Development and Validation of a Risk Prediction Model for Diastolic Dysfunction in Patients with Sepsis

Development and Validation of a Risk Prediction Model for Diastolic Dysfunction in Patients with Sepsis | 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 Development and Validation of a Risk Prediction Model for Diastolic Dysfunction in Patients with Sepsis Jin-xin Pan, Li-juan Duan, Li-wei Hua, Zhi-wei Yang, Jia-ding Xia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8639758/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 Background This study aimed to identify risk factors for diastolic dysfunction in septic patients, establish a predictive model, enable early intervention, reduce the incidence of septic cardiomyopathy (SCM), and improve patient prognosis. Methods Ninety-eight sepsis patients admitted to the Affiliated Hospital of Chengde Medical College were divided into two groups: the diastolic dysfunction group and the normal cardiac function group. Baseline clinical data, echocardiographic parameters related to diastolic function, and serum biomarkers of myocardial injury were collected. Least Absolute Shrinkage and Selection Operator (LASSO) regression combined with binary logistic regression was used for variable selection, followed by model construction, evaluation, visualization, and internal validation. Results Key predictive variables included Sequential Organ Failure Assessment (SOFA) score, 28-day mortality, Cardiac Troponin I (cTnI), A (Atrial systole peak), E/A (Early diastolic peak/Atrial systole peak), e' (Early diastolic myocardial velocity peak), E/e' (Early diastolic peak/Early diastolic myocardial velocity peak), and Relative Wall Thickness (RWT). Nomogram analysis confirmed these as risk factors for SCM. The model showed high predictive value (Receiver Operating Characteristic(ROC) curve area under the curve = 0.983, 95% Confidence Interval ( CI ): 0.951-1.000) and good calibration (Hosmer-Lemeshow test: χ² = 1.784, df = 8, P = 0.987). Its clinical utility was validated by Decision Curve Analysis (DCA). Conclusion SOFA score, 28-day mortality, cTnI, A, e', E/e', E/A, and RWT are independent risk factors for diastolic dysfunction in septic patients. The constructed predictive model exhibits excellent performance and clinical applicability. Septic cardiomyopathy Diastolic function Relative wall thickness Cardiac troponin I SOFA score 28-day mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background As a common life-threatening condition in the Intensive Care Unit (ICU),sepsis has shown an increasing incidence trend in recent years. The condition develops as a result of an unregulated and overly intense inflammatory reaction to an infection, which can progress to multiple organ dysfunction or failure and carries a high risk of mortality( 1 ). Sepsis-induced myocardial injury is associated with a significantly elevated mortality rate( 2 ). The incidence of cardiac diastolic dysfunction in sepsis remains unknown, and its diagnosis poses a challenge; furthermore, its impact on survival, independent of dysfunction in other organ systems, remains unclear. Early identification of myocardial injury is essential in sepsis management, as it enables timely and vigorous therapeutic measures, which significantly improve the clinical outcomes and prognosis for patients. Currently, the diagnosis of SCM mainly relies on a combination of echocardiography, myocardial injury biomarkers, and clinical features. Sepsis-induced myocardial dysfunction refers to an intrinsic impairment affecting both the systolic and diastolic functions of the heart muscle, which becomes evident during the course of sepsis. Ventricular diastolic dysfunction is a disorder characterized by impaired ventricular filling, which can result from compromised ventricular relaxation and/or reduced compliance( 3 ). Current research has consistently demonstrated that left ventricular diastolic dysfunction is a more effective predictor of clinical outcomes in patients with sepsis or septic shock than left ventricular systolic dysfunction( 4 ). This condition is clinically assessed through standards such as echocardiography, where parameters like the E/A ratio and Deceleration Time (DT) are used to evaluate the heart's diastolic function( 5 ). The diagnosis of diastolic dysfunction is based on multiple parameters derived from Doppler echocardiography, including the E/A ratio, E/e' ratio, and Left Ventricular End-Diastolic Dimension (LVIDD), which are crucial for assessing left ventricular diastolic function( 6 ). The detection of myocardial injury markers is often used as a supplementary evaluation to echocardiography in the diagnosis of SCM. However, several parameters, such as left ventricular end-diastolic dimension(LVIDD), Left Ventricular End-Systolic Dimension (LVISD),and RWT(RWT = 2 × Posterior Wall Thickness in Diastole (PWTd) / Left Ventricular End-Diastolic Diameter (LVEDD)( 7 )), have not yet been evaluated in the diagnostic assessment of ventricular diastolic dysfunction. Currently, the clinical diagnosis of sepsis-induced diastolic dysfunction relies heavily on echocardiographic evaluation and the assessment of biomarkers, as these methods have been shown to be crucial in identifying reversible cardiac dysfunction associated with sepsis. Nevertheless, echocardiography demands not only superior image quality but also a high level of technical expertise from the operator, which contributes to significant variability in its application and interpretation. There is currently significant debate surrounding the use of various biomarkers for diagnosing sepsis-induced diastolic dysfunction( 8 ). In clinical practice, healthcare providers frequently evaluate the diastolic and systolic functions of the heart by relying on specific diagnostic indicators. However, alterations in the heart's morphology and structural characteristics are often underappreciated or not given adequate attention during routine assessments. In this study, we employed a combination of biomarkers and echocardiographic techniques to diagnose septic diastolic dysfunction, identify its associated risk factors, and develop a predictive model. This approach aims to facilitate the early detection of patients with septic diastolic dysfunction, enabling timely clinical intervention. The ultimate goal is to reduce the incidence of sepsis and its complications, particularly septic cardiomyopathy, thereby improving patient outcomes. Methods Study Design and Setting Ninety-eight patients diagnosed with sepsis and admitted to the Department of Critical Care Medicine at the Affiliated Hospital of Chengde Medical College between November 2023 and November 2024 were enrolled in this study. Based on the presence or absence of diastolic dysfunction, the participants were divided into two groups: the sepsis-associated cardiac diastolic dysfunction group(observation group, D Group) and the normal cardiac diastolic function group(control group, N-D Group).The data screening process is shown in Fig. 1 Flowchart. Sample selection 1 Inclusion and exclusion criteria The inclusion criteria were as follows: ( 1 ) Patients were required to meet the diagnostic criteria for sepsis as outlined in the "2021 International Guidelines for Management of Sepsis and Septic Shock"( 9 ); ( 2 ) Participants in the observation group were additionally required to fulfill the diagnostic criteria for diastolic heart dysfunction; ( 3 ) All patients were required to have complete medical record documentation, and either the patient or their legally authorized guardian should have provided signed informed consent. Exclusion criteria are as follows: ( 1 ) Individuals under the age of 18, pregnant or lactating women; ( 2 ) Patients who die within 24 hours of being diagnosed with sepsis upon admission; ( 3 ) Patients with pre-existing cardiac insufficiency or myocardial disease; ( 4 ) Patients with severe hepatic or renal dysfunction; ( 5 ) Patients with concurrent autoimmune diseases or those who are on long-term treatment with steroids or other immune suppressive agents; ( 6 ) Patients with advanced-stage malignant tumors. 2 Variables and measurement 2.1 Experimental Instruments Bedside ultrasound machine(Philips CX50) 2.2 Experimental grouping Patients who met the inclusion and exclusion criteria were divided into a sepsis-related cardiac diastolic dysfunction group(observation group, D group) and anormal cardiac diastolic function group(control group, N-D group) based on the presence or absence of diastolic dysfunction. 2.3 Data collection For all patients, baseline clinical data were collected within the first 24 hours of admission, along with echocardiographic parameters assessing diastolic function. These echocardiographic indicators included the E,A,E/A, e, E/e, Left Ventricular Ejection Fraction (LVEF), LVEDD, Posterior Wall Thickness in Diastole (PWTd), and RWT. In addition, serum biomarkers indicative of myocardial injury—specifically cTnI and NT-proBNP—were also recorded at the time of admission. Statistical analysis Statistical analysis was performed using SPSS 26.0. Quantitative data were subjected to normality tests and homogeneity of variance tests. For data conforming to anormal distribution, the mean± standard deviation(x̄±s) was used, and intergroup comparisons were conducted using an independent samples t-test. In cases where indicators do not follow a normal distribution, statistical analysis was conducted using the median (M) and the interquartile range (IQR), denoted as M[QL, QU]. Mann-Whitney U rank-sum test for independent samples. Qualitative data were expressed as rates (%) and analyzed using the χ² test. To identify significant predictors of diastolic dysfunction in septic patients, univariate screening was initially performed using the LASSO regression method, as demonstrated in clinical studies. Variables that demonstrated potential relevance were then further analyzed through multivariate logistic regression to determine their independent associations with diastolic dysfunction. Based on the results of the multivariate analysis, a nomogram predictive model was constructed to estimate the probability of diastolic dysfunction in septic patients. The performance of the nomogram was rigorously evaluated using the Bootstrap method for internal validation, with key aspects of model performance including discrimination, accuracy, and clinical utility all being assessed. If the P -value is less than 0.05, the observed difference is regarded as statistically significant. Results Comparison of baseline data of enrolled patients This study enrolled a total of 108 patients diagnosed with sepsis. Of these, three were excluded due to the presence of primary malignant tumors, three were excluded because of severe cardiac dysfunction resulting from acute myocardial infarction, and four were excluded owing to significant liver or kidney impairment. After these exclusions, a final cohort of 98 sepsis patients was included in the analysis. This group was further divided into two subgroups based on diastolic function: 43 patients exhibited diastolic dysfunction and were assigned to the observation group(D Group), while 55 patients with normal diastolic function were placed in the control group(N-D Group). The overall gender distribution among the 98 patients was 52 males and 46 females. The most frequent site of infection was the abdomen, which accounted for 64.58% of all cases(Fig. 1 and Table 1 ). Table 1 Baseline characteristics of the two groups (N = 98) Baseline Information N-D Group(n = 55) D Group(n = 43) t/z/χ 2 P Age (years) 64.63 ± 12.86 66.35 ± 12.97 -8.475 < 0.001 Sex, n (%) -0.478 0.633 Male 28(51.00%) 24(56.00%) Female 27(49.00%) 19(44.00%) Height (cm) 166.92 ± 9.91 167.22 ± 10.24 -1.894 0.058 Weight (kg) 64.37 ± 11.82 64.38 ± 13.10 -0.052 0.959 BMI (kg/m²) 22.60(20.76,25.06) 23.43(19.59,26.12) -0.982 0.326 BSA (m²) 1.69 ± 0.19 1.69 ± 0.22 1.432 0.152 Past medical history, n (%) Hypertension 26(47.29%) 21(48.82%) 0.024 0.878 Type 2 diabetes 12(21.82%) 13(30.23%) 0.899 0.343 Site of infection, n (%) Nervous System 1(1.82%) 1(2.33%) 0.031 0.860 Lungs 10(18.18%) 11(25.58%) 0.785 0.376 Abdomen 30(54.55%) 22(51.16%) 0.111 0.739 Skin, soft tissue 5(9.09%) 2(4.65%) 0.717 0.397 Blood stream infection 3(5.45%) 2(4.65%) 0.032 0.858 Urinary system 6(10.91%) 5(11.63%) 0.013 0.911 Note : 1. Data are presented as mean±standard deviation (x̄±s) for normally distributed continuous variables, median (interquartile range, IQR) [M(QL, QU)] for non-normally distributed continuous variables, and n (%) for categorical variables. 