Project description:BackgroundSepsis is a serious consequence of acute pancreatitis (AP) that requires immediate detection and treatment. Triglyceride-glucose (TyG) index demonstrated predictive ability for a number of diseases. In an effort to enhance clinical care and early warning systems, this study examined the association between the TyG index and sepsis risk with the aim of improving clinical care and early warning systems.MethodsPatients who were first admitted and satisfied the diagnostic criteria for acute pancreatitis (ICD-9: 5770; ICD-10: K85) were chosen from the MIMIC-IV database, excluding those lacking essential demographic or laboratory data. Using the Sepsis-3.0 criteria. Depending on whether they had sepsis or not, patients were divided into sepsis group and non-sepsis group. Utilizing the formula ln[(triglycerides mg/dl) × (glucose mg/dl)/2], the TyG index was calculated. The Boruta algorithm and Xgboost model were used for feature selection in order to pinpoint the important variables affecting results. Logistic regression with univariate and multivariate factors were used to assess the association between the TyG index and the start of sepsis after admission.ResultsTwenty-eight thousand AP patients were screened in all, among which 661 patients were ultimately included in the study. Of these, 228 patients (34.5%) developed sepsis. The TyG index was shown to have a significant correlation (OR = 1.891, 95% CI: 1.408-2.555) with sepsis, and an increased risk of sepsis was observed with an increase in the TyG index (all P values for trend < 0.001). Subgroup analysis showed that among patients of various ages, sexes, and with hypertension and diabetes, there was a positive association between the TyG index and the probability of sepsis (all P values for trend < 0.05). The combination of the TyG index with clinical indicators had an area under the curve (AUC) of 0.828 (0.794-0.862), which was significantly greater than that of the TyG index alone (0.657 [0.613-0.701]), with a statistically significant difference (Z= -7.362, P < 0.001).ConclusionIn patients who have AP, the TyG index is substantially linked to a higher risk of sepsis, and when combined with clinical markers, its predictive power for sepsis is enhanced. The findings imply that the TyG index might be a helpful detection for determining which AP patients are at a higher risk of developing sepsis.

Project description:Triglyceride-glucose (TyG) index has emerged as a novel biomarker for detecting insulin resistance (IR) and has been proven to be associated with various diseases. However, its correlation with the prognosis of severe sepsis remains unraveled. This retrospective cohort study utilized patient records from the Medical Information Mart for Intensive Care (MIMIC-IV, version 2.2) to examine the outcomes of patients with sepsis. The primary outcomes were hospital mortality and intensive care unit (ICU) mortality. The correlation between the TyG index and outcomes was evaluated through the Kaplan-Meier method, the Log-rank test, and univariate and multivariate Cox regression analyses. Additionally, restricted cubic spline (RCS) regression analysis was employed to delve into the nonlinear relationship between baseline TyG index and outcomes, with trend significance assessed through quartile levels. Subgroup analyses were conducted to evaluate the consistency of the TyG index's prognostic value across various influencing factors. The study included 1,742 patients with sepsis requiring intensive care. The in-hospital mortality rate was 19.75% (344/1,742), and the ICU mortality rate was 14.75% (257/1,742). Cox regression analysis revealed that, in comparison to the first quartile (Q1), patients in the fourth quartile (Q4) had a 63% higher risk of in-hospital mortality (HR 1.63 [95% CI 1.22 to 2.18], P < 0.01) and a 79% higher risk of ICU mortality (HR 1.79 [95% CI 1.28 to 2.51], P < 0.001). Model 3 showed that ICU mortality risks for Q4, Q3, and Q2 were 240%, 75%, and 33% higher, respectively (HR 3.40 [95% CI 2.24 to 5.16], P < 0.001; HR 1.75 [95% CI 1.16 to 2.63], P = 0.007; HR 1.33 [95% CI 1.20 to 1.53], P < 0.001). RCS regression analysis identified a nonlinear association between the TyG index and mortality (overall P < 0.001; P for nonlinearity < 0.001, with an inflection point at 8.9). Subgroup analysis showed that the effect size and direction were consistent across different subgroups, suggesting the stability of the results. This study demonstrates that a higher TyG index is significantly associated with increased in-hospital and ICU mortality risk in critically ill sepsis patients, with evidence of non-linear correlation. Therefore, the TyG index helps identify the mortality prognosis of sepsis patients in the ICU.

