QRS Fragmentation: A Predictor of Mortality in Patients with Cardiovascular Diseases

QRS Fragmentation: A Predictor of Mortality in Patients with Cardiovascular Diseases | 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 Article QRS Fragmentation: A Predictor of Mortality in Patients with Cardiovascular Diseases Hanieh Gholamalizadeh, Azadeh Izadi-Moud, Sara Saffar Soflaei, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8708399/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Emerging research has highlighted the significance of fragmented QRS (fQRS), a notable marker on the electrocardiogram (ECG), in various cardiovascular diseases (CVDs). In this retrospective cohort study, we aimed to investigate the MASHAD study population regarding the presence of fQRS and its association with the incidence of subsequent CVDs, including coronary artery disease (CAD) and cerebrovascular event (CVA). A total of 8,834 individuals with available and interpretable ECGs and without a confirmed CVD at baseline were classified into the fQRS group (N = 146) and the non-fQRS group (N = 8,688). After a ten-year follow-up, CVD developed in 18 subjects from the fQRS group and 924 from the non-fQRS group. The mortality rates were 6 and 149 individuals in the fQRS and non-fQRS groups, respectively. Statistical analyses indicated a significant association between fQRS and the mortality from CVD and CAD (p = 0.029 and 0.023), but not CVA (p = 0.606). Furthermore, Kaplan-Meier survival analysis was performed to evaluate the prognostic significance of fQRS on the CVD-related and CAD-related mortality. We concluded that the presence of fQRS in ECG significantly influences a patient's prognosis and could serve as a predictor for mortality among CVD patients. Health sciences/Cardiology Health sciences/Diseases fragmented QRS QRS fragmentation fQRS Cardiovascular diseases Coronary artery disease Cerebrovascular accident Figures Figure 1 Figure 2 Figure 3 1. Introduction Cardiovascular diseases (CVDs) rank as the most prevalent non-communicable diseases and are the leading cause of death and disability globally ( 1 ). Their incidence and mortality rates are increasing, especially in developing countries, largely due to rapid shifts in aging and urbanization patterns ( 2 ). Atherosclerosis, the primary underlying pathogenesis behind the onset and progression of CVDs, evolves over many years and is significantly associated with conventional risk factors for CVD ( 3 ). Researchers believe that more than half of CVD deaths are attributable to the five risk factors, including obesity, diabetes, hypertension, dyslipidemia, and tobacco consumption ( 4 , 5 ). Early detection and appropriate management of CVDs and their risk factors could greatly alleviate this global health crisis. To achieve this goal, various screening and assessment tools have been introduced. Nowadays, clinical decisions regarding the initiation and optimization of preventive treatment are guided by risk assessment systems, such as the Framingham scoring system ( 6 , 7 ). However, data from routine 12-lead electrocardiograms (ECGs) have not yet been incorporated into these systems. Research indicates that both major and minor ECG abnormalities correlate positively with CVD and its risk factors ( 8 ). Recently, the fragmentation of the QRS complex has garnered significant attention due to its links to various CVD complications ( 9 , 10 ). Fragmented QRS (fQRS) is characterized by a high-frequency potential (spike or notch) within the QRS complex ( 11 ). This term was first introduced in a preclinical study by Flowers et al. in 1973, which focused on canine hearts with coronary occlusion ( 12 ). Its popularity emerged remarkably after the study published by Das et al., which showed a significant association between fQRS and myocardial scar using single photon emission tomography ( 13 ). Further studies have examined fQRS in various CVDs, including coronary artery disease (CAD) ( 14 ), arrhythmogenic right ventricular cardiomyopathy ( 15 ), hypertrophic cardiomyopathy ( 16 ), Brugada syndrome ( 17 ), and heart structural abnormalities ( 18 ). Most studies have reported an association between fQRS and both CVD mortality and arrhythmia-related events. It has been suggested that any condition that disrupts myocardial depolarization may lead to QRS fragmentation ( 9 ). On the other hand, several articles have reported a 5% prevalence of fQRS among healthy individuals, which may be attributed to factors such as left axis deviation, scarring, or fibrosis ( 19 ). Despite numerous attempts to explore the implications of fQRS in various cardiovascular conditions, there has been a lack of studies that follow healthy populations for the onset of cardiovascular diseases. The current cohort study aims to explore the relationship between fQRS and the incidence of CVD over a ten-year follow-up period. Furthermore, we will analyze the effects of fQRS on CVD-related mortality. By identifying the prognostic significance of fQRS, our findings could improve early detection strategies in clinical settings. 2. Results 2.1. Study Population Characteristics A total of 8,834 subjects were enrolled in this study, comprising 8,688 individuals without QRS fragmentation and 146 individuals with fQRS on their ECGs. The average age of participants in the fQRS group was 48.95±8.52 years, compared to 47.95±8.18 years in the non-fQRS group (p = 0.144). The proportion of males in the fQRS group was 44.5%, compared to 39.6% in the non-fQRS group (p = 0.130). No significant differences were observed between the two groups in terms of marital status (p = 0.285), job status (p = 0.548), smoking status (p = 0.405), obesity (p = 0.479), diabetes mellitus (p = 0.275), hypertension (p = 0.061), and dyslipidemia (p = 0.775). ( Table 1 ) Table 1. Demographics and Baseline Clinical Characteristics of the Study Population Characteristics Non-fQRS Group (N=8688) fQRS Group (N=146) P-value Age (year) 47.95±8.18 48.95±8.52 0.144 Gender Male 3438 (39.6%) 65 (44.5%) 0.130 Female 5250 (60.4%) 81 (55.5%) Marriage Status Single 54 (0.6%) 1 (0.7%) 0.285 Married 8097 (93.2%) 135 (92.7%) Divorced 119 (1.4%) 0 (0.0%) Widow 418 (4.8%) 10 (6.8%) Education Level Very Low (illiterate) 1098 (12.7%) 23 (15.9%) 0.003 Low (elementary) 3473 (40.1%) 75 (51.7%) Moderate (diploma) 3086 (35.7%) 39 (26.9%) High (university) 994 (11.5%) 18 (5.5%) Job Status Employed 3240 (37.4%) 51 (35.2%) 0.548 Un-employed 4576 (52.8%) 83 (57.2%) Retired 850 (9.8%) 11 (7.6%) Smoking Status Non-smoker 5997 (69%) 94 (64.4%) 0.405 Ex-smoker 827 (9.5%) 14 (9.6%) Current smoker 1864 (21.5%) 38 (26%) Obesity No 6057 (69.8%) 98 (67.1%) 0.479 Yes 2616 (30.2%) 48 (32.9%) DM No 7385 (86.2%) 120 (82.8%) 0.275 Yes 1186 (13.8%) 25 (17.2%) HTN No 6049 (69.7%) 90 (62.5%) 0.061 Yes 2624 (30.3%) 54 (37.5%) DLP No 1239 (14.3%) 22 (15.2%) 0.775 Yes 7405 (85.7%) 123 (84.8%) Data presented as mean ± SD or number and percentage. Abbreviation: DM, diabetes mellitus; HTN, hypertension; DLP, dyslipidemia. 