Diagnostic Accuracy of Bedside Cardiac Ultrasound EPSS for Rapid Assessment of Left Ventricular Ejection Fraction inPediatric Shock:A Prospective Study

Diagnostic Accuracy of Bedside Cardiac Ultrasound EPSS for Rapid Assessment of Left Ventricular Ejection Fraction inPediatric Shock:A Prospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diagnostic Accuracy of Bedside Cardiac Ultrasound EPSS for Rapid Assessment of Left Ventricular Ejection Fraction inPediatric Shock:A Prospective Study Samira Sayyah, Shabahang Jafarnejad, Seyedeh Mahsa Mahmoudinezhad Dezfouli, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7105889/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Introduction: With the rise of cardiovascular diseases, rapid and accurate diagnosis of cardiac injury in shock patients is crucial. Bedside cardiac ultrasound and E-Point Septal Separation (EPSS) provide fast, non-invasive methods for evaluating cardiac function. This study aims to assess EPSS's accuracy in estimating ejection fraction to enhance clinical diagnosis and management. Methods: A prospective study was conducted on children with shock in the emergency department. EF was measured by two methods: 1. Bedside echocardiography with EPSS (by pediatric heart fellowship) 2. Standard echocardiography (by cardiologist). The relationship between EPSS and left ventricular ejection fraction (LVEF) was analyzed by correlation and linear regression. ROC curves were drawn to evaluate the diagnostic performance of EPSS and LVEF. Data analysis was performed with SPSS 16 software. Results : Patients ranged in age from 1 to 17 years (mean 5.15 ± 4.732). EPSS values varied from 1.0 to 20.0 mm, with a mean of 5.318 ± 3.9263 mm, indicating significant variation in mitral valve function. LVEF was reported both by visual estimation (20–70%, mean 55.29 ± 12.582) and device measurement (30–78%, mean 62.41 ± 10.313). ROC analysis demonstrated excellent diagnostic performance of EPSS in detecting cardiac dysfunction, with an AUC of 0.983, standard error of 0.011, and p < 0.001, confirming the high discriminative power of the model. Additionally, ROC analysis for the Eponit variable showed an AUC of 0.975, standard error 0.013, and p = 0.001, indicating strong ability to differentiate positive and negative cases. The 95% confidence interval for AUC ranged from 0.950 to 1.000, confirming high reliability. Conclusion: The present study indicates that Eponit can serve as a suitable alternative for measuring ejection fraction and can be used as a valuable tool in clinical decision-making. Shock Ejection Fraction Cardiac Ultrasound Figures Figure 1 Figure 2 Figure 3 Figure 4 Background With the global rise in cardiovascular diseases and the critical need for rapid, accurate diagnosis of cardiac injuries in patients with shock, there is a strong demand for fast and non-invasive assessment tools. Bedside cardiac ultrasound has emerged as an innovative and effective approach, enabling clinicians to quickly gather essential information about cardiac function and hemodynamic(1) status in critically ill patients. E-Point Septal Separation (EPSS) is a straightforward, easily obtainable echocardiographic measurement that has gained attention for its utility in estimating left ventricular ejection fraction (LVEF).(2, 3) EPSS can serve as a predictive indicator for identifying patients with reduced ejection fraction who may require urgent therapeutic interventions(2). EPSS is particularly valuable in emergency and critical care settings due to its speed and simplicity(4, 5). Studies have shown that an EPSS greater than 7 mm is associated with significantly reduced LVEF, and higher values (e.g., ≥ 13 mm) reliably indicate severely depressed systolic function (LVEF ≤ 35%)(2). Recent research in emergency department settings found that an EPSS cutoff of 9.5 mm yielded a sensitivity of 80% and specificity of 91% for detecting LVEF < 50%, and a sensitivity of 91% and specificity of 80% for LVEF ≤ 40%. The area under the ROC curve (AUC) for diagnosing reduced LVEF was 0.90–0.91, demonstrating high diagnostic accuracy(2, 4). While shock can cause significant alterations in hemodynamics and cardiac function, bedside cardiac ultrasound, including EPSS measurement, remains a practical screening tool for rapid assessment(6, 7). EPSS is particularly suited for point-of-care use in the emergency department and intensive care unit, where rapid triage and decision-making are essential(5, 8). Certain conditions (e.g., mitral stenosis, aortic regurgitation, septal hypertrophy, atrial fibrillation) may affect EPSS accuracy, and severe hemodynamic changes in shock can introduce variability(4). Nonetheless, EPSS is still considered a valuable initial screening method for identifying patients with low ejection fraction in acute settings(2, 4). This research aims to clarify the effectiveness and predictive value of EPSS in estimating ejection fraction among patients with shock. By establishing the correlation between EPSS-derived ejection fraction and clinical status, the study seeks to enhance the accuracy of diagnosis and the quality of clinical management for these high-risk patients. Methods This prospective comparative study is conducted on children with shock who present to the emergency department. Patients are selected based on specific inclusion and exclusion criteria. The measurement of ejection fraction (EF) is performed using two different methods: 1. Bedside Echocardiography : Echocardiography is performed by an emergency medicine fellow using the EPSS method. 2. Standard Method : Echocardiography is conducted by a cardiologist following standard protocols. Data obtained from both methods are collected and prepared for analysis. To compare the ejection fraction between the two groups (bedside echocardiography and standard method), independent t-tests will be used. Data will be analyzed using SPSS statistical software, and results will be presented using appropriate tables and charts to clearly demonstrate the accuracy and efficacy of each method. Study Sample The sample consists of patients with shock who are transferred to the intensive care unit (ICU) and are under cardiac monitoring. Clinical data, including blood pressure, hemodynamic status, lactate levels, and other parameters related to various types of shock (cardiogenic shock, septic shock, etc.), will be collected. Additionally, bedside ultrasound images will be recorded in two primary positions: subcostal and parasternal, to calculate EPSS. Ultrasound Procedure Execution EPSS is measured as the distance between the E-point (the point of maximum movement of the mitral valve in diastole) and the septal wall. This index serves as an indirect measure for assessing left ventricular function and estimating EF. The measurement of EPSS should be performed periodically throughout the treatment course to accurately evaluate changes in cardiac function during shock and various treatments. Concurrent Testing In addition to EPSS, concurrent tests such as standard echocardiography will be performed for a more accurate estimation of ejection fraction (EF), which will serve as a reference standard for comparison and validation. These methods together provide a comprehensive and precise assessment of cardiac function in patients with shock. Inclusion Criteria Age : Patients under 18 years old and over one month old. Diagnosis : Patients with shock (cardiogenic shock, vascular shock, or hypovolemic shock) who present to the emergency department. Awareness : Patients or their legal representatives must be informed about the study and sign a consent form. Absence of Specific Conditions : Patients must be eligible regarding underlying diseases. Exclusion Criteria Specific Medical Conditions : Patients with underlying diseases that may negatively impact the results. Lack of Consent : Patients who do not provide informed consent to participate in the study. Cognitive Impairments : Patients who cannot respond to questions or understand the study process. Specific Assessment Conditions : Patients whose echocardiography results are deemed unreliable due to factors such as operator skill or other obstacles. Sample Size To determine the sample size, the following key parameters are considered: Confidence Level (α) : 0.05, which corresponds to a 95% confidence level. The corresponding Z value for this confidence level is 1.96. Study Power (β) : 0.8, which corresponds to an 80% statistical power. The corresponding Z value for this power is 0.84. Expected Difference Between Two Methods (µ₁ - µ₂) : 10%. Standard Deviation (σ) : 5%. The formula for calculating the sample size is as follows: By substituting the above values, the required sample size is calculated. Additionally, using a regression model with an assumed correlation coefficient of 0.4, a significance level of 0.05, and a statistical power of 80%, the estimated sample size is