Exploring Triglyceride-Glucose Index's Role in Sepsis-Associated Encephalopathy: A Comprehensive Study of Its Impact on Disease Severity and Prognostic Accuracy

Exploring Triglyceride-Glucose Index's Role in Sepsis-Associated Encephalopathy: A Comprehensive Study of Its Impact on Disease Severity and Prognostic Accuracy | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Article Exploring Triglyceride-Glucose Index's Role in Sepsis-Associated Encephalopathy: A Comprehensive Study of Its Impact on Disease Severity and Prognostic Accuracy Xiaopeng Shi, Lijun Xu, Jia Ren, Lijuan Jing, Kaifeng Wei, Lijie Qin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3865210/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: S epsis-associated encephalopathy (SAE) is a complex condition with variable outcomes. This study investigates the potential of the Triglyceride-glucose (TyG) index as a marker for disease severity and prognosis in SAE patients. Methods: Our cohort comprised 1578 SAE patients from the MIMIC-IV database, stratified based on TyG index tertiles. We analyzed baseline characteristics, disease severity, and prognostic outcomes. The Kaplan-Meier method and Cox regression analyses were employed for survival analysis, while Spearman rank correlation and various statistical tests were used to assess correlations between TyG index and clinical outcomes. Results: The study population's median age was 65.96 years, predominantly male (60.1%). Higher TyG index scores correlated with elevated clinical severity scores (APSIII, LODS, OASIS, SAPSII, and CCI) and increased ICU and hospital stay durations. TyG index categorization revealed significant differences in 90-day survival probabilities, with "high TyG" associated with a 25% increased mortality risk compared to "low TyG". Furthermore, TyG index showed a moderate positive correlation with ICU stay duration and use of norepinephrine and vasopressin, but not with dopamine and epinephrine use. Conclusions: The TyG index is a significant independent predictor of disease severity and prognosis in SAE patients. High TyG levels correlate with worse clinical outcomes and increased mortality risk, suggesting its potential as a valuable tool in managing SAE. Health sciences/Medical research/Biomarkers Health sciences/Diseases/Neurological disorders/Disorders of consciousness Health sciences/Diseases/Infectious diseases/Bacterial infection Sepsis-associated Encephalopathy Triglyceride-glucose Index Disease Severity Prognosis Clinical Outcomes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. INTRODUCTION The global incidence of sepsis, a life-threatening complex disease, continues to escalate. Recent studies indicate that approximately 31 million individuals worldwide experience sepsis annually, with new cases in the United States ranging from 300 to 1,000 per 100,000 people [ 1 ] . In China, the prevalence and mortality rates of sepsis surpass those observed in North America and Europe. A systematic review and meta-analysis estimate the incidence of sepsis in China to be 33.6% [ 2 ] . Furthermore, the 90-day mortality rate for sepsis in Chinese intensive care units (ICUs) is reported at 35.5% [ 3 ] .Sepsis-associated encephalopathy (SAE), a neurological dysfunction arising from severe infections and systemic inflammation, is linked to various factors, including inflammation, inadequate oxygenation, metabolic disorders, and drug effects [ 4 ] . The clinical manifestations of SAE span from mild cognitive dysfunction to profound impairment of consciousness. The pathogenesis is intricate and multifactorial, encompassing vascular injury, endothelial activation, disruption of the blood-brain barrier, altered brain signaling, brain inflammation, and apoptosis [ 5 ] . Extensive documentation affirms the independent association between SAE occurrence and short-term mortality. Even slight alterations in mental status independently elevate the risk of death [ 6 ] , with the potential for enduring neurological sequelae [ 7 ] . The evaluation of SAE in the ICU poses challenges and is presently conducted through clinical scoring systems [ 8 ] . Notably, no specific biomarkers for SAE have been identified for use in routine clinical practice [ 9 ] . The triglyceride-glucose (TyG) index, recognized as a surrogate marker of insulin resistance, has recently gained attention as a potential prognostic tool for various metabolic and cardiovascular diseases [ 10 ] . Its simplicity of calculation and cost-effectiveness render it an accessible instrument for clinicians to evaluate and monitor patients' metabolic status and associated risks. Preliminary investigations have revealed a correlation between metabolic disorders characterized by dysregulated lipid and glucose metabolism and the severity of sepsis. Notably, a high TyG index has been associated with increased in-hospital mortality in sepsis patients [ 11 ] . Additionally, studies have demonstrated a positive association between the TyG index and the risk of delirium, particularly in critically ill patients aged 65 years and older [ 12 ] . Considering these findings, the TyG index may be valuable as a surrogate for insulin resistance in assessing the risk and severity of SAE. This study investigated the TyG index as a potential predictor of severity and prognosis in SAE. The primary objective was to evaluate the correlation between TyG index and disease severity, survival, and the duration of ICU stay. Through the analysis of a substantial sample size and the application of reliable statistical methods, we aim to gain insights into the TyG index's potential as a dependable marker for clinicians managing SAE. This endeavor is poised to contribute to the enhancement of care and prognosis for patients with SAE in the intensive care setting. 2. MATERIALS AND METHODS 2.1 Data Source This research leveraged the open-source medical information from the Medical Information Mart for Intensive Care (MIMIC-IV, version 2.2) database. MIMIC-IV is a comprehensive repository that encompasses patient data from Beth Israel Deaconess Medical Center, spanning the years 2008 to 2019 [ 13 ] . This extensive database includes detailed medical records, medication regimens, laboratory results, patient demographics, and International Classification of Diseases (ICD) codes, offering a rich source of high-quality clinical data. Team member Xiaopeng Shi has successfully completed the Collaborative Institutional Training Initiative (CITI) course offered by the National Institutes of Health (NIH) and obtained the requisite certification (Certification Number: 38652558). This certification, along with authorization from the Institutional Review Board (IRB) of the Massachusetts Institute of Technology (MIT), permits Xiaopeng Shi to access and utilize the MIMIC-IV database for research purposes. Furthermore, this study received ethical approval from both the Massachusetts Institute of Technology (MIT IRB Number: 0403000206) and Beth Israel Deaconess Medical Center (BIDMC IRB Number: 2001-P-001699/14). Adhering to the highest standards of research integrity, the reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [ 14 ] . 2.2 Patients Inclusion Criteria : (1) Alignment with the Sepsis 3.0 Definition and Diagnostic Criteria as delineated by the American Society of Critical Care Medicine and the European Society of Critical Care Medicine in 2016. This entails a diagnosis of Sepsis 3.0, characterized by an infection coupled with a Sequential Organ Failure Assessment (SOFA) score of 2 or higher. (2)Admission to the Intensive Care Unit (ICU) for treatment. For patients with multiple hospitalizations, the study only considered data from their initial ICU admission during the first hospitalization period. (3) Diagnosis of septic encephalopathy, defined by either a Glasgow Coma Scale (GCS) score of 14 or lower or a positive delirium assessment on the first day of ICU admission. Exclusion Criteria : (1) Patients aged below 18 years. (2) Patients with an ICU stay of less than 24 hours. (3) Patients presenting with primary brain injuries, which include traumatic brain injury, ischemic stroke, hemorrhagic stroke, epilepsy, intracranial infection, psychiatric disorders, or dementia. (4) Individuals with a history of chronic alcohol or drug abuse. (5) Absence of triglyceride or glucose laboratory results from the first day of ICU admission. (6) Patients not diagnosed with septic encephalopathy. 2.3 Study Settings In this study, patients with septic encephalopathy were divided into three groups based on the values of the triglyceride-to-glucose index (TyG index).The TyG index was calculated using the following formula: TyG = Ln[triglyceride (mg/dL) × glucose (mg/dL) / 2]. This index is used to assess insulin resistance in patients and is a commonly used biomarker in sepsis research in recent years. The grouping was based on the following: Group 1: Patients with a TyG index of 8.81 or below. Group 2: Patients whose TyG index ranged between 8.81 and 9.43, denoted as 8.81 < TyG < 9.43. Group 3: Patients with a TyG index of 9.43 or above. We assessed the following clinical outcomes: Primary Outcome: Patient survival within the first 90 days following admission to the Intensive Care Unit (ICU). Secondary Outcomes: (1) The total duration of the hospital stay. (2)The length of stay in the ICU. (3) Utilization of vasoactive medications, including epinephrine, norepinephrine, dopamine, and vasopressin. 2.4 Data Collection In this research, data extraction from the Medical Information Mart for Intensive Care (MIMIC-IV) database was executed using Structured Query Language (SQL) via PostgreSQL. Our focus was directed towards comprehensively gathering key data areas, as outlined below: Demographic Information: This includes critical details such as the age and gender of the patients. Comorbidities:The presence of significant comorbidities was assessed, encompassing conditions like myocardial infarction, congestive heart failure, peripheral vascular disease, chronic lung disease, diabetes, chronic liver disease, and chronic kidney disease. Disease Severity Score at ICU Admission: We examined various scores indicative of disease severity on the first day of ICU admission. These included the Acute Physiology Score (APSIII), the Logistic Organ Dysfunction Score (LODS), the Oxford Acute Severity of Illness Score (OASIS), the Sequential Organ Failure Assessment (SOFA), and the Charlson Comorbidity Index (CCI). Vital Signs on ICU Admission Day: Vital signs recorded on the first day of ICU admission included heart rate (HR), respiratory rate (RR), mean arterial pressure (MAP), body temperature, and finger pulse oxygen saturation (SpO2). Laboratory Findings on ICU Admission Day: An array of laboratory parameters was collected, including hemoglobin, platelets, white blood cells, albumin, anion gap, bicarbonate, blood urea nitrogen, calcium, sodium, potassium, international normalized ratios (INR), prothrombin time (PT), partial thromboplastin time (PTT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), blood oxygen saturation (SpO2), blood glucose, triglycerides, and lactate levels. 