Assessment of Early Left Ventricular Systolic Function in Septic Cardiomyopathy:Non-invasive Myocardial Work

Assessment of Early Left Ventricular Systolic Function in Septic Cardiomyopathy:Non-invasive Myocardial Work | 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 Assessment of Early Left Ventricular Systolic Function in Septic Cardiomyopathy:Non-invasive Myocardial Work Yanan Li, Yun'an Chen, Yongpeng Xie, Yao Yan, Qixiang Hong, Jingjing Hou, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7021154/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To investigate the clinical value of noninvasive myocardial work (MW) in assessing early left ventricular systolic dysfunction in patients with septic cardiomyopathy(SCM). Methods This prospective cohort study enrolled septic patients admitted to the EICU of Lianyungang First People's Hospital between September 2024 and May 2025. Participants were stratified into the SCM group (the study group) and the non-SCM group (the control group) based on Global Longitudinal Strain (GLS) values.Intergroup comparisons were performed: baseline clinical characteristics,conventional echocardiographic parameters. Pearson correlation analysis was employed to quantify associations between myocardial work parameters and GLS. The diagnostic value of noninvasive myocardial work parameters for SCM identification was evaluated through ROC curve. Results The study cohort comprised 84 patients, with 42 cases allocated to the SCM group and 42 to the non-SCM group. Comparative analysis revealed significantly elevated heart rate and high-sensitivity cardiac troponin I levels in the SCM group versus controls (all P < 0.05).Cardiac functional assessment demonstrated marked reductions in left ventricular ejection fraction (LVEF), GLS, global constructive work (GCW), global work efficiency (GWE), and global work index (GWI) within the SCM group compared to controls (all parameters P < 0.01).Correlation analyses identified significant negative correlations between GWI, GCW, GWE and GLS (r=-0.81, -0.71, -0.70,all P < 0.01). ROC curve confirmed the superior diagnostic performance of myocardial work indices for SCM identification, with GWI demonstrating the highest predictive value (AUC = 0.863), followed by GCW (AUC = 0.826) and GWE (AUC = 0.793). Conclusion Noninvasive myocardial work parameters demonstrate significant potential for early identification of left ventricular systolic dysfunction in SCM. Septic Cardiomyopathy Left Ventricular Systolic Dysfunction Noninvasive Myocardial Work Pressure-Strain Loop Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction SCM is a potentially reversible syndrome of acute cardiac dysfunction. It is characterized by acute onset, where impairment of cardiac function is not directly caused by myocardial ischemia, but occurs in the context of sepsis and possesses the potential for recovery. Mortality rates can be as high as 70–90%. The concept of SCM was first introduced by Parker [ 1 ] in 1984, with its core pathophysiological alterations manifesting as reduced LVEF and increased end-diastolic volume. However, there is currently a lack of unified guidelines or expert consensus regarding the diagnostic criteria for SCM. Many scholars commonly reference the criteria proposed by Martin et al. [ 2 ]in their 2019 study published in Chest. The primary features of SCM according to this study are: first, exclusion of acute cardiac dysfunction due to coronary ischemia or pre-existing chronic cardiac dysfunction; it must occur in the setting of sepsis and satisfy at least one or more of the following criteria:①Left ventricular dilatation with normal or low filling pressures;②Depressed myocardial contractility;③Impaired right ventricular systolic function or left ventricular [systolic and/or diastolic] dysfunction accompanied by reduced fluid responsiveness. Given that current SCM diagnostic criteria predominantly rely on left ventricular systolic function indices (such as LVEF and GLS, the early assessment of left ventricular systolic dysfunction holds significant importance for SCM diagnosis. Studies [ 3 , 4 ] indicate that commonly used clinical parameters for evaluating left ventricular systolic function, including LVEF and strain parameters, are susceptible to loading conditions, which can reduce assessment accuracy. Noninvasive MW imaging [ 5 , 6 ] is an emerging technique for assessing myocardial contractile function. This technology combines left ventricular GLS with left ventricular pressure to generate pressure-strain loops (PSL), effectively accounting for loading variations and thereby providing a more reliable assessment basis. Prospective multicenter studies [ 7 ] have thoroughly validated that various parameters of left ventricular MW exhibit significant correlations with indices of myocardial contractile function and myocardial strain. This conclusion is likewise supported by the research of Galli et al. [ 8 ].However, current research on noninvasive MW predominantly focuses on conditions such as non-obstructive coronary artery disease [ 9 ], acute myocardial infarction [ 10 ], and diabetes mellitus [ 11 ].To date, there are no published studies, domestic or international, investigating noninvasive myocardial work specifically in septic cardiomyopathy. Consequently, this study aims to explore the clinical value of noninvasive myocardial work in assessing and diagnosing early left ventricular systolic dysfunction in patients with SCM. 2. Materials and Methods 2.1 Study Population This prospective observational cohort study enrolled patients with sepsis admitted to the EICU of the First People's Hospital of Lianyungang between September 2024 and May 2025. Inclusion criteria : (1) Diagnosis meeting the latest Sepsis-3 definition and diagnostic criteria [ 12 ]; (2) No history of cardiovascular diseases such as coronary artery disease, heart failure, or arrhythmia, and no prior cardiac surgery (such as valve replacement); (3) Echocardiographic examination performed within 24 hours of admission; (4) Age ≥ 18 years. Exclusion criteria : (1) Failure to meet Sepsis-3 diagnostic criteria; (2) History of cardiac surgery, existing or comorbid cardiomyopathy, valvular heart disease, congenital heart disease, or other cardiac conditions; (3) Pregnancy or lactation; (4) Failure to undergo echocardiography or poor acoustic windows precluding adequate image quality; (5) Transfer to another hospital during the study period, inability to complete the study protocol, or incomplete data; (6) Age < 18 years. 2.2 Ethics This study was conducted in accordance with the principles of medical ethics. Approval was obtained from the Institutional Review Board (IRB) of the First People's Hospital of Lianyungang, and written informed consent was acquired from all participants. Approval Number: KY-20220809001-01.F01. 2.3 Study Grouping Relevant literature jointly published in 2015 by two major authoritative imaging societies, the European Association of Cardiovascular Imaging (EACVI) and the American Society of Echocardiography (ASE) [ 13 ], indicates that significant advancements in ultrasound technology (such as 3D echocardiography and speckle-tracking strain imaging) and clinical practice needs have led to the inclusion of GLS in the assessment framework for left ventricular systolic function. The normal GLS value is approximately − 20%, with more negative values indicating better cardiac function. GLS demonstrates significantly higher sensitivity for detecting early myocardial dysfunction compared to LVEF. In a cohort study of septic patients conducted by Boissier et al. [ 14 ], the prevalence of abnormal left ventricular GLS was significantly higher than that of abnormal LVEF (70% vs. 32%, p < 0.001). Furthermore, reduced GLS was still detected in 58% of patients within the subgroup exhibiting preserved LVEF. Currently, the threshold for diagnosing SCM using GLS remains inconsistent. As noted in Reference [ 15 ], a GLS ≥ -15% demonstrates a sensitivity of 72% and a specificity of 68% for diagnosing septic myocardial dysfunction. Additionally, a recent review on SCM [ 16 ] indicates that GLS can detect myocardial fiber deformation capacity, enabling earlier and more sensitive identification of subclinical myocardial dysfunction compared to LVEF. The review further proposes that GLS >-15% serves as an independent predictor of poor prognosis in patients with septic cardiomyopathy, demonstrating superior value over LVEF.Therefore, based on this and previous literature [ 17 ], this study defines patients with GLS ≥ -15% as the Study Group, and those with GLS < -15% as the Control Group. 2.4 Instrumentation and Image Acquisition 2.4.1 Instrumentation The imaging system used in this study was a GE Vivid E95 cardiovascular ultrasound system equipped with an M5S broadband phased-array transducer (frequency range: 1.5–4.5 MHz, adaptive frequency tuning). All image data were processed and analyzed offline using an EchoPAC 204 workstation. 2.4.2 Image Acquisition All participants underwent standardized transthoracic echocardiography (TTE) within 24 hours of admission. During data acquisition, simultaneous electrocardiogram (ECG) monitoring was performed. Acoustic window parameters (width, gain, depth, and angle) were optimized to ensure clear visualization of endocardial and epicardial borders. Two-dimensional (2D) image acquisition was maintained at a frame rate of (57 ± 8) frames per second (fps).Measurements of left ventricular end-systolic volume (LVESV), left ventricular end-diastolic volume (LVEDV), and LVEF were performed according to the modified biplane Simpson's method. During the echocardiographic examination, 2D cine loops (each encompassing ≥ 3 consecutive cardiac cycles) of the apical four-chamber, three-chamber, and two-chamber views were acquired simultaneously and subsequently imported into the system for analysis. 2.4.3 Image Analysis Echocardiographic data were first loaded into the EchoPAC 204 analysis platform. The AutoStrain™ function imaging (AFI) quantitative analysis module was activated via the workstation interface. The system automatically identified the three aforementioned apical view cine loops and selected the cardiac cycle with optimal image quality for myocardial motion tracking. If suboptimal tracking occurred, the operator manually adjusted the position and size of the region of interest (ROI) to ensure optimal speckle-tracking signal quality throughout the entire cardiac cycle.Following tracking of the apical three-chamber view, the aortic valve closure timing was analyzed and confirmed. The remaining views were then analyzed sequentially. Finally, the system automatically generated a 17-segment bull's-eye plot. GLS was derived as the weighted average of the peak systolic longitudinal strain values across all segments.To obtain myocardial work parameters, the analysis mode was switched to "Myocardial Work". After inputting the brachial blood pressure values and selecting "Advanced", the left ventricular pressure-strain loop (LV-PSL) and myocardial work parameters were generated. Clicking on any individual segment within the bull's-eye plot displayed the corresponding segmental LV-PSL value.In this study, all ultrasound image acquisitions were performed by sonographers with standardized training certification. Image analysis was uniformly conducted by a sonographer blinded to the patients' clinical data. All parameters were measured three times, and the average value was used for analysis. 2.5 Data Collection and Statistical Methods 2.5.1 Data Collection General Clinical Data Collection: age, sex, height, weight, body mass index (BMI), respiratory rate (RR), temperature (T), heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), arterial blood pH (pH), procalcitonin (PCT), blood lactate (Lac), high-sensitivity cardiac troponin I (hs-TnI), B-type natriuretic peptide (BNP), creatinine, bilirubin, and Sequential Organ Failure Assessment score (SOFA score). Conventional Echocardiographic Data Collection: Transthoracic echocardiography (TTE) was performed within 24 hours of admission. Recorded parameters included: LAAD (Left Atrial Anterior-Posterior Diameter), LVIDd (Left Ventricular Internal Dimension at End-Diastole), LVIDs (Left Ventricular Internal Dimension at End-Systole), IVSd (Interventricular Septal Thickness at Diastole), LVPWd (Left Ventricular Posterior Wall Thickness at Diastole), LVEDV (Left Ventricular End-Diastolic Volume), LVESV (Left Ventricular End-Systolic Volume), LVEF (Left Ventricular Ejection Fraction),SV (Stroke Volume), CO (Cardiac Output), VTI (Velocity Time Integral), PASP (Pulmonary Artery Systolic Pressure) Speckle-Tracking Echocardiography (STE) Data Collection: Speckle-tracking echocardiography was performed within 24 hours of admission. The following parameters were included: GWI: Defined as the total work performed by the myocardium during the period from mitral valve closure to opening, representing the area encompassed by the LV-PSL. GCW: The work performed by the myocardium during shortening in systole and lengthening during isovolumic relaxation. This work contributes positively to left ventricular ejection. GWW (Global Wasted Work): The work generated when the myocardium undergoes lengthening during systole and shortening during isovolumic relaxation. This work has a detrimental effect on left ventricular ejection efficiency. GWE : Reflects the efficiency of work performed within a single cardiac cycle. GLS : The relative shortening rate of the entire left ventricular myocardium along the long axis (from apex to base) during systole. PSD (Peak Strain Delay): Evaluates the synchrony of myocardial mechanical or electrical activity. 