determined to be 69 individuals. Questionnaire Design A standardized questionnaire has been designed that includes both closed-ended and open-ended questions to collect demographic and clinical information about the patients. This questionnaire includes information such as age, gender, medical history, and risk factors, as well as questions about clinical symptoms and echocardiography results. The echocardiography protocol also includes detailed instructions for performing these methods to ensure data quality. Data Collection Process Data collection is carried out as follows: After obtaining the patient's or legal guardian's consent, the questionnaires are completed by the responsible person, and the echocardiography is performed by a subspecialty fellow or physician. The data is digitally entered into a database and managed in accordance with ethical principles. This comprehensive approach ensures the quality of the data and the validity of the research results. Data Analysis : In the data analysis phase, the EPSS, EF, and clinical data values are examined using descriptive statistics. The relationship between EPSS and EF is evaluated using Pearson's correlation test and linear regression analysis. Additionally, an ROC curve is analyzed to predict EF less than 35%. T-tests and ANOVA are used to compare means. Internal and external validation is also applied to ensure the accuracy and generalizability of the results. Results In this study, 100 patients were enrolled, all of whom gave their consent to participate in the research. Table 1 Demographic characteristics of patients at Ali Asghar Hospital Demographic Characteristics Frequency Percentage Gender Male 52 52.0 Female 48 48.0 History of Heart Disease No 79 79.0 Yes 21 21.0 Fever No 69 69.0 Yes 31 31.0 Tachycardia No 43 43.0 Yes 57 57.0 Tachypnea No 70 70.0 Yes 30 30.0 Hypoglycemia No 99 99.0 Yes 1 1.0 Diarrhea and Vomiting No 81 81.0 Yes 19 19.0 Poisoning No 98 98.0 Yes 2 2.0 Hypotension No 62 62.0 Yes 38 38.0 Respiratory Distress No 92 92.0 Yes 8 8.0 Description of Demographic and Clinical Characteristics This study included 100 hospitalized children at Hazrat Ali Asghar Hospital, distributed according to demographic and clinical characteristics as follows: The gender distribution of the samples was nearly balanced, with 52% male (52 cases) and 48% female (48 cases). Regarding the history of heart disease, a significant majority of the patients (79%, or 79 cases) had no history of heart conditions, while 21% (21 cases) had a history of cardiac issues. In terms of clinical symptoms, fever was observed in 31% of the patients (31 cases). Tachycardia (increased heart rate) was the most common symptom, occurring in 57% of the cases (57 cases), while tachypnea (rapid breathing) was noted in 30% of the cases (30 cases). Hypoglycemia (low blood sugar) was one of the least prevalent findings, occurring in only 1% (1 case), and poisoning was reported in 2% (2 cases). Gastrointestinal symptoms, including diarrhea and vomiting, were reported in 19% of the patients (19 cases). In terms of hemodynamic indicators, hypotension was present in 38% of the cases (38 cases), and respiratory distress was observed in 8% of the patients (8 cases). Notable findings include a relatively high prevalence of tachycardia (57%) and hypotension (38%), indicating unstable hemodynamic status in a significant portion of the patients. The very low prevalence of hypoglycemia (1%) and poisoning (2%) suggests that these factors played a minimal role in the etiology of shock in this population. Additionally, the high percentage of patients without respiratory distress (92%) may indicate that respiratory problems were not the primary cause of shock in most cases. Table 2 Demographic characteristics and echocardiographic parameters Variable Minimum Maximum Mean Std. Deviation Age 1 17 5.15 4.732 Weight (mm) 1.0 20.0 5.318 3.9263 EPSS 25.5 73.0 61.467 9.9907 LVEF (Eyeball %) 20 70 55.29 12.582 LVEF (Device %) 30 78 62.41 10.313 The age of the patients ranged from 1 to 17 years, with a mean age of 5.15 years (standard deviation ± 4.732), indicating a diverse age group in the study. Echocardiographic Parameters : EPSS (E-point septal separation) : Measured values varied from 1.0 to 20.0 mm, with a mean of 5.318 mm (standard deviation ± 3.9263). This wide range reflects significant variability in mitral valve function among the studied patients. LVEF (Left Ventricular Ejection Fraction) : Eyeball estimation ranged from 20–70%, with a mean of 55.29% (standard deviation ± 12.582). Device measurement ranged from 30–78%, with a mean of 62.41% (standard deviation ± 10.313). The average difference of approximately 7 percentage points between eyeball and device measurements of LVEF indicates a systematic discrepancy, potentially due to subjective estimation errors or differences in measurement techniques. The relatively high standard deviation in both methods (over 10 percentage points) suggests considerable data dispersion, highlighting the need for further analysis to identify factors influencing this variability. The results of the ROC curve for the E-Point Septal Separation (EPSS) variable demonstrate the excellent performance of this parameter in detecting cardiac dysfunction. The Area Under the Curve (AUC) is 0.983, with an ideal value of 1 indicating perfect detection. The standard deviation is 0.011, reflecting high precision in estimation. The significance level is p < 0.001, which rejects the null hypothesis of AUC = 0.5. The 95% confidence interval ranges from 0.962 to 1.000, indicating that even in the most pessimistic scenario, the AUC remains above 0.96. Clinically, an AUC close to 1 signifies excellent diagnostic accuracy for EPSS, exhibiting high sensitivity and specificity, which allows for the correct identification of both affected and unaffected patients (Fig. 1). Table 3 Key Results Parameter Value Interpretation AUC 0.983 Excellent diagnostic accuracy p-value < 0.001 Strong statistical significance Confidence Interval 0.962-1.000 High reliability The ROC analysis for the E-Point variable reveals its excellent performance in distinguishing between positive and negative groups, with an Area Under the Curve (AUC) of 0.983. This indicates a 98.3% probability of correctly identifying positive cases compared to negative ones, making it a highly favorable predictive model. The standard error of the AUC estimate is 0.011, reflecting high precision, while the Asymptotic Significance (p-value) of 0.000 shows that the AUC is significantly greater than 0.5, allowing us to reject the null hypothesis and affirm that our model outperforms random chance. The 95% confidence interval for the AUC ranges from 0.962 to 1.000, indicating with 95% certainty that the true AUC lies within this range, which further supports the model's high discriminative power. Youden’s Index, calculated as Youden’s Index = Sensitivity + Specificity − 1, yielded a value of 0.966, close to 1, signifying excellent diagnostic accuracy. The AUC value of 0.983 confirms that the model effectively differentiates between the two groups. Table 4 Area Under the Curve for the Eponit Test Parameter Value Area Under the Curve (AUC) 0.975 Standard Error 0.013 Asymptotic Significance (p-value) 0.000 95% Confidence Interval Lower Bound 0.950 Upper Bound 1.000 The ROC analysis for the E-Point variable indicates very good performance in distinguishing between positive and negative groups, with an Area Under the Curve (AUC) of 0.975. This suggests a high ability of the model to correctly identify positive cases compared to negative ones, meaning there is a 97.5% probability that a positive case will be accurately identified as opposed to a negative case. The Standard Error is 0.013, reflecting high precision in the AUC estimate. Additionally, the Asymptotic Significance (p-value) is 0.000, clearly demonstrating that the AUC is significantly greater than 0.5 (random performance), allowing us to reject the null hypothesis and conclude that our model performs better than random chance. The 95% Confidence Interval for the AUC ranges from 0.950 to 1.000, indicating with 95% certainty that the true AUC lies within this range, which further attests to the model's high discriminative power. In the medical context, Ejection Fraction (Eye) is a crucial metric for assessing cardiovascular performance. This measure aids in accurately evaluating heart status and, alongside the results from the ROC analysis, can enhance clinical decision-making (Fig. 2). The estimated Youden’s Index is approximately 0.95, indicating excellent diagnostic capability. This value suggests that the model has a very high ability to discriminate, with 1 representing perfect discrimination and 0.95 being very close to this ideal. The AUC of 0.975 further confirms this strong performance. The scatter diagram illustrates the relationship between Ejection Fraction (Eye) and Eponit (Fig. 3). 