased risk of postoperative left ventricular systolic dysfunction in patients undergoing isolated mitral valve replacement. When considered alongside baseline ventricular function and perioperative surgical stress, fragmented QRS provides incremental prognostic information, highlighting the role of underlying myocardial substrate in postoperative functional recovery. These findings suggest that readily available electrocardiographic markers may complement existing perioperative risk stratification strategies in selected patients undergoing mitral valve surgery. List of Abbreviations AUC: Area under the curve BMI: Body mass index CI: Confidence interval CKD: Chronic kidney disease CMR: Cardiac magnetic resonance ECG: Electrocardiography fQRS: Fragmented QRS HT: Hypertension IQR: Interquartile range LV: Left ventricle / Left ventricular LVEF: Left ventricular ejection fraction OR: Odds ratio ROC: Receiver operating characteristic SV: Stroke volume Declarations Ethics approval and consent to participate This study was approved by the Scientific Research Ethics Committee of Koşuyolu High Specialization Training and Research Hospital (approval number: 2026/03/1367, date: 10 February 2026). The study was conducted in accordance with the principles of the Declaration of Helsinki. Due to the retrospective nature of the study, informed consent was waived by the ethics committee. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request, if requested by the editor or reviewers with appropriate justification. Competing interests The authors declare that they have no competing interests. Funding Not applicable. Authors' contributions İsmail Balaban: Conception and design of the research; data collection; data analysis and interpretation; manuscript drafting. Seda Tanyeri Uzel: Conception and design of the research; data analysis and interpretation; manuscript drafting; critical revision of the manuscript for important intellectual content. Ahmet Karaduman: Conception and design of the research; data collection; statistical analysis. Zeynep Esra Güner: Data collection. Barkın Kültürsay: Data analysis and interpretation; statistical analysis; critical revision of the manuscript for important intellectual content. Cemalettin Yılmaz: Data analysis and interpretation; statistical analysis. Mustafa Ferhat Keten: Manuscript drafting; critical revision of the manuscript for important intellectual content. Kadir Bıyıklı: Data collection; data analysis and interpretation; critical revision of the manuscript for important intellectual content. Acknowledgements Not applicable. References Vahanian A, Beyersdorf F, Praz F, Milojevic M, Baldus S, Bauersachs J, Capodanno D, Conradi L, De Bonis M, De Paulis R, et al. 2021 ESC/EACTS Guidelines for the management of valvular heart disease. Eur Heart J. 2022;43:561–632. doi: 10.1093/eurheartj/ehab395. Jneid H, Chikwe J, Arnold S V., Bonow RO, Bradley SM, Chen EP, Diekemper RL, Fugar S, Johnston DR, Kumbhani DJ, et al. 2024 ACC/AHA Clinical Performance and Quality Measures for Adults With Valvular and Structural Heart Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Performance Measures. Circ Cardiovasc Qual Outcomes. 2024;17. doi: 10.1161/HCQ.0000000000000129. Enriquez-Sarano M, Avierinos J-F, Messika-Zeitoun D, Detaint D, Capps M, Nkomo V, Scott C, Schaff H V., Tajik AJ. Quantitative Determinants of the Outcome of Asymptomatic Mitral Regurgitation. New England Journal of Medicine. 2005;352:875–883. doi: 10.1056/NEJMoa041451. Gaasch WH, Meyer TE. Left Ventricular Response to Mitral Regurgitation. Circulation. 2008;118:2298–2303. doi: 10.1161/CIRCULATIONAHA.107.755942. Carabello BA. The Current Therapy for Mitral Regurgitation. J Am Coll Cardiol. 2008;52:319–326. doi: 10.1016/j.jacc.2008.02.084. Kitkungvan D, Nabi F, Kim RJ, Bonow RO, Khan MA, Xu J, Little SH, Quinones MA, Lawrie GM, Zoghbi WA, et al. Myocardial Fibrosis in Patients With Primary Mitral Regurgitation With and Without Prolapse. J Am Coll Cardiol. 2018;72:823–834. doi: 10.1016/j.jacc.2018.06.048. Badau Riebel CI, Agoston-Coldea L. Left Ventricular Fibrosis by Cardiac Magnetic Resonance Tissue Characterization in Chronic Mitral Regurgitation Patients. J Clin Med. 2024;13:3877. doi: 10.3390/jcm13133877. Altes A, Pécriaux V, Hanvi P, Hanet V, Belhakia I, Selin N, Vancraeynest D, Pasquet A, Delelis F, Toledano M, et al. Association of Preoperative Cardiac Magnetic Resonance and Echocardiography with Postoperative Left Ventricular Dysfunction in Primary Mitral Regurgitation. Journal of the American Society of Echocardiography. 2026;39:28–40. doi: 10.1016/j.echo.2025.09.015. Das MK, Khan B, Jacob S, Kumar A, Mahenthiran J. Significance of a Fragmented QRS Complex Versus a Q Wave in Patients With Coronary Artery Disease. Circulation. 2006;113:2495–2501. doi: 10.1161/CIRCULATIONAHA.105.595892. Das MK, Suradi H, Maskoun W, Michael MA, Shen C, Peng J, Dandamudi G, Mahenthiran J. Fragmented Wide QRS on a 12-Lead ECG. Circ Arrhythm Electrophysiol. 2008;1:258–268. doi: 10.1161/CIRCEP.107.763284. Das MK, Saha C, El Masry H, Peng J, Dandamudi G, Mahenthiran J, McHenry P, Zipes DP. Fragmented QRS on a 12-lead ECG: A predictor of mortality and cardiac events in patients with coronary artery disease. Heart Rhythm. 2007;4:1385–1392. doi: 10.1016/j.hrthm.2007.06.024. Pietrasik G, Zaręba W. QRS fragmentation: Diagnostic and prognostic significance. Cardiol J. 2012;19:114–121. doi: 10.5603/CJ.2012.0022. Azevedo CF, Nigri M, Higuchi ML, Pomerantzeff PM, Spina GS, Sampaio RO, Tarasoutchi F, Grinberg M, Rochitte CE. Prognostic Significance of Myocardial Fibrosis Quantification by Histopathology and Magnetic Resonance Imaging in Patients With Severe Aortic Valve Disease. J Am Coll Cardiol. 2010;56:278–287. doi: 10.1016/j.jacc.2009.12.074. Park S-J, On YK, Kim JS, Park SW, Yang J-H, Jun T-G, Kang I-S, Lee HJ, Choe YH, Huh J. Relation of Fragmented QRS Complex to Right Ventricular Fibrosis Detected by Late Gadolinium Enhancement Cardiac Magnetic Resonance in Adults With Repaired Tetralogy of Fallot. Am J Cardiol. 2012;109:110–115. doi: 10.1016/j.amjcard.2011.07.070. Viriyanukulvong K, Theerasuwipakorn N, Wongcharoen W, Kosum P, Chokesuwattanaskul R. Diagnostic performance of the fragmented QRS complex on electrocardiogram for detecting myocardial scars assessed by 3.0 Tesla cardiac magnetic resonance imaging. Heart Rhythm O2. 2025;6:1175–1181. doi: 10.1016/j.hroo.2025.05.014. Das MK, Zipes DP. Fragmented QRS: A predictor of mortality and sudden cardiac death. Heart Rhythm. 2009;6:S8–S14. doi: 10.1016/j.hrthm.2008.10.019. Rosengarten JA, Scott PA, Morgan JM. Fragmented QRS for the prediction of sudden cardiac death: a meta-analysis. Europace. 2015;17:969–977. doi: 10.1093/europace/euu279. Eyuboglu M. Fragmented QRS as a Marker of Myocardial Fibrosis in Hypertension: a Systematic Review. Curr Hypertens Rep. 2019;21:73. doi: 10.1007/s11906-019-0982-3. Yılmaz FK, Cakal B, Yılmaz F, Yazar A, Savur U, Akhundova A, Gunes HM, Guler E, Dursun A, Yousufzai N, et al. Relationship between fragmented QRS complex and early left ventricular dysfunction after mitral valve repair. J Electrocardiol. 2024;84:65–69. doi: 10.1016/j.jelectrocard.2024.03.001. Celik M, Yilmaz Y, Karagöz A, Kahyaoglu M, Kup A, Celik FB, Izci S, Candan O, Gecmen C, Kirma C, et al. Presence of fragmented QRS is associated with left ventricular systolic dysfunction after surgery in patients with severe aortic regurgitation. J Card Surg. 2021;36:1289–1297. doi: 10.1111/jocs.15370. Authors N. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. KIDNEYS. 2024;13:140–171. doi: 10.22141/2307-1257.13.2.2024.456. Lang RM, Badano LP, Mor-Avi V, Afilalo J, Armstrong A, Ernande L, Flachskampf FA, Foster E, Goldstein SA, Kuznetsova T, et al. Recommendations for Cardiac Chamber Quantification by Echocardiography in Adults: An Update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Journal of the American Society of Echocardiography. 2015;28:1-39.e14. doi: 10.1016/j.echo.2014.10.003. Lancellotti P, Pibarot P, Chambers J, Edvardsen T, Delgado V, Dulgheru R, Pepi M, Cosyns B, Dweck MR, Garbi M, et al. Recommendations for the imaging assessment of prosthetic heart valves: a report from the European Association of Cardiovascular Imaging endorsed by the Chinese Society of Echocardiography, the Inter-American Society of Echocardiography, and the Brazilian Department of Cardiovascular Imaging † . Eur Heart J Cardiovasc Imaging. 2016;17:589–590. doi: 10.1093/ehjci/jew025. Yücel M, Benli ED, Erdoğan KE, Sağlam MF, Deniz G, Çomaklı H, Uğuz E. Evaluation of Myocardial Protection in Prolonged Aortic Cross-Clamp Times: Del Nido and HTK Cardioplegia in Adult Cardiac Surgery. Medicina (B Aires). 2025;61:1420. doi: 10.3390/medicina61081420. Berretta P, Kempfert J, Van Praet F, Salvador L, Lamelas J, Nguyen TC, Wilbring M, Gerdisch M, Rinaldi M, Bonaros N, et al. Risk-related clinical outcomes after minimally invasive mitral valve surgery: insights from the Mini-Mitral International Registry. European Journal of Cardio-Thoracic Surgery. 2023;63. doi: 10.1093/ejcts/ezad090. García-de-la-Asunción J, Pastor E, Perez-Griera J, Belda FJ, Moreno T, García-del-Olmo E, Martí F. Oxidative stress injury after on-pump cardiac surgery: Effects of aortic cross clamp time and type of surgery. Redox Report. 