2.5.2 Statistical Methods Collected data were analyzed using SPSS software (version 26.0). The normality of continuous variables was assessed using the Kolmogorov-Smirnov test. Normally Distributed Data: Presented as mean ± standard deviation (mean ± SD). Differences between groups were analyzed using the independent samples *t*-test when variances were equal (assessed by Levene's test). When variances were unequal, Welch's analysis of variance (ANOVA) was used. Non-Normally Distributed Data: Presented as median with interquartile range [M(Q1, Q3)]. Differences between groups were analyzed using the nonparametric Mann-Whitney U test. Categorical Data: Presented as number and percentage (n, %). Differences between groups were analyzed using the Chi-square (χ²) test. A two-sided P-value < 0.05 was considered statistically significant for all group comparisons. Pearson correlation analysis was used to examine the relationships between myocardial work parameters and LVEF/GLS in normally distributed continuous variables. Statistical significance for correlations was defined as P < 0.05. ROC curve analysis was performed to evaluate the diagnostic value of myocardial work parameters for identifying patients with SCM. 3. Results 3 .1 Comparison of Baseline Clinical Characteristics Between Groups A total of 84 patients with sepsis were enrolled between September 2024 and May 2025. According to the predefined grouping criteria, all enrolled patients were categorized into the study group (n = 42) and the control group (n = 42).Compared to the control group, the study group demonstrated significantly higher values for HR and Hs-TnI (all P < 0.05;Table 1 ).No significant differences were observed between the study group and the control group regarding age, sex, height, weight, BMI,RR, T, BP, PCT, arterial blood pH, blood Lac, BNP, creatinine, bilirubin, or SOFA score (all P > 0.05;Table 1) Table 1 Comparison of Baseline Clinical Characteristics Between the two group Variables Study group (n=42) Control group (n=42) Statistical magnitude(t/F/χ²/z) P value Age[years,M(Q1,Q3)] 68.00(55.8,73.3) 67.00(54.0,77.0) -0.322 0.747 Male[n(%)] 21(50.00) 27(64.29) 1.75 0.186 Height[cm,M(Q1,Q3)] 168.00(160.0,175.3) 170.00(163.0,175.5) -0.601 0.548 Weight[kg,M(Q1,Q3)] 69.00(60.0,70.0) 65.00(59.0,73.5) -0.794 0.427 BMI(kg/m², ±s) 23.77±2.18 23.08±2.56 1.329 0.188 RR[b/min,M(Q1,Q3)] 20.50(16.0,27.0) 18.00(15.0,23.0) -1.372 0.17 T[℃,M(Q1,Q3)] 36.70(36.4,37.3) 36.75(36.2,37.6) -0.143 0.886 HR(bpm, ±s) 101.81±20.76 92.19±18.71 2.231 0.028* SBP[mmHg,M(Q1,Q3)] 124.00(1128,132.8) 122.50(113.0,133.3) -0.027 0.979 DBP(mmHg, ±s) 68.07±12.56 65.31±8.37 1.406 0.240 Blood PH[M(Q1,Q3)] 7.39(7.3,7.4) 7.42(7.3,7.5) -0.672 0.502 PCT[ng/ml,M(Q1,Q3)] 13.92(4.3,28.5) 3.67(1.0,27.6) -1.691 0.091 Blood Lac [mmol/L,M(Q1,Q3)] 2.82(1.9,5.2) 2.25(1.7,3.6) -1.884 0.060 Hs-TnⅠ[pg/ml,M(Q1,Q3)] 219.90(56.1,1494.8) 49.30(19.1,268.5) -2.594 0.009** BNP[pg/ml,M(Q1,Q3)] 1508.00(615.2,3758.6) 640.00(189.1,3063.2) -1.837 0.066 Creatinine[umol/L,M(Q1,Q3)] 112.00(80.0,198.7) 95.50(53.8,202.9) -1.082 0.279 Bilirubin[umol/L,M(Q1,Q3)] 21.00(13.9,37.4) 24.70(10.7,40.5) -0.031 0.975 SOFA score( ±s) 13.14±3.30 12.45±3.74 0.897 0.372 Abbreviations:BMI, body mass index ; RR, respiratory rate ; T, temperature ; HR, heart rate ; SBP, systolic blood pressure ; DBP, diastolic blood pressure ; PCT, procalcitonin ; Lac, lactate ; Hs-TnI, High-sensitivity cardiac troponin I ; BNP, B-type natriuretic peptide; SOFA score, Sequential Organ Failure Assessment score. * p <0.05 ，** p <0.01 3.2 Comparison of Conventional Echocardiographic Parameters Between the Two Groups There were statistically significant differences in LVIDs, LVEF, SV, CO, and VTI between the two groups (all P0.05 , Table 2). Table 2 Comparison of conventional echocardiographic parameters between the two groups Variables Study group( n =42) Control group( n =42) t/z P value LAAD[mm,M(Q1,Q3)] 41.00(37.0,44.0) 40.50(36.0,42.3) -0.885 0.376 LVIDd[mm,M(Q1,Q3)] 50.00(47.0,53.3) 50.00(45.0,52.0) -0.956 0.339 LVIDs[mm,M(Q1,Q3)] 29.50(25.0,35.3) 26.00(23.0,30.3) -2.376 0.017* IVSd[mm,M(Q1,Q3)] 9.00(9.0,10.0) 9.00(8.0,10.0) -0.458 0.647 LVPWd[mm,M(Q1,Q3)] 9.00(9.0,10.0) 9.00(8.0,10.0) -0.45 0.653 LVEDV[ml,M(Q1,Q3)] 78.50(60.5,96.3) 82.50(64.0,100.3) -0.796 0.426 LVESV[ml,M(Q1,Q3)] 35.00(28.0,55.3) 32.50(25.8,39.0) -1.822 0.069 LVEF[%,M(Q1,Q3)] 50.50(37.8,58.0) 61.00(58.0,64.0) -5.786 0.000** SV(ml, ±s) 37.12±12.01 50.10±16.08 -4.191 0.000** CO(L/min, ±s) 3.82±1.60 4.49±1.48 -2.007 0.048* VTI(cm, ±s) 21.40±5.84 26.99±7.68 -3.755 0.000** PASP[mmHg,M(Q1,Q3)] 33.50(29.8,40.3) 30.00(25.0,42.0) -1.224 0.221 Abbreviations:LAAD, Left Atrial Anterior-Posterior Diameter; LVIDd, Left Ventricular Internal Dimension at End-Diastole; LVIDs, Left Ventricular Internal Dimension at End-Systole; IVSd, Interventricular Septal Thickness at Diastole; LVPWd, Left Ventricular Posterior Wall Thickness at Diastole; LVEDV, Left Ventricular End-Diastolic Volume; LVESV, Left Ventricular End-Systolic Volume; LVEF, Left Ventricular Ejection Fraction; SV, Stroke Volume; CO, Cardiac Output; VTI, Velocity Time Integral; PASP, Pulmonary Artery Systolic Pressure. * p <0.05 ，** p <0.01 3.3 Comparison of speckle echocardiographic parameters between the two groups Compared with the control group, the absolute values of GLS, GWI, GCW, and GWE in the study group were significantly lower (all P <0.01, Table 3, Figure 1). GWW in the study group was significantly higher than that in the control group ( P 0.05, Table 3). Table 3 Comparison of speckle echocardiographic parameters between the two groups Variables Study group(n=42) Control group(n=42) t/ F/z P value GLS(%, ±s) -11.35±3.09 -18.10±2.18 133.397 0.000** GWI(mmHg%, ±s) 980.64±348.69 1543.81±368.86 -7.19 0.000** GCW(mmHg%, ±s) 1514.79±422.99 2049.26±473.57 -5.455 0.000** GWW(mmHg%, ±s) 300.45±126.83 237.02±161.60 2.001 0.049* GWE(%, ±s) 81.64±6.80 89.02±5.82 -5.348 0.000** PSD[ms,M（Q1，Q3）] 67.795(56.3,81.9) 55.865(44.9,79.2) -1.677 0.093 Abbreviations:GLS, Global Longitudinal Strain; GWI, Global Work Index; GCW, Global Constructive Work; GWW, Global Wasted Work; PSD , Peak Strain Delay. * p <0.05， ** p <0.01 3.4 Correlation Analysis Pearson correlation analysis was used to explore the correlation between myocardial work parameters and LVEF. The results showed significant positive correlations between LVEF and three parameters: GWI, GCW, and GWE (r=0.50, 0.48, 0.41, all P 0.05, Figure 2).The correlation between GLS and each myocardial work parameter was evaluated separately. The analysis showed significant negative correlations between GWI, GCW, GWE and GLS (r=-0.81, -0.71, -0.70, all P <0.01, Figure 3). The correlation between GLS and GWW was not significant (r=0.28, P <0.05, Figure 3). 3.5 Diagnostic Value of Myocardial Work Parameters for Septic Cardiomyopathy GWI, GCW, GWW, and GWE all showed high diagnostic values for septic cardiomyopathy (AUC = 0.863, 0.826, 0.672, 0.793, respectively, Figure 4). The corresponding cut-off values, sensitivities, and specificities are shown in Table 4,Among them, GWI showed the highest sensitivity and specificity. Table 4 Diagnostic Value of Myocardial Work Parameters for Septic Cardiomyopathy Myocardial Work Parameters AUC Youden's Index Sensitivity Specificity Cut-off 95% CI P value GWI(mmHg%) 0.863 0.619 79% 83% 1268.000 0.785 ~ 0.940 0.000** GCW(mmHg%) 0.826 0.500 57% 93% 2060.000 0.738 ~ 0.913 0.000** GWW(mmHg%) 0.672 0.357 71% 64% 227.000 0.553 ~ 0.792 0.007** GWE(%) 0.793 0.500 79% 71% 85.000 0.698 ~ 0.889 0.000** Abbreviations:GLS, Global Longitudinal Strain; GWI, Global Work Index; GCW, Global Constructive Work; GWW, Global Wasted Work; AUC ，Area Under the ROC Curve； CI ，Confidence Interval. * p <0.05 ，** p <0.01 4. Discussion During sepsis, a series of pathophysiological changes occur in patients, including activation of immune cells with inflammatory mediator cascade reactions, programmed cell death, mitochondrial dysfunction, etc. These mechanisms collectively contribute to the development of cardiac dysfunction [18]. According to relevant studies, approximately 25% of patients with septic shock develop myocardial dysfunction in the early stage (within 24 hours) [19].Therefore, there is an urgent need to explore additional biomarkers or detection methods to supplement the early diagnosis of SCM. In clinical practice, LVEF measured by conventional echocardiography is a common indicator for predicting cardiovascular diseases. Editorials have pointed out that GLS demonstrates higher reliability than LVEF as an index for evaluating left ventricular systolic function [20]. However, GLS is susceptible to pressure loading: this parameter decreases with increased afterload, which may be misinterpreted as reduced myocardial contractility, whereas it actually reflects afterload changes. In contrast, LV-PSL integrate myocardial deformation and left ventricular pressure parameters to achieve comprehensive assessment of myocardial work [21]. Russell’s team [4, 5] revealed that segmental differences in left ventricular MW evaluated by LV-PSL correlate closely with myocardial glucose metabolic rate measured by positron emission tomography (PET). This is consistent with previous studies [22, 23]. Subsequent studies have further indicated that in septic patients, elevated GWW is significantly correlated with lactate levels (reflecting tissue hypoxia) (r=0.45), suggesting that increased ineffective work may be associated with mitochondrial dysfunction. However, LVEF and GLS cannot directly reflect energy metabolic abnormalities [24]. With the proposal of non-invasive MW, its clinical application has increased progressively. A review on patients with early coronary artery disease (CAD) has suggested [25] that the GWI demonstrates extremely high sensitivity for diagnosing early CAD, with an AUC value of 0.86, which is significantly higher than that of GLS (0.79). In other related fields, a study [26] applied noninvasive MW to evaluate left ventricular systolic function in patients with cardiac amyloidosis (CA), finding that GWI significantly decreased at rest and recovered after exercise. In recent years, studies have also explored patients with transthyretin amyloid cardiomyopathy (ATTR-CM), indicating that myocardial work parameters (especially global work index, GWI) can more accurately reflect disease progression and energy metabolic efficiency in these patients [27]. These studies collectively confirm the clinical value of noninvasive myocardial work echocardiography in assessing early left ventricular systolic function in various cardiomyopathies, providing a solid basis for early intervention and targeted therapy. This study analyzed clinical data and ultrasound parameters of SCM and non-SCM groups, exploring intergroup differences. Except for Hs-TnI no significant differences were observed in other baseline clinical characteristics between groups. GWI, GCW, and GWE showed statistically significant differences (all P <0.01): their absolute values were notably lower in the SCM group, indicating reduced effective myocardial work, decreased overall cardiac contractility, or impaired effective ejection. GWW was significantly higher in the SCM group, reflecting inefficient cardiac energy conversion. These parameter changes (decreased GWI/GCW/GWE and increased GWW) collectively constitute a characteristic "myocardial energy-mechanical conversion disorder" profile in SCM, revealing that systolic dysfunction stems not only from reduced contractility but also from comprehensive deterioration of energy utilization efficiency. Correlation analysis showed significant positive correlations between LVEF and GWI/GCW/GWE (r=0.50, 0.48, 0.41, all P <0.01) and negative correlations between GLS and GWI/GCW/GWE (r=-0.81, -0.71, -0.70, all P <0.01), indicating that longitudinal myocardial fiber contraction drives overall mechanical work and determines work efficiency. ROC curve analysis further confirmed high diagnostic values of GWI, GCW, and GWE for SCM (AUC=0.863, 0.826, 0.793), with GWI showing the highest efficacy (sensitivity 79%, specificity 83%). MW parameters (especially GWI) can detect early, subtle impairments in myocardial contractility and efficiency before traditional indices (e.g., LVEF) change significantly, as evidenced by SCM patients with LVEF>50% but abnormal MW parameters in this study. Witthaut et al. [28] showed that BNP levels do not significantly increase in early sepsis when myocardial impact is mild, but rise markedly in severe sepsis or septic shock. This may result from BNP’s presence in spinal cord, brain, lung tissues, and diseases like renal insufficiency, inflammation, and tumors, limiting its specificity for SCM early diagnosis. Consistently, our study found no significant BNP difference between SCM and non-SCM groups ( P >0.05). During sepsis, inflammatory mediators directly damage myocardial cells, leading to the release of troponin. A prospective study [29] including 116 septic patients found that the level of Hs-TnⅠ in septic patients with subclinical left ventricular systolic dysfunction (LVSD) was significantly higher than that in patients without LVSD (median: 28.5 vs. 18.7 ng/L, p<0.001). Kim et al. [30] reported significantly higher peak Hs-TnI levels in sepsis patients with reduced myocardial contractility ( P <0.05), with stronger expression in left ventricular systolic dysfunction subgroups than preserved-function subgroups ( P <0.01). Similarly, our study showed higher Hs-TnI in the SCM group, supporting SCM classification and confirming myocardial damage. Thus, sepsis patients should be monitored for SCM after excluding other causes of troponin I elevation. 