2. P < 0.05 was considered statistically significant. Figure 1 Flow diagram of participant enrollment and grouping in sepsis patients. A total of 108 sepsis patients were initially screened between November 2023 and November 2024. Ten patients were excluded due to severe liver and renal insufficiency (n = 4), severe cardiac insufficiency caused by acute myocardial infarction (n = 3), and primary malignant tumors (n = 3). Finally, 98 eligible patients were enrolled and divided into the sepsis-associated cardiac diastolic dysfunction group (D Group, n = 43) and the normal cardiac diastolic function group (N-D Group, n = 55), with no loss to follow-up in either group. LASSO regression and multivariate logistic regression analysis Figure 2 and Fig. 3 present the results of univariate screening performed using LASSO regression analysis. This analysis identified eight risk factors with non-zero coefficients, namely: SOFA score,28-day mortality, cTnI, A,E/A, e', E/e', and RWT. These eight variables were further subjected to multivariate logistic regression analysis to determine their independent association with sepsis-induced diastolic dysfunction. The corresponding regression coefficients for these predictors were as follows: 0.528 for SOFA, 0.051 for 28-day mortality, 1.519 for cTnI, 0.018 for A, 0.922 for E/A, -0.290 for e', 0.495 for E/e', and 9.950 for RWT. Based on these coefficients, the predictive model formula was constructed as: Logistic(risk score) = -18.202 + 0.528 × SOFA + 0.051×28-day mortality + 1.519×cTnI + 0.018×A + 0.922×E/A + (-0.290)×e' + 0.495×E/e' + 9.950 × RWT. The goodness-of-fit of the model was assessed using the Hosmer-Lemeshow test, yielding a χ² value of 1.784, DF of 8, and a P value of 0.987, indicating a good fit of the model to the data. Detailed results are provided in Table 2 . Figure 2 LASSO regression coefficient path plot for predictor variable screening. This figure shows the coefficient path plot of LASSO regression combined with binary logistic regression for predictor variable screening. The curve represents the change trend of regression coefficients of each independent variable with the gradual decrease of Log(λ) value. "Coefficients" refer to the regression coefficients corresponding to each independent variable in the LASSO regression model. Figure 3 LASSO regression cross-validation curve for optimal λ selection. This is the LASSO regression cross-validation curve for optimal λ selection. The vertical dashed lines on the left and right represent Log(λ) corresponding to the minimum cross-validation error (lambda.min) and Log(λ) within one standard error of the minimum error (lambda.1se), respectively. "Binomial Deviance" is the binomial distribution loss function calculated for each fold during the cross-validation process, which is used to determine the optimal penalty parameter λ. Table 2 Results of binary logistic regression analysis Variables β SE OR (95%CI) Z P Constant -18.202 6.148 0.000 (0.000–0.000) -2.961 0.003 SOFA score 0.528 0.194 1.696 (1.163–2.475) 2.729 0.006 28-day mortality 0.051 0.025 1.053 (1.002–1.107) 2.049 0.040 cTnI 1.519 0.674 4.575 (1.211–17.273) 2.252 0.024 A 0.018 0.013 1.018 (0.993–1.044) 1.363 0.173 E/A 0.922 0.342 2.512 (1.283–4.918) 2.698 0.007 e' -0.290 0.133 0.748 (0.575–0.971) -2.182 0.029 E/e' 0.495 0.152 1.640 (1.213–2.216) 3.257 0.001 RWT 9.950 7.188 20949.860 (11.470-266700700000.000) 1.384 0.166 Notes: 1. This table presents the results of binary logistic regression analysis, with "sepsis-induced diastolic dysfunction" as the dependent variable. 2. P < 0.05 was considered statistically significant. Construct and validate a predictive model for sepsis-induced diastolic dysfunction After determining the final variables for constructing the predictive model of sepsis-induced diastolic dysfunction, we used the "rms" package in R Studio to build the model, and generated a nomogram(Fig. 4 )through the "nomogram" function of this package. The area under the receiver operating characteristic curve of the predictive model for sepsis-induced diastolic dysfunction was 0.983 (95% CI : 0.951-1.000), as shown in Fig. 5 . The calibration curve slope is approximately 1, which suggests that the developed nomogram model demonstrates strong predictive accuracy for assessing the risk of diastolic dysfunction in patients with sepsis (as illustrated in Fig. 6 ). The DCA curves demonstrated that the model constructed with 8 predictive variables yielded higher ordinate values compared to the single-variable predictive model across an extensive range of threshold probabilities. The predictive model established in the present study indicates a greater net benefit, as supported by the principles of predictive analytics and decision-making. Furthermore, it suggests that patients presenting with these predictive variables are at an elevated risk of developing septic diastolic dysfunction(as illustrated in Fig. 7 ). Figure 4 Nomogram for predicting diastolic dysfunction in sepsis patients. This nomogram is constructed to predict the probability of sepsis-induced diastolic dysfunction. Each predictive factor (SOFA score, 28-day mortality, cTnI, A, E/A, e', E/e', RWT) is assigned a corresponding score on the axis. The total score is obtained by summing the scores of all factors, and the predicted probability of diastolic dysfunction can be determined by mapping the total score to the "Predicted Value" axis. Figure 5 ROC curve of the predictive model for sepsis-induced diastolic dysfunction. This is the ROC curve of the predictive model for sepsis-induced diastolic dysfunction. The horizontal axis represents the false positive rate (1 - specificity), and the vertical axis represents the true positive rate (sensitivity). The area under the ROC curve (AUC) is 0.983 (95% CI : 0.951-1.000), indicating excellent discriminative ability of the model. Figure 6 Calibration curve of the predictive model for sepsis-induced diastolic dysfunction. This is the calibration curve of the predictive model. The horizontal axis represents the predicted probability of sepsis-induced diastolic dysfunction calculated by the model, and the vertical axis represents the actual occurrence rate of diastolic dysfunction in the study population. The "Ideal" line indicates perfect calibration, the "Apparent" line represents the observed calibration of the model, and the "Bias-corrected" line is the calibration curve after Bootstrap internal validation. The slope of the calibration curve is close to 1, indicating good consistency between the predicted probability and the actual occurrence rate. Figure 7 DCA of the predictive model for sepsis-induced diastolic dysfunction. This is the DCA of the predictive model. The horizontal axis represents the high-risk threshold probability of sepsis-induced diastolic dysfunction, and the vertical axis represents the standardized net benefit. The "All" curve represents the net benefit of treating all patients, the "None" curve represents the net benefit of not treating any patients, and the curve corresponding to "group~predicted_values" represents the net benefit of the predictive model. The model's curve maintains a higher standardized net benefit across the entire threshold probability range, indicating good clinical utility. Discussion Sepsis, characterized by a severe systemic inflammatory response due to microbial invasion in the bloodstream, is one of the most critical and life-threatening clinical syndromes within the domain of critical care, with a high incidence and mortality rate, and continues to exhibit persistently elevated incidence and mortality rates( 9 ). This is especially evident among elderly individuals and those with pre-existing medical conditions, who are at significantly higher risk( 10 ). The heart is one of the organs most frequently impacted in patients with sepsis( 11 ). According to a meta-analysis examining the epidemiology of sepsis, the overall incidence of septic cardiomyopathy was found to be 34.2%,with an associated in-hospital mortality rate of 45.6%. Notably, patients diagnosed with septic cardiomyopathy exhibited a significantly higher mortality rate compared to those with sepsis who did not develop cardiomyopathy( 12 ). Advancements in bedside ultrasound diagnostic techniques, particularly tissue Doppler imaging, have substantially improved the accuracy and feasibility of assessing diastolic function( 13 ). Consequently, there has been growing recognition within the medical community of the significance of sepsis-related cardiac diastolic dysfunction. This study found that 43.9% of sepsis patients exhibited signs of diastolic dysfunction. Furthermore, those with sepsis-induced diastolic dysfunction tended to have higher Acute Physiology and Chronic Health Evaluation II (APACHE-II) and SOFA scores, along with prolonged ICU stays, indicating a less favorable prognosis( 14 ). The evidence indicates a significant correlation between sepsis-induced cardiac diastolic dysfunction and an increased risk of subsequent cardiovascular events. Therefore, early identification of risk factors for this condition and the prompt implementation of targeted interventions are of paramount importance in improving patient outcomes. The pathogenesis of septic cardiac diastolic dysfunction remains incompletely understood and is mediated through a complex interplay of multiple pathophysiological mechanisms( 15 ). Throughout the development and progression of sepsis, there is a significant release of various inflammatory mediators. The activation of these inflammatory mediators, along with proteolytic enzymes, contributes to the degradation of the glycocalyx—a specialized carbohydrate-rich layer covering the vascular endothelium. This disruption impairs endothelial integrity and function, leading to compromised microcirculatory flow and reduced myocardial perfusion( 16 ). In addition, inflammatory mediators can directly influence cardiomyocyte function by activating L-type calcium channels, which results in an excessive influx of calcium ions into the cells. This calcium overload disrupts normal intracellular calcium homeostasis and induces mitochondrial dysfunction, further impairing cellular energy metabolism( 17 ). Moreover, sepsis-associated microcirculatory dysfunction and hemodynamic instability lead to inadequate delivery of oxygen and nutrients to myocardial tissue, resulting in myocardial hypoxia and metabolic derangements. These alterations collectively contribute to the development of diastolic dysfunction in the setting of sepsis( 18 ). In addition, the development of sepsis-related diastolic dysfunction may also involve a variety of other contributing factors, including but not limited to apoptosis and neurofunctional inhibition. These pathophysiological mechanisms do not act in isolation; rather, they interact intricately with one another, forming a complex and interconnected network. This interplay ultimately contributes to the progression of myocardial injury and the subsequent impairment of cardiac function( 19 ). During sepsis, microcirculatory disorders and hemodynamic disturbances lead to insufficient myocardial perfusion, which further causes myocardial cell hypoxia and metabolic disorders, resulting in diastolic dysfunction( 20 ).To date, however, there remains a relative scarcity of effective and reliable early diagnostic indicators specifically for identifying sepsis-associated diastolic dysfunction. Among the currently available clinical biomarkers, elevated serum levels of cTnI and NT-proBNP are widely recognized as dependable indicators of myocardial cell injury( 21 ). The findings of this study demonstrate that patients suffering from sepsis-induced diastolic dysfunction exhibit significantly higher serum concentrations of both cTnI and NT-proBNP when compared to those without diastolic dysfunction. Importantly, this difference between the two patient groups is statistically significant( P < 0.05). Troponin and NT-proBNP are essential biomarkers for assessing myocardial injury, with troponin being particularly significant in risk stratification and prognosis of sepsis-induced