2.2. Association between fQRS and the Development of Cardiovascular Events Figure 1 (A) illustrates the prevalence of fQRS across cardiovascular events over the follow-up period. Among 146 individuals with QRS fragmentation, 18 subjects developed CVDs, including 13 cases of CAD. From these 18 patients, 6 died—5 from CAD. In contrast, among the population without fQRS, 924 developed CVD, of whom 149 died. The prevalence of CVDs among survivors without fQRS in CAD and CVA subgroups was 777 and 111 individuals, respectively, while the prevalence among deceased individuals in these subgroups was 140 and 38 individuals, respectively. A notable association was observed between the QRS fragmentation and the incidence of CVD among deceased individuals (p = 0.043). This association was also significant within the CAD subgroup (p = 0.039) but not within the CVA subgroup (p = 0.74). Figure 1 (B) provides a closer look at CAD-specific complications. Our analysis revealed that, during the follow-up period, 3 individuals experienced stable angina, 3 had unstable angina, and 3 suffered myocardial infarction (MI). Additionally, 2 individuals underwent percutaneous coronary intervention (PCI), none received coronary artery bypass grafting (CABG), and 6 individuals died. No significant association was found between fQRS presence and the occurrence of stable angina, unstable angina, MI, or PCI. However, CAD patients with fQRS had a significantly higher mortality rate compared to those without fQRS (p = 0.002). 2.3. Cardiovascular Events and Mortality in the fQRS and Non-fQRS Groups We compared the fQRS and non-fQRS groups in terms of the incidence and mortality rates of CVD, CAD, and cerebrovascular event (CVA) during the follow-up period ( Table 2 ). Our findings suggest that the presence of fQRS is significantly associated with CVD mortality (p = 0.029) and CAD mortality (p = 0.023), but not CVA mortality (p = 0.606). Moreover, it has been shown that the incidence of cardiovascular events, including CVD, CAD, and CVA, did not statistically differ between the fQRS and non-fQRS groups. Table 2. Endpoint Cardiovascular Events and Mortality in the Study Population Events Non-fQRS Group (N=8688) fQRS Group (N=146) P-value CVD event No 7752 (89.3%) 128 (87.8%) 0.515 Yes 924 (10.7%) 18 (12.3%) CVD mortality No 8527 (98.3%) 140 (95.9%) 0.029 Yes 149 (1.7%) 6 (4.1%) CAD event No 7977 (91.9%) 133 (91.1%) 0.709 Yes 699 (8.1%) 13 (8.9%) CAD mortality No 8566 (98.7%) 141 (96.6%) 0.023 Yes 110 (1.3%) 5 (3.4%) CVA event No 8562 (98.7%) 145 (99.3%) 0.284 Yes 114 (1.3%) 1 (0.7%) CVA mortality No 8646 (99.7%) 146 (100.0%) 0.606 Yes 30 (0.3%) 0 (0.0%) Abbreviations: CVD, cardiovascular disease; CAD, coronary artery disease; CVA, cardiovascular accident. 2.4. Prognostic Significance of fQRS in Predicting CVD-Related Mortality We performed Cox regression analysis to evaluate the hazard of fQRS for CVD and CAD mortality, which are significant endpoints, as shown in Table 2 . Results indicate that individuals with fQRS had a significantly increased risk of mortality from CVD (HR: 2.533, 95% CI: 1.120-5.730, P = 0.026) and CAD (HR: 2.852, 95% CI: 1.164-6.990, P = 0.022) ( Table 3 ). Analyses were also conducted after adjustments for baseline characteristics related to fQRS, with p-values less than 0.2, including educational status and HTN. Similarly, the presence of fQRS was associated with higher mortality due to both CVD (HR: 2.350, 95% CI: 1.037-5.325, P = 0.041) and CAD (HR: 2.639, 95% CI: 1.074-6.484, P = 0.034). However, our results were not statistically significant when adjusted for all baseline characteristics in Tables 1 and 2 . As illustrated in Table 3 , having a fQRS complex in the ECG did not increase the risk of CVD-related (HR: 1.658, 95% CI: 0.723-3.802, P = 0.233) and CAD-related (HR: 1.827, 95% CI: 0.733-4.551, P = 0.196) deaths after adjustment for all baseline variables. Figure 2 demonstrates the Kaplan-Meier survival curve for CVD- and CAD-associated mortality in fQRS-positive and fQRS-negative participants. As shown, fQRS is correlated with decreased survival and duration to death in both CVD ( Figure 2-A ) and CAD ( Figure 2-B ) groups. Table 3. Hazard of fQRS for CVD and CAD mortality HR 95%CI P value Crude model CVD mortality 2.533 1.120-5.730 0.026 CAD mortality 2.852 1.164-6.990 0.022 Adjusted model 1# CVD mortality 2.350 1.037-5.325 0.041 CAD mortality 2.639 1.074-6.484 0.034 Adjusted model 2## CVD mortality 1.658 0.723-3.802 0.233 CAD mortality 1.827 0.733-4.551 0.196 # Adjusted for baseline characteristics related to fQRS with p-values less than 0.2 in Table 1 , including educational status and HTN. ## Adjusted for all baselines in Tables 1 and 2 . 3. Discussion This community-based cohort study highlights the significance of fQRS as a predictor of mortality in CVD patients, particularly among those with CAD. Our comprehensive analysis of 8,834 individuals confirmed a high prognostic value for fQRS. The findings suggest that incorporating ECG markers, notably fQRS, into routine clinical practice has the potential to transform the management of patients with CVD. Despite the widespread use of ECG, its effectiveness in CVD risk stratification remains fundamentally limited. Various ECG abnormalities have been proposed as potential predictors of cardiovascular outcomes. For instance, prolonged QT interval, pathological Q wave, and ST-segment depression have been recognized as indicators of arrhythmia, prior MI, and ischemic cardiac disease, respectively (21-23). While these abnormalities are incorporated into guidelines and clinical practice, their prognostic implications can differ among different populations, which poses obstacles in their application in universal risk assessment tools such as the Framingham risk score (24, 25). Regarding fQRS, the evidence supporting its reliability and utility for predicting CVD incidence and mortality is growing but less robust compared to other ECG indicators. However, our study, with its large population and substantial follow-up period, provides compelling evidence for the prognostic value of fQRS. We demonstrated that deceased CVD or CAD patients are at a > 2-fold higher likelihood to present fQRS compared to those without these events. More strikingly, the presence of fQRS increased the risk of MI by 3.6 times. These results resonate with a growing body of literature. Previous research conducted by Turkmen et al. on 500 patients diagnosed with ST-elevation myocardial infarction (STEMI) who underwent PCI indicated that the development of ventricular tachycardia (VT), major adverse cardiac event (MACE), the global registry of acute coronary events (GRACE) score, and the ratio of in-hospital mortality was significantly higher in patients in the fQRS group (26). Similarly, fQRS was recognized as an independent marker of in-hospital mortality in STEMI patients receiving emergent CABG (27). Another study, including 250 patients with unstable angina and non-ST elevation MI (NSTEMI), indicated that the prevalence of NSTEMI, arrhythmias, heart failure, and poor outcomes is significantly associated with fQRS (28). Moreover, an investigation by Cho et al. also highlighted the notable prognostic power of fQRS in patients with MI, surpassing several conventional risk factors, including age, gender, smoking status, HTN, DM, DLP, and ST-T changes (29). Their finding partially aligns with ours, strengthening the role of fQRS in risk assessment. With their retrospective nature, the mentioned studies have analyzed ECG data from patients who were previously admitted. However, an investigation by Yilmiz et al., similar to the present study, examined the predictive value of fQRS over a 10-year follow-up. A total of 1261 CAD patients who received PCI were included. Using multivariable Cox regression analysis, they have demonstrated that fQRS is an independent predictor of MACE (30). Despite this consensus, conflicting results from several studies highlight the complexity of validating fQRS as a reliable prognostic indicator. In particular, Das et al. revealed that fQRS is an independent predictor of the first arrhythmic event but not of mortality (31). These inconsistencies may stem from variations in sample size, population characteristics, and