2.5 Statistical Analysis In this study, our initial step involved utilizing the Shapiro-Wilk test to evaluate the distribution of continuous variables. Based on the test outcomes, all continuous variables were determined to be non-normally distributed. Consequently, these variables were presented as medians and interquartile ranges (IQR). For the comparison between two groups, we employed the Mann-Whitney U test or the Wilcoxon rank sum test, depending on the data characteristics. Regarding categorical variables, these were expressed in terms of counts and percentages. The analysis of these variables was conducted using either the chi-square test (χ2 test) or the Fisher exact test, selected based on data suitability. Additionally, we employed Pearson's correlation coefficient to measure the strength of association between the Triglyceride and Glucose index (TyG) and each scoring system. The relationships between these variables were visually depicted through heat maps for enhanced clarity. For the analysis of factors influencing prognostic primary outcomes, survival analysis was conducted. This involved the use of the "surv_cutpoint" function in the R programming language to ascertain the optimal cut-off point, followed by the construction of Kaplan-Meier survival curves. The log-rank test was then applied to assess survival disparities between groups. Further analytical depth was added through both univariate and multivariate Cox regression analyses, with the findings presented via forest plots. To examine the correlations between TyG and secondary prognostic outcomes, Spearman rank correlation coefficients were utilized, and these relationships were visually represented through violin plots. All statistical analyses were performed using R sversion 4.3.2, and P < 0.05 was considered statistically significant. 3. RESULTS 3.1 Baseline characteristics Following the delineated screening criteria, our study encompassed a cohort of 1578 patients with SAE. The recruitment process is detailed in Fig. 1 . The median age of these participants was 65.96 years, with an interquartile range of 54.61 to 76.77 years, and a majority of 60.1% being male. The cohort was stratified based on the TyG index tertiles, forming three distinct groups: those with TyG ≤ 8.81 (comprising 521 patients), 8.81 < TyG < 9.43 (536 patients), and TyG ≥ 9.43 (521 patients). In terms of hospitalization metrics, the median length of stay in the Intensive Care Unit (ICU) was recorded at 6.04 days (Interquartile Range, IQR: 3.38–11.88), while the total hospital stay averaged 14.88 days (IQR: 8.78–24.56). Notably, patients manifesting higher TyG index scores were observed to have elevated scores in multiple clinical assessments, including the Acute Physiology Score III (APSIII), Logistic Organ Dysfunction Score (LODS), Oxford Acute Severity of Illness Score (OASIS), Simplified Acute Physiology Score II (SAPSII), and Charlson Comorbidity Index (CCI), in comparison to their counterparts with lower TyG index scores. Furthermore, this subgroup with elevated TyG indexes exhibited significantly higher heart and respiratory rates, alongside increased body temperatures. Laboratory analyses revealed that these patients also presented with heightened levels of white blood cell count (WBC), blood urea nitrogen (BUN), creatinine (CRE), anion gap (AG), neutrophil count, and lactate dehydrogenase (LDH). Additionally, a correlation was observed between higher TyG levels and prolonged durations of stay in both the ICU and the hospital overall. Furthermore, the usage of pharmacological interventions such as epinephrine, norepinephrine, and vasopressin was more prevalent among patients with higher TyG indexes.(Table 1 ) 3.2 Correlation Between TyG Index and Disease Severity Scores In our investigation, the TyG index exhibited a significant positive correlation with various established severity scores, specifically the Acute Physiology Score III (APSIII), Logistic Organ Dysfunction Score (LODS), Oxford Acute Severity of Illness Score (OASIS), and Simplified Acute Physiology Score II (SAPSII), as evidenced by P-values less than 0.05. This finding implies that the TyG index could serve as a viable marker for reflecting the severity of disease in patients with SAE. Notably, the correlations between APSIII, LODS, and SAPSII were particularly pronounced, suggesting a high degree of congruence among these scoring systems in the assessment of disease severity.(Figure 2 ) In contrast, the Sequential Organ Failure Assessment (SOFA) score demonstrated a relatively weaker correlation with the other severity scores, particularly with the OASIS, as indicated by a Pearson correlation coefficient of 0.20297439. This discrepancy implies that the SOFA score might be quantifying somewhat different dimensions of disease severity compared to the other indices. This distinction underscores the complexity and multifaceted nature of severity assessment in SAE patients, highlighting the necessity of a multifactorial approach in evaluating patient conditions. 3.3 Correlation Between TyG and Prognostic Primary Outcomes in SAE Patients In this segment of our study, the TyG index was categorized into two groups based on an optimal cut-off value of 8.7639: "low TyG" and "high TyG." Analysis of Kaplan-Meier survival curves (as depicted in Fig. 3 ) revealed a temporal divergence in survival probabilities between these groups. Notably, the "low TyG" group exhibited a significantly higher survival probability compared to the "high TyG" group, as substantiated by a p-value of 0.0027.(Figure 3 ) Table 2 elucidates that the univariate analysis has revealed an extensive array of both clinical and laboratory determinants significantly influencing the 90-day survival rate of patients with septic encephalopathy. These determinants span metabolic indicators such as the TyG index and age, a variety of scores assessing critical illness, blood constituents like hematocrit and hemoglobin, electrolyte levels, and vital physiological metrics including heart rate, mean arterial pressure, respiratory rate, and body temperature. Subsequent multivariate Cox regression analysis has pinpointed a select group of these factors as independent prognostic indicators, encompassing metabolic rates, specific illness severity scores, coexisting conditions such as congestive heart failure and chronic liver disease, alongside particular hematological and physiological parameters. The univariate regression analysis demonstrates that the TyG index is a significant predictor of mortality, with a hazard ratio (HR) of 1.344 and a p-value of 0.00275, underscoring a strong statistical significance. Upon adjustment for confounding factors in the multivariate analysis, the TyG index retains its prognostic value (p = 0.036114), with an HR of 1.2517. This outcome suggests that individuals with higher TyG levels have a 25% greater risk of mortality compared to those with lower levels, even when other variables are taken into account.(Talbe2) Age also emerged as a significant predictor in the multivariate analysis. Each additional year of age was associated with a 1.6% increase in the risk of death (HR = 1.015743, p = 0.001619), a finding that remained robust even after controlling for other factors. Notably, the significance of certain variables, such as APSIII, SOFA, and CCI, diminished in the multivariate analysis. This attenuation could be attributed to their covariance with other variables or the adjustment effects of the latter. These findings underscore that both TyG index and age are critical determinants influencing the 90-day overall survival of SAE patients. TyG index, in particular, exerts a significant impact on survival outcomes, whether assessed independently or in conjunction with other factors. The multifactorial Cox regression analyses, as illustrated in the forest plot, affirm that TyG index is an independent prognostic factor in SAE, with high TyG index levels being indicative of a poorer prognosis. Furthermore, other variables such as the SOFA score, OASIS score, CCI score, APSII score, along with higher age, heart rate (HR), respiratory rate (RR), prothrombin time (PTT), and neutrophil counts, were identified as additional risk factors for an adverse prognosis.(Figure 4 ) 3.4 Correlation of TyG index with Prognostic Secondary Outcomes in SAE Patients In this aspect of our study, we employed the Spearman rank correlation coefficient to assess the relationship of the TyG index with secondary prognostic outcomes in SAE patients. Our analysis revealed that the TyG index had a moderately positive correlation with the number of days spent in the Intensive Care Unit (ICU) (denoted as los_icu). This correlation emerged as statistically highly significant, with a P-value less than 0.01. In a similar vein, the correlation between the TyG index and the total number of days spent in the hospital, as well as the administration of norepinephrine and vasopressin, also demonstrated statistical significance (P < 0.01). Conversely, the association of the TyG index with the use of dopamine and epinephrine was found to be statistically insignificant and notably weak. (Figure 5 ) 4. Discussion In the present study, a comprehensive analysis was conducted on a cohort of 1578 patients diagnosed with SAE. Among these patients, the median age was 65.96 years, with males being predominantly represented. This demographic profile aligns with the findings of Chen et al., where SAE patients had a median age of 67 years, with 57% being male [ 15 ] . These consistent findings suggest that SAE predominantly affects an older demographic, with a slight male predominance. This observation may be indicative of the elevated susceptibility of the elderly to sepsis, and possibly, a higher susceptibility of men to serious infections [ 16 ] . The current study affirms the TyG index as a significant predictor of disease severity and prognosis in SAE patients. Specifically, individuals in the high TyG index group exhibited elevated scores