5. Conclusion Noninvasive myocardial work echocardiography holds promise as a valuable indicator for evaluating septic myocardial systolic dysfunction, demonstrating broad clinical application prospects. 6. Limitations and advantages This study has several limitations: First, the GLS threshold (-15%) for defining SCM, derived from previous literature, may overestimate SCM frequency as some SCM patients had LVEF>50%, possibly because LVEF changes are masked by load conditions [3]. This highlights MW parameters’ value in identifying systolic dysfunction even with preserved LVEF. Second, noninvasive myocardial work technology faces challenges in diseases with increased aortic-left ventricular pressure gradients, as peripheral systolic pressure inaccurately reflects left ventricular peak pressure.Third, this single-center, small-sample study may lack generalizability, warranting larger multicenter studies. Fourth, obtaining standard ultrasound images is difficult in ICU patients due to altered consciousness and equipment interference. Fifth, the relationship between MW parameters and prognosis remains unanalyzed, representing a future research direction. Noninvasive myocardial work echocardiography offers advantages: noninvasiveness, bedside applicability, high temporal resolution for dynamic myocardial contraction tracking, and standardized afterload correction to eliminate peripheral resistance impacts, enabling comprehensive and accurate evaluation of local and global systolic function. In this study, abnormal myocardial work was detected in some sepsis patients with normal LVEF, and MW parameters showed high AUC values for SCM diagnosis, proving their utility in early sepsis myocardial injury identification and providing a novel assessment method alongside LVEF and GLS. Abbreviations MW M yocardial Work SCM Septic Cardiomyopathy GLS Global Longitudinal Strain LVEF L eft V entricular E jection F raction GCW G lobal C onstructive W ork GWE G lobal W ork E fficiency GWI G lobal W ork I ndex GWW Global Wasted Work) PSD Peak Strain Delay PSL Pressure-Strain Loops IRB Institutional Review Board EACVI European Association of Cardiovascular Imaging ASE American Society of Echocardiography TTE Transthoracic Echocardiography ECG Electrocardiogram 2D Two-Dimensional LVESV Left Ventricular End-Systolic Volume LVEDV Left Ventricular End-Diastolic Volume BMI Body Mass Index RR Respiratory Rate T Temperature HR Heart Rate SBP Systolic Blood Pressure DBP Diastolic Blood Pressure PCT Procalcitonin Lac Lactate Hs-TnI High-Sensitivity Cardiac Troponin I BNP B-type Natriuretic Peptide SOFA Sequential Organ Failure Assessment TTE Transthoracic Echocardiography LAAD Left Atrial Anterior-Posterior Diameter LVIDd Left Ventricular Internal Dimension at End-Diastole LVIDs Left Ventricular Internal Dimension at End-Systole IVSd Interventricular Septal Thickness at Diastole LVPWd Left Ventricular Posterior Wall Thickness at Diastole LVEDV Left Ventricular End-Diastolic Volume LVESV Left Ventricular End-Systolic Volume SV Stroke Volume CO Cardiac Output VTI Velocity Time Integral PASP Pulmonary Artery Systolic Pressure STE Speckle-Tracking Echocardiography PET Positron Emission Tomography CAD Coronary Artery Disease CA Cardiac Amyloidosis LVSD Left Ventricular Systolic Dysfunction Declarations Ethicsapproval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and the principles of medical ethics. Approval was obtained from the Institutional Review Board (IRB) of the First People's Hospital of Lianyungang, and written informed consent was acquired from all participants. Approval Number: KY-20220809001-01.F01.The data collected for this research will not be used for any other purposes. All methods were performed in accordance with the relevant guidelines and regulations. Clinical trial Not applicable. Consent for publication Not applicable. Availability of data and materials Data was collected based on available medical records. All of the data was analyzed anonymously. Competing interests All authors declare no conflict of interest. Funding This research was supported by Scientific Research Project of Jiangsu Provincial Health Commission (H2019109) and General Project for Social Development of Jiangsu Provincial Department of Science and Technology (BE2020670). Authors' Contributions YNL contributed to data collection, study design, statistical analysis, and manuscript writing. YAC and JJH contributed equally to data collection and image acquisition. YY participated in manuscript revision and provided work support. YPX provided research guidance, participated in manuscript revision, and offered work support. YHZ, YHM and QXH contributed equally to data collection and study design. XML provided research guidance, conducted manuscript review, and secured funding support. All authors critically revised the manuscript and gave final approval for the version to be published. Acknowledgments We thank the mentor team for their guidance and strong support during the scientific research and manuscript collaboration process. References Parker MM, Shelhamer JH, Bacharach SL, Green MV, Natanson C, Frederick TM, et al. Profound but reversible myocardial depression in patients with septic shock. Ann Intern Med. 1984;100(4):483–90. https://doi.org/10.7326/0003-4819-100-4-483 . Martin L, Derwall M, Al Zoubi S, Zechendorf E, Reuter DA, Thiemermann C, et al. The septic heart: Current understanding of molecular mechanisms and clinical implications. Chest. 2019;155(2):427–37. https://doi.org/10.1016/j.chest.2018.08.1037 . Ehrman RR, Sullivan AN, Favot MJ, Sherwin RL, Reynolds CA, Abidov A, et al. Pathophysiology, echocardiographic evaluation, biomarker findings, and prognostic implications of septic cardiomyopathy: A review of the literature. Crit Care. 2018;22(1):112. https://doi.org/10.1186/s13054-018-2043-8 . Hubert A, Le Rolle V, Leclercq C, Galli E, Samset E, Casset C, et al. Estimation of myocardial work from pressure-strain loops analysis: an experimental evaluation. Eur Heart J Cardiovasc Imaging. 2018;19(12):1372–9. https://doi.org/10.1093/ehjci/jey024 . Russell K, Eriksen M, Aaberge L, Wilhelmsen N, Skulstad H, Gjesdal O, et al. Assessment of wasted myocardial work: A novel method to quantify energy loss due to uncoordinated left ventricular contractions. Am J Physiol Heart Circ Physiol. 2013;305(7):H996–1003. https://doi.org/10.1152/ajpheart.00191.2013 . Russell K, Eriksen M, Aaberge L, Wilhelmsen N, Skulstad H, Remme EW, et al. A novel clinical method for quantification of regional left ventricular pressure-strain loop area: A non-invasive index of myocardial work. Eur Heart J. 2012;33(6):724–33. https://doi.org/10.1093/eurheartj/ehs016 . Manganaro R, Marchetta S, Dulgheru R, Sugimoto T, Tsugu T, Ilardi F, et al. Correlation between non-invasive myocardial work indices and main parameters of systolic and diastolic function: results from the EACVI NORRE study. Eur Heart J Cardiovasc Imaging. 2020;21(5):533–41. https://doi.org/10.1093/ehjci/jez203 . Galli E, Vitel E, Schnell F, Le Rolle V, Hubert A, Lederlin M, et al. Myocardial constructive work is impaired in hypertrophic cardiomyopathy and predicts left ventricular fibrosis. Echocardiography. 2018;36(1):74–82. https://doi.org/10.1111/echo.14210 . Li Y, Sun DD, Zhao HZ, Qin ZY, Ji W, Zhang HH, et al. Incremental value of non-invasive myocardial work for the evaluation and prediction of coronary microvascular dysfunction in angina with no obstructive coronary artery disease. Front Cardiovasc Med. 2023;10:1209122. https://doi.org/10.3389/fcvm.2023.1209122 . Coisne A, Fourdinier V, Lemesle G, Delsart P, Aghezzaf S, Lamblin N, et al. Clinical significance of myocardial work parameters after acute myocardial infarction. Eur Heart J Open. 2022;2(3):oeac037. https://doi.org/10.1093/ehjopen/oeac037 . Huang DQ, Cui CY, Zheng Q, Li YN, Liu YY, Hu YB, et al. Quantitative analysis of myocardial work by non-invasive left ventricular pressure-strain loop in patients with type 2 diabetes mellitus. Front Cardiovasc Med. 2021;8:733339. https://doi.org/10.3389/fcvm.2021.733339 . Shankar-Hari M, Phillips GS, Levy ML, Seymour CW, Liu VX, Deutschman CS, et al. Developing a new definition and assessing new clinical criteria for septic shock: For the third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):775. https://doi.org/10.1001/jama.2016.0289 . Lang RM, Badano LP, Mor-Avi V, Afilalo J, Armstrong A, Ernande L, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American society of echocardiography and the European association of cardiovascular imaging. J Am Soc Echocardiogr. 2015;28(1):1–e3914. https://doi.org/10.1016/j.echo.2014.10.003 . Boissier F, Razazi K, Seemann A, Bedet A, Thille AW, De Prost N, et al. Left ventricular systolic dysfunction during septic shock: the role of loading conditions. Intensive Care Med. 2017;43(5):633–42. https://doi.org/10.1007/s00134-017-4698-z . De Geer L, Engvall J, Oscarsson A. Strain echocardiography in septic shock - a comparison with systolic and diastolic function parameters, cardiac biomarkers and outcome. Crit Care. 2015;19(1):122. https://doi.org/10.1186/s13054-015-0857-1 . Torres JSS, Tamayo-Giraldo FJ, Bejarano-Zuleta A, Nati-Castillo HA, Quintero DA, Ospina-Mejía MJ, et al. Sepsis and post-sepsis syndrome: A multisystem challenge requiring comprehensive care and management-a review. Front Med (Lausanne). 2025;12:1560737. https://doi.org/10.3389/fmed.2025.1560737 . Voigt JU, Pedrizzetti G, Lysyansky P, Marwick TH, Houle H, Baumann R, et al. Definitions for a common standard for 2D speckle tracking echocardiography: Consensus document of the EACVI/ASE/Industry Task Force to standardize deformation imaging. Eur Heart J Cardiovasc Imaging. 2015;16(1):1–11. https://doi.org/10.1093/ehjci/jeu184 . Nong YX, Wei XB, Yu DQ. Inflammatory mechanisms and intervention strategies for sepsis-induced myocardial dysfunction. Immun Inflamm Dis. 2023;11(5):e860. https://doi.org/10.1002/iid3.860 . Singam A. Myocardial injury as a harbinger of multi-organ failure in septic shock: A comprehensive review. Cureus. 2024;16(2):e55021. https://doi.org/10.7759/cureus.55021 . Maslove DM. Echocardiography in the management of sepsis: Not all black and white. Crit Care Med. 2024;52(2):348–50. https://doi.org/10.1097/ccm.0000000000006125 . Chan J, Edwards NFA, Khandheria BK, Shiino K, Sabapathy S, Anderson B, et al. A new approach to assess myocardial work by non-invasive left ventricular pressure-strain relations in hypertension and dilated cardiomyopathy. Eur Heart J Cardiovasc Imaging. 2019;20(1):31–9. https://doi.org/10.1093/ehjci/jey131 . Bergman BC, Tsvetkova T, Lowes B, Wolfel EE. Myocardial glucose and lactate metabolism during rest and atrial pacing in humans. J Physiol. 2009;587(Pt 9):2087–99. https://doi.org/10.1113/jphysiol.2008.168286 . Wehrl HF, Wiehr S, Divine MR, Gatidis S, Gullberg GT, Maier FC et al. Preclinical and translational PET/MR imaging. J Nucl Med. 2014;55(2):11s-18s. https://doi.org/10.2967/jnumed.113.129221 Meng XY, Feng BY, Yang CG, Li Y, Xia CX, Guo Y, et al. Association between the triglyceride–glucose index and left ventricular myocardial work indices in patients with coronary artery disease. Front Endocrinol. 2024;15:1447984. https://doi.org/10.3389/fendo.2024.1447984 . Parlavecchio A, Vetta G, Caminiti R, Ajello M, Magnocavallo M, Vetta F, et al. Which is the best Myocardial Work index for the prediction of coronary artery disease? A data meta-analysis. Echocardiography. 2023;40(3):217–26. https://doi.org/10.1111/echo.15537 . Clemmensen TS, Eiskjær H, Mikkelsen F, Granstam SO, Flachskampf FA, Sørensen J, et al. Left ventricular pressure-strain–derived myocardial work at rest and during exercise in patients with cardiac amyloidosis. J Am Soc Echocardiogr. 2020;33(5):573–82. https://doi.org/10.1016/j.echo.2019.11.018 . Antonelli J, Neveu A, Kosmala W, L'official G, Curtis E, Oger E, et al. Evolution and prognostic value of left ventricular deformation and myocardial work parameters in transthyretin amyloid cardiomyopathy. Eur Heart J Cardiovasc Imaging. 