myocardial injury, as evidenced by a meta-analysis( 22 ). Sepsis-induced cardiomyopathy, a serious and potentially life-threatening complication of sepsis, is characterized by immune and metabolic dysregulation as well as direct damage to myocardial cells, all of which contribute to its underlying pathogenesis( 23 ). E, A, E/A, e', E/e', and RWT are echocardiographic parameters commonly used to assess cardiac function. In the context of sepsis, a decline in the values of E, A, E/A, and e', coupled with an increase in the E/e' ratio, may indicate underlying myocardial injury( 24 ). This study demonstrates that, when compared to the control group, patients experiencing sepsis-induced diastolic dysfunction exhibited statistically significant reductions in E, A, E/A, and e', alongside significant increases in E/e' ( P < 0.05). Research has demonstrated that an elevated RWT is strongly associated with the development of myocardial diastolic dysfunction. An increase in RWT can impair myocardial compliance, thereby exacerbating the severity of diastolic dysfunction. Furthermore, a higher RWT is not only linked to the onset of diastolic dysfunction but is also significantly correlated with patient outcomes. Elevated RWT values have been shown to be markedly associated with increased mortality rates and prolonged ICU hospital stays( 25 ). This study also showed that patients with diastolic dysfunction in sepsis had higher RWT and LVMI than patients with normal diastolic function, and had increased mortality and prolonged ICU stay, which may suggest a poor prognosis. The above results show that the structure of the myocardium changes in sepsis, but whether the morphology recovers in the later stage of the disease still needs to be verified by a large number of basic and clinical studies in the later stage. As previously discussed, the development of diastolic dysfunction in the context of sepsis involves a multifactorial and intricate pathogenic process. Given the complexity and the influence of numerous contributing factors, relying on a single parameter to assess diastolic dysfunction presents significant limitations. To address this, the current study developed a logistic regression model by integrating multiple clinical and echocardiographic indicators to enhance the prediction of sepsis-induced diastolic dysfunction. The findings of this study identify several key risk factors associated with the occurrence of diastolic dysfunction in sepsis patients, including the SOFA score, 28-day mortality, cTnI, A, E/A, e', E/e', and RWT. These variables were incorporated into a predictive model, which demonstrated satisfactory accuracy and specificity. To facilitate clinical application, individual risk scores were derived from the nomogram based on these identified risk factors, enabling the estimation of the likelihood of diastolic dysfunction in sepsis patients. The model's performance was evaluated using the area under the ROC curve(AUC), which was found to be 0.983( P < 0.001, 95% CI : 0.951-1.000), indicating excellent discriminatory power. Furthermore, the Hosmer-Lemeshow goodness-of-fittest confirmed a good model fit, with a χ² value of 1.784, DF of 8,and a P -value of 0.987. The calibration curve exhibited as lopecloseto1, further supporting the conclusion that the constructed nomogram model is both reliable and effective in predicting the risk of diastolic dysfunction in patients with sepsis. In the data analysis process, researchers utilized a combination of LASSO regression and binary logistic regression to systematically identify and select eight variables that demonstrated a strong association with septic diastolic dysfunction, as similarly applied in the identification of key variables in septic patients. The resulting predictive model exhibited an area under the ROC curve of 0.983, which signifies outstanding discriminatory power, predictive accuracy, and generalizability. Furthermore, evaluation through DCA and net gain curve analysis revealed that the model provided substantial clinical benefit and predictive advantage in identifying patients with septic diastolic dysfunction. The nomogram indicates that the SOFA score, 28-day mortality, cTnI, A, E/A, e', E/e', and RWT are all identified as significant risk factors for diastolic dysfunction in septic patients. The study constructed DCA curves to assess the clinical utility of the predictive model, which is a method commonly used in medical and biostatistical fields to evaluate the net benefit of different diagnostic or treatment strategies at various threshold probabilities. The findings reveal that the "Treat All" strategy demonstrates a greater net benefit at lower threshold probabilities, implying that treating all enrolled patients could be clinically advantageous when the likelihood of diastolic dysfunction is relatively low. Nevertheless, as the threshold probability increases, the net benefit of the "Treat All" approach progressively diminishes, likely due to the increasing risks and costs associated with overtreatment at higher risk levels. Conversely, the "Treat None" strategy consistently shows a low net benefit across the entire range of threshold probabilities, underscoring that withholding treatment entirely is not a viable clinical option at any risk threshold. This suggests that a non-treatment approach may result in missed opportunities for timely intervention, which is clinically undesirable. In contrast, the DCA curve for the nomogram-based predictive model developed in this study maintains a higher net benefit of threshold probabilities, particularly the mid-to-high range. This indicates that the model is more effective at identifying those septic patients who are at higher individual risk of diastolic dysfunction and thus more likely to benefit from targeted treatment. By employing such an individualized, risk-stratified treatment strategy, unnecessary interventions can be reduced, medical resources can be used more efficiently, and overall treatment efficacy can be enhanced. As illustrated in Fig. 7 , the full predictive model exhibits a consistently high standardized net benefit across all examined threshold probabilities, whereas the baseline model shows a declining net benefit as the threshold probability increases. This comparison highlights that the full model-incorporating all identified risk factors-demonstrates superior clinical applicability and predictive accuracy for diastolic dysfunction in septic patients. The limitations of this study encompass several key factors that may impact the validity and broader applicability of the findings. Firstly, the relatively small sample size and constraints associated with the data source may reduce the statistical power and limit the generalizability of the results. To strengthen the reliability and clinical applicability of the predictive model, it is essential to expand the sample size and undertake more comprehensive investigations in future studies. In addition, incorporating multicenter data would enhance the robustness of the model and facilitate its validation across diverse patient populations. Furthermore, this study focused primarily on the short-term prognosis of sepsis patients, with limited attention to the long-term outcomes, such as the quality of life following ICU discharge and the incidence of post-ICU complications. This narrow focus may lead to an incomplete representation of the overall prognosis among sepsis survivors. Conclusion SOFA score, 28-day mortality, cTnI, A, e', E/e', E/A, and RWT are independent risk factors for the development of diastolic dysfunction in septic patients. The prediction model constructed based on these parameters demonstrates good predictive value. Abbreviations SCM Septic cardiomyopathy LASSO Least Absolute Shrinkage and Selection Operator SOFA Sequential Organ Failure Assessment cTnI Cardiac Troponin I A Atrial systole peak E/A Early diastolic peak/Atrial systole peak e' Early diastolic myocardial velocity peak E/e' Early diastolic peak/Early diastolic myocardial velocity peak RWT Relative Wall Thickness ROC Receiver Operating Characteristic ICU Intensive Care Unit LVIDD Left Ventricular End-Diastolic Dimension LVISD Left Ventricular End-Systolic Dimension LVEF Left Ventricular Ejection Fraction LVEDD Left Ventricular End-Diastolic Diameter PWTd Posterior Wall Thickness in Diastole DT Deceleration Time DCA Decision Curve Analysis CI Confidence Interval OR Odds Ratio APACHE-II Acute Physiology and Chronic Health Evaluation II Declarations Ethics Approval and Consent to Participate Patient information has been approved by the Ethics Committee of Affiliated Hospital of Chengde Medical College (institute affiliation: Affiliated Hospital of Chengde Medical College, Chengde 067000, Hebei, China; ethics number: CYFYLL 2023132) and informed consent has been obtained from the patient or his/her authorized person. All procedures performed in this study involving human participants and/or human data were in full compliance with the principles outlined in the World Medical Association Declaration of Helsinki. Informed consent was obtained from all individual participants or their legally authorized guardians prior to data collection, including consent for the use of clinical and echocardiographic data for research purposes. The study protocol was reviewed and approved by the aforementioned ethics committee to ensure the protection of participants' privacy, autonomy, and welfare. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Clinical trial number Not applicable. Competing interests The authors declare that they have no competing interests. Funding This study was supported by S&T Program of Chengde (Project No. 202204A074). Government-Funded Clinical Medicine Outstanding Talent Training Program(2026) (Project No. ZF2026388). Authors' contributions Conceptualization: Jiading Xia. Methodology: Jinxin Pan, Lijuan Duan, Jiading Xia. Data acquisition: Jinxin Pan, Lijuan Duan. Formal analysis: Jiading Xia, Liwei Hua. Project administration: Liwei Hua. Writing-original draft: Jinxin Pan. Writing-Review and editing: Zhiwei Yang, Jiading Xia, Lijuan Duan. All authors reviewed the manuscript. Acknowledgements Not applicable. References L’Heureux M, Sternberg M, Brath L, Turlington J, Kashiouris MG. Sepsis-induced cardiomyopathy: A comprehensive review. Curr Cardiol Rep. 2020;22(5):35. https://doi.org/10.1007/s11886-020-01277-2. Vieillard-Baron A, Caille V, Charron C, Belliard G, Page B, Jardin F. Actual incidence of global left ventricular hypokinesia in adult septic shock. Crit Care Med. 2008;36(6):1701-6. https://doi.org/10.1097/CCM.0b013e318174db05. Devereux RB. Left ventricular diastolic dysfunction: Early diastolic relaxation and late diastolic compliance. J Am Coll Cardiol.1989;13(2):337-9. https://doi.org/10.1016/0735-1097(89)90508-1. Vallabhajosyula S, Pruthi S, Shah S, Wiley BM, Mankad SV, Jentzer JC. Basic and advanced echocardiographic evaluation of myocardial dysfunction in sepsis and septic shock. Anaesth Intensive Care.2018;46(1):13-24. https://doi.org/10.1177/0310057X1804600104. Laws JL, Maya TR, Gupta DK. Stress echocardiography for assessment of diastolic function. Curr Cardiol Rep. 2024;26(12):1461-9. https://doi.org/10.1007/s11886-024-02142-2. Valocik G, Rosochova I. Diastolic function of the left ventricle assessed by echocardiography. Bratisl Lek Listy. 2003;104(3):134-6. https://doi.org/10.4149/BLL_2003_03_134. Nagueh SF, Smiseth OA, Appleton CP, et al. 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Han X, Chen D, Liufu N, et al.MG53 protects against sepsis-induced myocardial dysfunction by upregulating peroxisome proliferator-activated receptor-α. Oxid Med Cell Longevity. 2020;2020:7413693. https://doi.org/10.1155/2020/7413693. Lv X, Wang H. Pathophysiology of sepsis-induced myocardial dysfunction. Mil Med Res. 2016;3:30. https://doi.org/10.1186/s40779-016-0099-9. Zhang ZY, Marrachelli VG, Thijs L, et al. Diastolic left ventricular function in relation to circulating metabolic biomarkers in a general population. J Am Heart Assoc. 