on the Clinical Criticality Score and more pronounced abnormalities in physiological parameters, including increased heart rate, respiratory rate, and body temperature. Moreover, the high TyG index group demonstrated a significantly heightened risk of mortality compared to the low TyG index group. These findings align with existing research; one study, for instance, established a relationship between SAE prognosis and sepsis severity, age, respiratory rate, body temperature, and heart rate [ 17 ] . Another study revealed that SAE patients exhibited higher heart rate, blood lactate, and serum sodium levels, alongside lower platelet counts, serum albumin levels, and serum pH compared to non-SAE patients [ 18 ] . Previous investigations have identified traditional severity scores, such as the Serial Organ Failure Assessment (SOFA) and the Acute Physiology and Chronic Health Evaluation (APACHE II), as important prognostic factors for SAE [ 15 ] . In contrast to these studies, our findings suggest a relatively weak correlation between the TyG index and SOFA score. This disparity may stem from the diverse clinical dimensions assessed by the SOFA score [ 19 ] , hinting at a more intricate interplay between various factors influencing SAE severity. SAE, characterized by diffuse cerebral dysfunction resulting from a systemic inflammatory response, is primarily diagnosed based on the manifestation of impaired consciousness or delirium. Currently, this diagnosis is primarily one of exclusion. Numerous screening tools are available to identify delirium, yet none are specifically tailored to SAE. In a multicenter study, the CAM-ICU exhibited a sensitivity of 47%, specificity of 98%, and positive and negative predictive values of 95% and 72%, respectively [ 20 ] . Consequently, the most suitable delirium screening tool for ICU settings remains a matter of debate. The ICDSC, with a higher sensitivity (99%) but lower specificity (64%) for delirium assessment compared to the CAM-ICU, presents an alternative option [ 21 ] . The application of coma scales in SAE remains unexplored, although the GCS score proves useful in predicting the course of SAE [ 22 ] . Neuroimaging findings in SAE patients exhibit variability, with acute abnormalities observed on MRI in a subset of cases, such as multiple ischemic strokes or hemianopic central white matter lesions [ 23 ] . Notably, some patients exhibit normal brain MRI scans despite the presence of SAE. SAE currently lacks specific biomarkers; hence, the identification of early warning and diagnostic indicators for SAE in critically ill septic patients holds significant importance for timely intervention and treatment [ 24 ] . The underlying mechanisms through which the TyG index predicts SAE disease severity and the risk of death remain unclear. This study endeavors to explore this association from a pathophysiological perspective. The TyG index, serving as an indicator of metabolic disorders, is likely intricately linked to the onset of sepsis and SAE. Insulin resistance, a key component of metabolic disorders, not only affects systemic metabolism but may also impact hemodynamics and brain metabolism [ 25 ] . Insulin resistance could contribute to blood-brain barrier dysfunction, increasing the brain tissue's susceptibility to inflammatory responses and oxidative stress [ 26 ] . These factors may exacerbate the symptoms and severity of SAE. Moreover, elevated levels of blood glucose and triglycerides may directly harm nerve cells, triggering the excessive release of inflammatory factors and cytokines [ 27 ] . This, in turn, exacerbates the pathological progression of SAE. Such pathological changes may culminate in nerve cell death, further worsening neurological dysfunction. Our study has several limitations that warrant consideration. Retrospective analyses based on a single database may introduce selection bias, thereby limiting the generalizability of our findings. Furthermore, while the TyG index proves valuable, it should be viewed as one component in the comprehensive assessment of patients with SAE, considering other relevant clinical parameters. Given the multitude of diseases that can lead to TyG alterations, its utility lies in identifying SAE, yet its specificity and sensitivity may be insufficient to distinguish SAE from other forms of encephalopathies. Nonetheless, owing to its ease of accessibility and lack of involvement of subjective factors, the TyG index holds importance in the early identification of SAE and monitoring treatment effects throughout the course of care. In conclusion, our study underscores the significance of the TyG index as a vital independent predictor of disease severity and prognosis in individuals with SAE. The observed association with a poorer clinical prognosis and an elevated risk of mortality suggests its potential applicability in clinical practice. Future investigations should seek to validate these findings through prospective cohorts and delve into the underlying pathophysiological mechanisms of these associations. Such endeavors will contribute to advancing our comprehension of SAE, potentially paving the way for more effective management strategies for this challenging disease. 5. Conclusion This study revealed a notable correlation between the TyG index and disease severity, length of hospital stay, and the risk of death in individuals with SAE. Patients exhibiting a high TyG index demonstrated elevated scores across various clinical assessment metrics and were linked to prolonged stays in both the intensive care unit and overall hospitalization. Furthermore, a heightened TyG index was associated with an increased risk of mortality, a correlation that remained significant even after adjusting for other variables. Consequently, the TyG index emerges as a potential valid marker for gauging the severity of SAE and predicting patient prognosis. Nevertheless, given the constraints inherent in the study design, these findings warrant further validation in broader populations and multicenter studies. Declarations Data availability statement The MIMIC IV database (version 2.2) is publicly available at https://mimic-iv.mit.edu/. Any researcher who adheres to the data use requirements can access these databases (Certification number 38652558). Code availability (software application or custom code) The codes are available at https://github.com/MIT-LCP. Ethical Approval In this study, all procedures involving human participants were meticulously conducted in compliance with the ethical standards set forth by both the Institutional and National Research Committees. Furthermore, these procedures adhered to the principles of the 1964 Helsinki Declaration, along with its subsequent amendments, or other equivalent ethical guidelines. Consent to participate The MIMIC-IV database, financially supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) at the National Institutes of Health (NIH) under grant numbers R01-EB001659 (2003–2013) and R01-EB017205 (2014-2018), has been duly authorized by the Institutional Review Boards of the Beth Israel Deaconess Medical Center, Boston, Massachusetts, and the Massachusetts Institute of Technology, Cambridge, Massachusetts. The database has successfully met the Safe Harbor standards set by Privacert (Cambridge, Massachusetts), evidenced by its HIPAA certification number 1031219-2, ensuring a minimized risk of re-identification. Given that the data, accessible in the MIMIC-IV database, does not influence clinical care and maintains patient confidentiality through comprehensive anonymization, this research has been granted an exemption from the customary requirements for ethical approval and informed consent. Competing interests The authors declare no competing interests. Funding No funding. Author contributions Shi XP: Played a significant role in the conception and design of the study. Led the data collection process and contributed to the initial draft of the manuscript. Xu LJ: Focused on the analysis and interpretation of data. Worked extensively on the statistical aspects of the study, ensuring accurate data analysis. Ren J: Provided expertise in Sepsis-associated encephalopathy, significantly contributing to the theoretical framework of the research. Assisted in the literature review and manuscript editing. Jing LJ: Actively involved in drafting the manuscript, particularly in refining the intellectual content. Collaborated closely with other authors in interpreting the study's findings. Wei KF: Contributed to the study design and methodology. Assisted in data acquisition and played a key role in the critical revision of the manuscript for important intellectual content. Qin LJ: Oversaw the overall research project and coordinated the efforts of the research team. Acknowledgments The authors extend their sincere appreciation to the Massachusetts Institute of Technology and the Beth Israel Deaconess Medical Center for their substantial contributions to the MIMIC project. References Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med. 2001;29(7):1303–1310. doi: 10.1097/00003246-200107000-00002 . Liu YC, Yao Y, Yu MM, et al. Frequency and mortality of sepsis and septic shock in China: a systematic review and meta-analysis. BMC Infect Dis. 2022;22(1):564. Published 2022 Jun 21. doi: 10.1186/s12879-022-07543-8 . Xie J, Wang H, Kang Y, et al. The Epidemiology of Sepsis in Chinese ICUs: A National Cross-Sectional Survey. Crit Care Med. 2020;48(3):e209-e218. doi: 10.1097/CCM.0000000000004155 . Ren C, Yao RQ, Zhang H, Feng YW, Yao YM. Sepsis-associated encephalopathy: a vicious cycle of immunosuppression. J Neuroinflammation. 2020;17(1):14. Published 2020 Jan 10. doi: 10.1186/s12974-020-1701-3 . Gofton TE, Young GB. Sepsis-associated encephalopathy. Nat Rev Neurol. 2012;8(10):557–566. doi: 10.1038/nrneurol.2012.183 . Sonneville R, de Montmollin E, Poujade J, et al. Potentially modifiable factors contributing to sepsis-associated encephalopathy. Intensive Care Med. 2017;43(8):1075–1084. doi: 10.1007/s00134-017-4807-z . Helbing DL, Böhm L, Witte OW. Sepsis-associated encephalopathy. CMAJ. 2018;190(36):E1083. doi: 10.1503/cmaj.180454 . Gusmao-Flores D, Salluh JI, Chalhub RÁ, Quarantini LC. The confusion assessment method for the intensive care unit (CAM-ICU) and intensive care delirium screening checklist (ICDSC) for the diagnosis of delirium: a systematic review and meta-analysis of clinical studies. Crit Care. 2012;16(4):R115. Published 2012 Jul 3. doi: 10.1186/cc11407 . Sonneville R, Benghanem S, Jeantin L, et al. The spectrum of sepsis-associated encephalopathy: a clinical perspective. Crit Care. 2023;27(1):386. doi: 10.1186/s13054-023-04655-8 . Tao LC, Xu JN, Wang TT, Hua F, Li JJ. Triglyceride-glucose index as a marker in cardiovascular diseases: landscape and limitations. Cardiovasc Diabetol. 