Project description:BackgroundThis study aimed to explore the association between the triglyceride-glucose (TyG) index and the risk of in-hospital mortality in critically ill patients with sepsis.MethodsThis was a retrospective observational cohort study and data were obtained from the Medical Information Mart for Intensive Care-IV (MIMIC IV2.2) database. The participants were grouped into three groups according to the TyG index tertiles. The primary outcome was in-hospital all-cause mortality. Multivariable logistics proportional regression analysis and restricted cubic spline regression was used to evaluate the association between the TyG index and in-hospital mortality in patients with sepsis. In sensitivity analysis, the feature importance of the TyG index was initially determined using machine learning algorithms and subgroup analysis based on different subgroups was also performed.Results1,257 patients (56.88% men) were included in the study. The in-hospital, 28-day and intensive care unit (ICU) mortality were 21.40%, 26.17%, and 15.43% respectively. Multivariate logistics regression analysis showed that the TyG index was independently associated with an elevated risk of in-hospital mortality (OR 1.440 [95% CI 1.106-1.875]; P = 0.00673), 28-day mortality (OR 1.391; [95% CI 1.52-1.678]; P = 0.01414) and ICU mortality (OR 1.597; [95% CI 1.188-2.147]; P = 0.00266). The restricted cubic spline regression model revealed that the risks of in-hospital, 28-day, and ICU mortality increased linearly with increasing TyG index. Sensitivity analysis indicate that the effect size and direction in different subgroups are consistent, the results is stability. Additionally, the machine learning results suggest that TyG index is an important feature for the outcomes of sepsis.ConclusionOur study indicates that a high TyG index is associated with an increased in-hospital mortality in critically ill sepsis patients. Larger prospective studies are required to confirm these findings.

Project description:BackgroundThe relationship of the first-trimester triglyceride-glucose (TyG) index with GDM (gestational diabetes mellitus) and other adverse pregnancy outcomes has yet to be fully understood. This study aims to investigate the relationship between the first-trimester TyG index and the risk of adverse pregnancy outcomes in pregnant women.MethodsThe data for the retrospective cohort study were derived from the Maternal and Child Health Hospital of Longgang District, Shenzhen, China. To calculate the TyG index, health indicators were measured in the early pregnancy period (<14 gestational weeks), including triglycerides and fasting blood glucose levels in pregnant women. Multivariable regression analysis and subgroup analysis were used to ascertain the independent association between the TyG index and the possibility of adverse pregnancy outcomes. Interaction analysis was performed to assess the potential heterogeneity of associations among subgroups. Nonlinear associations and the predictive value of the TyG index were explored using restricted cubic splines and receiver operating characteristic (ROC) curves. The discrimination and accuracy of the fully adjusted model were evaluated using calibration curves, Brier scores, and decision curve analysis (DCA). Mediation analysis was conducted to assess the impact of GDM (gestational diabetes mellitus) and PE (preeclampsia) as intermediaries on the risk of Preterm delivery.ResultsThe study included a cohort of 11,942 pregnant women, with an average TyG index of 8.36 ± 0.41. Logistic regression analysis showed that after adjusting for covariates, for each 1-unit increase in the TyG index, the risk of GDM increased by 2.21-fold, and this result was significantly different across all quartiles. Compared to the lowest quartile group, the highest TyG index group had the highest risk of PE (OR: 2.89; 95% CI 1.39 ~ 6.50), GH (gestational hypertension) (OR: 1.47; 95% CI 1.07 ~ 2.02), and Preterm delivery (OR: 1.75; 95% CI 1.21 ~ 2.56).The analysis of data stratification and interaction confirmed the validity of our study results. However, the analysis found no statistically significant association between the TyG index and low birth weight and macrosomia. GDM and PE were identified as partial mediating factors between TyG and the risk of preterm delivery, with variance contributions of 7.23% and 20.33%. The TyG index demonstrated the highest area under the curve (AUC) values in the ROC curves for GDM, PE, GH, and preterm delivery, with values of 0.61, 0.67, 0.58, and 0.56, respectively. The combination of the TyG index, maternal age, and pre-pregnancy body mass index predicted outcomes better than the TyG index alone (p < 0.01).After adjustment for confounders, the model showed good accuracy and net benefit in predicting adverse pregnancy outcomes, as supported by calibration curves, Brier scores, and decision curve analysis.ConclusionAn elevated first-trimester TyG index correlates with a heightened risk of GDM, PE, GH and Preterm delivery.The TyG index presents a promising tool for more effectively identifying populations at early risk for adverse pregnancy outcomes.