2016;5(3):e002681.https://doi.org/10.1161/JAHA.115.002681. Fleischmann-Struzek C, Mellhammar L, Rose N, et al. Incidence and mortality of hospital- and ICU-treated sepsis: Results from an updated and expanded systematic review and meta-analysis. Intensive Care Med. 2020;46(8):1552-1562. https://doi.org/10.1007/s00134-020-06151-x. Lin YM, Lee MC, Toh HS, et al. Association of sepsis-induced cardiomyopathy and mortality: A systematic review and meta-analysis. Ann Intensive Care. 2022;12(1):112. https://doi.org/10.1186/s13613-022-01089-3. Targoński R, Kowacz M, Oraczewski R, Thoene M, Targoński R. The emerging concept of glycocalyx damage as the trigger of heart failure onset and progression. Med Hypotheses. 2024;182:111234. https://doi.org/10.1016/j.mehy.2023.111234. Habimana R, Choi I, Cho HJ, Kim D, Lee K, Jeong I. Sepsis-induced cardiac dysfunction: A review of pathophysiology. Acute Crit Care. 2020;35(2):57-66. https://doi.org/10.4266/acc.2020.00248. El Missiri AM, Alzurfi AS, Keddeas VW. The relationship between tumor necrosis factor alpha and left ventricular diastolic function. J Cardiovasc Echogr. 2020;30(2):62-7. https://doi.org/10.4103/jcecho.jcecho_1_20. Pei C, Zheng H, Yang K, Song N. Identification of immunometabolism-associated genes and immune infiltration in sepsis-induced cardiomyopathy using integrated bioinformatics and machine learning approaches. J Int Med Res. 2025;53(11):3000605251395584. https://doi.org/10.1177/03000605251395584. Bansal M, Mehta A, Machanahalli Balakrishna A, et al. Right ventricular dysfunction in sepsis: An updated narrative review. Shock. 2023;59(6):829-837. https://doi.org/10.1097/SHK.0000000000002120. Ehrman RR, Sullivan AN, Favot MJ, 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. Nedel W, Portela LV. Association between troponin and NT-proBNP levels, cytokines, and clinical outcomes in early sepsis response: A cohort study. Braz J Anesthesiol. 2025;76(1):844712. https://doi.org/10.1016/j.bjane.2025.844712. Zheng WJ, Zhang Y, Fu WD, Fu XL, Yan X. Correction: Role of immune-related endoplasmic reticulum stress genes in sepsis-induced cardiomyopathy: novel insights from bioinformatics analysis. PLoS One. 2025;20(12):e0338149. https://doi.org/10.1371/journal.pone.0338149. Vetrugno L, Biasucci DG. Diastolic dysfunction in septic patients: An alice-in-wonderland perspective on mortality impact. Intensive Care Med. 2025;51(5):968-969. https://doi.org/10.1007/s00134-025-07887-0. Su H, Wang J, Wang ZF, Yang Z, Ma Y. Assessing left ventricular function in patients with hyperthyroidism across varied heart rates via press-strain loop analysis: A retrospective cross-sectional study. Quant Imaging Med Surg. 2025;15(2):1632-1640. https://doi.org/10.21037/qims-24-951. Additional Declarations No competing interests reported. Supplementary Files Funding1.pdf Funding2.pdf 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-8639758","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594548501,"identity":"ce01cdbc-d7e5-48dd-9643-e57685fc6c14","order_by":0,"name":"Jin-xin Pan","email":"","orcid":"","institution":"Affiliated Hospital of Chengde Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jin-xin","middleName":"","lastName":"Pan","suffix":""},{"id":594548504,"identity":"2d5da715-ee51-4265-9c91-c96073c10148","order_by":1,"name":"Li-juan Duan","email":"","orcid":"","institution":"Affiliated Hospital of Chengde Medical College","correspondingAuthor":false,"prefix":"","firstName":"Li-juan","middleName":"","lastName":"Duan","suffix":""},{"id":594548505,"identity":"6937f9ce-7186-4c58-84b2-8dab00701000","order_by":2,"name":"Li-wei Hua","email":"","orcid":"","institution":"Affiliated Hospital of Chengde Medical College","correspondingAuthor":false,"prefix":"","firstName":"Li-wei","middleName":"","lastName":"Hua","suffix":""},{"id":594548506,"identity":"50f739e8-f71e-42b5-a3fa-ae12effc1483","order_by":3,"name":"Zhi-wei Yang","email":"","orcid":"","institution":"Affiliated Hospital of Chengde Medical College","correspondingAuthor":false,"prefix":"","firstName":"Zhi-wei","middleName":"","lastName":"Yang","suffix":""},{"id":594548507,"identity":"c4fbe141-00e7-408e-8e19-c2ea73785945","order_by":4,"name":"Jia-ding Xia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACAwj1T86+vYFBAsw+QJyWA8YGPAdI1JK4QSKBSC3m7GcMH1cw3GHcLvn44a0bNQxyfDcSGD8X4NFi2ZNjbHiG4Rmz5ew0Y+ucYwzGkjcSmKVn4HPYgRwzyQYGZjaG2zls0rkNDIkbbiSwMfPg03L+jflPoBYehptnwFrqCWu5kWPG2MBwWMLgBg9YS4IBIS2WM54VAx2WZiDZA/aLhOHMMw+bpfFpMedP3vixgcGmvp/98MPbOTU28nzHkw9+xqeFgYHDgIHxH5wHihqgQ/ED9gcEFIyCUTAKRsGIBwArWEqdsG7DEAAAAABJRU5ErkJggg==","orcid":"","institution":"Affiliated Hospital of Chengde Medical College","correspondingAuthor":true,"prefix":"","firstName":"Jia-ding","middleName":"","lastName":"Xia","suffix":""}],"badges":[],"createdAt":"2026-01-19 12:58:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8639758/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8639758/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103506109,"identity":"33436020-753a-4313-8892-2abce3e9c325","added_by":"auto","created_at":"2026-02-26 13:34:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":252331,"visible":true,"origin":"","legend":"\u003cp\u003eFlow of participants through the study\u003c/p\u003e\n\u003cp\u003eFlow diagram of participant enrollment and grouping in sepsis patients. A total of 108 sepsis patients were initially screened between November 2023 and November 2024. Ten patients were excluded due to severe liver and renal insufficiency (n=4), severe cardiac insufficiency caused by acute myocardial infarction (n=3), and primary malignant tumors (n=3). Finally, 98 eligible patients were enrolled and divided into the sepsis-associated cardiac diastolic dysfunction group (D Group, n=43) and the normal cardiac diastolic function group (N-D Group, n=55), with no loss to follow-up in either group.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8639758/v1/f5d62a0ed956cfe4f1a355e8.png"},{"id":103505356,"identity":"0b7a432a-26a4-4680-b865-0247e1ae8c0d","added_by":"auto","created_at":"2026-02-26 13:30:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":200159,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO regression coefficient path plot\u003c/p\u003e\n\u003cp\u003eLASSO regression coefficient path plot for predictor variable screening. This figure shows the coefficient path plot of LASSO regression combined with binary logistic regression for predictor variable screening. The curve represents the change trend of regression coefficients of each independent variable with the gradual decrease of Log(λ) value. \"Coefficients\" refer to the regression coefficients corresponding to each independent variable in the LASSO regression model.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8639758/v1/7451c2120d86b14c8fa571cd.png"},{"id":103505657,"identity":"43fabf8f-1f65-4dfa-b921-83bde4b5ecd0","added_by":"auto","created_at":"2026-02-26 13:32:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":197990,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO regression cross-validation curve for variable selection\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;LASSO regression cross-validation curve for optimal λ selection. This is the LASSO regression cross-validation curve for optimal λ selection. The vertical dashed lines on the left and right represent Log(λ) corresponding to the minimum cross-validation error (lambda.min) and Log(λ) within one standard error of the minimum error (lambda.1se), respectively. \"Binomial Deviance\" is the binomial distribution loss function calculated for each fold during the cross-validation process, which is used to determine the optimal penalty parameter λ.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8639758/v1/e7e66e59176fcf3bf5d5c34f.png"},{"id":103259331,"identity":"a3a5856d-96a1-48f0-bbf7-7b80c3d708ca","added_by":"auto","created_at":"2026-02-23 17:37:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":265386,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting diastolic dysfunction in sepsis patients\u003c/p\u003e\n\u003cp\u003eNomogram for predicting diastolic dysfunction in sepsis patients. This nomogram is constructed to predict the probability of sepsis-induced diastolic dysfunction. Each predictive factor (SOFA score, 28-day mortality, cTnI, A, E/A, e', E/e', RWT) is assigned a corresponding score on the axis. The total score is obtained by summing the scores of all factors, and the predicted probability of diastolic dysfunction can be determined by mapping the total score to the \"Predicted Value\" axis.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8639758/v1/6fc37be15d8e68ee2ab58377.png"},{"id":103505623,"identity":"01ee3c69-32ae-4689-835f-5377cac1b2cc","added_by":"auto","created_at":"2026-02-26 13:32:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":140535,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the predictive model for sepsis-induced diastolic dysfunction\u003c/p\u003e\n\u003cp\u003eROC curve of the predictive model for sepsis-induced diastolic dysfunction. This is the ROC curve of the predictive model for sepsis-induced diastolic dysfunction. The horizontal axis represents the false positive rate (1 - specificity), and the vertical axis represents the true positive rate (sensitivity). The area under the ROC curve (AUC) is 0.983 (95% \u003cem\u003eCI\u003c/em\u003e: 0.951-1.000), indicating excellent discriminative ability of the model.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8639758/v1/cf49abc1418a333875239488.png"},{"id":103259334,"identity":"bb498587-a4b6-41ff-826f-3d12c679742b","added_by":"auto","created_at":"2026-02-23 17:37:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":95896,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the predictive model for sepsis-induced diastolic dysfunction\u003c/p\u003e\n\u003cp\u003eCalibration curve of the predictive model for sepsis-induced diastolic dysfunction. This is the calibration curve of the predictive model. The horizontal axis represents the predicted probability of sepsis-induced diastolic dysfunction calculated by the model, and the vertical axis represents the actual occurrence rate of diastolic dysfunction in the study population. The \"Ideal\" line indicates perfect calibration, the \"Apparent\" line represents the observed calibration of the model, and the \"Bias-corrected\" line is the calibration curve after Bootstrap internal validation. The slope of the calibration curve is close to 1, indicating good consistency between the predicted probability and the actual occurrence rate.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8639758/v1/5a96f0d2ee35b411d40a390d.png"},{"id":103505664,"identity":"9decc90b-cbb1-4900-a41d-94309ac86d46","added_by":"auto","created_at":"2026-02-26 13:32:30","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":104419,"visible":true,"origin":"","legend":"\u003cp\u003eDCA of the predictive model for sepsis-induced diastolic dysfunction\u003c/p\u003e\n\u003cp\u003eDCA of the predictive model for sepsis-induced diastolic dysfunction. This is the DCA of the predictive model. The horizontal axis represents the high-risk threshold probability of sepsis-induced diastolic dysfunction, and the vertical axis represents the standardized net benefit. The \"All\" curve represents the net benefit of treating all patients, the \"None\" curve represents the net benefit of not treating any patients, and the curve corresponding to \"group~predicted_values\" represents the net benefit of the predictive model. The model's curve maintains a higher standardized net benefit across the entire threshold probability range, indicating good clinical utility.