Background: The incidence and mortality of first-ever strokes have risen sharply, especially in the intensive care unit (ICU). Emerging surrogate for insulin resistance, triglyceride-glucose index (TyG), has been linked to stroke prognosis. We aims to explore the relationships between TyG with ICU all-cause mortality and other prognosis, and to develop machine learning (ML) models in predicting ICU all-cause mortality in the first-ever strokes. Methods: We included first-ever stroke patients from the eICU Collaborative Research Database in 2014–2015 as the primary analysis cohort (then divided into training and internal validation cohorts) and from local hospital’s ICUs as the external validation cohort. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to evaluate the association between TyG and ICU/hospital all-cause mortality. Linear regression and correlation analyses were performed to examine the relationships between TyG with length of ICU/hospital stay and Glasgow Coma Score. Results: The primary analysis cohort included 3173 first-ever strokes (median age 68.0 [55.0–68.0] years; 63.0% male), while the external validation cohort included 201 first-ever strokes (median age 71.0 [63.0–77.0] years; 62.3% male). Multivariate Cox proportional hazards models revealed that the high TyG group (TyG ≥ 9.265) was associated with higher ICU (HR 1.92, 95% CI 1.38–2.66) and hospital (HR 1.69, 95% CI 1.32–2.16) all-cause mortality, compared with low TyG group (TyG < 9.265). TyG was also correlated with ICU length of stay (r = 0.077), hospital length of stay (r = 0.042), and Glasgow Coma Score (r = -0.132). TyG and other six features were used to construct ML models. The random forest model performed best in internal validation with AUC (0.900) and G-mean (0.443), and in external validation with AUC (0.776) and G-mean (0.399). Conclusion: TyG (optimal cut-off: 9.265) was identified as an independent risk factor for ICU and hospital all-cause mortality in first-ever strokes. The ML model incorporating TyG demonstrated strong predictive performance. This emphasises the importance of insulin resistance (with TyG as a surrogate measure) in the prognostic assessment and early risk stratification of first-time stroke patients.

Consider that inflammatory factors are associated with short-term mortality in patients with acute myocardial infarction (AMI). In this retrospective analysis of 2,784 AMI patients from the Medical Information Mart for Intensive Care-IV database, we evaluated the impact of inflammatory markers on in-hospital mortality and predicted 30-day and 90-day outcomes. Patients were divided into groups based on in-hospital survival (n = 2,364) and mortality (n = 420). Analysis of initial hospital admission laboratory data, including inflammatory factors, revealed these factors as independent predictors of in-hospital mortality (Q4 of RDW: OR 1.96, NLR: OR 1.63, SII: OR 1.85, and SIRI: OR 2.23, all P < 0.05). Cox proportional hazards models confirmed their significance for predicting 30-day (Q4 of NLR: OR 1.83, SII: OR 1.86, and SIRI: OR 2.01, all P < 0.05) and 90-day mortality (Q4 of RDW: OR 1.46, NLR: OR 1.69, SII: OR 1.73, and SIRI: OR 1.72, all P < 0.05). Increasing levels of inflammatory markers correlated with higher odds and hazard ratios, as illustrated by Restricted Cubic Spline curves. Kaplan-Meier survival analysis showed better survival rates with lower inflammatory marker levels. Receiver operating characteristic curves demonstrated good predictive performance of individual inflammatory factors, with a new composite marker showing the highest predictive ability (AUC = 0.720). This study underscores the association of inflammatory factors with both hospital and short-term mortality in AMI patients.

Background: Cardiovascular-kidney-metabolic syndrome (CKM) represents a multisystem condition involving obesity, diabetes, chronic kidney disease, and cardiovascular diseases. Frailty, as measured by the Frailty Index (FI), is linked to adverse outcomes, but its association with CKM severity and mortality remains unclear. This study aimed to evaluate the relationship between frailty status, CKM staging, and mortality risk. Methods: We analysed data from 19,407 adults aged ≥ 45 years from NHANES 1999–2018. Frailty status was assessed using a 49-item Frailty Index (FI) and categorised as robust (FI ≤ 0.08), pre-frail (0.08 < FI < 0.25), or frail (FI ≥ 0.25). CKM was staged from 1 to 4 based on established clinical criteria. Multinomial logistic regression assessed the association between frailty status and CKM staging. Cox proportional hazards models evaluated the associations between frailty status and all-cause, cardiovascular, and non-cardiovascular mortality among CKM patients. Results: A total of 19,407 participants (median [IQR] age: 63.00 [54.00–72.00] years, 50.77% male), with 19,089 CKM patients. Frail individuals exhibited significantly higher odds of being assigned to advanced CKM stages. Over a median follow-up of 8.4 years, 4794 participants died. Kaplan–Meier curves and restricted cubic spline analyses demonstrated a clear gradient in mortality risk across frailty categories. Compared with the robust group, pre-frail and frail individuals had significantly higher risks of all-cause (HR = 1.47 and 2.83, respectively), cardiovascular (HR = 1.71 and 3.78), and non-cardiovascular mortality (HR = 1.40 and 2.57). Conclusions: Frailty status demonstrated a significant association with CKM staging and mortality outcomes. Early identification of frailty may help guide risk stratification and inform tailored interventions for individuals with CKM.