study design. Similarly, in the current study, our Cox regression analysis indicated that fQRS is a significant predictor of mortality related to CVD and CAD. Further meta-analyses on the data from cohorts and cross-sectional studies addressed the predictive value of fQRS in patients with various cardiovascular events, including MI, arrhythmias, heart failure, and Brugada syndrome. (32-35). Collectively, they underscored the potential of fQRS to be utilized as a subsequent marker for risk stratification in CVD patients. Several mechanisms have been proposed to explain the association between fQRS and adverse cardiovascular events. One prominent contributor is the myocardial scarring and fibrosis observed in CAD patients, which interfere with the heart’s electrical conduction, resulting in asynchronous ventricular activation that presents as fQRS on an ECG (9). Studies have identified subendocardial fibrosis in individuals with fQRS using cardiac magnetic resonance imaging (CMR) (36). Furthermore, changes in cardiac structural integrity and performance following a cardiovascular event contribute to the development of fQRS, as echocardiographic evaluations have shown reduced systolic and diastolic function and increased epicardial fat in individuals with fQRS (37). Additionally, a plausible explanation for the elevated mortality risk associated with fQRS is its strong correlation with life-threatening arrhythmias, including ventricular tachycardia (38). On the other hand, while the association between fQRS and both CAD complications and mortality is strongly supported by various investigations, existing evidence on the predictive significance of fQRS in patients with CVA is still limited. Sahin et al. prospectively enrolled 241 patients with ischemic stroke and followed them for two years. They found that fQRS is an independent marker predicting CVA-related mortality (39). Conversely, our analyses found no statistically significant relationship between fQRS and the incidence and mortality of CVA. Given the gaps in the prognostic relevance of fQRS in CVA patients, further prospective, well-designed research is warranted to promote our clinical insight. This study benefits from a robust participant base and comprehensive subgroup analyses. Furthermore, the comparability of our study groups (fQRS-positive and fQRS-negative individuals) regarding demographic and clinical features minimizes the potential role of confounding factors. However, due to the large sample size, several confounding variables, such as non-cardiac comorbidities and medication use, could not be fully controlled. Additionally, the role of fQRS in the development of arrhythmic events has not been assessed. Most significantly, the prospective design of our research, coupled with an extensive follow-up period, enhances the validity and reliability of our findings, especially when compared to most previous studies, which were retrospective. This study highlights the profound association between QRS fragmentation and long-term cardiovascular mortality, particularly among patients with CAD. However, the implications of our investigation go beyond its identification as an innovative prognostic marker, emphasizing its potential to refine risk-stratification models for CVDs. Healthcare providers could improve early detection and effective management of high-risk patients by incorporating fQRS into routine ECG evaluations. 4. Methods 4.1. Study Design and Participants The current retrospective cohort study was conducted on the population-based Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study, which was initiated in 2010. Using a stratified cluster random sampling technique, 9,704 participants aged between 35 and 65 were selected from three urban regions in Mashhad, the second-largest city in Iran ( Figure 3 ). Detailed information about the sampling methods and study design has been published previously (20). Demographic and socioeconomic data, including age, gender, marital status, job status, educational level, smoking status, and physical activity, were collected through interviews conducted by trained nurses or healthcare professionals. Body mass index (BMI) was calculated for each participant, with a measure of 30 kg/m 2 or above considered indicative of obesity. Blood pressure was measured using a standard mercury sphygmomanometer. Hypertension (HTN) was defined as a history of HTN or current HTN (systolic blood pressure≥140 mmHg or diastolic blood pressure ≥90 mmHg) or the use of antihypertensive medications. All subjects underwent fasting blood sampling for total cholesterol (TC) and fasting blood glucose (FBG). Hypercholesterolemia and diabetes mellitus were defined as TC ≥ 240 mg/dl and FBG ≥ 126 mg/dl, respectively. Participants with a history of diabetes mellitus (DM) or those treated with anti-hyperglycemic agents were also classified as diabetic patients. 4.2. ECG Recording and Definition of Fragmented QRS A single resting 12-lead ECG was recorded from each participant on paper, then digitally scanned and archived. Only standard ECGs with an amplitude of 10 mm/mv and a speed of 25 mm/s were included. All ECGs with insufficient quality, missing leads, and technical errors were rigorously excluded from the electrocardiographic data files. This ensured the accuracy and reliability of our data. Among the 9,704 participants, readable and available ECGs (n=9,035) were interpreted by skilled senior medical students according to the Minnesota ECG code classification system ( Figure 3 ). fQRS was defined as the notching of the R or S wave, the presence of an additional R wave (R’), or more than one R’ in at least two contiguous leads on an ECG with a narrow QRS complex without any bundle branch block (BBB) pattern. For wide QRS complexes, the presence of two or more notches in R or S waves was required to define fQRS. 4.3. Diagnosis of cardiovascular diseases Participants with pre-existing CVD at baseline (n=201) were excluded. Ultimately, a total of 8,834 individuals with accessible, interpretable ECGs and no prior history of confirmed CVD were included and followed up for ten years ( Figure 3 ). During the follow-up period, each individual with angina pectoris, relevant past medical history, and abnormal physical examination, along with electrocardiographic evidence of ischemia, was evaluated by a cardiologist for the diagnosis of CVD. Complementary diagnostic tests, including echocardiography, exercise tolerance testing, angiography, and computed tomography angiography, were conducted for suspected cases. The definitive diagnosis was made based on the consensus opinion of a panel of specialists. 4.4. Ethical Approval The present study was approved by the Human Research Ethics Committee of Mashhad University of Medical Sciences (IR.MUMS.IRH.REC.1401.080). Before data collection, all subjects provided informed consent. All procedures conducted in this study adhered to the ethical standards of the World Medical Association Declaration of Helsinki. 