Background: Hemorrhagic 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. Methods: Herein, 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. Results: The 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. Conclusion: In 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.

Background: The Triglyceride Glucose-Body Mass Index (TyG-BMI) has been established as a robust indicator of insulin resistance (IR), reflecting metabolic health across various populations. In general, lower TyG-BMI values are often associated with better metabolic health outcomes and a reduced risk of adverse health events in non-critically ill populations. Previous studies have highlighted a significant negative association between TyG-BMI and all-cause mortality (ACM) among critically ill atrial fibrillation patients. Given the high prevalence and severe outcomes associated with stroke, understanding how TyG-BMI at the time of ICU admission correlates with ACM in critically ill stroke patients becomes imperative. This study aims to assess the correlation between TyG-BMI and ACM in this specific patient cohort, exploring how traditional associations between TyG-BMI and metabolic health may differ in the context of acute, life-threatening illness. Methods: Patient data were retrieved by accessing the Medical Information Mart for Intensive Care IV (MIMIC-IV 2.2) database, categorizing patients into three groups on the basis of TyG-BMI tertiles. The study evaluated both primary and secondary outcomes: the primary outcomes included the 90-day, 180-day, and 1-year ACM, while secondary outcomes encompassed ICU, in-hospital, and 30-day ACM. Our study employed the Kaplan–Meier (K–M) curve method for outcome comparison across the groups while utilizing multivariate Cox proportional-hazards regression models and restricted cubic splines (RCS) to explore TyG-BMI association with these outcomes. Additionally, interaction and subgroup analyses were performed, focusing on different mortality time points. Results: Among a cohort of 1707 individuals diagnosed with stroke, the average age was 68 years (interquartile range [IQR]: 58–78 years), with 946 (55.42%) of the participants being male. The analysis of K-M curves suggested that patients having a lower TyG-BMI level faced a heightened risk of long-term ACM, whereas the short-term ACM exhibited no statistically significant differences across the three TyG-BMI groups. Furthermore, Cox proportional-hazards regression analysis validated a statistically significant increased risk of long-term ACM among patients belonging to the lowest TyG-BMI tertile. Additionally, RCS analysis results demonstrated L-shaped correlations between the TyG-BMI index and both short- and long-term ACM. These findings underscore the TyG-BMI predictive value for long-term mortality in stroke patients, highlighting a nuanced relationship that varies over different time frames. The results revealed no interactions between TyG-BMI and the stratified variables, with the exception of age. Conclusion: In our study, lower TyG-BMI levels in critically ill stroke patients are significantly related to a higher risk of long-term ACM within the context of the United States. This finding suggests the potential of TyG-BMI as a marker for stratifying long-term risk in this patient population. However, it's crucial to note that this association was not observed for short-term ACM, indicating that the utility of TyG-BMI may be more pronounced in long-term outcome prediction. Additionally, our conclusion that TyG-BMI could serve as a reliable indicator for managing and stratifying stroke patients over the long term is preliminary. To confirm our findings and assess the universal applicability of TyG-BMI as a prognostic tool, it is crucial to conduct rigorously designed research across various populations.

