ArticleCardiovascular diabetology2024
Exploring the prognostic impact of triglyceride-glucose index in critically ill patients with first-ever stroke: insights from traditional methods and machine learning-based mortality prediction.
Article in Cardiovascular diabetology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Article
- Multi-database pharmacovigilance identifies disproportionate reporting of hepatobiliary events with avacopan: an integrative study with network pharmacology and interpretable machine learning.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Dynamic Variability of Sodium and Potassium Predicts Mortality in Critically Ill Patients With Ischemic Stroke: Evidence From Two Critical Care Cohorts.Brain and behavior · 2026Article
- Development and external evaluation of an interpretable machine-learning model for early prediction of organ failure in higher-risk acute pancreatitis patients: A multicentre cohort study.PLOS digital health · 2026Article
- Enhanced prediction of coronary heart disease risk in diabetic patients via Machine learning incorporating multiple inflammatory and metabolic indices: A study with Dual-Cohort validation.International journal of cardiology. Heart & vasculature · 2026Article
- Lipoprotein(a) and atherosclerosis risk across estimated glucose disposal rate strata: evaluating synergistic predictive utility.Lipids in health and disease · 2026Article
- Immuno-metabolic biomarkers for 90-day prognostication after acute ischemic stroke: a classically solved QUBO-COPE modeling and biological contextualization study.Journal of translational medicine · 2026Article
- Association Between the Cholesterol-HDL-Glucose (CHG) Index and 90-Day Functional Outcomes in Patients with Acute Ischemic Stroke.Brain and behavior · 2026Article
- The prognostic value of the PALBI score for mortality prediction in stroke: an internal and external validation study.Scientific reports · 2026Article
- Relation of the stress hyperglycaemia ratio to residual risk in anticoagulated patients with atrial fibrillation: A report from the prospective Murcia AF Project III cohort.Diabetes, obesity & metabolism · 2026Article
- Post-marketing safety of tarlatamab in small cell lung cancer based on FAERS and WHO-VigiAccess with SHAP-based interpretable machine learning analysis of immune-related adverse events.Frontiers in pharmacology · 2026Article
- Co-recorded conditions in ischemic and hemorrhagic stroke: a 20-year national analysis of 598,186 natural deaths with machine learning.Frontiers in neurology · 2026Article
- Machine learning prediction of moderate-to-severe acute kidney injury after ICU admission and cardiac surgery with urine trace elements.European journal of clinical investigation · 2026Article
- The association of high-sensitivity C-reactive protein-triglyceride glucose index with functional outcome in elderly patients with acute ischemic stroke undergoing intravenous thrombolysis.Frontiers in neurology · 2026Observational
- Dynamic TyG trajectories cumulative TyG burden are associated with in-hospital mortality in acute brain injury: a multicenter interpretable machine-learning analysis.Frontiers in nutrition · 2026Article
- Atherogenic index of plasma and risk of acute kidney injury in critically ill patients: a multi-cohort study with machine learning and SHAP analysis.Renal failure · 2025Article
- Predictive value of systemic inflammation response index for atherosclerotic cardiovascular disease risk in patients with hypercholesterolemia: a machine learning study with dual-cohort validation.Lipids in health and disease · 2025Article
- Machine learning model development and validation using SHAP: predicting 28-day mortality risk in pulmonary fibrosis patients.BMC medical informatics and decision making · 2025Article
- Early blood pressure response index and mortality risk in cardiogenic shock: evidence from two critical care cohorts.BMC cardiovascular disorders · 2025Article
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13 authors.
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Abstract
backgroundThe 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.
methodsWe 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.
resultsThe 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).
conclusionTyG (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.
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