1. Linear Regression Line : Equation : y = 21.43 + 0.72x This indicates that for each unit increase in Ejection Fraction (Eye), Eponit increases by 0.72 units. 2. Coefficient of Determination (R-squared) : R-squared : 0.832 This shows that 83.2% of the variation in Eponit can be explained by Ejection Fraction (Eye), indicating a relatively strong linear relationship between the two variables. 3. Scatter Pattern : The scattered points in the diagram trend upwards with a positive slope. This pattern further confirms the positive relationship between the two variables. The scatter diagram illustrates the relationship between Ejection Fraction (Device) and Eponit (Fig. 4). 1. Linear Regression Line : Equation : y = 6.45 + 0.88x This indicates that for each unit increase in Ejection Fraction (Device), Eponit increases by 0.88 units. 2. Coefficient of Determination (R-squared) : R-squared : 0.828 This shows that 82.8% of the variation in Eponit can be explained by Ejection Fraction (Device), indicating a relatively strong linear relationship between the two variables. 3. Scatter Pattern : The scattered points in the diagram trend upwards with a positive slope. This pattern further confirms the positive relationship between the two variables. Table 4 Regression Coefficients and Statistical Significance of Predictors for Eponit Variable Standardized Coefficient (β) p-value Interpretation LVEF (Eye) 0.525 < 0.001 Strongest predictor of EPSS LVEF (Device) 0.418 < 0.001 Significant predictor History of Heart Disease -0.070 0.206 Significant only in Model 2 Age 0.038 0.485 Non-significant Gender -0.010 0.856 Non-significant The regression results indicate that the final model (Model 3), which includes the variables of age, gender, clinical symptoms (history of heart disease, fever, tachycardia, tachypnea, diarrhea, vomiting, hypotension, and respiratory distress), along with the two variables Ejection Fraction (Eye) and Ejection Fraction (Device), explains 77.3% of the variation in the Eponit variable (R-Square = 0.773). This model is significant at the 0.01 level (F = 27.303, p-value < 0.001). An examination of the regression coefficients shows that among the independent variables, only the two variables Ejection Fraction (Eye) (β = 0.525, p-value < 0.001) and Ejection Fraction (Device) (β = 0.418, p-value < 0.001) significantly influence Eponit. Other variables did not have a significant impact on Eponit. These results confirm that Eponit, as a non-invasive indicator, can effectively estimate Ejection Fraction, whether through the Device or echocardiography (Eye). In fact, Eponit shows a very high correlation with both Ejection Fraction variables ( Table 4). Discussion This study aimed to investigate the predictive value of Bedside Cardiac Ultrasound in estimating the Ejection Fraction using the E-Point Septal Separation (EPSS) method in children hospitalized at Ali Asghar Hospital with signs of shock. The findings clearly indicate that EPSS is a strong, reliable, and rapid indicator for assessing cardiac function in children with shock. Given the AUC close to one, this parameter has the potential to become a standard tool in inpatient evaluations. The results of the ROC analysis for the E-Point Septal Separation (EPSS) variable demonstrate the excellent performance of this parameter in predicting low LVEF. An area under the curve (AUC) of 0.983 indicates that EPSS can accurately differentiate between individuals with low and normal LVEF. Previous studies have also highlighted the high diagnostic value of EPSS in assessing cardiac function, with the study by Nagueh et al. reporting a strong correlation between EPSS and LVEF(9). The studies reviewed highlight the significant role of E-point Septal Separation (EPSS) as a non-invasive and reliable tool for estimating Left Ventricular Ejection Fraction (LVEF) across various patient populations, particularly in critically ill children and adults. A study found a notable correlation between EPSS and LVEF, as measured by standard echocardiography (r = -0.82). This aligns with the findings from our results, which indicated that EPSS effectively predicts LVEF, suggesting its utility in rapid assessment during acute clinical scenarios(4). The research demonstrated that EPSS can predict LVEF and fluid responsiveness in children with shock. With an EPSS > 7 mm correlating with EF < 55%, this study supports our findings that EPSS is a strong predictor of cardiac performance in pediatric patients, especially in critical conditions.(10). Given the high diagnostic accuracy of EPSS, this parameter can be used as a rapid and non-invasive screening method in children, and it may also reduce the need for more invasive and complex procedures. An AUC of 0.975 indicates that there is a 97.5% probability of correctly identifying a positive case from a negative one. These results are consistent with the study by Nagueh and colleagues, which highlighted the strong relationship between EPSS and LVEF(9). In the study by Leman and Johnson, the results showed that quantitative measurements of EPSS and FS had poor accuracy in estimating LVEF, even among experienced sonographers. In contrast, in the present study, the variable Eponit, which is similar to EPSS, was able to significantly estimate Ejection Fraction(11). On the other hand, other studies have also shown that EPSS measurement by emergency physicians can accurately estimate LVEF(4, 12, 13). These results are somewhat similar to the findings of the present study, which showed that Eponit can serve as a suitable alternative for measuring Ejection Fraction. Another study in 2021 demonstrated that measuring EPSS at the mitral valve has a significant negative correlation with LVEF and can be used as a simple and reliable tool for assessing left ventricular systolic function by anesthesiologists in preoperative settings. In other words, EPSS provides a rapid and non-invasive method for estimating left ventricular systolic function and can serve as a suitable alternative to more complex methods in clinical conditions, which aligns with the results of our study (14). Additionally, the study by Adnan and Neslihan showed that measuring EPSS has a strong correlation with LVEF and can serve as a complementary method for diagnosing patients in echocardiographic images. This finding is also somewhat consistent with the results of the present study (15). In summary, although some previous studies have reported poor accuracy for measurements similar to Eponit, the present study demonstrated that Eponit can serve as a non-invasive and independent indicator of Ejection Fraction, regardless of clinical symptoms. This difference may be due to variations in methodology and the study population. Limitations : Confounding Variables : Shock can arise from various factors that may significantly affect EPSS and EF, such as arrhythmias, the dosage of vasopressor medications, and respiratory status. To mitigate this limitation, it is essential to control for these confounding variables by collecting detailed clinical data and using statistical methods to adjust for their effects in the analysis. Variability in Device Results : The accuracy and reliability of ultrasound devices can vary across different centers. Consequently, results obtained from one center may not be generalizable to another. To address this issue, standardization of the ultrasound protocols and training for the personnel conducting the echocardiograms should be implemented. Additionally, using calibrated and validated equipment can help ensure consistency in results across different locations. Conclusion In conclusion, EPSS is a simple, rapid, and accurate parameter for assessing left ventricular function that can be used in various clinical settings, including emergency, intensive care, and perioperative settings, as a screening tool and even as an alternative to more complex methods. This could help improve diagnosis, expedite treatment decisions, and reduce the need for more invasive procedures. Declarations Informed Consent: Written informed consent was obtained from all subjects. Ethics Approval : Ethics Approval and Consent to Participate This study was approved by the Research Ethics Committee of Iran University of Medical Sciences (Approval ID: IR.IUMS.REC.1404.011, Approval Date: 2025-03-15). Written informed consent to participate was obtained from all patients or their legal guardians. Consent for Publication Not applicable. Clinical Trial Number Clinical trial number: not applicable. Human Ethics and Consent to Participate Declarations This study was conducted in accordance with the ethical principles of the Declaration of Helsinki and the national guidelines for medical research in Iran. All participants (or their guardians) provided informed consent before inclusion in the study. Conflict of interests : The authors report no conflict of interest. Data Reproducibility: The Dataset presented in the study is available on request from the corresponding author during submission or after publication. Funding/Support: The authors did not receive any funding support for this study. References Kost GJ. Principles & practice of point-of-care testing. (No Title). 