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8639758/v1/5abf50944fdbb5daae5b9721.png"},{"id":105215065,"identity":"93e7e011-c87a-4239-9ec0-8d5342b310a9","added_by":"auto","created_at":"2026-03-23 14:27:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1900260,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8639758/v1/ed3b9431-aa9d-4569-a216-b96290e56826.pdf"},{"id":103506569,"identity":"40f7ed50-90c7-46a1-b3d4-c9a29804e9dc","added_by":"auto","created_at":"2026-02-26 13:37:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":737502,"visible":true,"origin":"","legend":"","description":"","filename":"Funding1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8639758/v1/89df17b97d4454696c363a44.pdf"},{"id":103505662,"identity":"2cbb708a-8424-4d2f-a765-35bfab37cfb8","added_by":"auto","created_at":"2026-02-26 13:32:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1358505,"visible":true,"origin":"","legend":"","description":"","filename":"Funding2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8639758/v1/86fc2c3ccc29f96f4bab050b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Risk Prediction Model for Diastolic Dysfunction in Patients with Sepsis","fulltext":[{"header":"Background","content":"\u003cp\u003eAs a common life-threatening condition in the Intensive Care Unit (ICU),sepsis has shown an increasing incidence trend in recent years. The condition develops as a result of an unregulated and overly intense inflammatory reaction to an infection, which can progress to multiple organ dysfunction or failure and carries a high risk of mortality(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Sepsis-induced myocardial injury is associated with a significantly elevated mortality rate(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The incidence of cardiac diastolic dysfunction in sepsis remains unknown, and its diagnosis poses a challenge; furthermore, its impact on survival, independent of dysfunction in other organ systems, remains unclear. Early identification of myocardial injury is essential in sepsis management, as it enables timely and vigorous therapeutic measures, which significantly improve the clinical outcomes and prognosis for patients.\u003c/p\u003e \u003cp\u003eCurrently, the diagnosis of SCM mainly relies on a combination of echocardiography, myocardial injury biomarkers, and clinical features. Sepsis-induced myocardial dysfunction refers to an intrinsic impairment affecting both the systolic and diastolic functions of the heart muscle, which becomes evident during the course of sepsis. Ventricular diastolic dysfunction is a disorder characterized by impaired ventricular filling, which can result from compromised ventricular relaxation and/or reduced compliance(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Current research has consistently demonstrated that left ventricular diastolic dysfunction is a more effective predictor of clinical outcomes in patients with sepsis or septic shock than left ventricular systolic dysfunction(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). This condition is clinically assessed through standards such as echocardiography, where parameters like the E/A ratio and Deceleration Time (DT) are used to evaluate the heart's diastolic function(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The diagnosis of diastolic dysfunction is based on multiple parameters derived from Doppler echocardiography, including the E/A ratio, E/e' ratio, and Left Ventricular End-Diastolic Dimension (LVIDD), which are crucial for assessing left ventricular diastolic function(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The detection of myocardial injury markers is often used as a supplementary evaluation to echocardiography in the diagnosis of SCM. However, several parameters, such as left ventricular end-diastolic dimension(LVIDD), Left Ventricular End-Systolic Dimension (LVISD),and RWT(RWT\u0026thinsp;=\u0026thinsp;2 \u0026times; Posterior Wall Thickness in Diastole (PWTd) / Left Ventricular End-Diastolic Diameter (LVEDD)(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)), have not yet been evaluated in the diagnostic assessment of ventricular diastolic dysfunction.\u003c/p\u003e \u003cp\u003eCurrently, the clinical diagnosis of sepsis-induced diastolic dysfunction relies heavily on echocardiographic evaluation and the assessment of biomarkers, as these methods have been shown to be crucial in identifying reversible cardiac dysfunction associated with sepsis. Nevertheless, echocardiography demands not only superior image quality but also a high level of technical expertise from the operator, which contributes to significant variability in its application and interpretation. There is currently significant debate surrounding the use of various biomarkers for diagnosing sepsis-induced diastolic dysfunction(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In clinical practice, healthcare providers frequently evaluate the diastolic and systolic functions of the heart by relying on specific diagnostic indicators. However, alterations in the heart's morphology and structural characteristics are often underappreciated or not given adequate attention during routine assessments.\u003c/p\u003e \u003cp\u003eIn this study, we employed a combination of biomarkers and echocardiographic techniques to diagnose septic diastolic dysfunction, identify its associated risk factors, and develop a predictive model. This approach aims to facilitate the early detection of patients with septic diastolic dysfunction, enabling timely clinical intervention. The ultimate goal is to reduce the incidence of sepsis and its complications, particularly septic cardiomyopathy, thereby improving patient outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003e Ninety-eight patients diagnosed with sepsis and admitted to the Department of Critical Care Medicine at the Affiliated Hospital of Chengde Medical College between November 2023 and November 2024 were enrolled in this study. Based on the presence or absence of diastolic dysfunction, the participants were divided into two groups: the sepsis-associated cardiac diastolic dysfunction group(observation group, D Group) and the normal cardiac diastolic function group(control group, N-D Group).The data screening process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Flowchart.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample selection\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003e1 Inclusion and exclusion criteria\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe inclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Patients were required to meet the diagnostic criteria for sepsis as outlined in the \"2021 International Guidelines for Management of Sepsis and Septic Shock\"(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Participants in the observation group were additionally required to fulfill the diagnostic criteria for diastolic heart dysfunction; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) All patients were required to have complete medical record documentation, and either the patient or their legally authorized guardian should have provided signed informed consent. Exclusion criteria are as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Individuals under the age of 18, pregnant or lactating women; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Patients who die within 24 hours of being diagnosed with sepsis upon admission; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Patients with pre-existing cardiac insufficiency or myocardial disease; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Patients with severe hepatic or renal dysfunction; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Patients with concurrent autoimmune diseases or those who are on long-term treatment with steroids or other immune suppressive agents; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) Patients with advanced-stage malignant tumors.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2 Variables and measurement\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2.1 Experimental Instruments\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBedside ultrasound machine(Philips CX50)\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.2 Experimental grouping\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePatients who met the inclusion and exclusion criteria were divided into a sepsis-related cardiac diastolic dysfunction group(observation group, D group) and anormal cardiac diastolic function group(control group, N-D group) based on the presence or absence of diastolic dysfunction.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.3 Data collection\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor all patients, baseline clinical data were collected within the first 24 hours of admission, along with echocardiographic parameters assessing diastolic function. These echocardiographic indicators included the E,A,E/A, e, E/e, Left Ventricular Ejection Fraction (LVEF), LVEDD, Posterior Wall Thickness in Diastole (PWTd), and RWT. In addition, serum biomarkers indicative of myocardial injury\u0026mdash;specifically cTnI and NT-proBNP\u0026mdash;were also recorded at the time of admission.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS 26.0. Quantitative data were subjected to normality tests and homogeneity of variance tests. For data conforming to anormal distribution, the mean\u0026plusmn; standard deviation(x̄\u0026plusmn;s) was used, and intergroup comparisons were conducted using an independent samples t-test. In cases where indicators do not follow a normal distribution, statistical analysis was conducted using the median (M) and the interquartile range (IQR), denoted as M[QL, QU]. Mann-Whitney U rank-sum test for independent samples. Qualitative data were expressed as rates (%) and analyzed using the \u003cem\u003eχ\u0026sup2;\u003c/em\u003e test. To identify significant predictors of diastolic dysfunction in septic patients, univariate screening was initially performed using the LASSO regression method, as demonstrated in clinical studies. Variables that demonstrated potential relevance were then further analyzed through multivariate logistic regression to determine their independent associations with diastolic dysfunction. Based on the results of the multivariate analysis, a nomogram predictive model was constructed to estimate the probability of diastolic dysfunction in septic patients. The performance of the nomogram was rigorously evaluated using the Bootstrap method for internal validation, with key aspects of model performance including discrimination, accuracy, and clinical utility all being assessed. If the \u003cem\u003eP\u003c/em\u003e-value is less than 0.05, the observed difference is regarded as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eComparison of baseline data of enrolled patients\u003c/h2\u003e \u003cp\u003eThis study enrolled a total of 108 patients diagnosed with sepsis. Of these, three were excluded due to the presence of primary malignant tumors, three were excluded because of severe cardiac dysfunction resulting from acute myocardial infarction, and four were excluded owing to significant liver or kidney impairment. After these exclusions, a final cohort of 98 sepsis patients was included in the analysis. This group was further divided into two subgroups based on diastolic function: 43 patients exhibited diastolic dysfunction and were assigned to the observation group(D Group), while 55 patients with normal diastolic function were placed in the control group(N-D Group). The overall gender distribution among the 98 patients was 52 males and 46 females. The most frequent site of infection was the abdomen, which accounted for 64.58% of all cases(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the two groups (N\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline Information\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN-D Group(n\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD Group(n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et/z/χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.63\u0026thinsp;\u0026plusmn;\u0026thinsp;12.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.35\u0026thinsp;\u0026plusmn;\u0026thinsp;12.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(51.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(56.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(49.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(44.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166.92\u0026thinsp;\u0026plusmn;\u0026thinsp;9.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167.22\u0026thinsp;\u0026plusmn;\u0026thinsp;10.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.37\u0026thinsp;\u0026plusmn;\u0026thinsp;11.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.38\u0026thinsp;\u0026plusmn;\u0026thinsp;13.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.60(20.76,25.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.43(19.59,26.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBSA (m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast medical history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(47.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(48.