2013;18:193–199. doi: 10.1179/1351000213Y.0000000060. Additional Declarations No competing interests reported. Supplementary Files Graphicalabstract.jpg Graphical Abstract: Mechanistic framework linking preoperative fragmented QRS to postoperative LV systolic dysfunction after isolated mitral valve replacement. Preoperative fQRS reflects myocardial vulnerability despite preserved LVEF. Perioperative ischemic stress, particularly prolonged aortic cross-clamp time, may impair recovery, resulting in LV systolic dysfunction (LVEF <50%) and atrial fibrillation. Integration of fQRS with baseline LVEF, cross-clamp time, and chronic kidney disease enables individualized perioperative risk estimation. 4.jpg Supplementary Figure S1. Nomogram for individualized prediction of postoperative LV systolic dysfunction. Nomogram derived from the final stepwise logistic regression model to estimate individual risk of postoperative LV systolic dysfunction (postop LVEF <50%) based on cross-clamp time, preoperative LVEF, sex, fQRS, hypertension, and chronic kidney disease. Total points correspond to predicted probability of the outcome. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 04 May, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviews received at journal 11 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviews received at journal 27 Mar, 2026 Reviewers agreed at journal 27 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 16 Mar, 2026 Editor invited by journal 03 Mar, 2026 Editor assigned by journal 03 Mar, 2026 Submission checks completed at journal 03 Mar, 2026 First submitted to journal 27 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-8987964","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607535520,"identity":"5d0896c3-26c2-4a25-acee-4dc6213e85d1","order_by":0,"name":"Ismail Balaban","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYBACNhCRUHCAgY+BB8RklgORBx4Q1GJwAMiAaDEGa0kgaBeSlsQGsCF4FPPxn0788MDgjjwb+9mDnwtzrNPnhx1+CLTFTk63AYfDJHI3SyQYPDNs48lLlp65LT134+00A6CWZGOzA7i08G4AajnM2MaQYyDNu+1w7sbZCSAtBxK34dLCf3bzD6AW+zb+N8a/gVrSDWenf8CvhSF3G8iWxDaJHDOQLQny0jkEbJHI3WYB9Etym8S7NGvebemGG6RzCg4kGOD2i3z/2c03f1Tcse3nzz18m3ebtbz87PTNHz5U2Mnh0oIJDMAqDYhVDra3gRTVo2AUjIJRMBIAAL6uYUceuKuEAAAAAElFTkSuQmCC","orcid":"","institution":"Kosuyolu Yuksek Ihtisas Egitim ve Arastirma Hastanesi","correspondingAuthor":true,"prefix":"","firstName":"Ismail","middleName":"","lastName":"Balaban","suffix":""},{"id":607535521,"identity":"4d99f41f-528f-4d65-be15-31e46266e41f","order_by":1,"name":"Seda Tanyeri Uzel","email":"","orcid":"","institution":"Kosuyolu Yuksek Ihtisas Egitim ve Arastirma Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Seda","middleName":"Tanyeri","lastName":"Uzel","suffix":""},{"id":607535522,"identity":"2410ca64-59f9-4aea-9028-9ac8f5f06df4","order_by":2,"name":"Ahmet Karaduman","email":"","orcid":"","institution":"Emsey Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ahmet","middleName":"","lastName":"Karaduman","suffix":""},{"id":607535523,"identity":"c07a9ec4-efdf-47cf-abe1-a6e75d4fbc09","order_by":3,"name":"Zeynep Esra Güner","email":"","orcid":"","institution":"Uzunköprü State Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zeynep","middleName":"Esra","lastName":"Güner","suffix":""},{"id":607535524,"identity":"d2eb58d6-2647-4a8a-b123-190442e07c92","order_by":4,"name":"Barkın Kültürsay","email":"","orcid":"","institution":"Tunceli State Hospital","correspondingAuthor":false,"prefix":"","firstName":"Barkın","middleName":"","lastName":"Kültürsay","suffix":""},{"id":607535525,"identity":"609a2322-efd6-4802-b8f5-e56f8ebf0526","order_by":5,"name":"Cemalettin Yılmaz","email":"","orcid":"","institution":"Yalova University","correspondingAuthor":false,"prefix":"","firstName":"Cemalettin","middleName":"","lastName":"Yılmaz","suffix":""},{"id":607535526,"identity":"da9cd0ab-0686-4ba7-a86e-fb4792773604","order_by":6,"name":"Mustafa Ferhat Keten","email":"","orcid":"","institution":"Kosuyolu Yuksek Ihtisas Egitim ve Arastirma Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Mustafa","middleName":"Ferhat","lastName":"Keten","suffix":""},{"id":607535527,"identity":"db37615e-101d-42c8-8c3e-64ced68c4055","order_by":7,"name":"Kadir Bıyıklı","email":"","orcid":"","institution":"Kosuyolu Yuksek Ihtisas Egitim ve Arastirma Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Kadir","middleName":"","lastName":"Bıyıklı","suffix":""}],"badges":[],"createdAt":"2026-02-27 12:23:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8987964/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8987964/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104888563,"identity":"23054b79-480d-4917-a452-b93eb791d8ce","added_by":"auto","created_at":"2026-03-18 10:17:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89942,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of regression coefficients from the final stepwise multivariable logistic regression model predicting postoperative left ventricular systolic dysfunction (LVEF \u0026lt;50%).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eForest plot illustrating regression coefficients (β) and 95% confidence intervals derived from the final backward stepwise multivariable logistic regression model predicting postoperative left ventricular systolic dysfunction (LVEF \u0026lt;50%). Positive coefficients indicate an increased likelihood, whereas negative coefficients indicate a reduced likelihood of the outcome. Color intensity reflects the magnitude and direction of the regression coefficients.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8987964/v1/ae5b8f71689f957944c62612.jpg"},{"id":105033931,"identity":"bbd49aec-5f88-4f42-a15d-61d83bfeaac3","added_by":"auto","created_at":"2026-03-20 07:22:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68814,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves of the full and stepwise models for postoperative LV systolic dysfunction.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) curves comparing the full multivariable model and the parsimonious stepwise model for prediction of postoperative left ventricular systolic dysfunction (LVEF \u0026lt;50%). The area under the curve (AUC) was 0.79 for the full model and 0.78 for the stepwise model.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8987964/v1/ecdf0a4e532be00cee2cf73f.jpg"},{"id":105034363,"identity":"74974b32-ddce-403e-8e19-d9e519d253dd","added_by":"auto","created_at":"2026-03-20 07:23:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60851,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration of the stepwise prediction model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCalibration plot showing the agreement between predicted and observed probabilities of postoperative LV systolic dysfunction (postop LVEF \u0026lt;50%) for the stepwise model. The dashed diagonal line indicates perfect calibration, while the plotted points and connecting line represent observed event rates across increasing risk strata.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8987964/v1/c8c32e140f59867c32a931c0.jpg"},{"id":105036570,"identity":"a57ae9cd-847f-4fa2-9452-371d36455e08","added_by":"auto","created_at":"2026-03-20 07:34:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1863450,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8987964/v1/69d5c845-9601-4732-936c-b6023a47f08d.pdf"},{"id":105034048,"identity":"5db0aebb-f061-4cee-841c-1879f3c527d0","added_by":"auto","created_at":"2026-03-20 07:22:31","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":174223,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract: Mechanistic framework linking preoperative fragmented QRS to postoperative LV systolic dysfunction after isolated mitral valve replacement.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreoperative fQRS reflects myocardial vulnerability despite preserved LVEF. Perioperative ischemic stress, particularly prolonged aortic cross-clamp time, may impair recovery, resulting in LV systolic dysfunction (LVEF \u0026lt;50%) and atrial fibrillation. Integration of fQRS with baseline LVEF, cross-clamp time, and chronic kidney disease enables individualized perioperative risk estimation.\u003c/p\u003e","description":"","filename":"Graphicalabstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8987964/v1/daa1d93a39bed53aacc54d91.jpg"},{"id":105034097,"identity":"5f3fcd80-05e9-4c38-8de4-d82f33234f43","added_by":"auto","created_at":"2026-03-20 07:22:39","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":71735,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S1. Nomogram for individualized prediction of postoperative LV systolic dysfunction.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNomogram derived from the final stepwise logistic regression model to estimate individual risk of postoperative LV systolic dysfunction (postop LVEF \u0026lt;50%) based on cross-clamp time, preoperative LVEF, sex, fQRS, hypertension, and chronic kidney disease. Total points correspond to predicted probability of the outcome.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8987964/v1/47abbbc9699683822a78d349.