2022;21(1):68. doi: 10.1186/s12933-022-01511-x . Zheng R, Qian S, Shi Y, Lou C, Xu H, Pan J. Association between triglyceride-glucose index and in-hospital mortality in critically ill patients with sepsis: analysis of the MIMIC-IV database. Cardiovasc Diabetol. 2023;22(1):307. Published 2023 Nov 8. doi: 10.1186/s12933-023-02041-w . Huang X, Cheng H, Yuan S, et al. Triglyceride-glucose index as a valuable predictor for aged 65-years and above in critical delirium patients: evidence from a multi-center study. BMC Geriatr. 2023;23(1):701. doi: 10.1186/s12877-023-04420-0 . Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L. A., & Mark, R. (2023). MIMIC-IV (version 2.2). PhysioNet. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594. Published 2015 Jan 7. doi: 10.1136/bmj.g7594 . Chen J, Shi X, Diao M, et al. A retrospective study of sepsis-associated encephalopathy: epidemiology, clinical features and adverse outcomes. BMC Emerg Med. 2020;20(1):77. doi: 10.1186/s12873-020-00374-3 . van Vught LA, Klein Klouwenberg PM, Spitoni C, et al. Incidence, Risk Factors, and Attributable Mortality of Secondary Infections in the Intensive Care Unit After Admission for Sepsis. JAMA. 2016;315(14):1469–1479. doi: 10.1001/jama.2016.2691 . Peng L, Peng C, Yang F, et al. Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated encephalopathy. BMC Med Res Methodol. 2022;22(1):183. doi: 10.1186/s12874-022-01664-z Zhang LN, Wang XT, Ai YH, et al. Epidemiological features and risk factors of sepsis-associated encephalopathy in intensive care unit patients: 2008–2011. Chin Med J (Engl). 2012;125(5):828–831. Wang X, Guo Z, Chai Y, et al. Application Prospect of the SOFA Score and Related Modification Research Progress in Sepsis. J Clin Med. 2023;12(10):3493. Published 2023 May 16. doi: 10.3390/jcm12103493 . van Eijk MM, van den Boogaard M, van Marum RJ, et al. Routine use of the confusion assessment method for the intensive care unit: a multicenter study. Am J Respir Crit Care Med. 2011;184(3):340–344. doi: 10.1164/rccm.201101-0065OC . Bergeron N, Dubois MJ, Dumont M, Dial S, Skrobik Y. Intensive Care Delirium Screening Checklist: evaluation of a new screening tool. Intensive Care Med. 2001;27(5):859–864. doi: 10.1007/s001340100909 . Eidelman LA, Putterman D, Putterman C, Sprung CL. The spectrum of septic encephalopathy. Definitions, etiologies, and mortalities. JAMA. 1996;275(6):470–473. Sharshar T, Carlier R, Bernard F, et al. Brain lesions in septic shock: a magnetic resonance imaging study. Intensive Care Med. 2007;33(5):798–806. doi: 10.1007/s00134-007-0598-y . Catarina AV, Branchini G, Bettoni L, De Oliveira JR, Nunes FB. Sepsis-Associated Encephalopathy: from Pathophysiology to Progress in Experimental Studies. Mol Neurobiol. 2021;58(6):2770–2779. doi: 10.1007/s12035-021-02303-2 . Muhammad IF, Bao X, Nilsson PM, Zaigham S. Triglyceride-glucose (TyG) index is a predictor of arterial stiffness, incidence of diabetes, cardiovascular disease, and all-cause and cardiovascular mortality: A longitudinal two-cohort analysis. Front Cardiovasc Med. 2023;9:1035105. Published 2023 Jan 4. doi: 10.3389/fcvm.2022.1035105 . De La Monte SM. Metabolic derangements mediate cognitive impairment and Alzheimer's disease: role of peripheral insulin-resistance diseases. Panminerva Med. 2012;54(3):171–178. Mendes NF, Velloso LA. Perivascular macrophages in high-fat diet-induced hypothalamic inflammation. J Neuroinflammation. 2022;19(1):136. doi: 10.1186/s12974-022-02519-6 . Tables Table1: Baseline characteristics of the included patients. APSIII:Acute Physiology Score III; LODS:Logistic Organ Dysfunction System; OASIS:Oxford Acute Severity of Illness Score; SAPSII:Sequential Organ Failure Assessment (SOFA) Score; SOFA:Sequential Organ Failure Assessment; CCI：Charlson Comorbidity Index; AMI:Acute Myocardial Infarction; CHF:Congestive Heart Failure; PVD:Peripheral Vascular Disease; COPD:Chronic Obstructive Pulmonary Diseases; CLD：Chronic Liver Disease; DM:Diabetes Mellitus; CKD：Chronic Kidney Disease; HR:Heart Rate; RR:Respiratory Rate; MAP:Mean Arterial Pressure; BUN:Blood Urea Nitrogen; WBC:White Blood Cell count; INR:International Normalized Ratio; PT:Prothrombin Time; PTT:Partial Thromboplastin Time;ALT:Alanine Aminotransferase; AST:Aspartate Aminotransferase; TBIL:Total Bilirubin; NLR: Neutrophil to Lymphocyte Ratio; TyG:Triglyceride-Glucose Index. Table2: Univariate and multifactorial analyses of 90-day survival in patients with septic encephalopathy. Additional Declarations No competing interests reported. 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People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Xu","suffix":""},{"id":269603038,"identity":"a9dc56bb-c76a-49c5-986d-66df413fecba","order_by":2,"name":"Jia Ren","email":"","orcid":"","institution":"Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Ren","suffix":""},{"id":269603039,"identity":"017844bc-de46-4a74-9683-c990d177dd5c","order_by":3,"name":"Lijuan Jing","email":"","orcid":"","institution":"Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lijuan","middleName":"","lastName":"Jing","suffix":""},{"id":269603040,"identity":"0c8d229c-9190-4944-816a-3eb259d4b37b","order_by":4,"name":"Kaifeng Wei","email":"","orcid":"","institution":"Henan Provincial People's Hospital Yuxi Branch","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kaifeng","middleName":"","lastName":"Wei","suffix":""},{"id":269603041,"identity":"3fdbd5f2-d669-461d-a835-a21339af6b3c","order_by":5,"name":"Lijie Qin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYDACZgbGAwk8Egxs7I2NDz4Y2NgRo4UBrIWP53Cz4YyCtGSiLDoAIuQk0tukeT4cYmwgpJzvOO+BAw9kLOTYeA62SdsYHGBmYD98dAM+LZKH+RJADjMG+qXZOsfgDh8DT1raDXxaDA7zGIC0JLbxHGy8nWPwjJlBgseMKC31bRKJDdIWBocZG4jVksAmkdgkzUCMFkmoFkOgw5oNewzSktkI+YXv/BnDhz976uTl29sfPvjxx8aOn/3wMbxawJHC2IMkwIZXOUwLww+CykbBKBgFo2AkAwAx60tUVKda+AAAAABJRU5ErkJggg==","orcid":"","institution":"Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Lijie","middleName":"","lastName":"Qin","suffix":""}],"badges":[],"createdAt":"2024-01-15 02:44:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3865210/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3865210/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50277592,"identity":"4eed47ee-65a7-40e1-ad51-45160c956bb9","added_by":"auto","created_at":"2024-01-29 02:40:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44257,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart for patient selection.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3865210/v1/6876c202de1f6f82a105d480.png"},{"id":50277593,"identity":"7598182d-719e-49d7-ae88-3ee66a8bf54b","added_by":"auto","created_at":"2024-01-29 02:40:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72434,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Heatmap between TyG Index and Various Disease Severity Scores.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3865210/v1/9e3ce15da57d97f2168944a2.png"},{"id":50277594,"identity":"33be9bba-3220-4342-8e6e-a15327dad7a7","added_by":"auto","created_at":"2024-01-29 02:40:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":218496,"visible":true,"origin":"","legend":"\u003cp\u003e90-Day Kaplan-Meier Survival Comparison between the Low TyG Index Group and High TyG Index Group.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3865210/v1/8ccd52ac30dff247daf09f76.png"},{"id":50277595,"identity":"33251786-b3bc-4f2e-81ff-7f531a7c5b90","added_by":"auto","created_at":"2024-01-29 02:40:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":222657,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plot of Multifactorial Cox Regression Analysis Influencing the Primary Outcomes of SAE Patients.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3865210/v1/b6a025dc7bbed2cfc28b2ba9.png"},{"id":50277596,"identity":"8cd9d724-98e4-4f53-a0da-198dc7472c7c","added_by":"auto","created_at":"2024-01-29 02:40:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":418655,"visible":true,"origin":"","legend":"\u003cp\u003eViolin Plot Showing the Correlation between TyG Index and Secondary Prognostic Outcomes in SAE Patients.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-3865210/v1/c3cd014ace938667c7ab4106.png"},{"id":59045266,"identity":"5597b6ec-76d0-4763-9c01-9a8872a8cd45","added_by":"auto","created_at":"2024-06-25 18:04:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1524952,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3865210/v1/8531650a-8847-4a87-88ce-d1c86ae20b1a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Triglyceride-Glucose Index's Role in Sepsis-Associated Encephalopathy: A Comprehensive Study of Its Impact on Disease Severity and Prognostic Accuracy","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe global incidence of sepsis, a life-threatening complex disease, continues to escalate. Recent studies indicate that approximately 31\u0026nbsp;million individuals worldwide experience sepsis annually, with new cases in the United States ranging from 300 to 1,000 per 100,000 people\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In China, the prevalence and mortality rates of sepsis surpass those observed in North America and Europe. A systematic review and meta-analysis estimate the incidence of sepsis in China to be 33.6%\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Furthermore, the 90-day mortality rate for sepsis in Chinese intensive care units (ICUs) is reported at 35.5%\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.Sepsis-associated encephalopathy (SAE), a neurological dysfunction arising from severe infections and systemic inflammation, is linked to various factors, including inflammation, inadequate oxygenation, metabolic disorders, and drug effects\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The clinical manifestations of SAE span from mild cognitive dysfunction to profound impairment of consciousness. The pathogenesis is intricate and multifactorial, encompassing vascular injury, endothelial activation, disruption of the blood-brain barrier, altered brain signaling, brain inflammation, and apoptosis\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Extensive documentation affirms the independent association between SAE occurrence and short-term mortality. Even slight alterations in mental status independently elevate the risk of death\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, with the potential for enduring neurological sequelae\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. The evaluation of SAE in the ICU poses challenges and is presently conducted through clinical scoring systems\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Notably, no specific biomarkers for SAE have been identified for use in routine clinical practice\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe triglyceride-glucose (TyG) index, recognized as a surrogate marker of insulin resistance, has recently gained attention as a potential prognostic tool for various metabolic and cardiovascular diseases\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Its simplicity of calculation and cost-effectiveness render it an accessible instrument for clinicians to evaluate and monitor patients' metabolic status and associated risks. Preliminary investigations have revealed a correlation between metabolic disorders characterized by dysregulated lipid and glucose metabolism and the severity of sepsis. Notably, a high TyG index has been associated with increased in-hospital mortality in sepsis patients\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Additionally, studies have demonstrated a positive association between the TyG index and the risk of delirium, particularly in critically ill patients aged 65 years and older\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Considering these findings, the TyG index may be valuable as a surrogate for insulin resistance in assessing the risk and severity of SAE.