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 2 diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(21.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(30.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite of infection, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNervous System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(2.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLungs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(18.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(25.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(54.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(51.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin, soft tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(9.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(4.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood stream infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(5.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(4.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(10.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(11.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e: 1. Data are presented as mean\u0026plusmn;standard deviation (x̄\u0026plusmn;s) for normally distributed continuous variables, median (interquartile range, IQR) [M(QL, QU)] for non-normally distributed continuous variables, and n (%) for categorical variables. 2. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Flow diagram of participant enrollment and grouping in sepsis patients. A total of 108 sepsis patients were initially screened between November 2023 and November 2024. Ten patients were excluded due to severe liver and renal insufficiency (n\u0026thinsp;=\u0026thinsp;4), severe cardiac insufficiency caused by acute myocardial infarction (n\u0026thinsp;=\u0026thinsp;3), and primary malignant tumors (n\u0026thinsp;=\u0026thinsp;3). Finally, 98 eligible patients were enrolled and divided into the sepsis-associated cardiac diastolic dysfunction group (D Group, n\u0026thinsp;=\u0026thinsp;43) and the normal cardiac diastolic function group (N-D Group, n\u0026thinsp;=\u0026thinsp;55), with no loss to follow-up in either group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLASSO regression and multivariate logistic regression analysis\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e present the results of univariate screening performed using LASSO regression analysis. This analysis identified eight risk factors with non-zero coefficients, namely: SOFA score,28-day mortality, cTnI, A,E/A, e', E/e', and RWT. These eight variables were further subjected to multivariate logistic regression analysis to determine their independent association with sepsis-induced diastolic dysfunction. The corresponding regression coefficients for these predictors were as follows: 0.528 for SOFA, 0.051 for 28-day mortality, 1.519 for cTnI, 0.018 for A, 0.922 for E/A, -0.290 for e', 0.495 for E/e', and 9.950 for RWT. Based on these coefficients, the predictive model formula was constructed as: Logistic(risk score) = -18.202\u0026thinsp;+\u0026thinsp;0.528 \u0026times; SOFA\u0026thinsp;+\u0026thinsp;0.051\u0026times;28-day mortality\u0026thinsp;+\u0026thinsp;1.519\u0026times;cTnI\u0026thinsp;+\u0026thinsp;0.018\u0026times;A\u0026thinsp;+\u0026thinsp;0.922\u0026times;E/A + (-0.290)\u0026times;e' + 0.495\u0026times;E/e' + 9.950 \u0026times; RWT. The goodness-of-fit of the model was assessed using the Hosmer-Lemeshow test, yielding a χ\u0026sup2; value of 1.784, \u003cem\u003eDF\u003c/em\u003e of 8, and a \u003cem\u003eP\u003c/em\u003e value of 0.987, indicating a good fit of the model to the data. Detailed results are provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e LASSO regression coefficient path plot for predictor variable screening. This figure shows the coefficient path plot of LASSO regression combined with binary logistic regression for predictor variable screening. The curve represents the change trend of regression coefficients of each independent variable with the gradual decrease of Log(λ) value. \"Coefficients\" refer to the regression coefficients corresponding to each independent variable in the LASSO regression model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e LASSO regression cross-validation curve for optimal λ selection. This is the LASSO regression cross-validation curve for optimal λ selection. The vertical dashed lines on the left and right represent Log(λ) corresponding to the minimum cross-validation error (lambda.min) and Log(λ) within one standard error of the minimum error (lambda.1se), respectively. \"Binomial Deviance\" is the binomial distribution loss function calculated for each fold during the cross-validation process, which is used to determine the optimal penalty parameter λ.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of binary logistic regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOR (95%CI)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-18.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.696 (1.163\u0026ndash;2.475)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28-day mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.053 (1.002\u0026ndash;1.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecTnI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.575 (1.211\u0026ndash;17.273)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.018 (0.993\u0026ndash;1.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.512 (1.283\u0026ndash;4.918)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ee'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.748 (0.575\u0026ndash;0.971)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE/e'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.640 (1.213\u0026ndash;2.216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRWT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20949.860 (11.470-266700700000.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNotes: 1. This table presents the results of binary logistic regression analysis, with \"sepsis-induced diastolic dysfunction\" as the dependent variable. 2. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConstruct and validate a predictive model for sepsis-induced diastolic dysfunction\u003c/h3\u003e\n\u003cp\u003eAfter determining the final variables for constructing the predictive model of sepsis-induced diastolic dysfunction, we used the \"rms\" package in R Studio to build the model, and generated a nomogram(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)through the \"nomogram\" function of this package. The area under the receiver operating characteristic curve of the predictive model for sepsis-induced diastolic dysfunction was 0.983 (95%\u003cem\u003eCI\u003c/em\u003e: 0.951-1.000), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The calibration curve slope is approximately 1, which suggests that the developed nomogram model demonstrates strong predictive accuracy for assessing the risk of diastolic dysfunction in patients with sepsis (as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The DCA curves demonstrated that the model constructed with 8 predictive variables yielded higher ordinate values compared to the single-variable predictive model across an extensive range of threshold probabilities. The predictive model established in the present study indicates a greater net benefit, as supported by the principles of predictive analytics and decision-making. Furthermore, it suggests that patients presenting with these predictive variables are at an elevated risk of developing septic diastolic dysfunction(as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e Nomogram for predicting diastolic dysfunction in sepsis patients. This nomogram is constructed to predict the probability of sepsis-induced diastolic dysfunction. Each predictive factor (SOFA score, 28-day mortality, cTnI, A, E/A, e', E/e', RWT) is assigned a corresponding score on the axis. The total score is obtained by summing the scores of all factors, and the predicted probability of diastolic dysfunction can be determined by mapping the total score to the \"Predicted Value\" axis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e ROC curve of the predictive model for sepsis-induced diastolic dysfunction. This is the ROC curve of the predictive model for sepsis-induced diastolic dysfunction. The horizontal axis represents the false positive rate (1 - specificity), and the vertical axis represents the true positive rate (sensitivity). The area under the ROC curve (AUC) is 0.983 (95% \u003cem\u003eCI\u003c/em\u003e: 0.951-1.000), indicating excellent discriminative ability of the model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e Calibration curve of the predictive model for sepsis-induced diastolic dysfunction. This is the calibration curve of the predictive model. The horizontal axis represents the predicted probability of sepsis-induced diastolic dysfunction calculated by the model, and the vertical axis represents the actual occurrence rate of diastolic dysfunction in the study population. The \"Ideal\" line indicates perfect calibration, the \"Apparent\" line represents the observed calibration of the model, and the \"Bias-corrected\" line is the calibration curve after Bootstrap internal validation. The slope of the calibration curve is close to 1, indicating good consistency between the predicted probability and the actual occurrence rate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e DCA of the predictive model for sepsis-induced diastolic dysfunction. This is the DCA of the predictive model. The horizontal axis represents the high-risk threshold probability of sepsis-induced diastolic dysfunction, and the vertical axis represents the standardized net benefit. The \"All\" curve represents the net benefit of treating all patients, the \"None\" curve represents the net benefit of not treating any patients, and the curve corresponding to \"group~predicted_values\" represents the net benefit of the predictive model. The model's curve maintains a higher standardized net benefit across the entire threshold probability range, indicating good clinical utility.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSepsis, characterized by a severe systemic inflammatory response due to microbial invasion in the bloodstream, is one of the most critical and life-threatening clinical syndromes within the domain of critical care, with a high incidence and mortality rate, and continues to exhibit persistently elevated incidence and mortality rates(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This is especially evident among elderly individuals and those with pre-existing medical conditions, who are at significantly higher risk(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The heart is one of the organs most frequently impacted in patients with sepsis(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). According to a meta-analysis examining the epidemiology of sepsis, the overall incidence of septic cardiomyopathy was found to be 34.2%,with an associated in-hospital mortality rate of 45.6%. Notably, patients diagnosed with septic cardiomyopathy exhibited a significantly higher mortality rate compared to those with sepsis who did not develop cardiomyopathy(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Advancements in bedside ultrasound diagnostic techniques, particularly tissue Doppler imaging, have substantially improved the accuracy and feasibility of assessing diastolic function(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Consequently, there has been growing recognition within the medical community of the significance of sepsis-related cardiac diastolic dysfunction. This study found that 43.9% of sepsis patients exhibited signs of diastolic dysfunction. Furthermore, those with sepsis-induced diastolic dysfunction tended to have higher Acute Physiology and Chronic Health Evaluation II (APACHE-II) and SOFA scores, along with prolonged ICU stays, indicating a less favorable prognosis(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The evidence indicates a significant correlation between sepsis-induced cardiac diastolic dysfunction and an increased risk of subsequent cardiovascular events. Therefore, early identification of risk factors for this condition and the prompt