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preoperative Fragmented QRS as a Predictor of Postoperative Left Ventricular Systolic Dysfunction After Isolated Mitral Valve Replacement","fulltext":[{"header":"1. BACKGROUND","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePreservation of left ventricular (LV) systolic function after surgical treatment of mitral valve disease remains a key determinant of both short- and long-term clinical outcomes. Despite substantial advances in surgical techniques, myocardial protection strategies, and perioperative care, a considerable proportion of patients with preoperative left ventricular ejection fraction (LVEF) within guideline-recommended thresholds develop early or late postoperative LV systolic dysfunction after mitral valve surgery (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). This apparent dissociation between preserved preoperative systolic function and postoperative ventricular performance suggests that conventional functional parameters may not fully capture the underlying myocardial structural substrate governing postoperative recovery.\u003c/p\u003e\u003cp\u003eIn chronic mitral valve disease, sustained volume overload drives progressive myocardial remodeling characterized by cardiomyocyte hypertrophy, extracellular matrix expansion, and the development of interstitial and replacement fibrosis. Although surgical correction restores valvular competence and alters loading conditions, established myocardial fibrosis may persist and limit reverse remodeling, predisposing patients to postoperative LV dysfunction despite preserved preoperative systolic function (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Consistent with this concept, cardiac magnetic resonance studies using late gadolinium enhancement and T1 mapping have demonstrated that myocardial fibrosis burden is closely associated with postoperative LV functional trajectory and long-term outcomes following valvular surgery (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHowever, the routine use of CMR is limited by availability, cost, and practical constraints, prompting growing interest in simple and widely accessible surrogate markers of myocardial structural disease. Fragmented QRS (fQRS) on surface electrocardiography reflects heterogeneous ventricular conduction and has been consistently associated with myocardial scar and fibrosis across a broad spectrum of ischemic and non-ischemic heart diseases (\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHistopathological and CMR validation studies have shown that fQRS is associated with myocardial fibrosis with high specificity but limited sensitivity (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Accordingly, fQRS is best regarded not as a tool to exclude myocardial fibrosis, but rather as a practical electrocardiographic marker indicating the presence of an adverse myocardial substrate.\u003c/p\u003e \u003cp\u003eBeyond its diagnostic implications, fQRS has demonstrated prognostic relevance in various clinical settings, including coronary artery disease, hypertensive heart disease, and non-ischemic cardiomyopathies, where its presence has been associated with increased risks of arrhythmias, heart failure progression, and mortality (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In the context of mitral valve surgery, emerging evidence suggests that preoperative fQRS may identify patients at increased risk of postoperative LV systolic dysfunction, even in the presence of preserved preoperative LVEF (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNevertheless, most available studies have been conducted in relatively small, valve-specific populations and exhibit heterogeneity in outcome definitions. Moreover, the interaction between pre-existing myocardial structural vulnerability, as reflected by fQRS, and perioperative surgical stress has not been adequately explored in integrative, clinically applicable predictive models. Although current guidelines emphasize timely intervention and multimodality imaging in the management of valvular heart disease, electrocardiographic markers of myocardial fibrosis have not yet been systematically incorporated into perioperative risk stratification algorithms (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTherefore, the present study aimed to evaluate the prognostic significance of preoperative fQRS for predicting postoperative LV systolic dysfunction in patients undergoing isolated mitral valve replacement (MVR) and to develop a clinically applicable predictive model incorporating electrocardiographic, echocardiographic, and surgical variables. The conceptual and predictive framework underlying this study is graphically summarized in the \u003cb\u003eGraphical Abstract\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv description=\"\" class=\"Drawing\" id=\"1697902134\" name=\"Resim 5\"\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design\u003c/h2\u003e \u003cp\u003eThis study was conducted as a retrospective analysis of prospectively collected clinical, electrocardiographic, and echocardiographic data from patients undergoing elective isolated mitral valve replacement. The primary objective was to assess the prognostic value of preoperative fragmented QRS (fQRS) in predicting postoperative left ventricular (LV) systolic dysfunction and to develop a clinically applicable predictive model.\u003c/p\u003e \u003cp\u003e Given the retrospective nature of the analysis, written informed consent was obtained or waived in accordance with institutional ethics committee regulations. The study protocol complied with the principles outlined in the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Study Population\u003c/h2\u003e \u003cp\u003eConsecutive patients who underwent elective isolated mitral valve replacement with either mechanical or bioprosthetic prostheses between 2022 and 2025 were screened for eligibility. Inclusion criteria were as follows: (i) availability of a standard 12-lead surface electrocardiogram (ECG) obtained in the preoperative period; (ii) assessment of LV systolic function by transthoracic echocardiography both before surgery and during the early postoperative period; (iii) preserved preoperative left ventricular ejection fraction (LVEF\u0026thinsp;\u0026ge;\u0026thinsp;50%).\u003c/p\u003e \u003cp\u003ePatients who underwent mitral valve repair, concomitant aortic or other left-sided valvular surgery, prior cardiac surgery, or non-elective procedures were excluded; concomitant tricuspid annuloplasty was not considered an exclusion criterion. Additional exclusion criteria included bundle branch block, permanent pacemaker or implantable cardioverter-defibrillator rhythm, congenital intraventricular conduction abnormalities, significant ECG artifacts, inadequate echocardiographic image quality, or missing essential clinical data. These criteria were applied to ensure reliable assessment of fQRS and accurate evaluation of LV systolic function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Clinical and Perioperative Variables\u003c/h2\u003e \u003cp\u003eDemographic characteristics, cardiovascular risk factors, and comorbid conditions (including hypertension, diabetes mellitus, chronic kidney disease, and history of atrial fibrillation) were recorded using standardized data collection forms. Chronic kidney disease was defined in accordance with internationally accepted guidelines (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Surgical variables included mitral valve prosthesis type (mechanical or bioprosthetic), the presence of concomitant tricuspid annuloplasty, and aortic cross-clamp duration. Cross-clamp time was incorporated into the analyses as a surrogate marker of perioperative myocardial ischemic burden.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Electrocardiographic Assessment\u003c/h2\u003e \u003cp\u003ePreoperative ECGs were obtained using standard settings (paper speed 25 mm/s, calibration 10 mm/mV) and analyzed from 12-lead surface recordings. Fragmented QRS was defined as the presence of notching, multiple R waves, or additional R\u0026prime; deflections within the QRS complex in the absence of bundle branch block, observed in at least two contiguous leads (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). All ECGs were independently evaluated by two experienced investigators who were blinded to clinical, surgical, and echocardiographic data. In cases of disagreement, ECGs were re-reviewed jointly and a consensus decision was reached.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Echocardiographic Assessment\u003c/h2\u003e \u003cp\u003eTransthoracic echocardiography was performed in the preoperative period and during early postoperative follow-up in accordance with current echocardiographic guidelines (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Left ventricular ejection fraction was primarily calculated using the biplane Simpson method. Left atrial diameter, left ventricular end-diastolic and end-systolic dimensions, and other standard echocardiographic parameters were recorded concurrently. Postoperative echocardiographic assessment was performed within the first 7 days after surgery. All echocardiographic measurements were analyzed independently of electrocardiographic findings and clinical data, and assessments were conducted under blinded conditions to minimize measurement bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Outcome Definition\u003c/h2\u003e \u003cp\u003eThe primary endpoint of the study was the development of postoperative LV systolic dysfunction. Postoperative LV systolic dysfunction was defined as a reduction in LVEF to \u0026lt;\u0026thinsp;50% at postoperative follow-up. This threshold was selected in accordance with commonly used criteria in the literature for defining clinically relevant LV systolic impairment after mitral valve surgery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe distribution of continuous variables was assessed using visual (histograms and Q\u0026ndash;Q plots) and analytical methods. Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation if normally distributed, or as median with interquartile range (IQR) if non-normally distributed. Categorical variables are expressed as counts and percentages.\u003c/p\u003e \u003cp\u003eBetween-group comparisons were performed using the independent samples t test or the Mann\u0026ndash;Whitney U test for continuous variables, as appropriate, and the Pearson chi-square test or Fisher\u0026rsquo;s exact test for categorical variables.\u003c/p\u003e \u003cp\u003eTo identify potential determinants of postoperative LV systolic dysfunction, univariable analyses were initially performed. Variables considered clinically relevant and/or those demonstrating statistical significance in univariable analyses (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10) were entered into multivariable logistic regression models. A backward stepwise variable selection approach was applied to reduce model complexity and minimize the risk of overfitting, while preserving clinical interpretability. Odds ratios (ORs) with 95% confidence intervals (CIs) were reported.\u003c/p\u003e \u003cp\u003eModel discrimination was assessed using receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) was calculated. The discriminative performance of the full model and the more parsimonious stepwise model was compared. Model calibration was evaluated using calibration plots comparing observed and predicted event probabilities across risk strata.\u003c/p\u003e \u003cp\u003eBased on the final multivariable model, a nomogram was constructed to enable individualized risk estimation of postoperative LV systolic dysfunction at the patient level. All statistical analyses were performed using appropriate statistical software (R Foundation for Statistical Computing, Vienna, Austria), and a two-sided p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Study population and baseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 279 patients who underwent elective mitral valve replacement, with or without concomitant tricuspid annuloplasty, were included in the study. Fragmented QRS (fQRS) on preoperative electrocardiography was identified in 71 patients (25.4%), whereas 208 patients (74.6%) had no evidence of fQRS.\u003c/p\u003e \u003cp\u003eBaseline demographic, clinical, laboratory, echocardiographic, and operative characteristics according to fQRS status are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients with fQRS were significantly older than those without fQRS (p\u0026thinsp;=\u0026thinsp;0.019) and exhibited a markedly higher prevalence of preoperative atrial fibrillation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A history of smoking was also more frequent in the fQRS-positive group (p\u0026thinsp;=\u0026thinsp;0.040). Chronic kidney disease tended to be more common among patients with fQRS; however, this difference did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.083).\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 and Operative Characteristics According to fQRS Status\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll (n\u0026thinsp;=\u0026thinsp;279)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003efQRS negative (n\u0026thinsp;=\u0026thinsp;208)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003efQRS positive (n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDemographic \u0026amp; Clinical\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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\u003e57.4 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.3 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.4 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, (male) n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.2 (5.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.2 (5.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.1 (4.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHT, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112 (40.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHL, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreop AF, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (33.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory findings\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHgb, (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.7 (1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.7 (1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.7 (1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.35 (6.14\u0026ndash;8.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.27 (6.18\u0026ndash;8.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.51 (6.04\u0026ndash;9.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte, (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59 (0.47\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57 (0.46\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67 (0.54\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84 (0.73\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83 (0.73\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89 (0.74\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.89 (1.81\u0026ndash;9.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.92 (1.81\u0026ndash;9.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.74 (1.84\u0026ndash;12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.3 (5.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.1 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.7 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid, (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.03 (1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.4 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePreoperative echocardiography\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreop EF, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.5 (2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.5 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.4 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA diameter, (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.84 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.75 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.10 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDD, (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.19 (0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.15 (0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.30 (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVESD, (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.40 (0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.37 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.48 (0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEtiology (Rheumatic / Degenerative / Endocarditis), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 / 160 / 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 / 120 / 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 / 40 / 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOperative characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBioprosthesis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical valve, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228 (81.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176 (84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (73.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTricuspid ring, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaze, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCross-clamp time, (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189 (144\u0026ndash;224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185 (147\u0026ndash;224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e193 (140\u0026ndash;235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBypass time, (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224 (179\u0026ndash;259)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220 (182\u0026ndash;259)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e228 (175\u0026ndash;270)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.607\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\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e AF, atrial fibrillation; BMI, body mass index; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein; DM, diabetes mellitus; EF, ejection fraction; fQRS, fragmented QRS; Hgb, hemoglobin; HL, hyperlipidemia; HT, hypertension; LA, left atrium; LVEDD, left ventricular end-diastolic diameter; LVESD, left ventricular end-systolic diameter; PAD, peripheral arterial disease; WBC, white blood cell count.