\u003c/p\u003e\n\u003cp\u003eThis study investigated the TyG index as a potential predictor of severity and prognosis in SAE. The primary objective was to evaluate the correlation between TyG index and disease severity, survival, and the duration of ICU stay. Through the analysis of a substantial sample size and the application of reliable statistical methods, we aim to gain insights into the TyG index's potential as a dependable marker for clinicians managing SAE. This endeavor is poised to contribute to the enhancement of care and prognosis for patients with SAE in the intensive care setting.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Data Source\u003c/h2\u003e\nThis research leveraged the open-source medical information from the Medical Information Mart for Intensive Care (MIMIC-IV, version 2.2) database. MIMIC-IV is a comprehensive repository that encompasses patient data from Beth Israel Deaconess Medical Center, spanning the years 2008 to 2019\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. This extensive database includes detailed medical records, medication regimens, laboratory results, patient demographics, and International Classification of Diseases (ICD) codes, offering a rich source of high-quality clinical data.\u003cbr /\u003e\n\u003cp\u003eTeam member Xiaopeng Shi has successfully completed the Collaborative Institutional Training Initiative (CITI) course offered by the National Institutes of Health (NIH) and obtained the requisite certification (Certification Number: 38652558). This certification, along with authorization from the Institutional Review Board (IRB) of the Massachusetts Institute of Technology (MIT), permits Xiaopeng Shi to access and utilize the MIMIC-IV database for research purposes.\u003c/p\u003e\n\u003cp\u003eFurthermore, this study received ethical approval from both the Massachusetts Institute of Technology (MIT IRB Number: 0403000206) and Beth Israel Deaconess Medical Center (BIDMC IRB Number: 2001-P-001699/14). Adhering to the highest standards of research integrity, the reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Patients\u003c/h2\u003e\n\u003cstrong\u003eInclusion Criteria\u003c/strong\u003e: (1) Alignment with the Sepsis 3.0 Definition and Diagnostic Criteria as delineated by the American Society of Critical Care Medicine and the European Society of Critical Care Medicine in 2016. This entails a diagnosis of Sepsis 3.0, characterized by an infection coupled with a Sequential Organ Failure Assessment (SOFA) score of 2 or higher. (2)Admission to the Intensive Care Unit (ICU) for treatment. For patients with multiple hospitalizations, the study only considered data from their initial ICU admission during the first hospitalization period. (3) Diagnosis of septic encephalopathy, defined by either a Glasgow Coma Scale (GCS) score of 14 or lower or a positive delirium assessment on the first day of ICU admission.\u003cbr /\u003e\u003cstrong\u003eExclusion Criteria\u003c/strong\u003e: (1) Patients aged below 18 years. (2) Patients with an ICU stay of less than 24 hours. (3) Patients presenting with primary brain injuries, which include traumatic brain injury, ischemic stroke, hemorrhagic stroke, epilepsy, intracranial infection, psychiatric disorders, or dementia. (4) Individuals with a history of chronic alcohol or drug abuse. (5) Absence of triglyceride or glucose laboratory results from the first day of ICU admission. (6) Patients not diagnosed with septic encephalopathy.\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 Study Settings\u003c/h2\u003e\nIn this study, patients with septic encephalopathy were divided into three groups based on the values of the triglyceride-to-glucose index (TyG index).The TyG index was calculated using the following formula: TyG\u0026thinsp;=\u0026thinsp;Ln[triglyceride (mg/dL) \u0026times; glucose (mg/dL) / 2]. This index is used to assess insulin resistance in patients and is a commonly used biomarker in sepsis research in recent years.\u003cbr /\u003eThe grouping was based on the following:\u003cbr /\u003e\n\u003cp\u003eGroup 1: Patients with a TyG index of 8.81 or below.\u003c/p\u003e\n\u003cp\u003eGroup 2: Patients whose TyG index ranged between 8.81 and 9.43, denoted as 8.81\u0026thinsp;\u0026lt;\u0026thinsp;TyG\u0026thinsp;\u0026lt;\u0026thinsp;9.43.\u003c/p\u003e\n\u003cp\u003eGroup 3: Patients with a TyG index of 9.43 or above.\u003c/p\u003e\n\u003cp\u003eWe assessed the following clinical outcomes:\u003c/p\u003e\n\u003cp\u003ePrimary Outcome: Patient survival within the first 90 days following admission to the Intensive Care Unit (ICU).\u003c/p\u003e\n\u003cp\u003eSecondary Outcomes: (1) The total duration of the hospital stay. (2)The length of stay in the ICU. (3) Utilization of vasoactive medications, including epinephrine, norepinephrine, dopamine, and vasopressin.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4 Data Collection\u003c/h2\u003e\nIn this research, data extraction from the Medical Information Mart for Intensive Care (MIMIC-IV) database was executed using Structured Query Language (SQL) via PostgreSQL. Our focus was directed towards comprehensively gathering key data areas, as outlined below:\u003cbr /\u003e\n\u003cp\u003eDemographic Information: This includes critical details such as the age and gender of the patients. Comorbidities:The presence of significant comorbidities was assessed, encompassing conditions like myocardial infarction, congestive heart failure, peripheral vascular disease, chronic lung disease, diabetes, chronic liver disease, and chronic kidney disease. Disease Severity Score at ICU Admission: We examined various scores indicative of disease severity on the first day of ICU admission. These included the Acute Physiology Score (APSIII), the Logistic Organ Dysfunction Score (LODS), the Oxford Acute Severity of Illness Score (OASIS), the Sequential Organ Failure Assessment (SOFA), and the Charlson Comorbidity Index (CCI). Vital Signs on ICU Admission Day: Vital signs recorded on the first day of ICU admission included heart rate (HR), respiratory rate (RR), mean arterial pressure (MAP), body temperature, and finger pulse oxygen saturation (SpO2). Laboratory Findings on ICU Admission Day: An array of laboratory parameters was collected, including hemoglobin, platelets, white blood cells, albumin, anion gap, bicarbonate, blood urea nitrogen, calcium, sodium, potassium, international normalized ratios (INR), prothrombin time (PT), partial thromboplastin time (PTT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), blood oxygen saturation (SpO2), blood glucose, triglycerides, and lactate levels.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\nIn this study, our initial step involved utilizing the Shapiro-Wilk test to evaluate the distribution of continuous variables. Based on the test outcomes, all continuous variables were determined to be non-normally distributed. Consequently, these variables were presented as medians and interquartile ranges (IQR). For the comparison between two groups, we employed the Mann-Whitney U test or the Wilcoxon rank sum test, depending on the data characteristics. Regarding categorical variables, these were expressed in terms of counts and percentages. The analysis of these variables was conducted using either the chi-square test (\u0026chi;2 test) or the Fisher exact test, selected based on data suitability. Additionally, we employed Pearson's correlation coefficient to measure the strength of association between the Triglyceride and Glucose index (TyG) and each scoring system. The relationships between these variables were visually depicted through heat maps for enhanced clarity.\u003cbr /\u003eFor the analysis of factors influencing prognostic primary outcomes, survival analysis was conducted. This involved the use of the \"surv_cutpoint\" function in the R programming language to ascertain the optimal cut-off point, followed by the construction of Kaplan-Meier survival curves. The log-rank test was then applied to assess survival disparities between groups. Further analytical depth was added through both univariate and multivariate Cox regression analyses, with the findings presented via forest plots. To examine the correlations between TyG and secondary prognostic outcomes, Spearman rank correlation coefficients were utilized, and these relationships were visually represented through violin plots. All statistical analyses were performed using R sversion 4.3.2, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Baseline characteristics\u003c/h2\u003e\n\u003cp\u003eFollowing the delineated screening criteria, our study encompassed a cohort of 1578 patients with SAE. The recruitment process is detailed in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age of these participants was 65.96 years, with an interquartile range of 54.61 to 76.77 years, and a majority of 60.1% being male. The cohort was stratified based on the TyG index tertiles, forming three distinct groups: those with TyG\u0026thinsp;\u0026le;\u0026thinsp;8.81 (comprising 521 patients), 8.81\u0026thinsp;\u0026lt;\u0026thinsp;TyG\u0026thinsp;\u0026lt;\u0026thinsp;9.43 (536 patients), and TyG\u0026thinsp;\u0026ge;\u0026thinsp;9.43 (521 patients).