4.5. Statistical Analysis The quantitative and qualitative results were expressed as mean ± standard deviation (SD) and frequency (%), respectively. Independent t-tests and chi-square tests were employed to compare quantitative and qualitative variables between the two groups. The results for the logistic regression analyses were presented as odds ratios (OR) with respective 95% confidence intervals (CI). Furthermore, Kaplan-Meier survival analysis was conducted to assess the prognostic significance of fQRS in predicting CVD-related mortality. Data were analyzed using SPSS version 26, and a p-value < 0.05 was considered statistically significant. Declarations Acknowledgments We would like to thank Mashhad University of Medical Sciences for supporting this study. Author contribution Hanieh Gholamalizadeh, Azadeh Izadi-Moud (wrote manuscript), Mohsen Mohebati, Majid Ghayour-Mobarhan (study design), Sara Saffar Soflaei, Susan Darroudi (Data gathering and Data analysis), Bahram Shahri (Corresponding author). All authors read and approved the final manuscript. Data Availability The datasets generated and/or analyzed during the current study are not publicly available due to institutional restrictions and data use agreements with Mashhad University of Medical Sciences (MUMS), but are available from the corresponding author on reasonable request, provided that permission is obtained from Mashhad University of Medical Sciences (MUMS). Funding Mashhad University of Medical Sciences financially supported the collection of clinical data. Competing interests The authors declare no competing interests. References Banatvala, N., Bovet, P. & Noncommunicable Diseases A Compendium: Taylor & Francis; (2023). Soltani, S. et al. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviews received at journal 10 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 02 Mar, 2026 Editor invited by journal 30 Jan, 2026 Submission checks completed at journal 29 Jan, 2026 First submitted to journal 29 Jan, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8708399","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":620509062,"identity":"085c1f97-c87a-49d8-85ec-19aec0cba2e0","order_by":0,"name":"Hanieh Gholamalizadeh","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hanieh","middleName":"","lastName":"Gholamalizadeh","suffix":""},{"id":620509063,"identity":"e9ec34b3-a185-4fb4-8cc5-542b55b35146","order_by":1,"name":"Azadeh Izadi-Moud","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Azadeh","middleName":"","lastName":"Izadi-Moud","suffix":""},{"id":620509064,"identity":"0ff136ce-c0e8-470d-b4c9-8448aee045b1","order_by":2,"name":"Sara Saffar Soflaei","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"Saffar","lastName":"Soflaei","suffix":""},{"id":620509065,"identity":"8977a24a-67f7-4fe7-b361-d9577bea2402","order_by":3,"name":"Susan Darroudi","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Susan","middleName":"","lastName":"Darroudi","suffix":""},{"id":620509066,"identity":"b3c7fc2c-057a-4e81-97e0-6ebe4af7cfdc","order_by":4,"name":"Majid Ghayour-Mobarhan","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Majid","middleName":"","lastName":"Ghayour-Mobarhan","suffix":""},{"id":620509067,"identity":"aefd7032-1a67-4c83-8fe6-4c08fdb07f28","order_by":5,"name":"Mohsen Mohebati","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mohsen","middleName":"","lastName":"Mohebati","suffix":""},{"id":620509068,"identity":"53c55700-6d77-46a2-a50b-d2eb7611346f","order_by":6,"name":"Bahram Shahri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYDACCQY2BgaDA3JwATZitRiTqoXhQGID0e6Sn9387HFFwZ30tTOykz8w1Ngx8EkfwK/F4M4xc8MzBs9yt93I3SbBcCyZgY0vgYAWiQQzyQaDw2AtQI8cYGDjIeSwGenfQFrSzW7kbv7A8I8ILQw3csC2JAC1bJBgbCNCi8GNnDKglmeG28683SaR2JfMQ4zDtkk2/Lkjb3Yc6LAP3+zk5HsIOQwFJDAwEPTJKBgFo2AUjAIiAADL+UCXfJomkQAAAABJRU5ErkJggg==","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Bahram","middleName":"","lastName":"Shahri","suffix":""}],"badges":[],"createdAt":"2026-01-27 09:23:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8708399/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8708399/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106703556,"identity":"55ee7938-4291-4d13-b28b-8004064dfa24","added_by":"auto","created_at":"2026-04-12 07:43:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51156,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrevalence of QRS fragmentation among (A) various cardiovascular events occurred over a 10-year follow-up and (B) Complications associated with CAD.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8708399/v1/d5de7ef17027635620ce036c.png"},{"id":106703557,"identity":"6af42031-3648-46e4-a0da-7980ac5358cd","added_by":"auto","created_at":"2026-04-12 07:43:07","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":336151,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival analysis showing (A) CVD-related mortality and (B) CAD-related mortality in patients with and without fQRS.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8708399/v1/923077a6d90805b8469c9839.jpeg"},{"id":106728165,"identity":"246f5053-ff0c-43b8-b3ff-9475539c49fd","added_by":"auto","created_at":"2026-04-12 18:42:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37660,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the Study Cohort\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8708399/v1/c40ff5a7cf2aa674453fe233.png"},{"id":106959121,"identity":"c90e3bdc-2668-475f-a832-809070151726","added_by":"auto","created_at":"2026-04-15 08:47:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1315685,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8708399/v1/1f417d74-ee09-4d6c-8c2f-173d2d82da04.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"QRS Fragmentation: A Predictor of Mortality in Patients with Cardiovascular Diseases","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCardiovascular diseases (CVDs) rank as the most prevalent non-communicable diseases and are the leading cause of death and disability globally (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Their incidence and mortality rates are increasing, especially in developing countries, largely due to rapid shifts in aging and urbanization patterns (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Atherosclerosis, the primary underlying pathogenesis behind the onset and progression of CVDs, evolves over many years and is significantly associated with conventional risk factors for CVD (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Researchers believe that more than half of CVD deaths are attributable to the five risk factors, including obesity, diabetes, hypertension, dyslipidemia, and tobacco consumption (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Early detection and appropriate management of CVDs and their risk factors could greatly alleviate this global health crisis. To achieve this goal, various screening and assessment tools have been introduced. Nowadays, clinical decisions regarding the initiation and optimization of preventive treatment are guided by risk assessment systems, such as the Framingham scoring system (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, data from routine 12-lead electrocardiograms (ECGs) have not yet been incorporated into these systems. Research indicates that both major and minor ECG abnormalities correlate positively with CVD and its risk factors (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Recently, the fragmentation of the QRS complex has garnered significant attention due to its links to various CVD complications (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Fragmented QRS (fQRS) is characterized by a high-frequency potential (spike or notch) within the QRS complex (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This term was first introduced in a preclinical study by Flowers et al. in 1973, which focused on canine hearts with coronary occlusion (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Its popularity emerged remarkably after the study published by Das et al., which showed a significant association between fQRS and myocardial scar using single photon emission tomography (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Further studies have examined fQRS in various CVDs, including