2024;25(4):469–79. https://doi.org/10.1093/ehjci/jead318 . Witthaut R, Busch C, Fraunberger P, Walli A, Seidel D, Pilz G, et al. Plasma atrial natriuretic peptide and brain natriuretic peptide are increased in septic shock: impact of interleukin-6 and sepsis-associated left ventricular dysfunction. Intensive Care Med. 2003;29(10):1696–702. https://doi.org/10.1007/s00134-003-1910-0 . Hai PD, Binh NT, Tot NH, Hung HM, Hoa LTV, Hien NVQ, et al. Diagnostic value of high-sensitivity troponin T for subclinical left ventricular systolic dysfunction in patients with sepsis. Cardiol Res Pract. 2021;2021:8897738. https://doi.org/10.1155/2021/8897738 . Kim JS, Kim M, Kim YJ, Ryoo SM, Sohn CH, Ahn S, et al. Troponin testing for assessing sepsis-induced myocardial dysfunction in patients with septic shock. J Clin Med. 2019;8(2):239. https://doi.org/10.3390/jcm8020239 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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-7021154","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496145665,"identity":"b5327a52-53b5-4ea3-8ffc-e0eaa3e75cea","order_by":0,"name":"Yanan Li","email":"","orcid":"","institution":"Department of Emergency Medicine, Affiliated Lianyungang Hospital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanan","middleName":"","lastName":"Li","suffix":""},{"id":496145666,"identity":"a410dbed-5310-4356-929d-e3035aa2e9aa","order_by":1,"name":"Yun'an Chen","email":"","orcid":"","institution":"Department of Ultrasound, Affiliated Lianyungang Hospital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yun'an","middleName":"","lastName":"Chen","suffix":""},{"id":496145668,"identity":"14e6ddc1-34dc-4cca-8516-ab6369a7fef9","order_by":2,"name":"Yongpeng Xie","email":"","orcid":"","institution":"Department of Emergency Medicine, Affiliated Lianyungang Hospital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yongpeng","middleName":"","lastName":"Xie","suffix":""},{"id":496145675,"identity":"c4648ab4-6d34-4092-867e-eb2b2f113f1a","order_by":3,"name":"Yao Yan","email":"","orcid":"","institution":"Department of Emergency Medicine, Lianyungang Clinical Medical College, Nanjing Medical 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Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuhua","middleName":"","lastName":"Zhang","suffix":""},{"id":496145683,"identity":"a057ee11-8ec6-4ff5-b903-cfde782aae02","order_by":7,"name":"Yuhao Meng","email":"","orcid":"","institution":"Department of Emergency Medicine, Lianyungang Clinical Medical College, Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuhao","middleName":"","lastName":"Meng","suffix":""},{"id":496145684,"identity":"21b72d27-24f4-41d0-9aa5-73c87b6fe879","order_by":8,"name":"Xiaomin 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14:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7021154/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7021154/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88490749,"identity":"26c7dfb8-36a5-4947-b8bd-be085b04a2a5","added_by":"auto","created_at":"2025-08-07 04:15:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55057,"visible":true,"origin":"","legend":"\u003cp\u003ePressure-strain loop and bull's-eye map of myocardial work parameters\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e: Study group (male, 43 years old, GLS -4%);\u003cstrong\u003eB\u003c/strong\u003e: Study group (female, 72 years old, GLS -10%);\u003cstrong\u003eC\u003c/strong\u003e: Control group (male, 59 years old, GLS -25%);\u003cstrong\u003eD\u003c/strong\u003e: Control group (female, 55 years old, GLS -21%).Compared with the control group, the study group showed significantly lower absolute values of GLS, GWI, GCW, and GWE, and significantly higher GWW. \u003cstrong\u003eUpper left corner\u003c/strong\u003e: Red curve represents the left ventricular pressure-strain curve (X-axis = strain, Y-axis = left ventricular pressure). The area under the curve denotes the GWI.\u003cstrong\u003eLower left corner\u003c/strong\u003e: Bar chart showing left ventricular GCW and GWW.\u003cstrong\u003eUpper right corner\u003c/strong\u003e: 17-segment bullseye map of myocardial work indices.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7021154/v1/04f6f50c5e20759483f5f5c2.jpg"},{"id":88490752,"identity":"3a83994a-941e-45fa-9a7b-5c9a1da15bcf","added_by":"auto","created_at":"2025-08-07 04:15:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50168,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between LVEF and GWI, GCW, GWW, GWE\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7021154/v1/6dcd48d8562cae14d4d951f0.jpg"},{"id":88492932,"identity":"6d9aa563-e08a-49b8-ba3e-2936d661dfe6","added_by":"auto","created_at":"2025-08-07 04:39:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49192,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between GLS and GWI, GCW, GWW, GWE\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7021154/v1/f885006feebeb2cd0b422e00.jpg"},{"id":88491746,"identity":"3beadae2-f114-4731-bb37-e16f2988b760","added_by":"auto","created_at":"2025-08-07 04:23:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33330,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curves of GWI, GCW, GWW, and GWE for Diagnosing Septic Cardiomyopathy\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7021154/v1/a44498c9cae823a0337de26a.jpg"},{"id":98884115,"identity":"d7ea4d53-e669-4cd4-ba0c-92562d72499a","added_by":"auto","created_at":"2025-12-23 14:40:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1142119,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7021154/v1/5b52e56b-6f3d-4cc8-878c-6d47ce206c22.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of Early Left Ventricular Systolic Function in Septic Cardiomyopathy:Non-invasive Myocardial Work","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSCM is a potentially reversible syndrome of acute cardiac dysfunction. It is characterized by acute onset, where impairment of cardiac function is not directly caused by myocardial ischemia, but occurs in the context of sepsis and possesses the potential for recovery. Mortality rates can be as high as 70\u0026ndash;90%. The concept of SCM was first introduced by Parker [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] in 1984, with its core pathophysiological alterations manifesting as reduced LVEF and increased end-diastolic volume. However, there is currently a lack of unified guidelines or expert consensus regarding the diagnostic criteria for SCM. Many scholars commonly reference the criteria proposed by Martin et al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]in their 2019 study published in Chest. The primary features of SCM according to this study are: first, exclusion of acute cardiac dysfunction due to coronary ischemia or pre-existing chronic cardiac dysfunction; it must occur in the setting of sepsis and satisfy at least one or more of the following criteria:①Left ventricular dilatation with normal or low filling pressures;②Depressed myocardial contractility;③Impaired right ventricular systolic function or left ventricular [systolic and/or diastolic] dysfunction accompanied by reduced fluid responsiveness. Given that current SCM diagnostic criteria predominantly rely on left ventricular systolic function indices (such as LVEF and GLS, the early assessment of left ventricular systolic dysfunction holds significant importance for SCM diagnosis. Studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] indicate that commonly used clinical parameters for evaluating left ventricular systolic function, including LVEF and strain parameters, are susceptible to loading conditions, which can reduce assessment accuracy. Noninvasive MW imaging [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] is an emerging technique for assessing myocardial contractile function. This technology combines left ventricular GLS with left ventricular pressure to generate pressure-strain loops (PSL), effectively accounting for loading variations and thereby providing a more reliable assessment basis. Prospective multicenter studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] have thoroughly validated that various parameters of left ventricular MW exhibit significant correlations with indices of myocardial contractile function and myocardial strain. This conclusion is likewise supported by the research of Galli et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].However, current research on noninvasive MW predominantly focuses on conditions such as non-obstructive coronary artery disease [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], acute myocardial infarction [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and diabetes mellitus [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].To date, there are no published studies, domestic or international, investigating noninvasive myocardial work specifically in septic cardiomyopathy. Consequently, this study aims to explore the clinical value of noninvasive myocardial work in assessing and diagnosing early left ventricular systolic dysfunction in patients with SCM.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Population\u003c/h2\u003e\u003cp\u003eThis prospective observational cohort study enrolled patients with sepsis admitted to the EICU of the First People's Hospital of Lianyungang between September 2024 and May 2025.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInclusion criteria\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e(1) Diagnosis meeting the latest Sepsis-3 definition and diagnostic criteria [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e];\u003c/p\u003e\u003cp\u003e(2) No history of cardiovascular diseases such as coronary artery disease, heart failure, or arrhythmia, and no prior cardiac surgery (such as valve replacement);\u003c/p\u003e\u003cp\u003e(3) Echocardiographic examination performed within 24 hours of admission;\u003c/p\u003e\u003cp\u003e(4) Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExclusion criteria\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e(1) Failure to meet Sepsis-3 diagnostic criteria;\u003c/p\u003e\u003cp\u003e(2) History of cardiac surgery, existing or comorbid cardiomyopathy, valvular heart disease, congenital heart disease, or other cardiac conditions;\u003c/p\u003e\u003cp\u003e(3) Pregnancy or lactation;\u003c/p\u003e\u003cp\u003e(4) Failure to undergo echocardiography or poor acoustic windows precluding adequate image quality;\u003c/p\u003e\u003cp\u003e(5) Transfer to another hospital during the study period, inability to complete the study protocol, or incomplete data;\u003c/p\u003e\u003cp\u003e(6) Age\u0026thinsp;\u0026lt;\u0026thinsp;18 years.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Ethics\u003c/h2\u003e\u003cp\u003e This study was conducted in accordance with the principles of medical ethics. Approval was obtained from the Institutional Review Board (IRB) of the First People's Hospital of Lianyungang, and written informed consent was acquired from all participants. Approval Number: KY-20220809001-01.F01.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Study Grouping\u003c/h2\u003e\u003cp\u003eRelevant literature jointly published in 2015 by two major authoritative imaging societies, the European Association of Cardiovascular Imaging (EACVI) and the American Society of Echocardiography (ASE) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], indicates that significant advancements in ultrasound technology (such as 3D echocardiography and speckle-tracking strain imaging) and clinical practice needs have led to the inclusion of GLS in the assessment framework for left ventricular systolic function. The normal GLS value is approximately \u0026minus;\u0026thinsp;20%, with more negative values indicating better cardiac function. GLS demonstrates significantly higher sensitivity for detecting early myocardial dysfunction compared to LVEF. In a cohort study of septic patients conducted by Boissier et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], the prevalence of abnormal left ventricular GLS was significantly higher than that of abnormal LVEF (70% vs. 32%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, reduced GLS was still detected in 58% of patients within the subgroup exhibiting preserved LVEF. Currently, the threshold for diagnosing SCM using GLS remains inconsistent. As noted in Reference [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], a GLS \u0026ge; -15% demonstrates a sensitivity of 72% and a specificity of 68% for diagnosing septic myocardial dysfunction. Additionally, a recent review on SCM [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] indicates that GLS can detect myocardial fiber deformation capacity, enabling earlier and more sensitive identification of subclinical myocardial dysfunction compared to LVEF. The review further proposes that GLS \u0026gt;-15% serves as an independent predictor of poor prognosis in patients with septic cardiomyopathy, demonstrating superior value over LVEF.Therefore, based on this and previous literature [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], this study defines patients with GLS \u0026ge; -15% as the Study Group, and those with GLS \u0026lt; -15% as the Control Group.