\u003c/p\u003e\n\u003cp\u003ePreoperative left ventricular ejection fraction (LVEF) was similar between groups (p = 0.725). Likewise, no significant differences were observed in left ventricular dimensions, major laboratory parameters, or cardiopulmonary bypass and aortic cross-clamp durations (\u003cstrong\u003eTable 1\u003c/strong\u003e). Concomitant tricuspid annuloplasty was more frequently performed in patients with fQRS compared with those without fQRS (40.8% vs. 25.5%, p = 0.014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. \u0026nbsp; \u0026nbsp; \u0026nbsp; Postoperative left ventricular systolic function\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite comparable preoperative LVEF values, postoperative echocardiographic findings differed significantly according to fQRS status (\u003cstrong\u003eTable 2\u003c/strong\u003e). Patients with fQRS exhibited a significantly lower postoperative LVEF compared with those without fQRS (p = 0.036). Accordingly, the incidence of postoperative left ventricular systolic dysfunction\u0026mdash;defined as an LVEF \u0026lt;50%\u0026mdash;was significantly higher in the fQRS-positive group than in the fQRS-negative group (45.1% vs. 28.8%; p = 0.012).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Postoperative Echocardiographic and Clinical Outcomes According to fQRS Status\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll (n=279)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003efQRS negative (n=208)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003efQRS positive (n=71)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePostoperative echocardiography\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePostop EF, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.8 (9.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56.5 (9.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.7 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePostop EF \u0026lt;50, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e92 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60 (28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026Delta; LVEF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (0\u0026ndash;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (0\u0026ndash;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (0\u0026ndash;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePostop LA, (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.67 (0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.60 (0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.87 (0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePostop LVEDD, (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.92 (0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.89 (0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.01 (0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePostop LVESD, (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.27 (0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.24 (0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.34 (0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePostoperative rhythm \u0026amp; outcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePostop AF, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e124 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84 (40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40 (56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePostop complication, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePostop stroke, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePostop dialysis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePostop pacemaker, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePostop tamponade, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePostop revision, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIn-hospital mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30-day mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eAF, atrial fibrillation; EF, ejection fraction; fQRS, fragmented QRS; LA, left atrium; LVEDD, left ventricular end-diastolic diameter; LVESD, left ventricular end-systolic diameter; LVEF, left ventricular ejection fraction.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;In addition, the change in LVEF from the preoperative to postoperative assessment (\u0026Delta;LVEF) was significantly more pronounced in patients with fQRS, indicating a greater postoperative deterioration in LV systolic function (p = 0.038).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. \u0026nbsp; \u0026nbsp; \u0026nbsp; Postoperative rhythm disturbances and early clinical outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Postoperative atrial fibrillation occurred significantly more frequently in patients with fQRS than in those without fQRS (56.3% vs. 40.4%; p = 0.020). In contrast, the incidence of other early postoperative complications\u0026mdash;including stroke, need for dialysis, permanent pacemaker implantation, cardiac tamponade, surgical revision, in-hospital mortality, and 30-day mortality\u0026mdash;did not differ significantly between the two groups (all p \u0026gt; 0.05) (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. \u0026nbsp; \u0026nbsp; \u0026nbsp; Multivariable logistic regression analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;To identify independent determinants of postoperative left ventricular systolic dysfunction (LVEF \u0026lt; 50%), a two-step multivariable logistic regression approach was applied.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;In the first step, a full model including age, aortic cross-clamp time, preoperative LVEF, body mass index, sex, presence of fragmented QRS (fQRS), hypertension, diabetes mellitus, and chronic kidney disease was constructed (\u003cstrong\u003eTable 3\u003c/strong\u003e). In this model, aortic cross-clamp time and preoperative LVEF were independently associated with postoperative left ventricular systolic dysfunction. Each 1-minute increase in aortic cross-clamp time was associated with a significantly higher likelihood of postoperative LVEF \u0026lt; 50% (OR 1.014, 95% CI 1.010\u0026ndash;1.020; p \u0026lt; 0.001), whereas higher preoperative LVEF exerted a protective effect (OR 0.868, 95% CI 0.782\u0026ndash;0.962; p = 0.007). The presence of fQRS, hypertension, and chronic kidney disease demonstrated borderline statistical significance in the full model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Results of the full multivariable logistic regression model for postoperative left ventricular systolic dysfunction (LVEF \u0026lt;50%).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDependent variable:\u003c/strong\u003e Postoperative LVEF \u0026lt;50%\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; (Estimate)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAortic cross-clamp time (min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.010\u0026ndash;1.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.992\u0026ndash;1.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePreoperative LVEF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.782\u0026ndash;0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026minus;0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBody mass index (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.932\u0026ndash;1.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026minus;0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.90\u0026ndash;2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFragmented QRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.99\u0026ndash;3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.28\u0026ndash;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026minus;0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.47\u0026ndash;2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.96\u0026ndash;7.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable footnote:\u0026nbsp;\u003c/strong\u003eOdds ratios (ORs) were calculated as the exponentiated logistic regression coefficients [exp(\u0026beta;)]. The 95% confidence intervals were obtained using the profile likelihood method. For continuous variables, ORs represent the change in odds per 1-unit increase in the respective variable. A p value \u0026lt;0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;In the second step, backward stepwise variable selection was applied to enhance clinical applicability and derive a parsimonious model (\u003cstrong\u003eTable 4\u003c/strong\u003e). In the final backward stepwise model, aortic cross-clamp time, preoperative LVEF, presence of fQRS, chronic kidney disease, sex, and hypertension were retained as predictors of postoperative left ventricular systolic dysfunction. Sex and hypertension demonstrated borderline statistical significance but were retained to preserve clinical interpretability and model stability. Among these variables, the presence of fQRS was associated with an approximately twofold increase in the risk of postoperative LVEF \u0026lt; 50% (OR 2.00, 95% CI 1.06\u0026ndash;3.81; p = 0.033), while chronic kidney disease emerged as one of the strongest predictors (OR 3.06, 95% CI 1.11\u0026ndash;8.56; p = 0.030).\u003c/p\u003e\n\u003cp\u003eRegression coefficients and 95% confidence intervals from the stepwise model are illustrated in \u003cstrong\u003eFigure 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Backward Stepwise Multivariable Logistic Regression Analysis for Postoperative Left Ventricular Systolic Dysfunction (LVEF \u0026lt;50%)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDependent variable:\u0026nbsp;\u003c/strong\u003ePostoperative LVEF \u0026lt;50%\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAortic cross-clamp time (min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.010\u0026ndash;1.