coronary artery disease (CAD) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), arrhythmogenic right ventricular cardiomyopathy (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), hypertrophic cardiomyopathy (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), Brugada syndrome (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and heart structural abnormalities (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Most studies have reported an association between fQRS and both CVD mortality and arrhythmia-related events. It has been suggested that any condition that disrupts myocardial depolarization may lead to QRS fragmentation (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). On the other hand, several articles have reported a 5% prevalence of fQRS among healthy individuals, which may be attributed to factors such as left axis deviation, scarring, or fibrosis (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Despite numerous attempts to explore the implications of fQRS in various cardiovascular conditions, there has been a lack of studies that follow healthy populations for the onset of cardiovascular diseases. The current cohort study aims to explore the relationship between fQRS and the incidence of CVD over a ten-year follow-up period. Furthermore, we will analyze the effects of fQRS on CVD-related mortality. By identifying the prognostic significance of fQRS, our findings could improve early detection strategies in clinical settings.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003e\u003cstrong\u003e2.1. \u0026nbsp; \u0026nbsp; \u0026nbsp;Study Population Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 8,834 subjects were enrolled in this study, comprising 8,688 individuals without QRS fragmentation and 146 individuals with fQRS on their ECGs. The average age of participants in the fQRS group was 48.95\u0026plusmn;8.52 years, compared to 47.95\u0026plusmn;8.18 years in the non-fQRS group (p = 0.144). The proportion of males in the fQRS group was 44.5%, compared to 39.6% in the non-fQRS group (p = 0.130). No significant differences were observed between the two groups in terms of marital status (p = 0.285), job status (p = 0.548), smoking status (p = 0.405), obesity (p = 0.479), diabetes mellitus (p = 0.275), hypertension (p = 0.061), and dyslipidemia (p = 0.775). (\u003cem\u003eTable 1\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Demographics and Baseline Clinical Characteristics of the Study Population\u003c/strong\u003e\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-fQRS Group (N=8688)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003efQRS Group (N=146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47.95\u0026plusmn;8.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48.95\u0026plusmn;8.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3438 (39.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65 (44.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5250 (60.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81 (55.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eMarriage Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8097 (93.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e135 (92.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e119 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWidow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e418 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eEducation Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVery Low (illiterate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1098 (12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23 (15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow (elementary)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3473 (40.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75 (51.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModerate (diploma)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3086 (35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39 (26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh (university)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e994 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eJob Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3240 (37.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51 (35.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUn-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4576 (52.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83 (57.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e850 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eSmoking Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5997 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94 (64.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEx-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e827 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1864 (21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6057 (69.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98 (67.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2616 (30.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48 (32.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7385 (86.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e120 (82.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1186 (13.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eHTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6049 (69.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2624 (30.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eDLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1239 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22 (15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7405 (85.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e123 (84.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData presented as mean \u0026plusmn; SD or number and percentage. Abbreviation: DM, diabetes mellitus; HTN, hypertension; DLP, dyslipidemia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. \u0026nbsp; \u0026nbsp; \u0026nbsp;Association between fQRS and the Development of Cardiovascular Events\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFigure 1 (A)\u0026nbsp;\u003c/em\u003eillustrates the prevalence of fQRS across cardiovascular events over the follow-up period. Among 146 individuals with QRS fragmentation, 18 subjects developed CVDs, including 13 cases of CAD. From these 18 patients, 6 died\u0026mdash;5 from CAD. In contrast, among the population without fQRS, 924 developed CVD, of whom 149 died. The prevalence of CVDs among survivors without fQRS in CAD and CVA subgroups was 777 and 111 individuals, respectively, while the prevalence among deceased individuals in these subgroups was 140 and 38 individuals, respectively. A notable association was observed between the QRS fragmentation and the incidence of CVD among deceased individuals (p = 0.043). This association was also significant within the CAD subgroup (p = 0.039) but not within the CVA subgroup (p = 0.74).