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Instrumentation and Image Acquisition\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1 Instrumentation\u003c/h2\u003e\u003cp\u003eThe imaging system used in this study was a GE Vivid E95 cardiovascular ultrasound system equipped with an M5S broadband phased-array transducer (frequency range: 1.5\u0026ndash;4.5 MHz, adaptive frequency tuning). All image data were processed and analyzed offline using an EchoPAC 204 workstation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2 Image Acquisition\u003c/h2\u003e\u003cp\u003eAll participants underwent standardized transthoracic echocardiography (TTE) within 24 hours of admission. During data acquisition, simultaneous electrocardiogram (ECG) monitoring was performed. Acoustic window parameters (width, gain, depth, and angle) were optimized to ensure clear visualization of endocardial and epicardial borders. Two-dimensional (2D) image acquisition was maintained at a frame rate of (57\u0026thinsp;\u0026plusmn;\u0026thinsp;8) frames per second (fps).Measurements of left ventricular end-systolic volume (LVESV), left ventricular end-diastolic volume (LVEDV), and LVEF were performed according to the modified biplane Simpson's method. During the echocardiographic examination, 2D cine loops (each encompassing\u0026thinsp;\u0026ge;\u0026thinsp;3 consecutive cardiac cycles) of the apical four-chamber, three-chamber, and two-chamber views were acquired simultaneously and subsequently imported into the system for analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.4.3 Image Analysis\u003c/h2\u003e\u003cp\u003eEchocardiographic data were first loaded into the EchoPAC 204 analysis platform. The AutoStrain\u0026trade; function imaging (AFI) quantitative analysis module was activated via the workstation interface. The system automatically identified the three aforementioned apical view cine loops and selected the cardiac cycle with optimal image quality for myocardial motion tracking. If suboptimal tracking occurred, the operator manually adjusted the position and size of the region of interest (ROI) to ensure optimal speckle-tracking signal quality throughout the entire cardiac cycle.Following tracking of the apical three-chamber view, the aortic valve closure timing was analyzed and confirmed. The remaining views were then analyzed sequentially. Finally, the system automatically generated a 17-segment bull's-eye plot. GLS was derived as the weighted average of the peak systolic longitudinal strain values across all segments.To obtain myocardial work parameters, the analysis mode was switched to \"Myocardial Work\". After inputting the brachial blood pressure values and selecting \"Advanced\", the left ventricular pressure-strain loop (LV-PSL) and myocardial work parameters were generated. Clicking on any individual segment within the bull's-eye plot displayed the corresponding segmental LV-PSL value.In this study, all ultrasound image acquisitions were performed by sonographers with standardized training certification. Image analysis was uniformly conducted by a sonographer blinded to the patients' clinical data. All parameters were measured three times, and the average value was used for analysis.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Data Collection and Statistical Methods\u003c/h2\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.5.1 Data Collection\u003c/h2\u003e\u003cp\u003eGeneral Clinical Data Collection: age, sex, height, weight, body mass index (BMI), respiratory rate (RR), temperature (T), heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), arterial blood pH (pH), procalcitonin (PCT), blood lactate (Lac), high-sensitivity cardiac troponin I (hs-TnI), B-type natriuretic peptide (BNP), creatinine, bilirubin, and Sequential Organ Failure Assessment score (SOFA score).\u003c/p\u003e\u003cp\u003eConventional Echocardiographic Data Collection: Transthoracic echocardiography (TTE) was performed within 24 hours of admission. Recorded parameters included: LAAD (Left Atrial Anterior-Posterior Diameter), LVIDd (Left Ventricular Internal Dimension at End-Diastole), LVIDs (Left Ventricular Internal Dimension at End-Systole), IVSd (Interventricular Septal Thickness at Diastole), LVPWd (Left Ventricular Posterior Wall Thickness at Diastole), LVEDV (Left Ventricular End-Diastolic Volume), LVESV (Left Ventricular End-Systolic Volume), LVEF (Left Ventricular Ejection Fraction),SV (Stroke Volume), CO (Cardiac Output), VTI (Velocity Time Integral), PASP (Pulmonary Artery Systolic Pressure)\u003c/p\u003e\u003cp\u003eSpeckle-Tracking Echocardiography (STE) Data Collection: Speckle-tracking echocardiography was performed within 24 hours of admission. The following parameters were included: GWI: Defined as the total work performed by the myocardium during the period from mitral valve closure to opening, representing the area encompassed by the LV-PSL. GCW: The work performed by the myocardium during shortening in systole and lengthening during isovolumic relaxation. This work contributes positively to left ventricular ejection. GWW (Global Wasted Work): The work generated when the myocardium undergoes lengthening during systole and shortening during isovolumic relaxation. This work has a detrimental effect on left ventricular ejection efficiency. GWE : Reflects the efficiency of work performed within a single cardiac cycle. GLS : The relative shortening rate of the entire left ventricular myocardium along the long axis (from apex to base) during systole. PSD (Peak Strain Delay): Evaluates the synchrony of myocardial mechanical or electrical activity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.5.2 Statistical Methods\u003c/h2\u003e\u003cp\u003eCollected data were analyzed using SPSS software (version 26.0). The normality of continuous variables was assessed using the Kolmogorov-Smirnov test. Normally Distributed Data: Presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). Differences between groups were analyzed using the independent samples *t*-test when variances were equal (assessed by Levene's test). When variances were unequal, Welch's analysis of variance (ANOVA) was used. Non-Normally Distributed Data: Presented as median with interquartile range [M(Q1, Q3)]. Differences between groups were analyzed using the nonparametric Mann-Whitney U test. Categorical Data: Presented as number and percentage (n, %). Differences between groups were analyzed using the Chi-square (χ\u0026sup2;) test. A two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for all group comparisons. Pearson correlation analysis was used to examine the relationships between myocardial work parameters and LVEF/GLS in normally distributed continuous variables. Statistical significance for correlations was defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. ROC curve analysis was performed to evaluate the diagnostic value of myocardial work parameters for identifying patients with SCM.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.1 Comparison of Baseline Clinical Characteristics Between Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 84 patients with sepsis were enrolled between September 2024 and May 2025. According to the predefined grouping criteria, all enrolled patients were categorized into the study group (n = 42) and the control group (n = 42).Compared to the control group, the study group demonstrated significantly higher values for HR and Hs-TnI (all P \u0026lt; 0.05;Table 1\u0026nbsp;).No significant differences were observed between the\u0026nbsp;study\u0026nbsp;group and the control group regarding age, sex, height, weight, BMI,RR, T, BP, PCT, arterial blood pH, blood Lac, BNP, creatinine, bilirubin, or SOFA score (all P \u0026gt; 0.05;Table 1)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e Comparison of Baseline Clinical Characteristics Between the two group\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eVariables\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\u003cstrong\u003eStudy group\u0026nbsp;\u003c/strong\u003e(n=42)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003eControl group (n=42)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003eStatistical magnitude(t/F/\u0026chi;\u0026sup2;/z)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003eP value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eAge[years,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e68.00(55.8,73.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e67.00(54.0,77.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e-0.322\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.747\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eMale[n(%)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e21(50.00)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e27(64.29)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e1.75\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.186\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eHeight[cm,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e168.00(160.0,175.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e170.00(163.0,175.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e-0.601\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.548\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eWeight[kg,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e69.00(60.0,70.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e65.00(59.0,73.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e-0.794\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.427\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eBMI(kg/m\u0026sup2;,\u003cimg width=\"7\" height=\"14\" src=\"data:image/png;base64,R0lGODlhCgAVAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABAAKAA0AhAAAAAAAAAAAOgAAZgA6ZgA6kDoAADpmtjqQ22YAAGZmkGa2/5A6AJA6ZpBmOpC2kJDb/7ZmOrb//9uQOtuQZtu2Ztv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwU6YCCOJGCeaKqup+YEiBkJEEBB0yBRS3oJj4MqkyBIVBpGTHUxFCypjKKiQ2kaEoxhUelNaIBkQAgIAQA7\" alt=\"image\"\u003e\u0026plusmn;s)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e23.77\u0026plusmn;2.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e23.08\u0026plusmn;2.56\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e1.329\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.188\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eRR[b/min,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e20.50(16.0,27.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e18.00(15.0,23.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e-1.372\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eT[℃,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e36.70(36.4,37.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e36.75(36.2,37.6)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e-0.143\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.886\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eHR(bpm,\u003cimg width=\"7\" height=\"14\" src=\"data:image/png;base64,R0lGODlhCgAVAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABAAKAA0AhAAAAAAAAAAAOgAAZgA6ZgA6kDoAADpmtjqQ22YAAGZmkGa2/5A6AJA6ZpBmOpC2kJDb/7ZmOrb//9uQOtuQZtu2Ztv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwU6YCCOJGCeaKqup+YEiBkJEEBB0yBRS3oJj4MqkyBIVBpGTHUxFCypjKKiQ2kaEoxhUelNaIBkQAgIAQA7\" alt=\"image\"\u003e\u0026plusmn;s)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e101.81\u0026plusmn;20.76\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e92.19\u0026plusmn;18.71\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e2.231\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.028*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eSBP[mmHg,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e124.00(1128,132.8)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e122.50(113.0,133.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e-0.027\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.979\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eDBP(mmHg,\u003cimg width=\"7\" height=\"14\" src=\"data:image/png;base64,R0lGODlhCgAVAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABAAKAA0AhAAAAAAAAAAAOgAAZgA6ZgA6kDoAADpmtjqQ22YAAGZmkGa2/5A6AJA6ZpBmOpC2kJDb/7ZmOrb//9uQOtuQZtu2Ztv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwU6YCCOJGCeaKqup+YEiBkJEEBB0yBRS3oJj4MqkyBIVBpGTHUxFCypjKKiQ2kaEoxhUelNaIBkQAgIAQA7\" alt=\"image\"\u003e\u0026plusmn;s)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e68.07\u0026plusmn;12.56\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e65.31\u0026plusmn;8.37\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e1.406\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.240\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eBlood PH[M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e7.39(7.3,7.4)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e7.42(7.3,7.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e-0.672\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.502\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003ePCT[ng/ml,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e13.92(4.3,28.