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePreoperative LVEF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.776\u0026ndash;0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.91\u0026ndash;2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFragmented QRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.06\u0026ndash;3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.31\u0026ndash;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.11\u0026ndash;8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable footnote:\u003c/strong\u003e Odds ratios (ORs) were calculated as the exponentiated logistic regression coefficients [exp(\u0026beta;)]. The 95% confidence intervals were obtained using the profile likelihood method. For continuous variables, ORs represent the change in odds per 1-unit increase in the respective variable. A p value \u0026lt;0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. \u0026nbsp; \u0026nbsp; \u0026nbsp; Model performance: discrimination and calibration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModel performance was assessed in terms of both discrimination and calibration. The discriminatory ability of the full multivariable logistic regression model, evaluated using receiver operating characteristic (ROC) curve analysis, yielded an area under the curve (AUC) of 0.79. In the simplified stepwise model, the AUC was 0.78, indicating that discrimination was largely preserved despite model simplification and supporting the clinical applicability of the parsimonious model (\u003cstrong\u003eFigure 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eModel calibration was evaluated by comparing predicted probabilities with observed event rates using calibration plots. The calibration curve of the stepwise model closely followed the reference 45-degree line, particularly across low- and intermediate-risk ranges. The absence of a systematic deviation at higher predicted risk levels further supports the reliability of the simplified model for absolute risk estimation (\u003cstrong\u003eFigure 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6. \u0026nbsp; \u0026nbsp; \u0026nbsp; Nomogram development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the final stepwise logistic regression model, a nomogram was developed to facilitate individualized prediction of postoperative left ventricular systolic dysfunction (LVEF \u0026lt; 50%) (\u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e). In the nomogram, each variable is assigned a score proportional to its corresponding regression coefficient, and the sum of these scores is used to estimate an individual patient\u0026rsquo;s risk.\u003c/p\u003e\n\u003cp\u003eAortic cross-clamp time, preoperative LVEF, presence of fragmented QRS, and chronic kidney disease constituted the major contributors to the total risk score. This visual tool provides an integrated representation of the relative impact of independent predictors identified in the multivariable model and may support potential clinical risk stratification.\u003c/p\u003e"},{"header":"4.\tDISCUSSION ","content":"\u003cp\u003eIn this study, preoperative fragmented QRS (fQRS) was independently associated with an increased risk of postoperative left ventricular (LV) systolic dysfunction in patients undergoing mitral valve replacement, with or without concomitant tricuspid annuloplasty. This association persisted after adjustment for baseline LV systolic function and perioperative surgical stress and remained robust in a simplified multivariable model. Collectively, these findings suggest that postoperative LV functional recovery following mitral valve surgery is influenced not only by the hemodynamic effects of valve correction but also by pre-existing myocardial structural and electrical characteristics.\u003c/p\u003e\n\u003cp\u003eClinically, it is well recognized that a subset of patients develop postoperative deterioration or incomplete recovery of LV systolic function despite preserved preoperative ejection fraction and technically successful surgery. The present findings are consistent with this observation and support the concept that conventional functional indices alone may not fully capture latent myocardial vulnerability that becomes clinically relevant after surgical intervention (1,3–5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Myocardial substrate and postoperative LV functional recovery\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChronic mitral valve disease is associated with sustained volume overload, leading to progressive myocardial remodeling characterized by cardiomyocyte hypertrophy, extracellular matrix expansion, and the development of interstitial and replacement fibrosis. Although mitral valve replacement restores valvular competence and abruptly alters loading conditions, established myocardial fibrosis may persist and limit reverse remodeling. Consequently, postoperative LV systolic dysfunction may occur even in patients with preserved preoperative ejection fraction, underscoring the importance of the underlying myocardial substrate in shaping postoperative functional trajectories (4,5).\u003c/p\u003e\n\u003cp\u003eCardiac magnetic resonance (CMR) studies using late gadolinium enhancement and quantitative tissue characterization techniques have consistently demonstrated that myocardial fibrosis burden is closely associated with impaired postoperative LV recovery and adverse outcomes after mitral valve surgery (6–8). However, despite its high diagnostic accuracy, routine use of CMR in the perioperative evaluation of valvular heart disease remains constrained by availability, cost, and logistical considerations, limiting its widespread application in everyday clinical practice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Fragmented QRS as a surrogate marker of myocardial vulnerability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFragmented QRS (fQRS) on surface electrocardiography reflects heterogeneous ventricular conduction resulting from discontinuous myocardial activation. Experimental, histopathological, and cardiac magnetic resonance–based studies have demonstrated a close association between fQRS and the presence of myocardial scar and fibrosis across a wide spectrum of ischemic and non-ischemic heart diseases (9–12,14,15). Importantly, fragmented QRS should not be interpreted as a direct surrogate for imaging-based quantification of myocardial fibrosis. Instead, it represents a pragmatic and widely available electrocardiographic phenotype identifying patients with an adverse myocardial substrate characterized by electrical heterogeneity and reduced myocardial reserve (12,15).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Interaction with perioperative stress and surgical factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA key observation of this study is the interaction between pre-existing myocardial vulnerability, as reflected by fragmented QRS (fQRS), and perioperative ischemic burden, represented by aortic cross-clamp duration. While cross-clamp time consistently emerged as a determinant of postoperative left ventricular (LV) systolic dysfunction, the persistence of an independent association with fQRS suggests that baseline myocardial susceptibility modulates tolerance to surgical ischemia–reperfusion injury rather than acting as an isolated determinant of postoperative outcome. This substrate–stress interaction aligns with contemporary cardiac surgery literature emphasizing that postoperative ventricular performance is shaped by the combined effects of intrinsic myocardial health and procedural stress, rather than by either factor alone (24–26).\u003c/p\u003e\n\u003cp\u003eIn addition to ischemic burden, concomitant surgical factors warrant consideration. In the present cohort, tricuspid annuloplasty was more frequently performed in patients with fQRS, likely reflecting more advanced atrial remodeling, long-standing volume overload, and right-sided involvement rather than a direct effect on LV systolic performance. Given that tricuspid annuloplasty itself is not expected to impair LV systolic function, it should be regarded as a marker of more advanced underlying disease rather than a confounder of the observed association between fQRS and postoperative LV dysfunction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Relation to previous studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEvidence linking fQRS to postoperative LV systolic dysfunction in valvular heart disease is limited but increasingly consistent. Prior studies have demonstrated that preoperative fQRS predicts postoperative LV dysfunction in patients undergoing surgery for aortic regurgitation or mitral valve repair (19,20). The present study extends these observations by focusing specifically on mitral valve replacement and by integrating fQRS into a multivariable framework that accounts for baseline LV function and perioperative ischemic burden, thereby providing a clinically applicable approach to postoperative risk stratification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Predictive modeling and clinical applicability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBeyond identifying associations, the present study provides a clinically applicable predictive framework by integrating fragmented QRS with baseline ventricular function and perioperative surgical factors. The parsimonious model preserved discriminatory performance despite simplification and demonstrated acceptable calibration, supporting its internal robustness. The use of a clinically relevant LVEF threshold and the development of a nomogram further facilitate individualized risk estimation in routine practice.