Association Between Hemoglobin Glycation Index and Delirium Risk in Sepsis Patients in the Intensive Care Unit

Association Between Hemoglobin Glycation Index and All-Cause Mortality Among Critically Ill Patients with Hemorrhagic Stroke: A Multicenter Retrospective Study.

The hemoglobin glycation index (HGI) is a promising marker for assessing glycemic control and outcomes in critically ill patients, but its prognostic value in Trauma/Surgical Intensive Care Units (TSICU/SICU) remains unclear. This study investigates the predictive value of HGI in relation to mortality. This retrospective analysis used data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, a publicly available critical care database, focusing on TSICU/SICU patients. HGI was calculated as the difference between observed and predicted HbA1c levels. Associations between HGI quartiles and 28-day and 360-day mortality were assessed using Kaplan–Meier survival analysis, multivariate Cox regression, and restricted cubic spline (RCS) models. A stacked ensemble machine learning model validated HGI’s predictive power, and mediation analysis evaluated sodium as a mediator. Kaplan–Meier analysis showed significant survival differences across HGI quartiles (log-rank p < 0.001), with the lowest quartile demonstrating the worst outcomes. Cox regression revealed that higher HGI was independently associated with lower 28-day and 360-day mortality (HR 0.76, 95% CI 0.72–0.81, p < 0.001). ROC analysis confirmed HGI outperformed HbA1c and glucose in predictive performance. The stacked ensemble model achieved an AUC of 0.85, highlighting HGI’s predictive strength. Mediation analysis found sodium mediated only 3.1% of HGI’s total effect on 28-day mortality, suggesting direct mechanisms. HGI is a robust predictor of short- and long-term mortality in TSICU/SICU patients, surpassing traditional glycemic markers. Incorporating HGI into risk stratification models could improve patient management, and further studies are needed to validate its clinical utility and explore underlying mechanisms.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-21524-2.

This study investigates the association between the hemoglobin glycation index (HGI) and 90-day and 365-day all-cause mortality (ACM) in hospitalized patients with chronic heart failure (CHF). A total of included 2964 hospitalized CHF patients from the Medical Information Mart for Intensive Care IV database were included. HGI was calculated as observed glycated hemoglobin (HbA1c) minus the cohort-specific predicted HbA1c estimated from a linear regression of HbA1c on fasting plasma glucose. We ascertained 90-day and 365-day ACM from database-recorded dates of death, counting events during the index hospitalization and after discharge within each window. Cox proportional hazards regression models were used to examine the association of HGI with 90-day and 365-day ACM. Restricted cubic spline (RCS) curves assessed for nonlinear relationships, while Kaplan–Meier (KM) survival curves compared survival differences among HGI groups. Subgroup analysis, the Boruta algorithm, and mediation analysis were employed to explore the underlying mechanisms. Logistic regression models were used for sensitivity analysis. Multivariate Cox regression analysis revealed that a higher HGI was significantly associated with decreased ACM at both 90 and 365 days in CHF patients. The KM survival curve demonstrated that patients in the lowest HGI quartile (Q1 group) had significantly lower survival rates. Sensitivity analysis further confirmed the effect of HGI, which was consistent across various subgroups. The Boruta algorithm identified HGI as an independent predictor of mortality. Mediation analysis indicated that the leukocyte count partially mediated the association between HGI and mortality. HGI is significantly and negatively associated with 90-day and 365-day IHM in patients with CHF. HGI was independently associated with better survival, an effect that is partially mediated by leukocytes.