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFigure 1 (B)\u003c/em\u003e provides a closer look at CAD-specific complications. Our analysis revealed that, during the follow-up period, 3 individuals experienced stable angina, 3 had unstable angina, and 3 suffered myocardial infarction (MI). Additionally, 2 individuals underwent percutaneous coronary intervention (PCI), none received coronary artery bypass grafting (CABG), and 6 individuals died. No significant association was found between fQRS presence and the occurrence of stable angina, unstable angina, MI, or PCI. However, CAD patients with fQRS had a significantly higher mortality rate compared to those without fQRS (p = 0.002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. \u0026nbsp; \u0026nbsp; \u0026nbsp;Cardiovascular Events and Mortality in the fQRS and Non-fQRS Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe compared the fQRS and non-fQRS groups in terms of the incidence and mortality rates of CVD, CAD, and cerebrovascular event (CVA) during the follow-up period (\u003cem\u003eTable 2\u003c/em\u003e). Our findings suggest that the presence of fQRS is significantly associated with CVD mortality (p = 0.029) and CAD mortality (p = 0.023), but not CVA mortality (p = 0.606). Moreover, it has been shown that the incidence of cardiovascular events, including CVD, CAD, and CVA, did not statistically differ between the fQRS and non-fQRS groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Endpoint Cardiovascular Events and Mortality in the Study Population\u003c/strong\u003e\u003c/p\u003e\n \u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eEvents\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-fQRS Group (N=8688)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003efQRS Group (N=146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eCVD event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7752 (89.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e128 (87.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e924 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18 (12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eCVD mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8527 (98.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e140 (95.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e149 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eCAD event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7977 (91.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e133 (91.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e699 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eCAD mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8566 (98.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e141 (96.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e110 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eCVA event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8562 (98.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e145 (99.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e114 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eCVA mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8646 (99.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e146 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: CVD, cardiovascular disease; CAD, coronary artery disease; CVA, cardiovascular accident.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. \u0026nbsp; \u0026nbsp; \u0026nbsp;Prognostic Significance of fQRS in Predicting CVD-Related Mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed Cox regression analysis to evaluate the hazard of fQRS for CVD and CAD mortality, which are significant endpoints, as shown in \u003cem\u003eTable 2\u003c/em\u003e. Results indicate that individuals with fQRS had a significantly increased risk of mortality from CVD (HR: 2.533, 95% CI: 1.120-5.730, P = 0.026) and CAD (HR: 2.852, 95% CI: 1.164-6.990, P = 0.022) (\u003cem\u003eTable 3\u003c/em\u003e). Analyses were also conducted after adjustments for baseline characteristics related to fQRS, with p-values less than 0.2, including educational status and HTN. Similarly, the presence of fQRS was associated with higher mortality due to both CVD (HR: 2.350, 95% CI: 1.037-5.325, P = 0.041) and CAD (HR: 2.639, 95% CI: 1.074-6.484, P = 0.034). However, our results were not statistically significant when adjusted for all baseline characteristics in \u003cem\u003eTables 1 and 2\u003c/em\u003e. As illustrated in \u003cem\u003eTable 3\u003c/em\u003e, having a fQRS complex in the ECG did not increase the risk of CVD-related (HR: 1.658, 95% CI: 0.723-3.802, P = 0.233) and CAD-related (HR: 1.827, 95% CI: 0.733-4.551, P = 0.196) deaths after adjustment for all baseline variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFigure 2\u003c/em\u003e demonstrates the Kaplan-Meier survival curve for CVD- and CAD-associated mortality in fQRS-positive and fQRS-negative participants. As shown, fQRS is correlated with decreased survival and duration to death in both CVD (\u003cem\u003eFigure 2-A\u003c/em\u003e) and CAD (\u003cem\u003eFigure 2-B\u003c/em\u003e) groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Hazard of fQRS for CVD and CAD mortality\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"width: 4.7e+2pt;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003eCrude model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCVD mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.120-5.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCAD mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.164-6.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003eAdjusted model 1#\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCVD mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.037-5.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCAD mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.074-6.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003eAdjusted model 2##\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCVD mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.723-3.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCAD mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.733-4.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e# Adjusted for baseline characteristics related to fQRS with p-values less than 0.2 in \u003cem\u003eTable 1\u003c/em\u003e, including educational status and HTN.\u003c/p\u003e\n\u003cp\u003e## Adjusted for all baselines in \u003cem\u003eTables 1 and 2\u003c/em\u003e.\u003c/p\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThis community-based cohort study highlights the significance of fQRS as a predictor of mortality in CVD patients, particularly among those with CAD. Our comprehensive analysis of 8,834 individuals confirmed a high prognostic value for fQRS. The findings suggest that incorporating ECG markers, notably fQRS, into routine clinical practice has the potential to transform the management of patients with CVD. Despite the widespread use of ECG, its effectiveness in CVD risk stratification remains fundamentally limited. Various ECG abnormalities have been proposed as potential predictors of cardiovascular outcomes. For instance, prolonged QT interval, pathological Q wave, and ST-segment depression have been recognized as indicators of arrhythmia, prior MI, and ischemic cardiac disease, respectively (21-23). While these abnormalities are incorporated into guidelines and clinical practice, their prognostic implications can differ among different populations, which poses obstacles in their application in universal risk assessment tools such as the Framingham risk score (24, 25). Regarding fQRS, the evidence supporting its reliability and utility for predicting CVD incidence and mortality is growing but less robust compared to other ECG indicators. However, our study, with its large population and substantial follow-up period, provides compelling evidence for the prognostic value of fQRS. We demonstrated that deceased CVD or CAD patients are at a \u0026gt; 2-fold higher likelihood to present fQRS compared to those without these events. More strikingly, the presence of fQRS increased the risk of MI by 3.6 