2002. Núñez-Ramos JA, Pana-Toloza MC, Palacio-Held SC. E-point septal separation accuracy for the diagnosis of mild and severe reduced ejection fraction in emergency department patients. POCUS journal. 2022;7(1):160. Quesada-González D, Merkoçi A. Nanomaterial-based devices for point-of-care diagnostic applications. Chemical Society Reviews. 2018;47(13):4697-709. McKaigney CJ, Krantz MJ, La Rocque CL, Hurst ND, Buchanan MS, Kendall JL. E-point septal separation: a bedside tool for emergency physician assessment of left ventricular ejection fraction. The American Journal of Emergency Medicine. 2014;32(6):493-7. Price S, Via G, Sloth E, Guarracino F, Breitkreutz R, Catena E, et al. Echocardiography practice, training and accreditation in the intensive care: document for the World Interactive Network Focused on Critical Ultrasound (WINFOCUS). Cardiovascular ultrasound. 2008;6:1-35. McLean AS. Echocardiography in shock management. Critical Care. 2016;20:1-10. Vieillard-Baron A, Caille V, Charron C, Belliard G, Page B, Jardin F. Actual incidence of global left ventricular hypokinesia in adult septic shock. Critical care medicine. 2008;36(6):1701-6. Breitkreutz R, Walcher F, Seeger FH. Focused echocardiographic evaluation in resuscitation management: Concept of an advanced life support–conformed algorithm. Critical care medicine. 2007;35(5):S150-S61. Nagueh SF, Kopelen HA, Quin˜ ones MA. Assessment of left ventricular filling pressures by Doppler in the presence of atrial fibrillation. Circulation. 1996;94(9):2138-45. Vorel ES, Jacquemyn X, Cohen JS, Kutty S, Deanehan JK. Pediatric Reference Ranges and Test Characteristics of E-point Septal Separation as a Marker for Left Ventricular Dysfunction: A Retrospective Study. Pediatric emergency care. 2024:10.1097. Bahl A, Johnson S, Altwail M, Brackney A, Xiao J, Price J, et al. Left ventricular ejection fraction assessment by emergency physician-performed bedside echocardiography: a prospective comparative evaluation of multiple modalities. The Journal of emergency medicine. 2021;61(6):711-9. Secko MA, Lazar JM, Salciccioli LA, Stone MB. Can junior emergency physicians use E‐point septal separation to accurately estimate left ventricular function in acutely dyspneic patients? Academic Emergency Medicine. 2011;18(11):1223-6. Tamanna RJ, Hoque SJ, Pasha FM. E-Point Septal Separation: A Bedside Tool for Emergency Physician Assessment of Left Ventricular Ejection Fraction. Bangladesh Critical Care Journal. 2023;11(2):90-4. Joshi P, Borde D, Asegaonkar B, Daunde V, Joshi S, Jaspara A. Utility of E point septal separation as screening tool for left ventricular ejection fraction in perioperative settings by anesthetists. Annals of Cardiac Anaesthesia. 2022;25(3):304-10. Satılmış Siliv N, Yamanoglu A, Pınar P, Celebi Yamanoglu NG, Torlak F, Parlak I. Estimation of cardiac systolic function based on mitral valve movements: An accurate bedside tool for emergency physicians in dyspneic patients. Journal of Ultrasound in Medicine. 2019;38(4):1027-38. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 26 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers invited by journal 08 Aug, 2025 Editor invited by journal 17 Jul, 2025 Editor assigned by journal 16 Jul, 2025 Submission checks completed at journal 16 Jul, 2025 First submitted to journal 12 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7105889","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":497920813,"identity":"e67f3cd6-7e10-43c5-bd65-a17c37b24211","order_by":0,"name":"Samira Sayyah","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Samira","middleName":"","lastName":"Sayyah","suffix":""},{"id":497920814,"identity":"e1a29535-ac0f-45e5-beff-b5499c090ad8","order_by":1,"name":"Shabahang 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1","display":"","copyAsset":false,"role":"figure","size":85522,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis for EPSS in predicting low LVEF\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7105889/v1/0b7ca26ccba63d51962ee129.png"},{"id":89230845,"identity":"c4eecc96-eded-40d1-b866-1799234e23f5","added_by":"auto","created_at":"2025-08-17 14:16:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":98668,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curve Analysis of EPSS for Predicting Low Ejection Fraction in Children with Shock\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7105889/v1/112f33432f45d8ca8a8cfe57.png"},{"id":89232209,"identity":"d66da0f5-759a-4798-b915-752c3e9a3779","added_by":"auto","created_at":"2025-08-17 14:24:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":171611,"visible":true,"origin":"","legend":"\u003cp\u003eScatter diagram between Ejection Fraction (Eye) and Eponit\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7105889/v1/92e14a9a76cdedf32583867e.png"},{"id":89233213,"identity":"38e0f684-0c33-4b64-8bbc-cd2ae77baec4","added_by":"auto","created_at":"2025-08-17 14:32:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":151809,"visible":true,"origin":"","legend":"\u003cp\u003eScatter diagram between Ejection Fraction (Device) and Eponit\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7105889/v1/383a557710a4a36b58a8ecdc.png"},{"id":89233643,"identity":"f0a07d65-e01b-4d17-ba8b-3a1b61929e16","added_by":"auto","created_at":"2025-08-17 14:40:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1666186,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7105889/v1/674a71a9-ef27-4e5a-beed-6b20ebc78354.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic Accuracy of Bedside Cardiac Ultrasound EPSS for Rapid Assessment of Left Ventricular Ejection Fraction inPediatric Shock:A Prospective Study","fulltext":[{"header":"Background","content":"\u003cp\u003eWith the global rise in cardiovascular diseases and the critical need for rapid, accurate diagnosis of cardiac injuries in patients with shock, there is a strong demand for fast and non-invasive assessment tools. Bedside cardiac ultrasound has emerged as an innovative and effective approach, enabling clinicians to quickly gather essential information about cardiac function and hemodynamic(1) status in critically ill patients. E-Point Septal Separation (EPSS) is a straightforward, easily obtainable echocardiographic measurement that has gained attention for its utility in estimating left ventricular ejection fraction (LVEF).(2, 3) EPSS can serve as a predictive indicator for identifying patients with reduced ejection fraction who may require urgent therapeutic interventions(2).\u003c/p\u003e\u003cp\u003eEPSS is particularly valuable in emergency and critical care settings due to its speed and simplicity(4, 5). Studies have shown that an EPSS greater than 7 mm is associated with significantly reduced LVEF, and higher values (e.g., ≥ 13 mm) reliably indicate severely depressed systolic function (LVEF ≤ 35%)(2). Recent research in emergency department settings found that an EPSS cutoff of 9.5 mm yielded a sensitivity of 80% and specificity of 91% for detecting LVEF \u0026lt; 50%, and a sensitivity of 91% and specificity of 80% for LVEF ≤ 40%. The area under the ROC curve (AUC) for diagnosing reduced LVEF was 0.90–0.91, demonstrating high diagnostic accuracy(2, 4).\u003c/p\u003e\u003cp\u003eWhile shock can cause significant alterations in hemodynamics and cardiac function, bedside cardiac ultrasound, including EPSS measurement, remains a practical screening tool for rapid assessment(6, 7). EPSS is particularly suited for point-of-care use in the emergency department and intensive care unit, where rapid triage and decision-making are essential(5, 8). Certain conditions (e.g., mitral stenosis, aortic regurgitation, septal hypertrophy, atrial fibrillation) may affect EPSS accuracy, and severe hemodynamic changes in shock can introduce variability(4). Nonetheless, EPSS is still considered a valuable initial screening method for identifying patients with low ejection fraction in acute settings(2, 4).\u003c/p\u003e\u003cp\u003eThis research aims to clarify the effectiveness and predictive value of EPSS in estimating ejection fraction among patients with shock. By establishing the correlation between EPSS-derived ejection fraction and clinical status, the study seeks to enhance the accuracy of diagnosis and the quality of clinical management for these high-risk patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis prospective comparative study is conducted on children with shock who present to the emergency department. Patients are selected based on specific inclusion and exclusion criteria. The measurement of ejection fraction (EF) is performed using two different methods:\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp;\u003cstrong\u003eBedside Echocardiography\u003c/strong\u003e:\u003cbr\u003e\u0026nbsp;Echocardiography is performed by an emergency medicine fellow using the EPSS method.