\u003c/p\u003e\n\u003cp\u003eFrom a practical perspective, identification of fragmented QRS on standard preoperative electrocardiography offers incremental insight into myocardial vulnerability in patients undergoing mitral valve surgery. In particular, fQRS may help identify patients who remain at increased risk for postoperative left ventricular systolic dysfunction despite preserved preoperative ejection fraction, thereby informing perioperative myocardial protection strategies, closer early postoperative echocardiographic surveillance, and more individualized follow-up. Given its limited sensitivity for myocardial fibrosis, fQRS should not be interpreted in isolation but rather integrated within a comprehensive clinical, echocardiographic, and procedural risk assessment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be considered when interpreting these findings. First, the retrospective, single-center design limits causal inference and may affect the generalizability of the results. Second, myocardial fibrosis was not directly quantified using cardiac magnetic resonance imaging or histopathology; instead, fragmented QRS was used as a surrogate marker of myocardial structural disease, which may be associated with misclassification and limited sensitivity for diffuse fibrosis.\u003c/p\u003e\n\u003cp\u003eIn addition, the analysis focused on early postoperative left ventricular systolic dysfunction defined by an LVEF threshold and did not capture more subtle changes in myocardial mechanics or long-term left ventricular remodeling trajectories. Although concomitant tricuspid annuloplasty was included as a perioperative variable, residual confounding related to disease severity and right-sided involvement cannot be fully excluded. Finally, external validation of the predictive model in independent cohorts is required before broader clinical application.\u003c/p\u003e"},{"header":"5.\tCONCLUSIONS","content":"\u003cp\u003ePreoperative fragmented QRS is independently associated with an increased risk of postoperative left ventricular systolic dysfunction in patients undergoing isolated mitral valve replacement. When considered alongside baseline ventricular function and perioperative surgical stress, fragmented QRS provides incremental prognostic information, highlighting the role of underlying myocardial substrate in postoperative functional recovery. These findings suggest that readily available electrocardiographic markers may complement existing perioperative risk stratification strategies in selected patients undergoing mitral valve surgery.\u003c/p\u003e"},{"header":"List of Abbreviations","content":"\u003cp\u003eAUC: Area under the curve\u003c/p\u003e\n\u003cp\u003eBMI: Body mass index\u003c/p\u003e\n\u003cp\u003eCI: Confidence interval\u003c/p\u003e\n\u003cp\u003eCKD: Chronic kidney disease\u003c/p\u003e\n\u003cp\u003eCMR: Cardiac magnetic resonance\u003c/p\u003e\n\u003cp\u003eECG: Electrocardiography\u003c/p\u003e\n\u003cp\u003efQRS: Fragmented QRS\u003c/p\u003e\n\u003cp\u003eHT: Hypertension\u003c/p\u003e\n\u003cp\u003eIQR: Interquartile range\u003c/p\u003e\n\u003cp\u003eLV: Left ventricle / Left ventricular\u003c/p\u003e\n\u003cp\u003eLVEF: Left ventricular ejection fraction\u003c/p\u003e\n\u003cp\u003eOR: Odds ratio\u003c/p\u003e\n\u003cp\u003eROC: Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eSV: Stroke volume\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Scientific Research Ethics Committee of Koşuyolu High Specialization Training and Research Hospital (approval number: 2026/03/1367, date: 10 February 2026). The study was conducted in accordance with the principles of the Declaration of Helsinki. Due to the retrospective nature of the study, informed consent was waived by the ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request, if requested by the editor or reviewers with appropriate justification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eİsmail Balaban:\u003c/strong\u003e Conception and design of the research; data collection; data analysis and interpretation; manuscript drafting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSeda Tanyeri Uzel:\u003c/strong\u003e Conception and design of the research; data analysis and interpretation; manuscript drafting; critical revision of the manuscript for important intellectual content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAhmet Karaduman:\u003c/strong\u003e Conception and design of the research; data collection; statistical analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZeynep Esra G\u0026uuml;ner:\u003c/strong\u003e Data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBarkın K\u0026uuml;lt\u0026uuml;rsay:\u003c/strong\u003e Data analysis and interpretation; statistical analysis; critical revision of the manuscript for important intellectual content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCemalettin Yılmaz:\u003c/strong\u003e Data analysis and interpretation; statistical analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMustafa Ferhat Keten:\u003c/strong\u003e Manuscript drafting; critical revision of the manuscript for important intellectual content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKadir Bıyıklı:\u0026nbsp;\u003c/strong\u003eData collection; data analysis and interpretation; critical revision of the manuscript for important intellectual content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVahanian A, Beyersdorf F, Praz F, Milojevic M, Baldus S, Bauersachs J, Capodanno D, Conradi L, De Bonis M, De Paulis R, et al. 2021 ESC/EACTS Guidelines for the management of valvular heart disease. Eur Heart J. 2022;43:561\u0026ndash;632. doi: 10.1093/eurheartj/ehab395.\u003c/li\u003e\n\u003cli\u003eJneid H, Chikwe J, Arnold S V., Bonow RO, Bradley SM, Chen EP, Diekemper RL, Fugar S, Johnston DR, Kumbhani DJ, et al. 2024 ACC/AHA Clinical Performance and Quality Measures for Adults With Valvular and Structural Heart Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Performance Measures. Circ Cardiovasc Qual Outcomes. 2024;17. doi: 10.1161/HCQ.0000000000000129.\u003c/li\u003e\n\u003cli\u003eEnriquez-Sarano M, Avierinos J-F, Messika-Zeitoun D, Detaint D, Capps M, Nkomo V, Scott C, Schaff H V., Tajik AJ. Quantitative Determinants of the Outcome of Asymptomatic Mitral Regurgitation. New England Journal of Medicine. 2005;352:875\u0026ndash;883. doi: 10.1056/NEJMoa041451.\u003c/li\u003e\n\u003cli\u003eGaasch WH, Meyer TE. Left Ventricular Response to Mitral Regurgitation. Circulation. 2008;118:2298\u0026ndash;2303. doi: 10.1161/CIRCULATIONAHA.107.755942.\u003c/li\u003e\n\u003cli\u003eCarabello BA. The Current Therapy for Mitral Regurgitation. J Am Coll Cardiol. 2008;52:319\u0026ndash;326. doi: 10.1016/j.jacc.2008.02.084.\u003c/li\u003e\n\u003cli\u003eKitkungvan D, Nabi F, Kim RJ, Bonow RO, Khan MA, Xu J, Little SH, Quinones MA, Lawrie GM, Zoghbi WA, et al. Myocardial Fibrosis in Patients With Primary Mitral Regurgitation With and Without Prolapse. J Am Coll Cardiol. 2018;72:823\u0026ndash;834. doi: 10.1016/j.jacc.2018.06.048.\u003c/li\u003e\n\u003cli\u003eBadau Riebel CI, Agoston-Coldea L. Left Ventricular Fibrosis by Cardiac Magnetic Resonance Tissue Characterization in Chronic Mitral Regurgitation Patients. J Clin Med. 2024;13:3877. doi: 10.3390/jcm13133877.\u003c/li\u003e\n\u003cli\u003eAltes A, P\u0026eacute;criaux V, Hanvi P, Hanet V, Belhakia I, Selin N, Vancraeynest D, Pasquet A, Delelis F, Toledano M, et al. Association of Preoperative Cardiac Magnetic Resonance and Echocardiography with Postoperative Left Ventricular Dysfunction in Primary Mitral Regurgitation. Journal of the American Society of Echocardiography. 2026;39:28\u0026ndash;40. doi: 10.1016/j.echo.2025.09.015.\u003c/li\u003e\n\u003cli\u003eDas MK, Khan B, Jacob S, Kumar A, Mahenthiran J. Significance of a Fragmented QRS Complex Versus a Q Wave in Patients With Coronary Artery Disease. Circulation. 2006;113:2495\u0026ndash;2501. doi: 10.1161/CIRCULATIONAHA.105.595892.\u003c/li\u003e\n\u003cli\u003eDas MK, Suradi H, Maskoun W, Michael MA, Shen C, Peng J, Dandamudi G, Mahenthiran J. Fragmented Wide QRS on a 12-Lead ECG. Circ Arrhythm Electrophysiol. 2008;1:258\u0026ndash;268. doi: 10.1161/CIRCEP.107.763284.\u003c/li\u003e\n\u003cli\u003eDas MK, Saha C, El Masry H, Peng J, Dandamudi G, Mahenthiran J, McHenry P, Zipes DP. Fragmented QRS on a 12-lead ECG: A predictor of mortality and cardiac events in patients with coronary artery disease. Heart Rhythm. 2007;4:1385\u0026ndash;1392. doi: 10.1016/j.hrthm.2007.06.024.\u003c/li\u003e\n\u003cli\u003ePietrasik G, Zaręba W. QRS fragmentation: Diagnostic and prognostic significance. Cardiol J. 2012;19:114\u0026ndash;121. doi: 10.5603/CJ.2012.0022.\u003c/li\u003e\n\u003cli\u003eAzevedo CF, Nigri M, Higuchi ML, Pomerantzeff PM, Spina GS, Sampaio RO, Tarasoutchi F, Grinberg M, Rochitte CE. Prognostic Significance of Myocardial Fibrosis Quantification by Histopathology and Magnetic Resonance Imaging in Patients With Severe Aortic Valve Disease. J Am Coll Cardiol. 2010;56:278\u0026ndash;287. doi: 10.1016/j.jacc.2009.12.074.