Project description:BackgroundHemorrhagic stroke (HS), including non-traumatic intracerebral hemorrhage (ICH) and subarachnoid hemorrhage (SAH), constitutes a substantial proportion of cerebrovascular incidents, accounting for around 30% of stroke cases. The triglyceride-glucose index (TyG-i) represents a precise insulin resistance (IR) indicator, a crucial metabolic disturbance. Existing literature has demonstrated an association between TyG-i and all-cause mortality (ACM) among individuals suffering from ischemic stroke (IS). Yet, the TyG-i prognostic implications for severe HS patients necessitating intensive care unit (ICU) admission are not clearly understood. Considering the notably elevated mortality and morbidity associated with HS relative to IS, investigating this association is warranted. Our primary aim was to investigate TyG-i and ACM association among critically ill HS patients within an ICU context.MethodsHerein, patients with severe HS were identified by accessing the Medical Information Mart for Intensive Care-IV (MIMIC-IV, version 2.2) database, using the International Classification of Diseases (ICD)-9/10 as diagnostic guidelines. Subsequently, we stratified the subjects into quartiles, relying on their TyG-i scores. Moreover, we measured mortality at ICU, in-hospital, 30 days, 90 days, and 1 year as the outcomes. Cox proportional hazards regression analysis and restricted cubic splines (RCS) were deployed for elucidating the relation between the TyG-i and ACM while utilizing the Kaplan-Meier (K-M) method to estimate survival curves. The findings' robustness was assessed by conducting subgroup analysis and interaction tests employing likelihood ratio tests.ResultsThe analysis included 1475 patients, with a male predominance of 54.4%. Observed mortality rates in the ICU, hospital, 30 days, 90 days, and 1 year were 7.3%, 10.9%, 13.8%, 19.7%, and 27.3%, respectively. Multivariate Cox regression analysis results manifested that heightened TyG-i was significantly related to ACM at 30 days (adjusted hazard ratio [aHR]: 1.32; 95% confidence interval [CI]: 1.05-1.67; P = 0.020), 90 days (aHR: 1.27; 95% CI: 1.04-1.55; P = 0.019), and 1 year (aHR: 1.22; 95% CI: 1.03-1.44; P = 0.023). The results of RCS analysis demonstrated a progressive elevation in ACM risk with rising TyG-i levels. Interaction tests found no significant effect modification in this relationship.ConclusionIn summary, TyG-i exhibits a significant correlation with ACM among patients enduring critical illness due to HS. This correlation underscores the probable utility of TyG-i as a prognostic tool for stratifying HS patients according to their risk of mortality. Applying TyG-i in clinical settings could enhance therapeutic decision-making and the management of disease trajectories. Additionally, this investigation augments existing research on the linkage between the TyG-i and IS, elucidating the TyG-i's role in predicting mortality across diverse stroke categories.