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e3.67(1.0,27.6)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e-1.691\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.091\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eBlood Lac\u003cbr\u003e[mmol/L,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e2.82(1.9,5.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e2.25(1.7,3.6)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e-1.884\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.060\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eHs-TnⅠ[pg/ml,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e219.90(56.1,1494.8)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e49.30(19.1,268.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e-2.594\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.009**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eBNP[pg/ml,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e1508.00(615.2,3758.6)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e640.00(189.1,3063.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e-1.837\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.066\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eCreatinine[umol/L,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e112.00(80.0,198.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e95.50(53.8,202.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e-1.082\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.279\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eBilirubin[umol/L,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e21.00(13.9,37.4)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e24.70(10.7,40.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e-0.031\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.975\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003eSOFA score(\u003cimg width=\"7\" height=\"14\" src=\"data:image/png;base64,R0lGODlhCgAVAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABAAKAA0AhAAAAAAAAAAAOgAAZgA6ZgA6kDoAADpmtjqQ22YAAGZmkGa2/5A6AJA6ZpBmOpC2kJDb/7ZmOrb//9uQOtuQZtu2Ztv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwU6YCCOJGCeaKqup+YEiBkJEEBB0yBRS3oJj4MqkyBIVBpGTHUxFCypjKKiQ2kaEoxhUelNaIBkQAgIAQA7\" alt=\"image\"\u003e\u0026plusmn;s)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e13.14\u0026plusmn;3.30\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e12.45\u0026plusmn;3.74\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e0.897\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.372\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations:BMI, body mass index ; RR, respiratory rate ; T, temperature ; HR, heart rate ; SBP, systolic blood pressure ; DBP, diastolic blood pressure ; PCT, procalcitonin ; Lac, lactate ; Hs-TnI, High-sensitivity cardiac troponin I ; BNP, B-type natriuretic peptide; SOFA score, Sequential Organ Failure Assessment score. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05\u0026nbsp;，**\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01\u003c/p\u003e\n\u003cp\u003e3.2 Comparison of Conventional Echocardiographic Parameters Between the Two Groups\u003c/p\u003e\n\u003cp\u003eThere were statistically significant differences in LVIDs, LVEF, SV, CO, and VTI between the two groups (all P\u0026lt;0.05,\u0026nbsp;Table 2), and there were no statistically significant differences in the comparison of other parameters between the two groups (all\u0026nbsp;P\u0026gt;0.05 ,\u0026nbsp;Table 2).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eComparison of conventional echocardiographic parameters between the two groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003eVariables\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003eStudy group(\u003cem\u003en\u003c/em\u003e=42)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003eControl group(\u003cem\u003en\u003c/em\u003e=42)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003et/z\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003eP value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003eLAAD[mm,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e41.00(37.0,44.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e40.50(36.0,42.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e-0.885\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e0.376\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003eLVIDd[mm,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e50.00(47.0,53.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e50.00(45.0,52.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e-0.956\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e0.339\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003eLVIDs[mm,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e29.50(25.0,35.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e26.00(23.0,30.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e-2.376\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e0.017*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003eIVSd[mm,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e9.00(9.0,10.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e9.00(8.0,10.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e-0.458\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e0.647\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003eLVPWd[mm,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e9.00(9.0,10.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e9.00(8.0,10.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e-0.45\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e0.653\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003eLVEDV[ml,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e78.50(60.5,96.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e82.50(64.0,100.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e-0.796\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e0.426\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003eLVESV[ml,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e35.00(28.0,55.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e32.50(25.8,39.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e-1.822\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e0.069\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003eLVEF[%,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e50.50(37.8,58.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e61.00(58.0,64.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e-5.786\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e0.000**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003eSV(ml,\u003cimg width=\"7\" height=\"14\" src=\"data:image/png;base64,R0lGODlhCgAVAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABAAKAA0AhAAAAAAAAAAAOgAAZgA6ZgA6kDoAADpmtjqQ22YAAGZmkGa2/5A6AJA6ZpBmOpC2kJDb/7ZmOrb//9uQOtuQZtu2Ztv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwU6YCCOJGCeaKqup+YEiBkJEEBB0yBRS3oJj4MqkyBIVBpGTHUxFCypjKKiQ2kaEoxhUelNaIBkQAgIAQA7\" alt=\"image\"\u003e\u0026plusmn;s)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e37.12\u0026plusmn;12.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e50.10\u0026plusmn;16.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e-4.191\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e0.000**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003eCO(L/min,\u003cimg width=\"7\" height=\"14\" src=\"data:image/png;base64,R0lGODlhCgAVAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABAAKAA0AhAAAAAAAAAAAOgAAZgA6ZgA6kDoAADpmtjqQ22YAAGZmkGa2/5A6AJA6ZpBmOpC2kJDb/7ZmOrb//9uQOtuQZtu2Ztv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwU6YCCOJGCeaKqup+YEiBkJEEBB0yBRS3oJj4MqkyBIVBpGTHUxFCypjKKiQ2kaEoxhUelNaIBkQAgIAQA7\" alt=\"image\"\u003e\u0026plusmn;s)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e3.82\u0026plusmn;1.60\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e4.49\u0026plusmn;1.48\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e-2.007\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e0.048*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003eVTI(cm,\u003cimg width=\"7\" height=\"14\" src=\"data:image/png;base64,R0lGODlhCgAVAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABAAKAA0AhAAAAAAAAAAAOgAAZgA6ZgA6kDoAADpmtjqQ22YAAGZmkGa2/5A6AJA6ZpBmOpC2kJDb/7ZmOrb//9uQOtuQZtu2Ztv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwU6YCCOJGCeaKqup+YEiBkJEEBB0yBRS3oJj4MqkyBIVBpGTHUxFCypjKKiQ2kaEoxhUelNaIBkQAgIAQA7\" alt=\"image\"\u003e\u0026plusmn;s)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e21.40\u0026plusmn;5.84\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e26.99\u0026plusmn;7.68\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e-3.755\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e0.000**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003ePASP[mmHg,M(Q1,Q3)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e33.50(29.8,40.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e30.00(25.0,42.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e-1.224\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e0.221\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations:LAAD, Left Atrial Anterior-Posterior Diameter; LVIDd, Left Ventricular Internal Dimension at End-Diastole; LVIDs, Left Ventricular Internal Dimension at End-Systole; IVSd, Interventricular Septal Thickness at Diastole; LVPWd, Left Ventricular Posterior Wall Thickness at Diastole; LVEDV, Left Ventricular End-Diastolic Volume; LVESV, Left Ventricular End-Systolic Volume; LVEF, Left Ventricular Ejection Fraction; SV, Stroke Volume; CO, Cardiac Output; VTI, Velocity Time Integral; PASP, Pulmonary Artery Systolic Pressure. \u0026nbsp; *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05\u0026nbsp;，**\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Comparison of speckle echocardiographic parameters between the two groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared with the control group, the absolute values of GLS, GWI, GCW, and GWE in the study group were significantly lower (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01,\u0026nbsp;Table 3,\u0026nbsp;Figure 1). GWW in the study group was significantly higher than that in the control group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, Table 3, Figure 1). There was no significant difference in PSD between the two groups (P\u0026gt;0.05, Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eComparison of speckle echocardiographic parameters between the two groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003eVariables\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003eStudy group(n=42)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003eControl group(n=42)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003et/ F/z\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003eP value\u003cem\u003e\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003eGLS(%,\u003cimg width=\"7\" height=\"14\" src=\"data:image/png;base64,R0lGODlhCgAVAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABAAKAA0AhAAAAAAAAAAAOgAAZgA6ZgA6kDoAADpmtjqQ22YAAGZmkGa2/5A6AJA6ZpBmOpC2kJDb/7ZmOrb//9uQOtuQZtu2Ztv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwU6YCCOJGCeaKqup+YEiBkJEEBB0yBRS3oJj4MqkyBIVBpGTHUxFCypjKKiQ2kaEoxhUelNaIBkQAgIAQA7\" alt=\"image\"\u003e\u0026plusmn;s)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e-11.35\u0026plusmn;3.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e-18.10\u0026plusmn;2.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e133.397\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e0.000**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003eGWI(mmHg%,\u003cimg width=\"7\" height=\"14\" src=\"data:image/png;base64,R0lGODlhCgAVAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABAAKAA0AhAAAAAAAAAAAOgAAZgA6ZgA6kDoAADpmtjqQ22YAAGZmkGa2/5A6AJA6ZpBmOpC2kJDb/7ZmOrb//9uQOtuQZtu2Ztv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwU6YCCOJGCeaKqup+YEiBkJEEBB0yBRS3oJj4MqkyBIVBpGTHUxFCypjKKiQ2kaEoxhUelNaIBkQAgIAQA7\" alt=\"image\"\u003e\u0026plusmn;s)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e980.64\u0026plusmn;348.69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e1543.81\u0026plusmn;368.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e-7.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e0.000**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003eGCW(mmHg%,\u003cimg width=\"7\" height=\"14\" src=\"data:image/png;base64,R0lGODlhCgAVAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABAAKAA0AhAAAAAAAAAAAOgAAZgA6ZgA6kDoAADpmtjqQ22YAAGZmkGa2/5A6AJA6ZpBmOpC2kJDb/7ZmOrb//9uQOtuQZtu2Ztv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwU6YCCOJGCeaKqup+YEiBkJEEBB0yBRS3oJj4MqkyBIVBpGTHUxFCypjKKiQ2kaEoxhUelNaIBkQAgIAQA7\" alt=\"image\"\u003e\u0026plusmn;s)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e1514.79\u0026plusmn;422.99\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e2049.26\u0026plusmn;473.57\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e-5.455\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e0.000**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003eGWW(mmHg%,\u003cimg width=\"7\" height=\"14\" src=\"data:image/png;base64,R0lGODlhCgAVAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABAAKAA0AhAAAAAAAAAAAOgAAZgA6ZgA6kDoAADpmtjqQ22YAAGZmkGa2/5A6AJA6ZpBmOpC2kJDb/7ZmOrb//9uQOtuQZtu2Ztv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwU6YCCOJGCeaKqup+YEiBkJEEBB0yBRS3oJj4MqkyBIVBpGTHUxFCypjKKiQ2kaEoxhUelNaIBkQAgIAQA7\" alt=\"image\"\u003e\u0026plusmn;s)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e300.45\u0026plusmn;126.83\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e237.02\u0026plusmn;161.60\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e2.