\u003c/p\u003e\n\u003cp\u003eIn terms of hospitalization metrics, the median length of stay in the Intensive Care Unit (ICU) was recorded at 6.04 days (Interquartile Range, IQR: 3.38\u0026ndash;11.88), while the total hospital stay averaged 14.88 days (IQR: 8.78\u0026ndash;24.56). Notably, patients manifesting higher TyG index scores were observed to have elevated scores in multiple clinical assessments, including the Acute Physiology Score III (APSIII), Logistic Organ Dysfunction Score (LODS), Oxford Acute Severity of Illness Score (OASIS), Simplified Acute Physiology Score II (SAPSII), and Charlson Comorbidity Index (CCI), in comparison to their counterparts with lower TyG index scores. Furthermore, this subgroup with elevated TyG indexes exhibited significantly higher heart and respiratory rates, alongside increased body temperatures. Laboratory analyses revealed that these patients also presented with heightened levels of white blood cell count (WBC), blood urea nitrogen (BUN), creatinine (CRE), anion gap (AG), neutrophil count, and lactate dehydrogenase (LDH). Additionally, a correlation was observed between higher TyG levels and prolonged durations of stay in both the ICU and the hospital overall. Furthermore, the usage of pharmacological interventions such as epinephrine, norepinephrine, and vasopressin was more prevalent among patients with higher TyG indexes.(Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Correlation Between TyG Index and Disease Severity Scores\u003c/h2\u003e\n\u003cp\u003eIn our investigation, the TyG index exhibited a significant positive correlation with various established severity scores, specifically the Acute Physiology Score III (APSIII), Logistic Organ Dysfunction Score (LODS), Oxford Acute Severity of Illness Score (OASIS), and Simplified Acute Physiology Score II (SAPSII), as evidenced by P-values less than 0.05. This finding implies that the TyG index could serve as a viable marker for reflecting the severity of disease in patients with SAE. Notably, the correlations between APSIII, LODS, and SAPSII were particularly pronounced, suggesting a high degree of congruence among these scoring systems in the assessment of disease severity.(Figure\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eIn contrast, the Sequential Organ Failure Assessment (SOFA) score demonstrated a relatively weaker correlation with the other severity scores, particularly with the OASIS, as indicated by a Pearson correlation coefficient of 0.20297439. This discrepancy implies that the SOFA score might be quantifying somewhat different dimensions of disease severity compared to the other indices. This distinction underscores the complexity and multifaceted nature of severity assessment in SAE patients, highlighting the necessity of a multifactorial approach in evaluating patient conditions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Correlation Between TyG and Prognostic Primary Outcomes in SAE Patients\u003c/h2\u003e\n\u003cp\u003eIn this segment of our study, the TyG index was categorized into two groups based on an optimal cut-off value of 8.7639: \"low TyG\" and \"high TyG.\" Analysis of Kaplan-Meier survival curves (as depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed a temporal divergence in survival probabilities between these groups. Notably, the \"low TyG\" group exhibited a significantly higher survival probability compared to the \"high TyG\" group, as substantiated by a p-value of 0.0027.(Figure\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e elucidates that the univariate analysis has revealed an extensive array of both clinical and laboratory determinants significantly influencing the 90-day survival rate of patients with septic encephalopathy. These determinants span metabolic indicators such as the TyG index and age, a variety of scores assessing critical illness, blood constituents like hematocrit and hemoglobin, electrolyte levels, and vital physiological metrics including heart rate, mean arterial pressure, respiratory rate, and body temperature. Subsequent multivariate Cox regression analysis has pinpointed a select group of these factors as independent prognostic indicators, encompassing metabolic rates, specific illness severity scores, coexisting conditions such as congestive heart failure and chronic liver disease, alongside particular hematological and physiological parameters. The univariate regression analysis demonstrates that the TyG index is a significant predictor of mortality, with a hazard ratio (HR) of 1.344 and a p-value of 0.00275, underscoring a strong statistical significance. Upon adjustment for confounding factors in the multivariate analysis, the TyG index retains its prognostic value (p\u0026thinsp;=\u0026thinsp;0.036114), with an HR of 1.2517. This outcome suggests that individuals with higher TyG levels have a 25% greater risk of mortality compared to those with lower levels, even when other variables are taken into account.(Talbe2)\u003c/p\u003e\n\u003cp\u003eAge also emerged as a significant predictor in the multivariate analysis. Each additional year of age was associated with a 1.6% increase in the risk of death (HR\u0026thinsp;=\u0026thinsp;1.015743, p\u0026thinsp;=\u0026thinsp;0.001619), a finding that remained robust even after controlling for other factors. Notably, the significance of certain variables, such as APSIII, SOFA, and CCI, diminished in the multivariate analysis. This attenuation could be attributed to their covariance with other variables or the adjustment effects of the latter.\u003c/p\u003e\n\u003cp\u003eThese findings underscore that both TyG index and age are critical determinants influencing the 90-day overall survival of SAE patients. TyG index, in particular, exerts a significant impact on survival outcomes, whether assessed independently or in conjunction with other factors. The multifactorial Cox regression analyses, as illustrated in the forest plot, affirm that TyG index is an independent prognostic factor in SAE, with high TyG index levels being indicative of a poorer prognosis. Furthermore, other variables such as the SOFA score, OASIS score, CCI score, APSII score, along with higher age, heart rate (HR), respiratory rate (RR), prothrombin time (PTT), and neutrophil counts, were identified as additional risk factors for an adverse prognosis.(Figure\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4 Correlation of TyG index with Prognostic Secondary Outcomes in SAE Patients\u003c/h2\u003e\n\u003cp\u003eIn this aspect of our study, we employed the Spearman rank correlation coefficient to assess the relationship of the TyG index with secondary prognostic outcomes in SAE patients. Our analysis revealed that the TyG index had a moderately positive correlation with the number of days spent in the Intensive Care Unit (ICU) (denoted as los_icu). This correlation emerged as statistically highly significant, with a P-value less than 0.01. In a similar vein, the correlation between the TyG index and the total number of days spent in the hospital, as well as the administration of norepinephrine and vasopressin, also demonstrated statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Conversely, the association of the TyG index with the use of dopamine and epinephrine was found to be statistically insignificant and notably weak. (Figure\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn the present study, a comprehensive analysis was conducted on a cohort of 1578 patients diagnosed with SAE. Among these patients, the median age was 65.96 years, with males being predominantly represented. This demographic profile aligns with the findings of Chen et al., where SAE patients had a median age of 67 years, with 57% being male\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. These consistent findings suggest that SAE predominantly affects an older demographic, with a slight male predominance. This observation may be indicative of the elevated susceptibility of the elderly to sepsis, and possibly, a higher susceptibility of men to serious infections\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. The current study affirms the TyG index as a significant predictor of disease severity and prognosis in SAE patients. Specifically, individuals in the high TyG index group exhibited elevated scores on the Clinical Criticality Score and more pronounced abnormalities in physiological parameters, including increased heart rate, respiratory rate, and body temperature. Moreover, the high TyG index group demonstrated a significantly heightened risk of mortality compared to the low TyG index group. These findings align with existing research; one study, for instance, established a relationship between SAE prognosis and sepsis severity, age, respiratory rate, body temperature, and heart rate\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Another study revealed that SAE patients exhibited higher heart rate, blood lactate, and serum sodium levels, alongside lower platelet counts, serum albumin levels, and serum pH compared to non-SAE patients\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Previous investigations have identified traditional severity scores, such as the Serial Organ Failure Assessment (SOFA) and the Acute Physiology and Chronic Health Evaluation (APACHE II), as important prognostic factors for SAE\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. In contrast to these studies, our findings suggest a relatively weak correlation between the TyG index and SOFA score. This disparity may stem from the diverse clinical dimensions assessed by the SOFA score\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, hinting at a more intricate interplay between various factors influencing SAE severity.