The endothelial activation and stress index (EASIX) can predict endothelial intricacies as well as survival in distinct clinical circumstances. Accordingly, we hypothesize that EASIX may also serve as a predictor for stroke prevalence and mortality outcomes-all-cause mortality (ACM) and cardiovascular mortality (CVM) due to stroke. To validate our hypothesis, we deployed the National Health and Nutrition Examination Survey data. This cohort study employed 1999–2018 NHANES data. To evaluate EASIX effects on stroke risk, weighted logistic regression models were deployed. Meanwhile, we utilized the weighted Cox proportional hazards model for the calculation of hazard ratios (HRs) and 95% confidence intervals (CI) for mortality outcomes. The Kaplan-Meier curves were utilized, seeking the assessment of the interplay between the EASIX and both ACM and CVM among stroke patients. Using restricted cubic spline (RCS) curves, the potential non-linear or linear relations were investigated between the EASIX and both stroke prevalence and mortality outcomes. Subgroup analyses were performed to further assess the interconnection that existed between the EASIX and both stroke presence and its mortality outcomes. Interaction tests between covariates were also performed. Assessment of diagnostic value was done via receiver operating characteristic (ROC) curves. The area under the curve (AUC) was measured to estimate the EASIX predictive values for stroke prevalence and mortality outcomes. Of 43,853 participants, 1674 had a stroke; among them, there were (743/1671, 44.46%) ACM and 225 (13.46%) CVM. After full adjustment, EASIX was positively related to the likelihood of having a stroke (odds ratio [OR] = 1.16, 95% CI: 1.07–1.25). Unlike participants whose EASIX was in the lowest quartile (Q1), those in other quartiles (Q3/Q4) had an increased likelihood of having a stroke (OR = 1.34, 95% CI: 1.04–1.74; OR = 1.54, 95% CI 1.20–1.96, correspondingly). During a median follow-up of 71 months, Cox regression analysis manifested a positive interplay between EASIX and ACM (HR = 1.30, 95% CI: 1.17–1.44) and CVM (HR = 1.29, 95% CI: 1.06–1.58). The results showcased a non-linear interplay between EASIX and stroke presence and mortality outcomes. Additionally, subgroup analysis indicated no significant interactions between EASIX and any categorical covariates for CVM. Nervelessness, significant interaction effects existed between EASIX and the prevalence of stroke in categorical covariates such as hypertension, diabetes, and coronary heart disease (CHD; P < 0.001), as well as between EASIX and ACM in the categorical covariate of gender (P = 0.03). The ROC curves illustrated that the cutoff value of EASIX for stroke incidence and CVM was 0.56, and 0.58 for ACM, with an AUC of 0.663 (95% CI: 0.649–0.677) for stroke prevalence, as well as for ACM with an AUC of 0.620 (95% CI: 0.595–0.649) and for CVM with an AUC of 0.587 (95% CI: 0.549–0.626). The EASIX demonstrated a positive non-linear association with the prevalence and mortality outcomes amongst individuals at the age of 20 years and older who experienced a stroke, as identified in the NHANES dataset. Heightened EASIX scores were significantly related to an escalated risk of both stroke prevalence and mortality outcomes. Collectively, EASIX may be a valuable biomarker for assessing mortality risks in stroke patients and stroke prevalence. Looking forward, implementing EASIX in primary care settings could facilitate the early screening of endothelial function, thereby identifying high-risk patients and guiding appropriate referrals for specialized medical care. This approach can promote patient outcomes and reduce mortality rates among those who have suffered a stroke. Nevertheless, further validation in diverse countries and among various ethnic groups is necessary.

Purpose: We aim to ascertain the extent to which the blood urea nitrogen (BUN) to serum albumin (ALB) ratio (BAR) could be implemented to anticipate the short- and long-term prognosis of acute ischemic stroke (AIS) patients in intensive care units (ICUs).Methods: The data was derived from the Marketplace for Intensive Care Medical Information-IV (MIMIC-IV v3.0) database, primarily pertaining to AIS patients as categorized by the International Classification of Diseases (ICD)-9 and ICD-10. The outcomes encompassed short-term ACM incorporating ICM admissions and 30-day, as well as longer-term ACM involving 90-day and 365-day. Any confounding effects were mitigated with a 1:1 propensity score matching (PSM) approach. We determined the critical BAR level affecting patient survival with the use of maximum chosen rank statistics. The connection between BAR and ACM at various time intervals was ascertained with the multivariate Cox regression (MCR) models after the adjustment for covariates. Kaplan–Meier (KM) survival curves were generated to illustrate variations in BAR and death over various time intervals. Additionally, the linear or non-linear connection between BAR and ACM was ascertained with restricted cubic spline (RCS) approaches, supplemented by interaction and subgroup analyses.Results: Prior to PSM, we incorporated 