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp;\u003cstrong\u003eStandard Method\u003c/strong\u003e:\u003cbr\u003e\u0026nbsp;Echocardiography is conducted by a cardiologist following standard protocols. Data obtained from both methods are collected and prepared for analysis. To compare the ejection fraction between the two groups (bedside echocardiography and standard method), independent t-tests will be used. Data will be analyzed using SPSS statistical software, and results will be presented using appropriate tables and charts to clearly demonstrate the accuracy and efficacy of each method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy Sample\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe sample consists of patients with shock who are transferred to the intensive care unit (ICU) and are under cardiac monitoring. Clinical data, including blood pressure, hemodynamic status, lactate levels, and other parameters related to various types of shock (cardiogenic shock, septic shock, etc.), will be collected. Additionally, bedside ultrasound images will be recorded in two primary positions: subcostal and parasternal, to calculate EPSS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUltrasound Procedure Execution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEPSS is measured as the distance between the E-point (the point of maximum movement of the mitral valve in diastole) and the septal wall. This index serves as an indirect measure for assessing left ventricular function and estimating EF. The measurement of EPSS should be performed periodically throughout the treatment course to accurately evaluate changes in cardiac function during shock and various treatments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConcurrent Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to EPSS, concurrent tests such as standard echocardiography will be performed for a more accurate estimation of ejection fraction (EF), which will serve as a reference standard for comparison and validation. These methods together provide a comprehensive and precise assessment of cardiac function in patients with shock.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e: Patients under 18 years old and over one month old.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosis\u003c/strong\u003e: Patients with shock (cardiogenic shock, vascular shock, or hypovolemic shock) who present to the emergency department.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eAwareness\u003c/strong\u003e: Patients or their legal representatives must be informed about the study and sign a consent form.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eAbsence of Specific Conditions\u003c/strong\u003e: Patients must be eligible regarding underlying diseases.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eSpecific Medical Conditions\u003c/strong\u003e: Patients with underlying diseases that may negatively impact the results.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eLack of Consent\u003c/strong\u003e: Patients who do not provide informed consent to participate in the study.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive Impairments\u003c/strong\u003e: Patients who cannot respond to questions or understand the study process.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eSpecific Assessment Conditions\u003c/strong\u003e: Patients whose echocardiography results are deemed unreliable due to factors such as operator skill or other obstacles.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSample Size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the sample size, the following key parameters are considered:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eConfidence Level (\u0026alpha;)\u003c/strong\u003e: 0.05, which corresponds to a 95% confidence level. The corresponding Z value for this confidence level is 1.96.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eStudy Power (\u0026beta;)\u003c/strong\u003e: 0.8, which corresponds to an 80% statistical power. The corresponding Z value for this power is 0.84.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eExpected Difference Between Two Methods (\u0026micro;₁ - \u0026micro;₂)\u003c/strong\u003e: 10%.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation (\u0026sigma;)\u003c/strong\u003e: 5%.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe formula for calculating the sample size is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eBy substituting the above values, the required sample size is calculated. Additionally, using a regression model with an assumed correlation coefficient of 0.4, a significance level of 0.05, and a statistical power of 80%, the estimated sample size is determined to be 69 individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuestionnaire Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA standardized questionnaire has been designed that includes both closed-ended and open-ended questions to collect demographic and clinical information about the patients. This questionnaire includes information such as age, gender, medical history, and risk factors, as well as questions about clinical symptoms and echocardiography results. The echocardiography protocol also includes detailed instructions for performing these methods to ensure data quality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection Process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection is carried out as follows: After obtaining the patient\u0026apos;s or legal guardian\u0026apos;s consent, the questionnaires are completed by the responsible person, and the echocardiography is performed by a subspecialty fellow or physician. The data is digitally entered into a database and managed in accordance with ethical principles. This comprehensive approach ensures the quality of the data and the validity of the research results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e :\u003c/p\u003e\n\u003cp\u003eIn the data analysis phase, the EPSS, EF, and clinical data values are examined using descriptive statistics. The relationship between EPSS and EF is evaluated using Pearson\u0026apos;s correlation test and linear regression analysis. Additionally, an ROC curve is analyzed to predict EF less than 35%. T-tests and ANOVA are used to compare means. Internal and external validation is also applied to ensure the accuracy and generalizability of the results.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e In this study, 100 patients were enrolled, all of whom gave their consent to participate in the research.\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\u003eDemographic characteristics of patients at Ali Asghar Hospital\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic Characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHistory of Heart Disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFever\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTachycardia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTachypnea\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypoglycemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiarrhea and Vomiting\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePoisoning\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypotension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRespiratory Distress\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDescription of Demographic and Clinical Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study included 100 hospitalized children at Hazrat Ali Asghar Hospital, distributed according to demographic and clinical characteristics as follows: The gender distribution of the samples was nearly balanced, with 52% male (52 cases) and 48% female (48 cases). Regarding the history of heart disease, a significant majority of the patients (79%, or 79 cases) had no history of heart conditions, while 21% (21 cases) had a history of cardiac issues.\u003c/p\u003e\u003cp\u003eIn terms of clinical symptoms, fever was observed in 31% of the patients (31 cases). Tachycardia (increased heart rate) was the most common symptom, occurring in 57% of the cases (57 cases), while tachypnea (rapid breathing) was noted in 30% of the cases (30 cases). Hypoglycemia (low blood sugar) was one of the least prevalent findings, occurring in only 1% (1 case), and poisoning was reported in 2% (2 cases).\u003c/p\u003e\u003cp\u003eGastrointestinal symptoms, including diarrhea and vomiting, were reported in 19% of the patients (19 cases). In terms of hemodynamic indicators, hypotension was present in 38% of the cases (38 cases), and respiratory distress was observed in 8% of the patients (8 cases).