\u003c/p\u003e\n\u003cp\u003eSAE, characterized by diffuse cerebral dysfunction resulting from a systemic inflammatory response, is primarily diagnosed based on the manifestation of impaired consciousness or delirium. Currently, this diagnosis is primarily one of exclusion. Numerous screening tools are available to identify delirium, yet none are specifically tailored to SAE. In a multicenter study, the CAM-ICU exhibited a sensitivity of 47%, specificity of 98%, and positive and negative predictive values of 95% and 72%, respectively\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Consequently, the most suitable delirium screening tool for ICU settings remains a matter of debate. The ICDSC, with a higher sensitivity (99%) but lower specificity (64%) for delirium assessment compared to the CAM-ICU, presents an alternative option\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. The application of coma scales in SAE remains unexplored, although the GCS score proves useful in predicting the course of SAE\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Neuroimaging findings in SAE patients exhibit variability, with acute abnormalities observed on MRI in a subset of cases, such as multiple ischemic strokes or hemianopic central white matter lesions\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Notably, some patients exhibit normal brain MRI scans despite the presence of SAE.\u003c/p\u003e\n\u003cp\u003eSAE currently lacks specific biomarkers; hence, the identification of early warning and diagnostic indicators for SAE in critically ill septic patients holds significant importance for timely intervention and treatment\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The underlying mechanisms through which the TyG index predicts SAE disease severity and the risk of death remain unclear. This study endeavors to explore this association from a pathophysiological perspective. The TyG index, serving as an indicator of metabolic disorders, is likely intricately linked to the onset of sepsis and SAE. Insulin resistance, a key component of metabolic disorders, not only affects systemic metabolism but may also impact hemodynamics and brain metabolism\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Insulin resistance could contribute to blood-brain barrier dysfunction, increasing the brain tissue's susceptibility to inflammatory responses and oxidative stress\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. These factors may exacerbate the symptoms and severity of SAE. Moreover, elevated levels of blood glucose and triglycerides may directly harm nerve cells, triggering the excessive release of inflammatory factors and cytokines\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. This, in turn, exacerbates the pathological progression of SAE. Such pathological changes may culminate in nerve cell death, further worsening neurological dysfunction.\u003c/p\u003e\n\u003cp\u003eOur study has several limitations that warrant consideration. Retrospective analyses based on a single database may introduce selection bias, thereby limiting the generalizability of our findings. Furthermore, while the TyG index proves valuable, it should be viewed as one component in the comprehensive assessment of patients with SAE, considering other relevant clinical parameters. Given the multitude of diseases that can lead to TyG alterations, its utility lies in identifying SAE, yet its specificity and sensitivity may be insufficient to distinguish SAE from other forms of encephalopathies. Nonetheless, owing to its ease of accessibility and lack of involvement of subjective factors, the TyG index holds importance in the early identification of SAE and monitoring treatment effects throughout the course of care.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study underscores the significance of the TyG index as a vital independent predictor of disease severity and prognosis in individuals with SAE. The observed association with a poorer clinical prognosis and an elevated risk of mortality suggests its potential applicability in clinical practice. Future investigations should seek to validate these findings through prospective cohorts and delve into the underlying pathophysiological mechanisms of these associations. Such endeavors will contribute to advancing our comprehension of SAE, potentially paving the way for more effective management strategies for this challenging disease.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study revealed a notable correlation between the TyG index and disease severity, length of hospital stay, and the risk of death in individuals with SAE. Patients exhibiting a high TyG index demonstrated elevated scores across various clinical assessment metrics and were linked to prolonged stays in both the intensive care unit and overall hospitalization. Furthermore, a heightened TyG index was associated with an increased risk of mortality, a correlation that remained significant even after adjusting for other variables. Consequently, the TyG index emerges as a potential valid marker for gauging the severity of SAE and predicting patient prognosis. Nevertheless, given the constraints inherent in the study design, these findings warrant further validation in broader populations and multicenter studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MIMIC IV database (version 2.2) is publicly available at https://mimic-iv.mit.edu/. Any researcher who adheres to the data use requirements can access these databases (Certification number 38652558).\u003c/p\u003e\n\u003ch3\u003eCode availability (software application or custom code)\u003c/h3\u003e\n\u003cp\u003eThe codes are available at https://github.com/MIT-LCP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, all procedures involving human participants were meticulously conducted in compliance with the ethical standards set forth by both the Institutional and National Research Committees. Furthermore, these procedures adhered to the principles of the 1964 Helsinki Declaration, along with its subsequent amendments, or other equivalent ethical guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MIMIC-IV database, financially supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) at the National Institutes of Health (NIH) under grant numbers R01-EB001659 (2003\u0026ndash;2013) and R01-EB017205 (2014-2018), has been duly authorized by the Institutional Review Boards of the Beth Israel Deaconess Medical Center, Boston, Massachusetts, and the Massachusetts Institute of Technology, Cambridge, Massachusetts. The database has successfully met the Safe Harbor standards set by Privacert (Cambridge, Massachusetts), evidenced by its HIPAA certification number 1031219-2, ensuring a minimized risk of re-identification. Given that the data, accessible in the MIMIC-IV database, does not influence clinical care and maintains patient confidentiality through comprehensive anonymization, this research has been granted an exemption from the customary requirements for ethical approval and informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eAuthor contributions\u003c/h3\u003e\n\u003cp\u003eShi XP: Played a significant role in the conception and design of the study. Led the data collection process and contributed to the initial draft of the manuscript.\u003c/p\u003e\n\u003cp\u003eXu LJ: Focused on the analysis and interpretation of data. Worked extensively on the statistical aspects of the study, ensuring accurate data analysis.\u003c/p\u003e\n\u003cp\u003eRen J: Provided expertise in Sepsis-associated encephalopathy, significantly contributing to the theoretical framework of the research. Assisted in the literature review and manuscript editing.\u003c/p\u003e\n\u003cp\u003eJing LJ: Actively involved in drafting the manuscript, particularly in refining the intellectual content. Collaborated closely with other authors in interpreting the study\u0026apos;s findings.\u003c/p\u003e\n\u003cp\u003eWei KF: Contributed to the study design and methodology. Assisted in data acquisition and played a key role in the critical revision of the manuscript for important intellectual content.\u003c/p\u003e\n\u003cp\u003eQin LJ: Oversaw the overall research project and coordinated the efforts of the research team.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eAcknowledgments\u003c/h3\u003e\n\u003cp\u003eThe authors extend their sincere appreciation to the Massachusetts Institute of Technology and the Beth Israel Deaconess Medical Center for their substantial contributions to the MIMIC project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAngus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med. 2001;29(7):1303\u0026ndash;1310. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/00003246-200107000-00002\u003c/span\u003e\u003cspan address=\"10.1097/00003246-200107000-00002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu YC, Yao Y, Yu MM, et al. Frequency and mortality of sepsis and septic shock in China: a systematic review and meta-analysis. BMC Infect Dis. 2022;22(1):564. Published 2022 Jun 21. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12879-022-07543-8\u003c/span\u003e\u003cspan address=\"10.1186/s12879-022-07543-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie J, Wang H, Kang Y, et al. The Epidemiology of Sepsis in Chinese ICUs: A National Cross-Sectional Survey. Crit Care Med. 2020;48(3):e209-e218. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/CCM.0000000000004155\u003c/span\u003e\u003cspan address=\"10.1097/CCM.0000000000004155\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen C, Yao RQ, Zhang H, Feng YW, Yao YM. Sepsis-associated encephalopathy: a vicious cycle of immunosuppression. J Neuroinflammation. 2020;17(1):14. Published 2020 Jan 10. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12974-020-1701-3\u003c/span\u003e\u003cspan address=\"10.1186/s12974-020-1701-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGofton TE, Young GB. Sepsis-associated encephalopathy. Nat Rev Neurol. 2012;8(10):557\u0026ndash;566. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrneurol.2012.183\u003c/span\u003e\u003cspan address=\"10.1038/nrneurol.2012.183\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSonneville R, de Montmollin E, Poujade J, et al. Potentially modifiable factors contributing to sepsis-associated encephalopathy. Intensive Care Med. 2017;43(8):1075\u0026ndash;1084. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00134-017-4807-z\u003c/span\u003e\u003cspan address=\"10.1007/s00134-017-4807-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelbing DL, B\u0026ouml;hm L, Witte OW. Sepsis-associated encephalopathy. CMAJ. 2018;190(36):E1083. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1503/cmaj.180454\u003c/span\u003e\u003cspan address=\"10.1503/cmaj.180454\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGusmao-Flores D, Salluh JI, Chalhub R\u0026Aacute;, Quarantini LC. The confusion assessment method for the intensive care unit (CAM-ICU) and intensive care delirium screening checklist (ICDSC) for the diagnosis of delirium: a systematic review and meta-analysis of clinical studies. Crit Care. 2012;16(4):R115. Published 2012 Jul 3. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/cc11407\u003c/span\u003e\u003cspan address=\"10.1186/cc11407\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSonneville R, Benghanem S, Jeantin L, et al. The spectrum of sepsis-associated encephalopathy: a clinical perspective. Crit Care. 2023;27(1):386. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13054-023-04655-8\u003c/span\u003e\u003cspan address=\"10.1186/s13054-023-04655-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao LC, Xu JN, Wang TT, Hua F, Li JJ. Triglyceride-glucose index as a marker in cardiovascular diseases: landscape and limitations. Cardiovasc Diabetol. 2022;21(1):68. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12933-022-01511-x\u003c/span\u003e\u003cspan address=\"10.1186/s12933-022-01511-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng R, Qian S, Shi Y, Lou C, Xu H, Pan J. Association between triglyceride-glucose index and in-hospital mortality in critically ill patients with sepsis: analysis of the MIMIC-IV database. Cardiovasc Diabetol. 2023;22(1):307. Published 2023 Nov 8. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12933-023-02041-w\u003c/span\u003e\u003cspan address=\"10.1186/s12933-023-02041-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang X, Cheng H, Yuan S, et al. Triglyceride-glucose index as a valuable predictor for aged 65-years and above in critical delirium patients: evidence from a multi-center study. BMC Geriatr. 2023;23(1):701. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12877-023-04420-0\u003c/span\u003e\u003cspan address=\"10.1186/s12877-023-04420-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L. A., \u0026amp; Mark, R. (2023). MIMIC-IV (version 2.2). PhysioNet.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594. Published 2015 Jan 7. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.g7594\u003c/span\u003e\u003cspan address=\"10.1136/bmj.g7594\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Shi X, Diao M, et al. A retrospective study of sepsis-associated encephalopathy: epidemiology, clinical features and adverse outcomes. BMC Emerg Med. 2020;20(1):77. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12873-020-00374-3\u003c/span\u003e\u003cspan address=\"10.1186/s12873-020-00374-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Vught LA, Klein Klouwenberg PM, Spitoni C, et al. Incidence, Risk Factors, and Attributable Mortality of Secondary Infections in the Intensive Care Unit After Admission for Sepsis. JAMA. 2016;315(14):1469\u0026ndash;1479. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2016.2691\u003c/span\u003e\u003cspan address=\"10.1001/jama.2016.2691\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng L, Peng C, Yang F, et al. Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated encephalopathy. BMC Med Res Methodol. 2022;22(1):183. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12874-022-01664-z\u003c/span\u003e\u003cspan address=\"10.1186/s12874-022-01664-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang LN, Wang XT, Ai YH, et al. Epidemiological features and risk factors of sepsis-associated encephalopathy in intensive care unit patients: 2008\u0026ndash;2011. Chin Med J (Engl). 2012;125(5):828\u0026ndash;831.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Guo Z, Chai Y, et al. Application Prospect of the SOFA Score and Related Modification Research Progress in Sepsis. J Clin Med. 2023;12(10):3493. Published 2023 May 16. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm12103493\u003c/span\u003e\u003cspan address=\"10.3390/jcm12103493\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Eijk MM, van den Boogaard M, van Marum RJ, et al. Routine use of the confusion assessment method for the intensive care unit: a multicenter study. Am J Respir Crit Care Med. 2011;184(3):340\u0026ndash;344. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/rccm.201101-0065OC\u003c/span\u003e\u003cspan address=\"10.1164/rccm.201101-0065OC\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergeron N, Dubois MJ, Dumont M, Dial S, Skrobik Y. Intensive Care Delirium Screening Checklist: evaluation of a new screening tool. Intensive Care Med. 2001;27(5):859\u0026ndash;864. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s001340100909\u003c/span\u003e\u003cspan address=\"10.1007/s001340100909\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEidelman LA, Putterman D, Putterman C, Sprung CL. The spectrum of septic encephalopathy. Definitions, etiologies, and mortalities. JAMA. 1996;275(6):470\u0026ndash;473.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharshar T, Carlier R, Bernard F, et al. Brain lesions in septic shock: a magnetic resonance imaging study. Intensive Care Med. 2007;33(5):798\u0026ndash;806. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00134-007-0598-y\u003c/span\u003e\u003cspan address=\"10.1007/s00134-007-0598-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCatarina AV, Branchini G, Bettoni L, De Oliveira JR, Nunes FB. Sepsis-Associated Encephalopathy: from Pathophysiology to Progress in Experimental Studies. Mol Neurobiol. 2021;58(6):2770\u0026ndash;2779. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12035-021-02303-2\u003c/span\u003e\u003cspan address=\"10.1007/s12035-021-02303-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuhammad IF, Bao X, Nilsson PM, Zaigham S. Triglyceride-glucose (TyG) index is a predictor of arterial stiffness, incidence of diabetes, cardiovascular disease, and all-cause and cardiovascular mortality: A longitudinal two-cohort analysis. Front Cardiovasc Med. 2023;9:1035105. Published 2023 Jan 4. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcvm.2022.1035105\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2022.1035105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe La Monte SM. Metabolic derangements mediate cognitive impairment and Alzheimer's disease: role of peripheral insulin-resistance diseases. Panminerva Med. 2012;54(3):171\u0026ndash;178.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMendes NF, Velloso LA. Perivascular macrophages in high-fat diet-induced hypothalamic inflammation. J Neuroinflammation. 2022;19(1):136. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12974-022-02519-6\u003c/span\u003e\u003cspan address=\"10.1186/s12974-022-02519-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTable1: Baseline characteristics of the included patients. APSIII:Acute Physiology Score III; LODS:Logistic Organ Dysfunction System; OASIS:Oxford Acute Severity of Illness Score; \u0026nbsp; SAPSII:Sequential Organ Failure Assessment (SOFA) Score; SOFA:Sequential Organ Failure Assessment; CCI：Charlson Comorbidity Index; AMI:Acute Myocardial Infarction; CHF:Congestive Heart Failure; PVD:Peripheral Vascular Disease; COPD:Chronic Obstructive Pulmonary Diseases; CLD：Chronic Liver Disease; DM:Diabetes Mellitus; CKD：Chronic Kidney Disease; \u0026nbsp;HR:Heart Rate; RR:Respiratory Rate; MAP:Mean Arterial Pressure; BUN:Blood Urea Nitrogen; WBC:White Blood Cell count; INR:International Normalized Ratio; PT:Prothrombin Time; PTT:Partial Thromboplastin Time;ALT:Alanine Aminotransferase; AST:Aspartate Aminotransferase; TBIL:Total Bilirubin; NLR: Neutrophil to Lymphocyte Ratio; TyG:Triglyceride-Glucose Index.\u003c/p\u003e\u003cimg 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\"\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTable2: Univariate and multifactorial analyses of 90-day survival in patients with septic encephalopathy.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\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":"Sepsis-associated Encephalopathy, Triglyceride-glucose Index, Disease Severity, Prognosis, Clinical Outcomes","lastPublishedDoi":"10.21203/rs.3.rs-3865210/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3865210/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: S\u003c/strong\u003eepsis-associated encephalopathy (SAE) is a complex condition with variable outcomes. This study investigates the potential of the Triglyceride-glucose (TyG) index as a marker for disease severity and prognosis in SAE patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eOur cohort comprised 1578 SAE patients from the MIMIC-IV database, stratified based on TyG index tertiles. We analyzed baseline characteristics, disease severity, and prognostic outcomes. The Kaplan-Meier method and Cox regression analyses were employed for survival analysis, while Spearman rank correlation and various statistical tests were used to assess correlations between TyG index and clinical outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The study population's median age was 65.96 years, predominantly male (60.1%). Higher TyG index scores correlated with elevated clinical severity scores (APSIII, LODS, OASIS, SAPSII, and CCI) and increased ICU and hospital stay durations. TyG index categorization revealed significant differences in 90-day survival probabilities, with \"high TyG\" associated with a 25% increased mortality risk compared to \"low TyG\". Furthermore, TyG index showed a moderate positive correlation with ICU stay duration and use of norepinephrine and vasopressin, but not with dopamine and epinephrine use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe TyG index is a significant independent predictor of disease severity and prognosis in SAE patients. High TyG levels correlate with worse clinical outcomes and increased mortality risk, suggesting its potential as a valuable tool in managing SAE.\u003c/p\u003e","manuscriptTitle":"Exploring Triglyceride-Glucose Index's Role in Sepsis-Associated Encephalopathy: A Comprehensive Study of Its Impact on Disease Severity and Prognostic Accuracy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-29 02:40:32","doi":"10.21203/rs.3.rs-3865210/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":"db1b2642-8b77-443f-a04c-ea749eb94519","owner":[],"postedDate":"January 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28406975,"name":"Health sciences/Medical research/Biomarkers"},{"id":28406976,"name":"Health sciences/Diseases/Neurological disorders/Disorders of consciousness"},{"id":28406977,"name":"Health sciences/Diseases/Infectious diseases/Bacterial infection"}],"tags":[],"updatedAt":"2024-06-25T17:56:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-29 02:40:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3865210","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3865210","identity":"rs-3865210","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}