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e0.049*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003eGWE(%,\u003cimg width=\"7\" height=\"14\" src=\"data:image/png;base64,R0lGODlhCgAVAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABAAKAA0AhAAAAAAAAAAAOgAAZgA6ZgA6kDoAADpmtjqQ22YAAGZmkGa2/5A6AJA6ZpBmOpC2kJDb/7ZmOrb//9uQOtuQZtu2Ztv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwU6YCCOJGCeaKqup+YEiBkJEEBB0yBRS3oJj4MqkyBIVBpGTHUxFCypjKKiQ2kaEoxhUelNaIBkQAgIAQA7\" alt=\"image\"\u003e\u0026plusmn;s)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e81.64\u0026plusmn;6.80\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e89.02\u0026plusmn;5.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e-5.348\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e0.000**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003ePSD[ms,M（Q1，Q3）]\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e67.795(56.3,81.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e55.865(44.9,79.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e-1.677\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e0.093\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations:GLS, Global Longitudinal Strain; GWI, Global Work Index; GCW, Global Constructive Work; GWW, Global Wasted Work; PSD , Peak Strain Delay. \u0026nbsp; \u0026nbsp;*\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05，\u0026nbsp;**\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Correlation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePearson correlation analysis was used to explore the correlation between myocardial work parameters and LVEF. The results showed significant positive correlations between LVEF and three parameters: GWI, GCW, and GWE (r=0.50, 0.48, 0.41, all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, Figure 2). There was no significant correlation between LVEF and GWW (r=0.10, *P*\u0026gt;0.05, Figure 2).The correlation between GLS and each myocardial work parameter was evaluated separately. The analysis showed significant negative correlations between GWI, GCW, GWE and GLS (r=-0.81, -0.71, -0.70, all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, Figure 3). The correlation between GLS and GWW was not significant (r=0.28, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, Figure 3).\u003c/p\u003e\n\u003cp\u003e3.5 Diagnostic Value of Myocardial Work Parameters for Septic Cardiomyopathy\u003c/p\u003e\n\u003cp\u003eGWI, GCW, GWW, and GWE all showed high diagnostic values for septic cardiomyopathy (AUC = 0.863, 0.826, 0.672, 0.793, respectively, Figure 4). The corresponding cut-off values, sensitivities, and specificities are shown in Table 4,Among them, GWI showed the highest sensitivity and specificity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eDiagnostic Value of Myocardial Work Parameters for Septic Cardiomyopathy\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\u003cstrong\u003eMyocardial Work Parameters\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003eAUC\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\u003cstrong\u003eYouden\u0026apos;s Index\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003eSensitivity\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003eSpecificity\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003eCut-off\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e95% CI\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003eP value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003eGWI(mmHg%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e0.863\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e0.619\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e79%\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e83%\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e1268.000 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e0.785 ~ 0.940\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e0.000**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003eGCW(mmHg%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e0.826\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e0.500\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e57%\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e93%\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e2060.000 \u0026nbsp; \u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e0.738 ~ 0.913\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e0.000**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003eGWW(mmHg%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e0.672\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e0.357\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e71%\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e64%\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e227.000\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e0.553 ~ 0.792\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e0.007**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003eGWE(%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e0.793\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e0.500\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e79%\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e71%\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e85.000\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e0.698 ~ 0.889\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e0.000**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations:GLS, Global Longitudinal Strain; GWI, Global Work Index; GCW, Global Constructive Work; GWW, Global Wasted Work; \u003cstrong\u003eAUC\u003c/strong\u003e，Area Under the ROC Curve；\u003cstrong\u003eCI\u003c/strong\u003e，Confidence Interval. * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 ，** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eDuring sepsis, a series of pathophysiological changes occur in patients, including activation of immune cells with inflammatory mediator cascade reactions, programmed cell death, mitochondrial dysfunction, etc. These mechanisms collectively contribute to the development of cardiac dysfunction [18]. According to relevant studies, approximately 25% of patients with septic shock develop myocardial dysfunction in the early stage (within 24 hours) [19].Therefore, there is an urgent need to explore additional biomarkers or detection methods to supplement the early diagnosis of SCM.\u003c/p\u003e\n\u003cp\u003eIn clinical practice, LVEF measured by conventional echocardiography is a common indicator for predicting cardiovascular diseases. Editorials have pointed out that GLS demonstrates higher reliability than LVEF as an index for evaluating left ventricular systolic function [20]. However, GLS is susceptible to pressure loading: this parameter decreases with increased afterload, which may be misinterpreted as reduced myocardial contractility, whereas it actually reflects afterload changes. In contrast, LV-PSL integrate myocardial deformation and left ventricular pressure parameters to achieve comprehensive assessment of myocardial work [21]. Russell’s team [4, 5] revealed that segmental differences in left ventricular MW evaluated by LV-PSL correlate closely with myocardial glucose metabolic rate measured by positron emission tomography (PET). This is consistent with previous studies [22, 23]. Subsequent studies have further indicated that in septic patients, elevated GWW is significantly correlated with lactate levels (reflecting tissue hypoxia) (r=0.45), suggesting that increased ineffective work may be associated with mitochondrial dysfunction. However, LVEF and GLS cannot directly reflect energy metabolic abnormalities [24]. With the proposal of non-invasive MW, its clinical application has increased progressively. A review on patients with early coronary artery disease (CAD) has suggested [25] that the GWI demonstrates extremely high sensitivity for diagnosing early CAD, with an AUC value of 0.86, which is significantly higher than that of GLS (0.79). In other related fields, a study [26] applied noninvasive MW to evaluate left ventricular systolic function in patients with cardiac amyloidosis (CA), finding that GWI significantly decreased at rest and recovered after exercise. In recent years, studies have also explored patients with transthyretin amyloid cardiomyopathy (ATTR-CM), indicating that myocardial work parameters (especially global work index, GWI) can more accurately reflect disease progression and energy metabolic efficiency in these patients [27]. These studies collectively confirm the clinical value of noninvasive myocardial work echocardiography in assessing early left ventricular systolic function in various cardiomyopathies, providing a solid basis for early intervention and targeted therapy.\u003c/p\u003e\n\u003cp\u003eThis study analyzed clinical data and ultrasound parameters of SCM and non-SCM groups, exploring intergroup differences. Except for Hs-TnI no significant differences were observed in other baseline clinical characteristics between groups. GWI, GCW, and GWE showed statistically significant differences (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01): their absolute values were notably lower in the SCM group, indicating reduced effective myocardial work, decreased overall cardiac contractility, or impaired effective ejection. GWW was significantly higher in the SCM group, reflecting inefficient cardiac energy conversion. These parameter changes (decreased GWI/GCW/GWE and increased GWW) collectively constitute a characteristic \"myocardial energy-mechanical conversion disorder\" profile in SCM, revealing that systolic dysfunction stems not only from reduced contractility but also from comprehensive deterioration of energy utilization efficiency. Correlation analysis showed significant positive correlations between LVEF and GWI/GCW/GWE (r=0.50, 0.48, 0.41, all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01) and negative correlations between GLS and GWI/GCW/GWE (r=-0.81, -0.71, -0.70, all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01), indicating that longitudinal myocardial fiber contraction drives overall mechanical work and determines work efficiency. ROC curve analysis further confirmed high diagnostic values of GWI, GCW, and GWE for SCM (AUC=0.863, 0.826, 0.793), with GWI showing the highest efficacy (sensitivity 79%, specificity 83%). MW parameters (especially GWI) can detect early, subtle impairments in myocardial contractility and efficiency before traditional indices (e.g., LVEF) change significantly, as evidenced by SCM patients with LVEF\u0026gt;50% but abnormal MW parameters in this study.\u003c/p\u003e\n\u003cp\u003eWitthaut et al. [28] showed that BNP levels do not significantly increase in early sepsis when myocardial impact is mild, but rise markedly in severe sepsis or septic shock. This may result from BNP’s presence in spinal cord, brain, lung tissues, and diseases like renal insufficiency, inflammation, and tumors, limiting its specificity for SCM early diagnosis. Consistently, our study found no significant BNP difference between SCM and non-SCM groups (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eDuring sepsis, inflammatory mediators directly damage myocardial cells, leading to the release of troponin. A prospective study [29] including 116 septic patients found that the level of Hs-TnⅠ in septic patients with subclinical left ventricular systolic dysfunction (LVSD) was significantly higher than that in patients without LVSD (median: 28.5 vs. 18.7 ng/L, p\u0026lt;0.001). Kim et al. [30] reported significantly higher peak Hs-TnI levels in sepsis patients with reduced myocardial contractility (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), with stronger expression in left ventricular systolic dysfunction subgroups than preserved-function subgroups (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01). Similarly, our study showed higher Hs-TnI in the SCM group, supporting SCM classification and confirming myocardial damage. Thus, sepsis patients should be monitored for SCM after excluding other causes of troponin I elevation.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eNoninvasive myocardial work echocardiography holds promise as a valuable indicator for evaluating septic myocardial systolic dysfunction, demonstrating broad clinical application prospects.\u003c/p\u003e"},{"header":"6.