implementation of targeted interventions are of paramount importance in improving patient outcomes. The pathogenesis of septic cardiac diastolic dysfunction remains incompletely understood and is mediated through a complex interplay of multiple pathophysiological mechanisms(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Throughout the development and progression of sepsis, there is a significant release of various inflammatory mediators. The activation of these inflammatory mediators, along with proteolytic enzymes, contributes to the degradation of the glycocalyx\u0026mdash;a specialized carbohydrate-rich layer covering the vascular endothelium. This disruption impairs endothelial integrity and function, leading to compromised microcirculatory flow and reduced myocardial perfusion(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In addition, inflammatory mediators can directly influence cardiomyocyte function by activating L-type calcium channels, which results in an excessive influx of calcium ions into the cells. This calcium overload disrupts normal intracellular calcium homeostasis and induces mitochondrial dysfunction, further impairing cellular energy metabolism(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Moreover, sepsis-associated microcirculatory dysfunction and hemodynamic instability lead to inadequate delivery of oxygen and nutrients to myocardial tissue, resulting in myocardial hypoxia and metabolic derangements. These alterations collectively contribute to the development of diastolic dysfunction in the setting of sepsis(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In addition, the development of sepsis-related diastolic dysfunction may also involve a variety of other contributing factors, including but not limited to apoptosis and neurofunctional inhibition. These pathophysiological mechanisms do not act in isolation; rather, they interact intricately with one another, forming a complex and interconnected network. This interplay ultimately contributes to the progression of myocardial injury and the subsequent impairment of cardiac function(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). During sepsis, microcirculatory disorders and hemodynamic disturbances lead to insufficient myocardial perfusion, which further causes myocardial cell hypoxia and metabolic disorders, resulting in diastolic dysfunction(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).To date, however, there remains a relative scarcity of effective and reliable early diagnostic indicators specifically for identifying sepsis-associated diastolic dysfunction. Among the currently available clinical biomarkers, elevated serum levels of cTnI and NT-proBNP are widely recognized as dependable indicators of myocardial cell injury(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The findings of this study demonstrate that patients suffering from sepsis-induced diastolic dysfunction exhibit significantly higher serum concentrations of both cTnI and NT-proBNP when compared to those without diastolic dysfunction. Importantly, this difference between the two patient groups is statistically significant(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Troponin and NT-proBNP are essential biomarkers for assessing myocardial injury, with troponin being particularly significant in risk stratification and prognosis of sepsis-induced myocardial injury, as evidenced by a meta-analysis(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Sepsis-induced cardiomyopathy, a serious and potentially life-threatening complication of sepsis, is characterized by immune and metabolic dysregulation as well as direct damage to myocardial cells, all of which contribute to its underlying pathogenesis(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eE, A, E/A, e', E/e', and RWT are echocardiographic parameters commonly used to assess cardiac function. In the context of sepsis, a decline in the values of E, A, E/A, and e', coupled with an increase in the E/e' ratio, may indicate underlying myocardial injury(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). This study demonstrates that, when compared to the control group, patients experiencing sepsis-induced diastolic dysfunction exhibited statistically significant reductions in E, A, E/A, and e', alongside significant increases in E/e' (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Research has demonstrated that an elevated RWT is strongly associated with the development of myocardial diastolic dysfunction. An increase in RWT can impair myocardial compliance, thereby exacerbating the severity of diastolic dysfunction. Furthermore, a higher RWT is not only linked to the onset of diastolic dysfunction but is also significantly correlated with patient outcomes. Elevated RWT values have been shown to be markedly associated with increased mortality rates and prolonged ICU hospital stays(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This study also showed that patients with diastolic dysfunction in sepsis had higher RWT and LVMI than patients with normal diastolic function, and had increased mortality and prolonged ICU stay, which may suggest a poor prognosis. The above results show that the structure of the myocardium changes in sepsis, but whether the morphology recovers in the later stage of the disease still needs to be verified by a large number of basic and clinical studies in the later stage.\u003c/p\u003e \u003cp\u003eAs previously discussed, the development of diastolic dysfunction in the context of sepsis involves a multifactorial and intricate pathogenic process. Given the complexity and the influence of numerous contributing factors, relying on a single parameter to assess diastolic dysfunction presents significant limitations. To address this, the current study developed a logistic regression model by integrating multiple clinical and echocardiographic indicators to enhance the prediction of sepsis-induced diastolic dysfunction. The findings of this study identify several key risk factors associated with the occurrence of diastolic dysfunction in sepsis patients, including the SOFA score, 28-day mortality, cTnI, A, E/A, e', E/e', and RWT. These variables were incorporated into a predictive model, which demonstrated satisfactory accuracy and specificity. To facilitate clinical application, individual risk scores were derived from the nomogram based on these identified risk factors, enabling the estimation of the likelihood of diastolic dysfunction in sepsis patients. The model's performance was evaluated using the area under the ROC curve(AUC), which was found to be 0.983(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% \u003cem\u003eCI\u003c/em\u003e: 0.951-1.000), indicating excellent discriminatory power. Furthermore, the Hosmer-Lemeshow goodness-of-fittest confirmed a good model fit, with a \u003cem\u003eχ\u0026sup2;\u003c/em\u003e value of 1.784,\u003cem\u003eDF\u003c/em\u003e of 8,and a \u003cem\u003eP\u003c/em\u003e-value of 0.987. The calibration curve exhibited as lopecloseto1, further supporting the conclusion that the constructed nomogram model is both reliable and effective in predicting the risk of diastolic dysfunction in patients with sepsis. In the data analysis process, researchers utilized a combination of LASSO regression and binary logistic regression to systematically identify and select eight variables that demonstrated a strong association with septic diastolic dysfunction, as similarly applied in the identification of key variables in septic patients. The resulting predictive model exhibited an area under the ROC curve of 0.983, which signifies outstanding discriminatory power, predictive accuracy, and generalizability. Furthermore, evaluation through DCA and net gain curve analysis revealed that the model provided substantial clinical benefit and predictive advantage in identifying patients with septic diastolic dysfunction.\u003c/p\u003e \u003cp\u003eThe nomogram indicates that the SOFA score, 28-day mortality, cTnI, A, E/A, e', E/e', and RWT are all identified as significant risk factors for diastolic dysfunction in septic patients. The study constructed DCA curves to assess the clinical utility of the predictive model, which is a method commonly used in medical and biostatistical fields to evaluate the net benefit of different diagnostic or treatment strategies at various threshold probabilities. The findings reveal that the \"Treat All\" strategy demonstrates a greater net benefit at lower threshold probabilities, implying that treating all enrolled patients could be clinically advantageous when the likelihood of diastolic dysfunction is relatively low. Nevertheless, as the threshold probability increases, the net benefit of the \"Treat All\" approach progressively diminishes, likely due to the increasing risks and costs associated with overtreatment at higher risk levels. Conversely, the \"Treat None\" strategy consistently shows a low net benefit across the entire range of threshold probabilities, underscoring that withholding treatment entirely is not a viable clinical option at any risk threshold. This suggests that a non-treatment approach may result in missed opportunities for timely intervention, which is clinically undesirable. In contrast, the DCA curve for the nomogram-based predictive model developed in this study maintains a higher net benefit of threshold probabilities, particularly the mid-to-high range. This indicates that the model is more effective at identifying those septic patients who are at higher individual risk of diastolic dysfunction and thus more likely to benefit from targeted treatment. By employing such an individualized, risk-stratified treatment strategy, unnecessary interventions can be reduced, medical resources can be used more efficiently, and overall treatment efficacy can be enhanced. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the full predictive model exhibits a consistently high standardized net benefit across all examined threshold probabilities, whereas the baseline model shows a declining net benefit as the threshold probability increases. This comparison highlights that the full model-incorporating all identified risk factors-demonstrates superior clinical applicability and predictive accuracy for diastolic dysfunction in septic patients.\u003c/p\u003e \u003cp\u003eThe limitations of this study encompass several key factors that may impact the validity and broader applicability of the findings. Firstly, the relatively small sample size and constraints associated with the data source may reduce the statistical power and limit the generalizability of the results. To strengthen the reliability and clinical applicability of the predictive model, it is essential to expand the sample size and undertake more comprehensive investigations in future studies. In addition, incorporating multicenter data would enhance the robustness of the model and facilitate its validation across diverse patient populations. Furthermore, this study focused primarily on the short-term prognosis of sepsis patients, with limited attention to the long-term outcomes, such as the quality of life following ICU discharge and the incidence of post-ICU complications. This narrow focus may lead to an incomplete representation of the overall prognosis among sepsis survivors.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSOFA score, 28-day mortality, cTnI, A, e', E/e', E/A, and RWT are independent risk factors for the development of diastolic dysfunction in septic patients. The prediction model constructed based on these parameters demonstrates good predictive value.