\u003c/p\u003e\u003cp\u003eNotable findings include a relatively high prevalence of tachycardia (57%) and hypotension (38%), indicating unstable hemodynamic status in a significant portion of the patients. The very low prevalence of hypoglycemia (1%) and poisoning (2%) suggests that these factors played a minimal role in the etiology of shock in this population. Additionally, the high percentage of patients without respiratory distress (92%) may indicate that respiratory problems were not the primary cause of shock in most cases.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic characteristics and echocardiographic parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd. Deviation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.732\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWeight (mm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.9263\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEPSS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.9907\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLVEF (Eyeball %)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.582\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLVEF (Device %)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.313\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe age of the patients ranged from 1 to 17 years, with a mean age of 5.15 years (standard deviation\u0026thinsp;\u0026plusmn;\u0026thinsp;4.732), indicating a diverse age group in the study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEchocardiographic Parameters\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEPSS (E-point septal separation)\u003c/b\u003e: Measured values varied from 1.0 to 20.0 mm, with a mean of 5.318 mm (standard deviation\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9263). This wide range reflects significant variability in mitral valve function among the studied patients.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLVEF (Left Ventricular Ejection Fraction)\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eEyeball estimation ranged from 20\u0026ndash;70%, with a mean of 55.29% (standard deviation\u0026thinsp;\u0026plusmn;\u0026thinsp;12.582).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDevice measurement ranged from 30\u0026ndash;78%, with a mean of 62.41% (standard deviation\u0026thinsp;\u0026plusmn;\u0026thinsp;10.313).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe average difference of approximately 7 percentage points between eyeball and device measurements of LVEF indicates a systematic discrepancy, potentially due to subjective estimation errors or differences in measurement techniques. The relatively high standard deviation in both methods (over 10 percentage points) suggests considerable data dispersion, highlighting the need for further analysis to identify factors influencing this variability.\u003c/p\u003e\u003cp\u003eThe results of the ROC curve for the E-Point Septal Separation (EPSS) variable demonstrate the excellent performance of this parameter in detecting cardiac dysfunction. The Area Under the Curve (AUC) is 0.983, with an ideal value of 1 indicating perfect detection. The standard deviation is 0.011, reflecting high precision in estimation. The significance level is p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, which rejects the null hypothesis of AUC\u0026thinsp;=\u0026thinsp;0.5. The 95% confidence interval ranges from 0.962 to 1.000, indicating that even in the most pessimistic scenario, the AUC remains above 0.96. Clinically, an AUC close to 1 signifies excellent diagnostic accuracy for EPSS, exhibiting high sensitivity and specificity, which allows for the correct identification of both affected and unaffected patients (Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eKey Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExcellent diagnostic accuracy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStrong statistical significance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConfidence Interval\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.962-1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh reliability\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe ROC analysis for the E-Point variable reveals its excellent performance in distinguishing between positive and negative groups, with an Area Under the Curve (AUC) of 0.983. This indicates a 98.3% probability of correctly identifying positive cases compared to negative ones, making it a highly favorable predictive model. The standard error of the AUC estimate is 0.011, reflecting high precision, while the Asymptotic Significance (p-value) of 0.000 shows that the AUC is significantly greater than 0.5, allowing us to reject the null hypothesis and affirm that our model outperforms random chance.\u003c/p\u003e\u003cp\u003eThe 95% confidence interval for the AUC ranges from 0.962 to 1.000, indicating with 95% certainty that the true AUC lies within this range, which further supports the model's high discriminative power. Youden\u0026rsquo;s Index, calculated as Youden\u0026rsquo;s Index\u0026thinsp;=\u0026thinsp;Sensitivity\u0026thinsp;+\u0026thinsp;Specificity\u0026thinsp;\u0026minus;\u0026thinsp;1,\u003c/p\u003e\u003cp\u003eyielded a value of 0.966, close to 1, signifying excellent diagnostic accuracy. The AUC value of 0.983 confirms that the model effectively differentiates between the two groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eArea Under the Curve for the Eponit Test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArea Under the Curve (AUC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.975\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsymptotic Significance (p-value)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower Bound\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.950\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper Bound\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe ROC analysis for the E-Point variable indicates very good performance in distinguishing between positive and negative groups, with an Area Under the Curve (AUC) of 0.975. This suggests a high ability of the model to correctly identify positive cases compared to negative ones, meaning there is a 97.5% probability that a positive case will be accurately identified as opposed to a negative case. The Standard Error is 0.013, reflecting high precision in the AUC estimate. Additionally, the Asymptotic Significance (p-value) is 0.000, clearly demonstrating that the AUC is significantly greater than 0.5 (random performance), allowing us to reject the null hypothesis and conclude that our model performs better than random chance.\u003c/p\u003e\u003cp\u003eThe 95% Confidence Interval for the AUC ranges from 0.950 to 1.000, indicating with 95% certainty that the true AUC lies within this range, which further attests to the model's high discriminative power. In the medical context, Ejection Fraction (Eye) is a crucial metric for assessing cardiovascular performance. This measure aids in accurately evaluating heart status and, alongside the results from the ROC analysis, can enhance clinical decision-making (Fig.\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eThe estimated Youden\u0026rsquo;s Index is approximately 0.95, indicating excellent diagnostic capability. This value suggests that the model has a very high ability to discriminate, with 1 representing perfect discrimination and 0.95 being very close to this ideal. The AUC of 0.975 further confirms this strong performance.\u003c/p\u003e\u003cp\u003eThe scatter diagram illustrates the relationship between Ejection Fraction (Eye) and Eponit (Fig. 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Linear Regression Line\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eEquation\u003c/strong\u003e: y\u0026thinsp;=\u0026thinsp;21.43\u0026thinsp;+\u0026thinsp;0.72x\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThis indicates that for each unit increase in Ejection Fraction (Eye), Eponit increases by 0.72 units.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e2. Coefficient of Determination (R-squared)\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eR-squared\u003c/strong\u003e: 0.832\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThis shows that 83.2% of the variation in Eponit can be explained by Ejection Fraction (Eye), indicating a relatively strong linear relationship between the two variables.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e3. Scatter Pattern\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe scattered points in the diagram trend upwards with a positive slope.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThis pattern further confirms the positive relationship between the two variables.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe scatter diagram illustrates the relationship between Ejection Fraction (Device) and Eponit (Fig. 