\u003c/li\u003e\n\u003cli\u003ePark S-J, On YK, Kim JS, Park SW, Yang J-H, Jun T-G, Kang I-S, Lee HJ, Choe YH, Huh J. Relation of Fragmented QRS Complex to Right Ventricular Fibrosis Detected by Late Gadolinium Enhancement Cardiac Magnetic Resonance in Adults With Repaired Tetralogy of Fallot. Am J Cardiol. 2012;109:110\u0026ndash;115. doi: 10.1016/j.amjcard.2011.07.070.\u003c/li\u003e\n\u003cli\u003eViriyanukulvong K, Theerasuwipakorn N, Wongcharoen W, Kosum P, Chokesuwattanaskul R. Diagnostic performance of the fragmented QRS complex on electrocardiogram for detecting myocardial scars assessed by 3.0 Tesla cardiac magnetic resonance imaging. Heart Rhythm O2. 2025;6:1175\u0026ndash;1181. doi: 10.1016/j.hroo.2025.05.014.\u003c/li\u003e\n\u003cli\u003eDas MK, Zipes DP. Fragmented QRS: A predictor of mortality and sudden cardiac death. Heart Rhythm. 2009;6:S8\u0026ndash;S14. doi: 10.1016/j.hrthm.2008.10.019.\u003c/li\u003e\n\u003cli\u003eRosengarten JA, Scott PA, Morgan JM. Fragmented QRS for the prediction of sudden cardiac death: a meta-analysis. Europace. 2015;17:969\u0026ndash;977. doi: 10.1093/europace/euu279.\u003c/li\u003e\n\u003cli\u003eEyuboglu M. Fragmented QRS as a Marker of Myocardial Fibrosis in Hypertension: a Systematic Review. Curr Hypertens Rep. 2019;21:73. doi: 10.1007/s11906-019-0982-3.\u003c/li\u003e\n\u003cli\u003eYılmaz FK, Cakal B, Yılmaz F, Yazar A, Savur U, Akhundova A, Gunes HM, Guler E, Dursun A, Yousufzai N, et al. Relationship between fragmented QRS complex and early left ventricular dysfunction after mitral valve repair. J Electrocardiol. 2024;84:65\u0026ndash;69. doi: 10.1016/j.jelectrocard.2024.03.001.\u003c/li\u003e\n\u003cli\u003eCelik M, Yilmaz Y, Karag\u0026ouml;z A, Kahyaoglu M, Kup A, Celik FB, Izci S, Candan O, Gecmen C, Kirma C, et al. Presence of fragmented QRS is associated with left ventricular systolic dysfunction after surgery in patients with severe aortic regurgitation. J Card Surg. 2021;36:1289\u0026ndash;1297. doi: 10.1111/jocs.15370.\u003c/li\u003e\n\u003cli\u003eAuthors N. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. KIDNEYS. 2024;13:140\u0026ndash;171. doi: 10.22141/2307-1257.13.2.2024.456.\u003c/li\u003e\n\u003cli\u003eLang RM, Badano LP, Mor-Avi V, Afilalo J, Armstrong A, Ernande L, Flachskampf FA, Foster E, Goldstein SA, Kuznetsova T, et al. Recommendations for Cardiac Chamber Quantification by Echocardiography in Adults: An Update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Journal of the American Society of Echocardiography. 2015;28:1-39.e14. doi: 10.1016/j.echo.2014.10.003.\u003c/li\u003e\n\u003cli\u003eLancellotti P, Pibarot P, Chambers J, Edvardsen T, Delgado V, Dulgheru R, Pepi M, Cosyns B, Dweck MR, Garbi M, et al. Recommendations for the imaging assessment of prosthetic heart valves: a report from the European Association of Cardiovascular Imaging endorsed by the Chinese Society of Echocardiography, the Inter-American Society of Echocardiography, and the Brazilian Department of Cardiovascular Imaging \u003csup\u003e\u0026dagger;\u003c/sup\u003e. Eur Heart J Cardiovasc Imaging. 2016;17:589\u0026ndash;590. doi: 10.1093/ehjci/jew025.\u003c/li\u003e\n\u003cli\u003eY\u0026uuml;cel M, Benli ED, Erdoğan KE, Sağlam MF, Deniz G, \u0026Ccedil;omaklı H, Uğuz E. Evaluation of Myocardial Protection in Prolonged Aortic Cross-Clamp Times: Del Nido and HTK Cardioplegia in Adult Cardiac Surgery. Medicina (B Aires). 2025;61:1420. doi: 10.3390/medicina61081420.\u003c/li\u003e\n\u003cli\u003eBerretta P, Kempfert J, Van Praet F, Salvador L, Lamelas J, Nguyen TC, Wilbring M, Gerdisch M, Rinaldi M, Bonaros N, et al. Risk-related clinical outcomes after minimally invasive mitral valve surgery: insights from the Mini-Mitral International Registry. European Journal of Cardio-Thoracic Surgery. 2023;63. doi: 10.1093/ejcts/ezad090.\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-de-la-Asunci\u0026oacute;n J, Pastor E, Perez-Griera J, Belda FJ, Moreno T, Garc\u0026iacute;a-del-Olmo E, Mart\u0026iacute; F. Oxidative stress injury after on-pump cardiac surgery: Effects of aortic cross clamp time and type of surgery. Redox Report. 2013;18:193\u0026ndash;199. doi: 10.1179/1351000213Y.0000000060.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Fragmented QRS, Mitral valve replacement, Postoperative outcomes, Left ventricular dysfunction, Risk prediction","lastPublishedDoi":"10.21203/rs.3.rs-8987964/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8987964/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDespite preserved preoperative left ventricular ejection fraction (LVEF), a substantial proportion of patients develop postoperative left ventricular (LV) systolic dysfunction after mitral valve surgery. Myocardial structural abnormalities and limited myocardial reserve may impair postoperative recovery and are not fully captured by conventional functional indices. Fragmented QRS (fQRS) on surface electrocardiography has been proposed as a simple marker of an adverse myocardial substrate. To evaluate the prognostic value of preoperative fragmented QRS for predicting postoperative LV systolic dysfunction in patients undergoing isolated mitral valve replacement and to develop a clinically applicable predictive model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective analysis included 279 consecutive patients who underwent elective isolated mitral valve replacement, with or without concomitant tricuspid annuloplasty, between 2022 and 2025. Fragmented QRS was assessed on preoperative 12-lead electrocardiography. Postoperative LV systolic dysfunction was defined as an LVEF\u0026thinsp;\u0026lt;\u0026thinsp;50% at early postoperative echocardiographic follow-up. Multivariable logistic regression analyses, including a backward stepwise approach, were performed to identify independent predictors. Model discrimination and calibration were evaluated, and a nomogram was constructed for individualized risk estimation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFragmented QRS was present in 71 patients (25.4%). Although preoperative LVEF was similar between groups, patients with fQRS had lower postoperative LVEF (p\u0026thinsp;=\u0026thinsp;0.036) and a higher incidence of postoperative LV systolic dysfunction compared with those without fQRS (45.1% vs. 28.8%; p\u0026thinsp;=\u0026thinsp;0.012). In the stepwise multivariable model, fQRS independently predicted postoperative LV systolic dysfunction (odds ratio [OR] 2.00, 95% confidence interval [CI] 1.06\u0026ndash;3.81; p\u0026thinsp;=\u0026thinsp;0.033), together with aortic cross-clamp time and chronic kidney disease. The model demonstrated good discrimination (area under the curve 0.78). A nomogram was developed to facilitate individualized risk prediction.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePreoperative fragmented QRS is independently associated with an increased risk of postoperative left ventricular systolic dysfunction after isolated mitral valve replacement. When integrated with baseline ventricular function and perioperative surgical factors, fQRS may serve as a simple and widely available marker to enhance perioperative risk stratification and support individualized clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Preoperative Fragmented QRS as a Predictor of Postoperative Left Ventricular Systolic Dysfunction After Isolated Mitral Valve Replacement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 10:17:03","doi":"10.21203/rs.3.rs-8987964/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-11T13:25:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T13:42:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159932800960502871135120171790131481530","date":"2026-04-29T17:43:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T10:38:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-11T23:33:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241457306878659155329906651718743376343","date":"2026-04-10T01:14:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-27T21:14:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202318948050964392320930786650330771815","date":"2026-03-27T20:45:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289286092692360750357799000177891943771","date":"2026-03-27T01:59:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70392795544024043602797950377393063536","date":"2026-03-26T19:58:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69394367445331405810915219817999391147","date":"2026-03-18T20:31:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-16T14:54:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-03T15:05:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-03T10:44:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-03T10:43:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-02-27T12:15:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b172dc6-38a4-469a-a798-4f5d6958e0e5","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-11T13:25:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T13:42:25+00:00","index":281,"fulltext":""},{"type":"reviewerAgreed","content":"159932800960502871135120171790131481530","date":"2026-04-29T17:43:00+00:00","index":279,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T13:43:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 10:17:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8987964","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8987964","identity":"rs-8987964","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}