\tLimitations and advantages","content":"\u003cp\u003eThis study has several limitations: First, the GLS threshold (-15%) for defining SCM, derived from previous literature, may overestimate SCM frequency as some SCM patients had LVEF\u0026gt;50%, possibly because LVEF changes are masked by load conditions [3]. This highlights MW parameters’ value in identifying systolic dysfunction even with preserved LVEF. Second, noninvasive myocardial work technology faces challenges in diseases with increased aortic-left ventricular pressure gradients, as peripheral systolic pressure inaccurately reflects left ventricular peak pressure.Third, this single-center, small-sample study may lack generalizability, warranting larger multicenter studies. Fourth, obtaining standard ultrasound images is difficult in ICU patients due to altered consciousness and equipment interference. Fifth, the relationship between MW parameters and prognosis remains unanalyzed, representing a future research direction.\u003c/p\u003e\n\u003cp\u003eNoninvasive myocardial work echocardiography offers advantages: noninvasiveness, bedside applicability, high temporal resolution for dynamic myocardial contraction tracking, and standardized afterload correction to eliminate peripheral resistance impacts, enabling comprehensive and accurate evaluation of local and global systolic function. In this study, abnormal myocardial work was detected in some sepsis patients with normal LVEF, and MW parameters showed high AUC values for SCM diagnosis, proving their utility in early sepsis myocardial injury identification and providing a novel assessment method alongside LVEF and GLS.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eMW\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;M\u003c/strong\u003eyocardial\u0026nbsp;Work\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;SCM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Septic\u0026nbsp;Cardiomyopathy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;GLS\u003c/strong\u003e\u003cstrong\u003eGlobal Longitudinal Strain\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLVEF\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;L\u003c/strong\u003e\u003cstrong\u003eeft\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eV\u003c/strong\u003e\u003cstrong\u003eentricular\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003cstrong\u003ejection\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003cstrong\u003eraction\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGCW\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;G\u003c/strong\u003e\u003cstrong\u003elobal\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003eonstructive\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eW\u003c/strong\u003e\u003cstrong\u003eork\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGWE\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;G\u003c/strong\u003e\u003cstrong\u003elobal\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eW\u003c/strong\u003e\u003cstrong\u003eork\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003cstrong\u003efficiency\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGWI\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;G\u003c/strong\u003e\u003cstrong\u003elobal\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eW\u003c/strong\u003e\u003cstrong\u003eork\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003cstrong\u003endex\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGWW \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Global Wasted Work)\u003c/p\u003e\n\u003cp\u003ePSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Peak Strain Delay\u003c/p\u003e\n\u003cp\u003ePSL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Pressure-Strain Loops\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIRB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Institutional Review Board\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEACVI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;European Association of Cardiovascular Imaging\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eASE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;American Society of Echocardiography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTTE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Transthoracic Echocardiography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eECG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Electrocardiogram\u003c/p\u003e\n\u003cp\u003e2D \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Two-Dimensional\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLVESV \u0026nbsp; \u0026nbsp; \u0026nbsp; Left Ventricular End-Systolic Volume\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLVEDV \u0026nbsp; \u0026nbsp; \u0026nbsp; Left Ventricular End-Diastolic Volume\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Body Mass Index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Respiratory Rate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Temperature \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Heart Rate\u003c/p\u003e\n\u003cp\u003eSBP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Systolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eDBP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Diastolic Blood Pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Procalcitonin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLac \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Lactate \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHs-TnI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;High-Sensitivity Cardiac Troponin I\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBNP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;B-type Natriuretic Peptide\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSOFA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sequential Organ Failure Assessment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTTE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Transthoracic Echocardiography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLAAD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Left Atrial Anterior-Posterior Diameter\u003c/p\u003e\n\u003cp\u003eLVIDd \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Left Ventricular Internal Dimension at End-Diastole\u003c/p\u003e\n\u003cp\u003eLVIDs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Left Ventricular Internal Dimension at End-Systole\u003c/p\u003e\n\u003cp\u003eIVSd \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Interventricular Septal Thickness at Diastole\u003c/p\u003e\n\u003cp\u003eLVPWd \u0026nbsp; \u0026nbsp; \u0026nbsp; Left Ventricular Posterior Wall Thickness at Diastole\u003c/p\u003e\n\u003cp\u003eLVEDV \u0026nbsp; \u0026nbsp; \u0026nbsp; Left Ventricular End-Diastolic Volume\u003c/p\u003e\n\u003cp\u003eLVESV \u0026nbsp; \u0026nbsp; \u0026nbsp; Left Ventricular End-Systolic Volume\u003c/p\u003e\n\u003cp\u003eSV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Stroke Volume\u003c/p\u003e\n\u003cp\u003eCO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Cardiac Output\u003c/p\u003e\n\u003cp\u003eVTI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Velocity Time Integral\u003c/p\u003e\n\u003cp\u003ePASP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Pulmonary Artery Systolic Pressure\u003c/p\u003e\n\u003cp\u003eSTE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Speckle-Tracking Echocardiography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePET \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Positron Emission Tomography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCAD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Coronary Artery Disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cardiac Amyloidosis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLVSD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Left\u0026nbsp;Ventricular\u0026nbsp;Systolic\u0026nbsp;Dysfunction\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthicsapproval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and the principles of medical ethics. Approval was obtained from the Institutional Review Board (IRB) of the First People\u0026apos;s Hospital of Lianyungang, and written informed consent was acquired from all participants. Approval Number: KY-20220809001-01.F01.The data collected for this research will not be used for any other purposes. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData was collected based on available medical records. All of the data was analyzed anonymously.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Scientific Research Project of Jiangsu Provincial Health Commission (H2019109)\u0026nbsp;and\u0026nbsp;General Project for Social Development of Jiangsu Provincial Department of Science and Technology (BE2020670).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYNL contributed to data collection, study design, statistical analysis, and manuscript writing. YAC and JJH contributed equally to data collection and image acquisition. YY participated in manuscript revision and provided work support. YPX provided research guidance, participated in manuscript revision, and offered work support. YHZ, YHM and QXH contributed equally to data collection and study design. XML provided research guidance, conducted manuscript review, and secured funding support. All authors critically revised the manuscript and gave final approval for the version to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the mentor team for their guidance and strong support during the scientific research and manuscript collaboration process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eParker MM, Shelhamer JH, Bacharach SL, Green MV, Natanson C, Frederick TM, et al. Profound but reversible myocardial depression in patients with septic shock. 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Troponin testing for assessing sepsis-induced myocardial dysfunction in patients with septic shock. J Clin Med. 2019;8(2):239. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jcm8020239\u003c/span\u003e\u003cspan address=\"10.3390/jcm8020239\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Septic Cardiomyopathy, Left Ventricular Systolic Dysfunction, Noninvasive Myocardial Work, Pressure-Strain Loop","lastPublishedDoi":"10.21203/rs.3.rs-7021154/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7021154/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo investigate the clinical value of noninvasive myocardial work (MW) in assessing early left ventricular systolic dysfunction in patients with septic cardiomyopathy(SCM).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis prospective cohort study enrolled septic patients admitted to the EICU of Lianyungang First People's Hospital between September 2024 and May 2025. Participants were stratified into the SCM group (the study group) and the non-SCM group (the control group) based on Global Longitudinal Strain (GLS) values.Intergroup comparisons were performed: baseline clinical characteristics,conventional echocardiographic parameters. Pearson correlation analysis was employed to quantify associations between myocardial work parameters and GLS. The diagnostic value of noninvasive myocardial work parameters for SCM identification was evaluated through ROC curve.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe study cohort comprised 84 patients, with 42 cases allocated to the SCM group and 42 to the non-SCM group. Comparative analysis revealed significantly elevated heart rate and high-sensitivity cardiac troponin I levels in the SCM group versus controls (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).Cardiac functional assessment demonstrated marked reductions in left ventricular ejection fraction (LVEF), GLS, global constructive work (GCW), global work efficiency (GWE), and global work index (GWI) within the SCM group compared to controls (all parameters P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).Correlation analyses identified significant negative correlations between GWI, GCW, GWE and GLS (r=-0.81, -0.71, -0.70,all P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). ROC curve confirmed the superior diagnostic performance of myocardial work indices for SCM identification, with GWI demonstrating the highest predictive value (AUC\u0026thinsp;=\u0026thinsp;0.863), followed by GCW (AUC\u0026thinsp;=\u0026thinsp;0.826) and GWE (AUC\u0026thinsp;=\u0026thinsp;0.793).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eNoninvasive myocardial work parameters demonstrate significant potential for early identification of left ventricular systolic dysfunction in SCM.\u003c/p\u003e","manuscriptTitle":"Assessment of Early Left Ventricular Systolic Function in Septic Cardiomyopathy:Non-invasive Myocardial Work","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 04:15:54","doi":"10.21203/rs.3.rs-7021154/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"996d5b1e-d701-4af9-980e-00cff3fdfec7","owner":[],"postedDate":"August 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-23T14:39:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-07 04:15:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7021154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7021154","identity":"rs-7021154","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}