times. These results resonate with a growing body of literature. Previous research conducted by Turkmen et al. on 500 patients diagnosed with ST-elevation myocardial infarction (STEMI) who underwent PCI indicated that the development of ventricular tachycardia (VT), major adverse cardiac event (MACE), the global registry of acute coronary events (GRACE) score, and the ratio of in-hospital mortality was significantly higher in patients in the fQRS group (26). Similarly, fQRS was recognized as an independent marker of in-hospital mortality in STEMI patients receiving emergent CABG (27). Another study, including 250 patients with unstable angina and non-ST elevation MI (NSTEMI), indicated that the prevalence of NSTEMI, arrhythmias, heart failure, and poor outcomes is significantly associated with fQRS (28). Moreover, an investigation by Cho et al. also highlighted the notable prognostic power of fQRS in patients with MI, surpassing several conventional risk factors, including age, gender, smoking status, HTN, DM, DLP, and ST-T changes (29). Their finding partially aligns with ours, strengthening the role of fQRS in risk assessment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith their retrospective nature, the mentioned studies have analyzed ECG data from patients who were previously admitted. \u0026nbsp;However, an investigation by Yilmiz et al., similar to the present study, examined the predictive value of fQRS over a 10-year follow-up. A total of 1261 CAD patients who received PCI were included. Using multivariable Cox regression analysis, they have demonstrated that fQRS is an independent predictor of MACE (30). Despite this consensus, conflicting results from several studies highlight the complexity of validating fQRS as a reliable prognostic indicator. In particular, Das et al. revealed that fQRS is an independent predictor of the first arrhythmic event but not of mortality (31). These inconsistencies may stem from variations in sample size, population characteristics, and study design. Similarly, in the current study, our Cox regression analysis indicated that fQRS is a significant predictor of mortality related to CVD and CAD. Further meta-analyses on the data from cohorts and cross-sectional studies addressed the predictive value of fQRS in patients with various cardiovascular events, including MI, arrhythmias, heart failure, and Brugada syndrome. (32-35). Collectively, they underscored the potential of fQRS to be utilized as a subsequent marker for risk stratification in CVD patients.\u003c/p\u003e\n\u003cp\u003eSeveral mechanisms have been proposed to explain the association between fQRS and adverse cardiovascular events. One prominent contributor is the myocardial scarring and fibrosis observed in CAD patients, which interfere with the heart\u0026rsquo;s electrical conduction, resulting in asynchronous ventricular activation that presents as fQRS on an ECG (9). Studies have identified subendocardial fibrosis in individuals with fQRS using cardiac magnetic resonance imaging (CMR) (36). Furthermore, changes in cardiac structural integrity and performance following a cardiovascular event contribute to the development of fQRS, as echocardiographic evaluations have shown reduced systolic and diastolic function and increased epicardial fat in individuals with fQRS (37). Additionally, a plausible explanation for the elevated mortality risk associated with fQRS is its strong correlation with life-threatening arrhythmias, including ventricular tachycardia (38).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the other hand, while the association between fQRS and both CAD complications and mortality is strongly supported by various investigations, existing evidence on the predictive significance of fQRS in patients with CVA is still limited. Sahin et al. prospectively enrolled 241 patients with ischemic stroke and followed them for two years. They found that fQRS is an independent marker predicting CVA-related mortality (39). Conversely, our analyses found no statistically significant relationship between fQRS and the incidence and mortality of CVA. Given the gaps in the prognostic relevance of fQRS in CVA patients, further prospective, well-designed research is warranted to promote our clinical insight.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study benefits from a robust participant base and comprehensive subgroup analyses. Furthermore, the comparability of our study groups (fQRS-positive and fQRS-negative individuals) regarding demographic and clinical features minimizes the potential role of confounding factors. However, due to the large sample size, several confounding variables, such as non-cardiac comorbidities and medication use, could not be fully controlled. \u0026nbsp;Additionally, the role of fQRS in the development of arrhythmic events has not been assessed. Most significantly, the prospective design of our research, coupled with an extensive follow-up period, enhances the validity and reliability of our findings, especially when compared to most previous studies, which were retrospective.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study highlights the profound association between QRS fragmentation and long-term cardiovascular mortality, particularly among patients with CAD. However, the implications of our investigation go beyond its identification as an innovative prognostic marker, emphasizing its potential to refine risk-stratification models for CVDs. Healthcare providers could improve early detection and effective management of high-risk patients by incorporating fQRS into routine ECG evaluations.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Methods","content":"\u003cp\u003e\u003cstrong\u003e4.1.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Study Design and Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current retrospective cohort study was conducted on the population-based Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study, which was initiated in 2010. Using a stratified cluster random sampling technique, 9,704 participants aged between 35 and 65 were selected from three urban regions in Mashhad, the second-largest city in Iran (\u003cem\u003eFigure 3\u003c/em\u003e). Detailed information about the sampling methods and study design has been published previously (20). Demographic and socioeconomic data, including age, gender, marital status, job status, educational level, smoking status, and physical activity, were collected through interviews conducted by trained nurses or healthcare professionals. Body mass index (BMI) was calculated for each participant, with a measure of 30 kg/m\u003csup\u003e2\u003c/sup\u003e or above considered indicative of obesity. Blood pressure was measured using a standard mercury sphygmomanometer. Hypertension (HTN) was defined as a history of HTN or current HTN (systolic blood pressure\u0026ge;140\u0026thinsp;mmHg or diastolic blood pressure \u0026ge;90\u0026thinsp;mmHg) or the use of antihypertensive medications. All subjects underwent fasting blood sampling for total cholesterol (TC) and fasting blood glucose (FBG). Hypercholesterolemia and diabetes mellitus were defined as TC \u0026ge; 240 mg/dl and FBG \u0026ge; 126\u0026thinsp;mg/dl, respectively. Participants with a history of diabetes mellitus (DM) or those treated with anti-hyperglycemic agents were also classified as diabetic patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;ECG Recording and Definition of Fragmented QRS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA single resting 12-lead ECG was recorded from each participant on paper, then digitally scanned and