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Linear Regression Line\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eEquation\u003c/strong\u003e: y\u0026thinsp;=\u0026thinsp;6.45\u0026thinsp;+\u0026thinsp;0.88x\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThis indicates that for each unit increase in Ejection Fraction (Device), Eponit increases by 0.88 units.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e2. Coefficient of Determination (R-squared)\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eR-squared\u003c/strong\u003e: 0.828\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThis shows that 82.8% of the variation in Eponit can be explained by Ejection Fraction (Device), indicating a relatively strong linear relationship between the two variables.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e3. Scatter Pattern\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe scattered points in the diagram trend upwards with a positive slope.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThis pattern further confirms the positive relationship between the two variables.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eRegression Coefficients and Statistical Significance of Predictors for Eponit\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandardized Coefficient (\u0026beta;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInterpretation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEF (Eye)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrongest predictor of EPSS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEF (Device)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificant predictor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistory of Heart Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificant only in Model 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-significant\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\u003eThe regression results indicate that the final model (Model 3), which includes the variables of age, gender, clinical symptoms (history of heart disease, fever, tachycardia, tachypnea, diarrhea, vomiting, hypotension, and respiratory distress), along with the two variables Ejection Fraction (Eye) and Ejection Fraction (Device), explains 77.3% of the variation in the Eponit variable (R-Square\u0026thinsp;=\u0026thinsp;0.773). This model is significant at the 0.01 level (F\u0026thinsp;=\u0026thinsp;27.303, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003eAn examination of the regression coefficients shows that among the independent variables, only the two variables Ejection Fraction (Eye) (\u0026beta;\u0026thinsp;=\u0026thinsp;0.525, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Ejection Fraction (Device) (\u0026beta;\u0026thinsp;=\u0026thinsp;0.418, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly influence Eponit. Other variables did not have a significant impact on Eponit.\u003c/p\u003e\n\u003cp\u003eThese results confirm that Eponit, as a non-invasive indicator, can effectively estimate Ejection Fraction, whether through the Device or echocardiography (Eye). In fact, Eponit shows a very high correlation with both Ejection Fraction variables \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to investigate the predictive value of Bedside Cardiac Ultrasound in estimating the Ejection Fraction using the E-Point Septal Separation (EPSS) method in children hospitalized at Ali Asghar Hospital with signs of shock. The findings clearly indicate that EPSS is a strong, reliable, and rapid indicator for assessing cardiac function in children with shock. Given the AUC close to one, this parameter has the potential to become a standard tool in inpatient evaluations.\u003c/p\u003e\u003cp\u003eThe results of the ROC analysis for the E-Point Septal Separation (EPSS) variable demonstrate the excellent performance of this parameter in predicting low LVEF. An area under the curve (AUC) of 0.983 indicates that EPSS can accurately differentiate between individuals with low and normal LVEF. Previous studies have also highlighted the high diagnostic value of EPSS in assessing cardiac function, with the study by Nagueh et al. reporting a strong correlation between EPSS and LVEF(9).\u003c/p\u003e\u003cp\u003eThe studies reviewed highlight the significant role of E-point Septal Separation (EPSS) as a non-invasive and reliable tool for estimating Left Ventricular Ejection Fraction (LVEF) across various patient populations, particularly in critically ill children and adults. A study found a notable correlation between EPSS and LVEF, as measured by standard echocardiography (r = -0.82). This aligns with the findings from our results, which indicated that EPSS effectively predicts LVEF, suggesting its utility in rapid assessment during acute clinical scenarios(4).\u003c/p\u003e\u003cp\u003eThe research demonstrated that EPSS can predict LVEF and fluid responsiveness in children with shock. With an EPSS\u0026thinsp;\u0026gt;\u0026thinsp;7 mm correlating with EF\u0026thinsp;\u0026lt;\u0026thinsp;55%, this study supports our findings that EPSS is a strong predictor of cardiac performance in pediatric patients, especially in critical conditions.(10).\u003c/p\u003e\u003cp\u003eGiven the high diagnostic accuracy of EPSS, this parameter can be used as a rapid and non-invasive screening method in children, and it may also reduce the need for more invasive and complex procedures. An AUC of 0.975 indicates that there is a 97.5% probability of correctly identifying a positive case from a negative one. These results are consistent with the study by Nagueh and colleagues, which highlighted the strong relationship between EPSS and LVEF(9). In the study by Leman and Johnson, the results showed that quantitative measurements of EPSS and FS had poor accuracy in estimating LVEF, even among experienced sonographers. In contrast, in the present study, the variable Eponit, which is similar to EPSS, was able to significantly estimate Ejection Fraction(11).\u003c/p\u003e\u003cp\u003eOn the other hand, other studies have also shown that EPSS measurement by emergency physicians can accurately estimate LVEF(4, 12, 13).\u003c/p\u003e\u003cp\u003eThese results are somewhat similar to the findings of the present study, which showed that Eponit can serve as a suitable alternative for measuring Ejection Fraction. Another study in 2021 demonstrated that measuring EPSS at the mitral valve has a significant negative correlation with LVEF and can be used as a simple and reliable tool for assessing left ventricular systolic function by anesthesiologists in preoperative settings. In other words, EPSS provides a rapid and non-invasive method for estimating left ventricular systolic function and can serve as a suitable alternative to more complex methods in clinical conditions, which aligns with the results of our study (14).\u003c/p\u003e\u003cp\u003eAdditionally, the study by Adnan and Neslihan showed that measuring EPSS has a strong correlation with LVEF and can serve as a complementary method for diagnosing patients in echocardiographic images. This finding is also somewhat consistent with the results of the present study (15). In summary, although some previous studies have reported poor accuracy for measurements similar to Eponit, the present study demonstrated that Eponit can serve as a non-invasive and independent indicator of Ejection Fraction, regardless of clinical symptoms. This difference may be due to variations in methodology and the study population.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eConfounding Variables\u003c/b\u003e: Shock can arise from various factors that may significantly affect EPSS and EF, such as arrhythmias, the dosage of vasopressor medications, and respiratory status. To mitigate this limitation, it is essential to control for these confounding variables by collecting detailed clinical data and using statistical methods to adjust for their effects in the analysis.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eVariability in Device Results\u003c/b\u003e: The accuracy and reliability of ultrasound devices can vary across different centers. Consequently, results obtained from one center may not be generalizable to another. To address this issue, standardization of the ultrasound protocols and training for the personnel conducting the echocardiograms should be implemented. Additionally, using calibrated and validated equipment can help ensure consistency in results across different locations.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, EPSS is a simple, rapid, and accurate parameter for assessing left ventricular function that can be used in various clinical settings, including emergency, intensive care, and perioperative settings, as a screening tool and even as an alternative to more complex methods. This could help improve diagnosis, expedite treatment decisions, and reduce the need for more invasive procedures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cdiv dir=\"RTL\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eInformed Consent:\u003c/strong\u003e Written informed consent was obtained from all subjects.