Project description:BackgroundThe TyG-BMI index, which is a reliable indicator of insulin resistance (IR), has been found to have a significant correlation with the occurrence of cardiovascular events. However, there still lacks study on the TyG-BMI index and prognosis in patients with atrial fibrillation (AF). The objective of the present study was to evaluate the relationship between TyG-BMI index at admission to ICU and all-cause mortality in critically ill patients with AF.MethodsThe patient's data were extracted from Medical Information Mart for Intensive Care IV(MIMIC-IV) database. All patients were divided into four groups according to TyG-BMI index. Outcomes include primary and secondary endpoints, with the primary endpoint being the 30-day and 365-day all-cause mortality and the secondary endpoint being the 90-day and 180-day all-cause mortality. TyG-BMI index was quartile and Kaplan-Meier curve was used to compare the outcome of each group. Cox proportional-hazards regression model and restricted cubic splines (RCS) were conducted to assess the relationship between TyG-BMI index and outcomes.ResultsOut of a total of 2509 participants, the average age was 73.26 ± 11.87 years, with 1555 (62.0%) being males. Patients with lower level of TyG-BMI had higher risk of 30-day, 90-day, 180-day and 365-day all-cause mortality, according to the Kaplan-Meier curves (log-rank P < 0.001). In addition, cox proportional-hazards regression analysis revealed that the risk of 30-day, 90-day, 180-day and 365-day all-cause mortality was significantly higher in the lowest quartile of TyG-BMI. Meanwhile, the RCS analysis indicated that L-typed relationships between TyG-BMI index and all-cause mortality, with inflection points at 223.60 for 30-day and 255.02 for 365-day all-cause mortality, respectively. Compared to patients with TyG-BMI levels below the inflection points, those with higher levels had a 1.8% lower risk for 30-day all-cause mortality (hazard ratio [HR] 0.982, 95% confidence interval [CI] 0.9676-0.988) and 1.1% lower risk for 365-day all-cause mortality (HR 0.989, 95% CI 0.986-0.991).ConclusionIn critically ill patients with AF, a lower TyG-BMI level is significantly associated with a higher risk of 30-day, 90-day, 180-day and 365-day all-cause mortality. TyG-BMI index could be used as a valid indicator for grading and treating patients with AF in the ICU.

Project description:BackgroundWe determined utilizing a sepsis participant cohort whether there is a significant association between TyG-BMI (triglyceride glucose body mass index) and mortality rates at any stage.MethodsHerein, a historical cohort investigation approach was adopted, using information provided by the Medical Information Mart for Intensive Care-IV (MIMIC-IV). We categorized the included individuals in accordance with their TyG-BMI data quartiles, and the primary outcomes were mortality during the hospital stay and death rate due to any reason at postadmission day 28, 90, and 365. To evaluate TyG-BMI mortality's relationship with sepsis-induced mortality risk, we employed restricted cubic spline regression (RCS) and Cox regression models. Additionally, we confirmed TyG-BMI's significant predictive value for mortality via machine learning methods. Furthermore, we performed subgroup analyses to investigate possible differences among various patient groups.ResultsThe cohort included 4759 individuals, aged 63.9 ± 15.0 years, involving 2885 males (60.6%). The rates of death that took place during hospital stay and at 28, 90 and 365 days postadmission were respectively 19.60%, 24.70%, 28.80%, and 35.20%. As reflected by Cox models, TyG-BMI was negatively associated with mortality risk at various intervals: in-hospital [hazard ratio (HR) 0.47 (0.39-0.56), P = 0.003], 28 days postadmission [HR 0.42 (0.35-0.49), P < 0.001], 90 days postadmission [HR 0.41 (0.35-0.48), P < 0.001], and 365 days postadmission [HR 0.41 (0.35-0.47), P < 0.001]. Additionally, the relationship between TyG-BMI and death rates was L-shaped, as reflected by the RCS, with a TyG-BMI of 249 being the turning point.ConclusionsAmong sepsis patients in critical care, TyG-BMI is negatively correlated with mortality possibility at various intervals: during hospital stay and 28 days, 90 days, and one year postadmission. TyG-BMI is a beneficial parameter for categorizing risk levels among sepsis patients and for predicting their mortality risk within one year.

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Triglyceride‐glucose index and clinical outcomes in sepsis: A retrospective cohort study of MIMIC‐IV

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