archived. Only standard ECGs with an amplitude of 10\u0026thinsp;mm/mv and a speed of 25\u0026thinsp;mm/s were included. All ECGs with insufficient quality, missing leads, and technical errors were rigorously excluded from the electrocardiographic data files. This ensured the accuracy and reliability of our data. Among the 9,704 participants, readable and available ECGs (n=9,035) were interpreted by skilled senior medical students according to the Minnesota ECG code classification system (\u003cem\u003eFigure 3\u003c/em\u003e). fQRS was defined as the notching of the R or S wave, the presence of an additional R wave (R\u0026rsquo;), or more than one R\u0026rsquo; in at least two contiguous leads on an ECG with a narrow QRS complex without any bundle branch block (BBB) pattern. For wide QRS complexes, the presence of two or more notches in R or S waves was required to define fQRS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Diagnosis of cardiovascular diseases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants with pre-existing CVD at baseline (n=201) were excluded. Ultimately, a total of 8,834 individuals with accessible, interpretable ECGs and no prior history of confirmed CVD were included and followed up for ten years (\u003cem\u003eFigure 3\u003c/em\u003e). \u0026nbsp;During the follow-up period, each individual with angina pectoris, relevant past medical history, and abnormal physical examination, along with electrocardiographic evidence of ischemia, was evaluated by a cardiologist for the diagnosis of CVD. Complementary diagnostic tests, including echocardiography, exercise tolerance testing, angiography, and computed tomography angiography, were conducted for suspected cases. The definitive diagnosis was made based on the consensus opinion of a panel of specialists.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Ethical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was approved by the Human Research Ethics Committee of Mashhad University of Medical Sciences (IR.MUMS.IRH.REC.1401.080). Before data collection, all subjects provided informed consent. All procedures conducted in this study adhered to the ethical standards of the World Medical Association Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe quantitative and qualitative results were expressed as mean \u0026plusmn; standard deviation (SD) and frequency (%), respectively. Independent t-tests and chi-square tests were employed to compare quantitative and qualitative variables between the two groups. The results for the logistic regression analyses were presented as odds ratios (OR) with respective 95% confidence intervals (CI). Furthermore, Kaplan-Meier survival analysis was conducted to assess the prognostic significance of fQRS in predicting CVD-related mortality. Data were analyzed using SPSS version 26, and a p-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Mashhad University of Medical Sciences for supporting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHanieh Gholamalizadeh, Azadeh Izadi-Moud (wrote manuscript), Mohsen Mohebati, Majid Ghayour-Mobarhan (study design), Sara Saffar Soflaei,\u0026nbsp;Susan Darroudi (Data gathering and Data analysis), Bahram Shahri (Corresponding author).\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to institutional restrictions and data use agreements with Mashhad University of Medical Sciences (MUMS), but are available from the corresponding author on reasonable request, provided that permission is obtained from Mashhad University of Medical Sciences (MUMS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMashhad University of Medical Sciences financially supported the collection of clinical data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBanatvala, N., Bovet, P. \u0026amp; Noncommunicable Diseases A Compendium: Taylor \u0026amp; Francis; (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoltani, S. et al. 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Noninvasive Electrocardiol.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e (1), e12910 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSahin, I. et al. Prognostic significance of fragmented qrs in patients with acute ischemic stroke. \u003cem\u003eJ. Stroke Cerebrovasc. Dis.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e (9), 105986 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"fragmented QRS, QRS fragmentation, fQRS, Cardiovascular diseases, Coronary artery disease, Cerebrovascular accident","lastPublishedDoi":"10.21203/rs.3.rs-8708399/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8708399/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEmerging research has highlighted the significance of fragmented QRS (fQRS), a notable marker on the electrocardiogram (ECG), in various cardiovascular diseases (CVDs). In this retrospective cohort study, we aimed to investigate the MASHAD study population regarding the presence of fQRS and its association with the incidence of subsequent CVDs, including coronary artery disease (CAD) and cerebrovascular event (CVA). A total of 8,834 individuals with available and interpretable ECGs and without a confirmed CVD at baseline were classified into the fQRS group (N\u0026thinsp;=\u0026thinsp;146) and the non-fQRS group (N\u0026thinsp;=\u0026thinsp;8,688). After a ten-year follow-up, CVD developed in 18 subjects from the fQRS group and 924 from the non-fQRS group. The mortality rates were 6 and 149 individuals in the fQRS and non-fQRS groups, respectively. Statistical analyses indicated a significant association between fQRS and the mortality from CVD and CAD (p\u0026thinsp;=\u0026thinsp;0.029 and 0.023), but not CVA (p\u0026thinsp;=\u0026thinsp;0.606). Furthermore, Kaplan-Meier survival analysis was performed to evaluate the prognostic significance of fQRS on the CVD-related and CAD-related mortality. We concluded that the presence of fQRS in ECG significantly influences a patient's prognosis and could serve as a predictor for mortality among CVD patients.\u003c/p\u003e","manuscriptTitle":"QRS Fragmentation: A Predictor of Mortality in Patients with Cardiovascular Diseases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-12 07:43:04","doi":"10.21203/rs.3.rs-8708399/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-17T20:07:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-14T20:48:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T18:32:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115832007180196404957509970229224640883","date":"2026-04-09T18:02:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135900097936668464464146488759346499988","date":"2026-04-06T17:45:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215437960291807793522059741991420301421","date":"2026-04-06T17:45:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-06T17:42:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-02T10:08:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-30T20:36:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-29T08:10:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-29T07:43:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a55351a-51c3-4cc8-a797-d96894f1f7ea","owner":[],"postedDate":"April 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66038949,"name":"Health sciences/Cardiology"},{"id":66038950,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2026-05-10T17:08:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-12 07:43:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8708399","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8708399","identity":"rs-8708399","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}