\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e :\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eEthics Approval and Consent to Participate This study was approved by the Research Ethics Committee of Iran University of Medical Sciences (Approval ID: IR.IUMS.REC.1404.011, Approval Date: 2025-03-15). Written informed consent to participate was obtained from all patients or their legal guardians. Consent for Publication Not applicable. Clinical Trial Number Clinical trial number: not applicable. Human Ethics and Consent to Participate Declarations This study was conducted in accordance with the ethical principles of the Declaration of Helsinki and the national guidelines for medical research in Iran. All participants (or their guardians) provided informed consent before inclusion in the study.\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eConflict of interests\u003c/strong\u003e: The authors report no conflict of interest.\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eData Reproducibility:\u003c/strong\u003e The Dataset presented in the study is available on request from the corresponding author during submission or after publication.\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eFunding/Support:\u003c/strong\u003e The authors did not receive any funding support for this study.\u003c/p\u003e\n\u003c/div\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKost GJ. Principles \u0026amp; practice of point-of-care testing. (No Title). 2002.\u003c/li\u003e\n\u003cli\u003eN\u0026uacute;\u0026ntilde;ez-Ramos JA, Pana-Toloza MC, Palacio-Held SC. E-point septal separation accuracy for the diagnosis of mild and severe reduced ejection fraction in emergency department patients. POCUS journal. 2022;7(1):160.\u003c/li\u003e\n\u003cli\u003eQuesada-Gonz\u0026aacute;lez D, Merko\u0026ccedil;i A. Nanomaterial-based devices for point-of-care diagnostic applications. Chemical Society Reviews. 2018;47(13):4697-709.\u003c/li\u003e\n\u003cli\u003eMcKaigney CJ, Krantz MJ, La Rocque CL, Hurst ND, Buchanan MS, Kendall JL. E-point septal separation: a bedside tool for emergency physician assessment of left ventricular ejection fraction. The American Journal of Emergency Medicine. 2014;32(6):493-7.\u003c/li\u003e\n\u003cli\u003ePrice S, Via G, Sloth E, Guarracino F, Breitkreutz R, Catena E, et al. Echocardiography practice, training and accreditation in the intensive care: document for the World Interactive Network Focused on Critical Ultrasound (WINFOCUS). Cardiovascular ultrasound. 2008;6:1-35.\u003c/li\u003e\n\u003cli\u003eMcLean AS. Echocardiography in shock management. Critical Care. 2016;20:1-10.\u003c/li\u003e\n\u003cli\u003eVieillard-Baron A, Caille V, Charron C, Belliard G, Page B, Jardin F. Actual incidence of global left ventricular hypokinesia in adult septic shock. Critical care medicine. 2008;36(6):1701-6.\u003c/li\u003e\n\u003cli\u003eBreitkreutz R, Walcher F, Seeger FH. Focused echocardiographic evaluation in resuscitation management: Concept of an advanced life support\u0026ndash;conformed algorithm. Critical care medicine. 2007;35(5):S150-S61.\u003c/li\u003e\n\u003cli\u003eNagueh SF, Kopelen HA, Quin\u0026tilde; ones MA. Assessment of left ventricular filling pressures by Doppler in the presence of atrial fibrillation. Circulation. 1996;94(9):2138-45.\u003c/li\u003e\n\u003cli\u003eVorel ES, Jacquemyn X, Cohen JS, Kutty S, Deanehan JK. Pediatric Reference Ranges and Test Characteristics of E-point Septal Separation as a Marker for Left Ventricular Dysfunction: A Retrospective Study. Pediatric emergency care. 2024:10.1097.\u003c/li\u003e\n\u003cli\u003eBahl A, Johnson S, Altwail M, Brackney A, Xiao J, Price J, et al. Left ventricular ejection fraction assessment by emergency physician-performed bedside echocardiography: a prospective comparative evaluation of multiple modalities. The Journal of emergency medicine. 2021;61(6):711-9.\u003c/li\u003e\n\u003cli\u003eSecko MA, Lazar JM, Salciccioli LA, Stone MB. Can junior emergency physicians use E‐point septal separation to accurately estimate left ventricular function in acutely dyspneic patients? Academic Emergency Medicine. 2011;18(11):1223-6.\u003c/li\u003e\n\u003cli\u003eTamanna RJ, Hoque SJ, Pasha FM. E-Point Septal Separation: A Bedside Tool for Emergency Physician Assessment of Left Ventricular Ejection Fraction. Bangladesh Critical Care Journal. 2023;11(2):90-4.\u003c/li\u003e\n\u003cli\u003eJoshi P, Borde D, Asegaonkar B, Daunde V, Joshi S, Jaspara A. Utility of E point septal separation as screening tool for left ventricular ejection fraction in perioperative settings by anesthetists. Annals of Cardiac Anaesthesia. 2022;25(3):304-10.\u003c/li\u003e\n\u003cli\u003eSatılmış Siliv N, Yamanoglu A, Pınar P, Celebi Yamanoglu NG, Torlak F, Parlak I. Estimation of cardiac systolic function based on mitral valve movements: An accurate bedside tool for emergency physicians in dyspneic patients. Journal of Ultrasound in Medicine. 2019;38(4):1027-38.\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-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Shock, Ejection Fraction, Cardiac Ultrasound","lastPublishedDoi":"10.21203/rs.3.rs-7105889/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7105889/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntroduction:\u003c/p\u003e\n\u003cp\u003eWith the rise of cardiovascular diseases, rapid and accurate diagnosis of cardiac injury in shock patients is crucial. Bedside cardiac ultrasound and E-Point Septal Separation (EPSS) provide fast, non-invasive methods for evaluating cardiac function. This study aims to assess EPSS's accuracy in estimating ejection fraction to enhance clinical diagnosis and management.\u003c/p\u003e\n\u003cp\u003eMethods:\u003c/p\u003e\n\u003cp\u003eA prospective study was conducted on children with shock in the emergency department. EF was measured by two methods: 1. Bedside echocardiography with EPSS (by pediatric heart fellowship) 2. Standard echocardiography (by cardiologist). The relationship between EPSS and left ventricular ejection fraction (LVEF) was analyzed by correlation and linear regression. ROC curves were drawn to evaluate the diagnostic performance of EPSS and LVEF. Data analysis was performed with SPSS 16 software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePatients ranged in age from 1 to 17 years (mean 5.15 ± 4.732). EPSS values varied from 1.0 to 20.0 mm, with a mean of 5.318 ± 3.9263 mm, indicating significant variation in mitral valve function. LVEF was reported both by visual estimation (20–70%, mean 55.29 ± 12.582) and device measurement (30–78%, mean 62.41 ± 10.313). ROC analysis demonstrated excellent diagnostic performance of EPSS in detecting cardiac dysfunction, with an AUC of 0.983, standard error of 0.011, and p \u0026lt; 0.001, confirming the high discriminative power of the model. Additionally, ROC analysis for the Eponit variable showed an AUC of 0.975, standard error 0.013, and p = 0.001, indicating strong ability to differentiate positive and negative cases. The 95% confidence interval for AUC ranged from 0.950 to 1.000, confirming high reliability.\u003c/p\u003e\n\u003cp\u003eConclusion:\u003c/p\u003e\n\u003cp\u003eThe present study indicates that Eponit can serve as a suitable alternative for measuring ejection fraction and can be used as a valuable tool in clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Diagnostic Accuracy of Bedside Cardiac Ultrasound EPSS for Rapid Assessment of Left Ventricular Ejection Fraction inPediatric Shock:A Prospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-17 14:15:55","doi":"10.21203/rs.3.rs-7105889/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-08-26T19:44:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20040439087927992893197230391904405166","date":"2025-08-19T18:20:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-08T14:17:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-17T04:52:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-16T08:46:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-16T08:45:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2025-07-12T05:31:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dc5a8d59-5108-43b7-a36a-13263bb0211d","owner":[],"postedDate":"August 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-17T14:15:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-17 14:15:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7105889","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7105889","identity":"rs-7105889","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}