Background: Diastolic heart failure (DHF) and type 2 diabetes mellitus (T2DM) often coexist, causing increased mortality rates. Glycaemic variability (GV) exacerbates cardiovascular complications, but its impact on outcomes in patients with DHF and T2DM remains unclear. This study examined the relationships between GV with mortality outcomes, and developed a machine learning (ML) model for long-term mortality in these patients. Methods: Patients with DHF and T2DM were included from the Medical Information Mart for Intensive Care IV, with admissions (2008–2019) as primary analysis cohort and admissions (2020–2022) as external validation cohort. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to evaluate the associations of GV with 90-day, 1-year, and 3-year all-cause mortality. The primary analysis cohort was split into training and internal validation cohorts, then developing ML models for predicting 1-year all-cause mortality in training cohort, which were validated using the internal and external validation cohorts. Results: 2,128 patients with DHF and T2DM were included in primary analysis cohort (meidian age 71.0years [IQR: 62.0–79.0]; 46.9% male), 498 patients with DHF and T2DM were included in the external validation cohort (meidian age 75.0years [IQR: 67.0–81.0]; 54.0% male). Multivariate Cox proportional hazards models showed that high GV tertiles were associated with higher risk of 90-day (T2: HR 1.45, 95%CI 1.09–1.93; T3: HR 1.96, 95%CI 1.48–2.60), 1-year (T2: HR 1.25, 95%CI 1.02–1.53; T3: HR 1.54, 95%CI 1.26–1.89), and 3-year (T2: HR 1.31, 95%CI: 1.10–1.56; T3: HR 1.48, 95%CI 1.23–1.77) all-cause mortality, compared with lowest GV tertile. Chronic kidney disease, creatinine, potassium, haemoglobin, and white blood cell were identified as mediators of GV and 1-year all-cause mortality. Additionally, GV and other clinical features were pre-selected to construct ML models. The random forest model performed best, with AUC (0.770) and G-mean (0.591) in internal validation, with AUC (0.753) and G-mean (0.599) in external validation. Conclusion: GV was determined as an independent risk factor for short-term and long-term all-cause mortality in patients with DHF and T2DM, with a potential intervention threshold around 25.0%. The ML model incorporating GV demonstrated strong predictive performance for 1-year all-cause mortality, highlighting its importance in early risk stratification management of these patients.

Objective: This study explores the relationship between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and the severity of coronary artery stenosis in acute coronary syndrome (ACS) patients. Methods: We included patients who were first admitted to the cardiac intensive care unit of Tianjin Medical University General Hospital between July 1, 2022, and June 30, 2024, with a diagnosis of ACS. Coronary stenosis severity was assessed using the Gensini score (GS), with patients divided into high (≥ 68.5) and low (< 68.5) GS groups. Patients in the high GS group represented those with more severe coronary artery stenosis. General clinical data from the first admission were compared between the two groups. Logistic regression identified independent risk factors, and receiver operating characteristic (ROC) curves evaluated the predictive value of NHHR and other lipid parameters. C-statistics, calibration, and decision curves assessed the Nomogram’s predictive accuracy. Sensitivity analysis was performed to further validate the robustness of the results. Results: The study included 1,799 ACS patients, 907 with severe coronary artery lesions (GS ≥ 68.5). The median age was 71, and 72.9% were male. NHHR was significantly higher in the high GS group. Logistic regression showed NHHR as an independent risk factor for high GS (odds ratio, OR = 1.15, P = 0.001), with higher NHHR levels indicating greater risk. Compared to the first quartile (Q1), the third and fourth quartiles showed increased risk (OR = 1.36, P = 0.044; OR = 1.66, P = 0.002). Incorporating NHHR into the model for predicting severe coronary artery lesions (Model 1) increased the predictive value from 0.696 (95% CI: 0.674–0.717) to 0.703 (95% CI: 0.682–0.724). Conclusion: NHHR was an independent risk factor for severe coronary artery stenosis (GS ≥ 68.5) in ACS patients, with higher values linked to increased lesion severity. It outperformed traditional lipid parameters in predicting severity and improved the prediction model’s accuracy. Subgroup analysis showed stronger associations in high-risk populations, though further studies are needed to confirm its clinical utility.

Background: Propofol and midazolam are commonly used sedative drugs in mechanically ventilated patients in the Intensive Care Unit (ICU). However, there is still a lack of relevant studies exploring the influence of midazolam and propofol on the prognosis of patients with Sepsis-associated Acute Kidney Injury (S-AKI).Patients and methods: A statistical analysis was conducted on 3,745 patients with S-AKI in the Medical Information Mart for Intensive Care IV database. The patients’ baseline characteristics were grouped based on the use of either propofol or midazolam as sedatives. Cox proportional hazards models, logistic regression models, and subgroup analyses were used to compare the effects of propofol and midazolam on the short-term prognosis of S-AKI patients, including 30-day mortality, ICU mortality, and duration of mechanical ventilation.Results: In the statistical analysis, a total of 3,745 patients were included, with 649 patients using midazolam and 3,096 patients using propofol. In terms of the 30-day mortality, compared to patients using midazolam, S-AKI patients using propofol had a lower ICU mortality (hazard ratio = 0.62, 95% confidence interval: 0.52–0.74, p < 0.001), lower 30-day mortality (hazard ratio = 0.56, 95% confidence interval: 0.47–0.67, p < 0.001), and shorter mechanical ventilation time (odds ratio = 0.72, 95% confidence interval: 0.59–0.88, p < 0.001). Kaplan–Meier curves showed lower survival probabilities in the midazolam group (p < 0.001). Subgroup analyses showed that propofol was strongly protective of short-term prognosis in older, male, smaller SOFA score CCI score, no heart failure, and comorbid chronic kidney disease patients with S-AKI.Conclusion: Compared to midazolam, propofol was considered a protective factor for short-term mortality risk and ICU mortality risk in S-AKI patients. Additionally, S-AKI patients using propofol had a lower risk of requiring prolonged mechanical ventilation. Overall, propofol may be more beneficial for the short-term prognosis of S-AKI patients compared to midazolam.