Assessment of stress hyperglycemia ratio to predict all-cause mortality in patients with critical cerebrovascular disease: a retrospective cohort study from the MIMIC-IV database

Cardiovascular Diabetology, Journal Year: 2025, Volume and Issue: 24(1)

Published: Feb. 7, 2025

The association between the stress hyperglycemia ratio (SHR), which represents degree of acute hyperglycemic status, and risk mortality in cerebrovascular disease patients intensive care unit (ICU) remains unclear. This study aims to investigate predictive ability SHR for in-hospital critically ill assess its potential enhance existing models. We extracted data from Medical Information Mart Intensive Care (MIMIC-IV) database diagnosed with used Cox regression mortality. To nature this association, we applied restricted cubic spline analysis determine if it is linear. was evaluated using receiver operating characteristic (ROC) curves C-index. included a total 2,461 patients, mean age 70.55 ± 14.59 years, 1,221 (49.61%) being female. revealed that independently associated both (per standard deviation (SD) increase: hazard (HR) 1.35, 95% confidence interval (CI) 1.23-1.48) ICU SD HR 1.37, CI 1.21-1.54). death increased an approximately linear fashion when exceeded 0.77-0.79. Subgroup indicated more pronounced non-diabetic individuals. Additionally, incorporating into models improved discrimination reclassification performance. serves as independent factor ICU. Adding enhances their performance, offering clinical value identification high-risk patients.

Language: Английский

Association between stress hyperglycemia ratio and contrast-induced nephropathy in ACS patients undergoing PCI: a retrospective cohort study from the MIMIC-IV database

BMC Cardiovascular Disorders, Journal Year: 2025, Volume and Issue: 25(1)

Published: Feb. 25, 2025

Contrast-induced nephropathy (CIN) is a significant complication in acute coronary syndrome (ACS) patients undergoing percutaneous intervention (PCI). The role of the stress hyperglycemia ratio (SHR) as predictor CIN and mortality these remains unclear warrants investigation. To assess relationship between SHR CIN, well its impact on short-term ACS PCI. We conducted retrospective cohort study using MIMIC-IV database, including 552 patients. was calculated admission glucose to estimated average from hemoglobin A1c. defined ≥ 0.5 mg/dL or 25% increase serum creatinine within 48 h Logistic regression spline models were used analyze association while Kaplan–Meier curves assessed 30-day mortality. Higher levels independently associated with increased risk (OR 2.36, 95% CI: 1.56–3.57, P < 0.0001). A J-shaped observed, rising sharply when exceeded 1.06. also higher (P Subgroup analysis revealed stronger SHR-CIN non-diabetic an independent It offers potential for stratification clinical decision-making, especially

Language: Английский

Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in cardiovascular-kidney-metabolic syndrome stages 0–3 and the development of a machine learning prediction model: a nationwide prospective cohort study

Cardiovascular Diabetology, Journal Year: 2025, Volume and Issue: 24(1)

Published: April 16, 2025

Background The American Heart Association recently introduced the concept of cardiovascular-kidney-metabolic (CKM) syndrome, highlighting increasing importance complex interplay between metabolic, renal, and cardiovascular diseases (CVD). While substantial evidence supports a correlation estimated glucose disposal rate (eGDR) CVD events, its predictive value compared with other insulin resistance (IR) indices, such as triglyceride–glucose (TyG) index, TyG-waist circumference, TyG-body mass TyG-waist-to-height ratio, triglyceride-to-high density lipoprotein cholesterol metabolic score for resistance, remains unclear. Methods This prospective cohort study utilized data from China Health Retirement Longitudinal Study (CHARLS). individuals were categorized into four subgroups based on quartiles eGDR. associations eGDR incident evaluated using multivariate logistic regression analyses restricted cubic spline. Seven machine learning models to assess index events. To model’s performance, we applied receiver operating characteristic (ROC) precision-recall (PR) curves, calibration decision curve analysis. Results A total 4,950 participants (mean age: 73.46 ± 9.93 years), including 50.4% females, enrolled in study. During follow-up 2011 2018, 697 (14.1%) developed CVD, 486 (9.8%) heart disease 263 (5.3%) stroke. outperformed six IR indices predicting demonstrating significant linear relationship all outcomes. Each 1-unit increase was associated 14%, 19% lower risk disease, stroke, respectively, fully adjusted model. incorporation significantly improved prediction performance area under ROC PR curves equal or exceeding 0.90 both training testing sets. Conclusions outperforms stroke CKM syndrome stages 0–3. Its enhances stratification may aid early identification high-risk this population. Further studies are needed validate these findings external cohorts. Graphical abstract

Language: Английский