1,764 suitable subjects with a median BAR of 5.52 mg/g. This cohort was composed of 1,395 and 369 patients in the BAR <10.42 and ≥10.42 groups, respectively. The ICU ACM rates were 9.53 and 19.24% (p < 0.001), respectively, while the 30-day ACM rates were 19.00 and 40.11% (p < 0.001). The 90- and 365-day ACM rates were 26.95 and 52.57% (p < 0.001), and 33.12 and 62.87%, respectively (p < 0.001). After fully adjustment, MCR models indicated a heightened mortality risk for the ICU (hazard ratio [HR] = 1.55, 95% confidence interval [CI]: 1.08–2.22; p = 0.02), 30-day (HR = 1.87, 95% CI: 1.46–2.38; p < 0.001), 90-day (HR = 1.75, 95% CI: 1.42–2.15; p < 0.001), and 365-day (HR = 1.81, 95% CI: 1.50–2.19; p < 0.001) in the high BAR group as opposed to the low BAR group. Following PSM, the analysis included 352 matched patient pairs, revealing persistent links between the higher BAR group and increased ACM risk throughout ICU, 30-, 90-, and 365-day intervals. Subsequent RCS studies before and after PSM highlighted a positive non-linear correlation between BAR and ACM in the short and long-term. In the subgroup investigation of ICU ACM, a subgroup of diabetes had an interaction effect (P_{for interaction} = 0.02). In the subgroup analysis of 90-day ACM, subgroups of hypertension and CRRT had an interaction effect (all P_{for interaction} < 0.05). In the subgroup analysis of 365-day ACM, subgroups of HTN, CRRT, and malignancy tumor had an interaction effect (all P_{for interaction} < 0.05).Conclusion: In this retrospective cohort study, our findings reveal that a confluence of deteriorated nutritional and renal function is significantly linked to heightened risks of ACM, and BAR may operate as an effective predictive indicator for AIS patients in ICUs. These findings have substantial importance for public health policy and practice. A comprehensive knowledge of these linkages may enable public health specialists and researchers to formulate more precisely targeted drugs and policies tailored to the unique requirements of the AIS patient group, hence improving their health outcomes. We reveal a significant link between the BAR and ACM in persons with AIS, highlighting the BAR’s potential as an innovative, economical, and accessible measure for forecasting ACM in this demographic. However, further research is needed on other racial and ethnic groups before these findings can be widely applied in clinical practice.

Objective: This investigation aimed to delineate the association between the advanced lung cancer inflammation index (ALI) and all-cause mortality (ACM) in individuals experiencing acute ischemic stroke (AIS).Methods: Drawing on information from the Medical Information Mart for Intensive Care (MIMIC)-IV database, release 2.2, covering the years 2012 to 2019, this research assessed the advanced lung cancer inflammation index (ALI) by factoring in body mass index (BMI), serum albumin levels (ALB), and the neutrophil-to-lymphocyte ratio (NLR). Patients with AIS were identified using codes from the International Classification of Diseases (ICD). To address potential confounding factors, a 1:1 propensity score matching (PSM) method was utilized. The investigation identified the pivotal ALI level impacting patient survival using maximally selected rank statistics. It then examined the effects on short- and long-term ACM through multivariate Cox proportional hazards regression models and Kaplan–Meier (K–M) survival analysis. Additionally, restricted cubic spline (RCS) methods were applied to delve into the linear or nonlinear nature of the relationship between ALI and ACM, with further insights gained from interaction and subgroup analyses.Results: The cohort comprised 838 AIS patients. Post-PSM, analysis involved 199 matched patient pairs. Adjusted Cox proportional hazard models indicated a significant association of low ALI (<10.38) with increased in-hospital ACM, both before (HR: 1.98; 95% CI: 1.36–2.88; p < 0.001) and after PSM (HR: 2.16; 95% CI: 1.32–3.52; p = 0.002). Associations of low ALI with elevated risk were consistent across ICU, 30 days, 90 days, and 1 year ACM pre- and post-PSM. Subsequent RCS analysis post-PSM underscored a negative nonlinear relationship between ALI and ACM over both short and long terms, without significant interaction effects across different subgroups for ACM.Conclusion: In this retrospective cohort study, by utilizing a nationally representative sample of United States patients with AIS, our analysis elucidates a negative correlation between the ALI and ACM in individuals with AIS, underscoring the utility of ALI as a novel, efficacious, and accessible inflammatory biomarker for prognosticating ACM. These results carry profound implications for public health policy and practice. A deeper comprehension of these associations can empower public health practitioners and researchers to devise more targeted interventions and policies, aimed specifically at catering to the distinct needs of the AIS patient population, thereby enhancing their health outcomes. The further research in other races/ethnicity is urgent, particularly before applying these findings in clinical practice.