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eSCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eSeptic cardiomyopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eLASSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eSOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eSequential Organ Failure Assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003ecTnI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eCardiac Troponin I\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eAtrial systole peak\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eE/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eEarly diastolic peak/Atrial systole peak\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003ee\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eEarly diastolic myocardial velocity peak\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eE/e\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eEarly diastolic peak/Early diastolic myocardial velocity peak\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eRWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eRelative Wall Thickness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eIntensive Care Unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eLVIDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eLeft Ventricular End-Diastolic Dimension\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eLVISD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eLeft Ventricular End-Systolic Dimension\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eLVEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eLeft Ventricular Ejection Fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eLVEDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eLeft Ventricular End-Diastolic Diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003ePWTd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003ePosterior Wall Thickness in Diastole\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eDeceleration Time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eDecision Curve Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eAPACHE-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eAcute Physiology and Chronic Health Evaluation II\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;Patient information has been approved by the Ethics Committee of Affiliated Hospital of Chengde Medical College (institute affiliation: Affiliated Hospital of Chengde Medical College, Chengde 067000, Hebei, China; ethics number: CYFYLL 2023132) and informed consent has been obtained from the patient or his/her authorized person. All procedures performed in this study involving human participants and/or human data were in full compliance with the principles outlined in the World Medical Association Declaration of Helsinki. Informed consent was obtained from all individual participants or their legally authorized guardians prior to data collection, including consent for the use of clinical and echocardiographic data for research purposes. The study protocol was reviewed and approved by the aforementioned ethics committee to ensure the protection of participants\u0026apos; privacy, autonomy, and welfare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; This study was supported by S\u0026amp;T Program of Chengde (Project No. 202204A074). Government-Funded Clinical Medicine Outstanding Talent Training Program(2026) (Project No. ZF2026388).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; Conceptualization: Jiading Xia. Methodology: Jinxin Pan, Lijuan Duan, Jiading Xia. Data acquisition: Jinxin Pan, Lijuan Duan. Formal analysis: Jiading Xia, Liwei Hua. Project administration: Liwei Hua. Writing-original draft: Jinxin Pan. Writing-Review and editing: Zhiwei Yang, Jiading Xia, Lijuan Duan. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eL\u0026rsquo;Heureux M, Sternberg M, Brath L, Turlington J, Kashiouris MG. Sepsis-induced cardiomyopathy: A comprehensive review. Curr Cardiol Rep. 2020;22(5):35. https://doi.org/10.1007/s11886-020-01277-2.\u003c/li\u003e\n \u003cli\u003eVieillard-Baron A, Caille V, Charron C, Belliard G, Page B, Jardin F. Actual incidence of global left ventricular hypokinesia in adult septic shock. Crit Care Med. 2008;36(6):1701-6. https://doi.org/10.1097/CCM.0b013e318174db05.\u003c/li\u003e\n \u003cli\u003eDevereux RB. Left ventricular diastolic dysfunction: Early diastolic relaxation and late diastolic compliance. J Am Coll Cardiol.1989;13(2):337-9. https://doi.org/10.1016/0735-1097(89)90508-1.\u003c/li\u003e\n \u003cli\u003eVallabhajosyula S, Pruthi S, Shah S, Wiley BM, Mankad SV, Jentzer JC. Basic and advanced echocardiographic evaluation of myocardial dysfunction in sepsis and septic shock. Anaesth Intensive Care.2018;46(1):13-24. https://doi.org/10.1177/0310057X1804600104.\u003c/li\u003e\n \u003cli\u003eLaws JL, Maya TR, Gupta DK. Stress echocardiography for assessment of diastolic function. Curr Cardiol Rep. 2024;26(12):1461-9. https://doi.org/10.1007/s11886-024-02142-2.\u003c/li\u003e\n \u003cli\u003eValocik G, Rosochova I. Diastolic function of the left ventricle assessed by echocardiography. Bratisl Lek Listy. 2003;104(3):134-6. https://doi.org/10.4149/BLL_2003_03_134.\u003c/li\u003e\n \u003cli\u003eNagueh SF, Smiseth OA, Appleton CP, et al. Recommendations for the evaluation of left ventricular diastolic function by echocardiography: An update from the American society of echocardiography and the european association of cardiovascular imaging. J Am Soc Echocardiogr. 2016;29(4):277-314. https://doi.org/10.1016/j.echo.2016.01.011.\u003c/li\u003e\n \u003cli\u003eLandesberg G, Gilon D, Meroz Y, et al. Diastolic dysfunction and mortality in severe sepsis and septic shock. Eur Heart J. 2012;33(7):895-903. https://doi.org/10.1093/eurheartj/ehr351.\u003c/li\u003e\n \u003cli\u003eEvans L, Rhodes A, Alhazzani W, et al. Surviving sepsis campaign: International guidelines for management of sepsis and septic shock 2021. Crit Care Med. 2021;49(11):e1063-e1143. https://doi.org/10.1097/CCM.0000000000005337.\u003c/li\u003e\n \u003cli\u003eZhang M, Duan M, Zhi D, Lin J, Liu P, Wang Y. Risk factors for 28-day mortality in patients with sepsis-related myocardial injury in intensive care units. J Int Med Res. 2021;49(4):3000605211004759. https://doi.org/10.1177/03000605211004759.\u003c/li\u003e\n \u003cli\u003eHan X, Chen D, Liufu N, et al.MG53 protects against sepsis-induced myocardial dysfunction by upregulating peroxisome proliferator-activated receptor-\u0026alpha;. Oxid Med Cell Longevity. 2020;2020:7413693. https://doi.org/10.1155/2020/7413693.\u003c/li\u003e\n \u003cli\u003eLv X, Wang H. Pathophysiology of sepsis-induced myocardial dysfunction. Mil Med Res. 2016;3:30. https://doi.org/10.1186/s40779-016-0099-9.\u003c/li\u003e\n \u003cli\u003eZhang ZY, Marrachelli VG, Thijs L, et al. Diastolic left ventricular function in relation to circulating metabolic biomarkers in a general population. J Am Heart Assoc. 2016;5(3):e002681.https://doi.org/10.1161/JAHA.115.002681.\u003c/li\u003e\n \u003cli\u003eFleischmann-Struzek C, Mellhammar L, Rose N, et al. Incidence and mortality of hospital- and ICU-treated sepsis: Results from an updated and expanded systematic review and meta-analysis. Intensive Care Med. 2020;46(8):1552-1562. https://doi.org/10.1007/s00134-020-06151-x.\u003c/li\u003e\n \u003cli\u003eLin YM, Lee MC, Toh HS, et al. Association of sepsis-induced cardiomyopathy and mortality: A systematic review and meta-analysis. Ann Intensive Care. 2022;12(1):112. https://doi.org/10.1186/s13613-022-01089-3.\u003c/li\u003e\n \u003cli\u003eTargoński R, Kowacz M, Oraczewski R, Thoene M, Targoński R. The emerging concept of glycocalyx damage as the trigger of heart failure onset and progression. Med Hypotheses. 2024;182:111234. https://doi.org/10.1016/j.mehy.2023.111234.\u003c/li\u003e\n \u003cli\u003eHabimana R, Choi I, Cho HJ, Kim D, Lee K, Jeong I. Sepsis-induced cardiac dysfunction: A review of pathophysiology. Acute Crit Care. 2020;35(2):57-66. https://doi.org/10.4266/acc.2020.00248.\u003c/li\u003e\n \u003cli\u003eEl Missiri AM, Alzurfi AS, Keddeas VW. The relationship between tumor necrosis factor alpha and left ventricular diastolic function. J Cardiovasc Echogr. 2020;30(2):62-7. https://doi.org/10.4103/jcecho.jcecho_1_20.\u003c/li\u003e\n \u003cli\u003ePei C, Zheng H, Yang K, Song N. Identification of immunometabolism-associated genes and immune infiltration in sepsis-induced cardiomyopathy using integrated bioinformatics and machine learning approaches. J Int Med Res. 2025;53(11):3000605251395584. https://doi.org/10.1177/03000605251395584.\u003c/li\u003e\n \u003cli\u003eBansal M, Mehta A, Machanahalli Balakrishna A, et al. Right ventricular dysfunction in sepsis: An updated narrative review. Shock. 2023;59(6):829-837. https://doi.org/10.1097/SHK.0000000000002120.\u003c/li\u003e\n \u003cli\u003eEhrman RR, Sullivan AN, Favot MJ, 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.\u003c/li\u003e\n \u003cli\u003eNedel W, Portela LV. Association between troponin and NT-proBNP levels, cytokines, and clinical outcomes in early sepsis response: A cohort study. Braz J Anesthesiol. 2025;76(1):844712. https://doi.org/10.1016/j.bjane.2025.844712.\u003c/li\u003e\n \u003cli\u003eZheng WJ, Zhang Y, Fu WD, Fu XL, Yan X. Correction: Role of immune-related endoplasmic reticulum stress genes in sepsis-induced cardiomyopathy: novel insights from bioinformatics analysis. PLoS One. 2025;20(12):e0338149. https://doi.org/10.1371/journal.pone.0338149.\u003c/li\u003e\n \u003cli\u003eVetrugno L, Biasucci DG. Diastolic dysfunction in septic patients: An alice-in-wonderland perspective on mortality impact. Intensive Care Med. 2025;51(5):968-969. https://doi.org/10.1007/s00134-025-07887-0.\u003c/li\u003e\n \u003cli\u003eSu H, Wang J, Wang ZF, Yang Z, Ma Y. Assessing left ventricular function in patients with hyperthyroidism across varied heart rates via press-strain loop analysis: A retrospective cross-sectional study. Quant Imaging Med Surg. 2025;15(2):1632-1640. https://doi.org/10.21037/qims-24-951.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Septic cardiomyopathy, Diastolic function, Relative wall thickness, Cardiac troponin I, SOFA score, 28-day mortality","lastPublishedDoi":"10.21203/rs.3.rs-8639758/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8639758/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aimed to identify risk factors for diastolic dysfunction in septic patients, establish a predictive model, enable early intervention, reduce the incidence of septic cardiomyopathy (SCM), and improve patient prognosis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eNinety-eight sepsis patients admitted to the Affiliated Hospital of Chengde Medical College were divided into two groups: the diastolic dysfunction group and the normal cardiac function group. Baseline clinical data, echocardiographic parameters related to diastolic function, and serum biomarkers of myocardial injury were collected. Least Absolute Shrinkage and Selection Operator (LASSO) regression combined with binary logistic regression was used for variable selection, followed by model construction, evaluation, visualization, and internal validation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eKey predictive variables included Sequential Organ Failure Assessment (SOFA) score, 28-day mortality, Cardiac Troponin I (cTnI), A (Atrial systole peak), E/A (Early diastolic peak/Atrial systole peak), e' (Early diastolic myocardial velocity peak), E/e' (Early diastolic peak/Early diastolic myocardial velocity peak), and Relative Wall Thickness (RWT). Nomogram analysis confirmed these as risk factors for SCM. The model showed high predictive value (Receiver Operating Characteristic(ROC) curve area under the curve\u0026thinsp;=\u0026thinsp;0.983, 95% \u003cem\u003eConfidence Interval\u003c/em\u003e (\u003cem\u003eCI\u003c/em\u003e): 0.951-1.000) and good calibration (Hosmer-Lemeshow test: \u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 1.784, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.987). Its clinical utility was validated by Decision Curve Analysis (DCA).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSOFA score, 28-day mortality, cTnI, A, e', E/e', E/A, and RWT are independent risk factors for diastolic dysfunction in septic patients. The constructed predictive model exhibits excellent performance and clinical applicability.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Risk Prediction Model for Diastolic Dysfunction in Patients with Sepsis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 17:37:35","doi":"10.21203/rs.3.rs-8639758/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":"43fc01f1-f527-44fa-ac19-0b1a18dbe738","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T14:26:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 17:37:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8639758","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8639758","identity":"rs-8639758","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}