An increasing number of studies have reported the close relation of the hemoglobin glycation index (HGI) with metabolism, inflammation, and disease prognosis. However, the prognostic relationship between the HGI and patients with sepsis remains unclear. Thus, this study aimed to analyze the association between the HGI and all-cause mortality in patients with sepsis using data from the MIMIC-IV database. In this study, 2605 patients with sepsis were retrospectively analyzed. The linear regression equation was established by incorporating glycated hemoglobin (HbA1c) and fasting plasma glucose levels. Subsequently, the HGI was calculated based on the difference between the predicted and observed HbA1c levels. Furthermore, the HGI was divided into the following three groups using X-tile software: Q1 (HGI ≤ − 0.50%), Q2 (− 0.49% ≤ HGI ≤ 1.18%), and Q3 (HGI ≥ 1.19%). Kaplan–Meier survival curves were further plotted to analyze the differences in 28-day and 365-day mortality among patients with sepsis patients in these HGI groups. Multivariate corrected Cox proportional risk model and restricted cubic spline (RCS) were used. Lastly, mediation analysis was performed to assess the factors through which HGI affects sepsis prognosis. This study included 2605 patients with sepsis, and the 28-day and 365-day mortality rates were 19.7% and 38.9%, respectively. The Q3 group had the highest mortality risk at 28 days (HR = 2.55, 95% CI: 1.89–3.44, p < 0.001) and 365 days (HR = 1.59, 95% CI: 1.29–1.97, p < 0.001). In the fully adjusted multivariate Cox proportional hazards model, patients in the Q3 group still displayed the highest mortality rates at 28 days (HR = 2.02, 95% CI: 1.45–2.80, p < 0.001) and 365 days (HR = 1.28, 95% CI: 1.08–1.56, p < 0.001). The RCS analysis revealed that HGI was positively associated with adverse clinical outcomes. Finally, the mediation effect analysis demonstrated that the HGI might influence patient survival prognosis via multiple indicators related to the SOFA and SAPS II scores. There was a significant association between HGI and all-cause mortality in patients with sepsis, and patients with higher HGI values had a higher risk of death. Therefore, HGI can be used as a potential indicator to assess the prognostic risk of death in patients with sepsis.

BackgroundThe hemoglobin glycation index (HGI) represents the difference between the observed and predicted values of haemoglobin A1c (HbA1c). However, the association between HGI and prognosis of heart failure (HF) is not completely clarified yet and requires more investigation. This study aimed to explore the connection between HGI and mortality in HF patients.MethodsThe data for the study were derived from the MIMIC-IV database from 2008 to 2019, a publicly available clinical database in intensive care. A linear regression equation between HbA1c and fasting blood glucose (FBG) was established to calculate predicted HbA1c. The endpoints were 30-day and 365-day all-cause mortality. Kaplan–Meier analysis was utilized to compare survival rates across groups differentiated by their HGI levels. The Cox regression models and restricted cubic spline (RCS) analysis were utilized to analyze the association between HGI and mortality.ResultsThe study collected a total of 2846 patients with HF (40.1% male), of whom 305 patients (10.7%) died within 30 days and 954 patients (33.5%) died within 365 days. Kaplan–Meier curves revealed patients with higher HGI had significantly higher mortality risks (log-rank P < 0.001). A high HGI was significantly associated with 30-day mortality (adjusted HR [aHR]: 2.36, 95% CI: 1.74–3.20, P < 0.001) and 365-day mortality (aHR: 1.40, 95% CI: 1.16–1.68, P < 0.001) after adjustment for potential confounders. Likewise, each unit increase in the HGI correlated with a 1.42-fold higher risk of 30-day mortality (aHR: 1.42, 95% CI: 1.28–1.57, P < 0.001) and 1.19-fold higher risk of 365-day mortality (aHR: 1.19, 95% CI: 1.11–1.68, P < 0.001). RCS analysis suggested an L-shaped nonlinear association between HGI and clinical endpoints (P for nonlinearity < 0.001), with an inflection point value of − 1.295. Subgroup analysis and sensitivity analysis revealed that the correlation between HGI and 30-day and 365-day all-cause mortality remained consistent.ConclusionsIn ICU-admitted HF patients, HGI was independently associated with increased risks of 30-day and 365-day mortality and the identification of high HGI (> 0.709) provided a valuable tool for clinicians to detect high-risk populations. Integrating HGI into routine clinical practice might strengthen the prognosis-based decision making improve HF patient outcomes.

Background:The hemoglobin glycation index (HGI) presents a discrepancy between observed and predicted glycosylated hemoglobin (HbA1c) and fasting blood glucose values. Meanwhile, compared to the HbA1c values, the HGI provides a more comprehensive reflection of blood glucose variability across populations. However, no studies have examined the association between the HGI and all-cause, cardiac, and cardiovascular mortalities in the general population. Hence, this study aimed to investigate these relationships using data from the National Health and Nutrition Examination Survey (NHANES) database.Methods:Participants were stratified into four groups based on the HGI quartiles. Weighted multivariable Cox proportional hazards models were used to assess the associations between HGI and all-cause, cardiovascular, and cardiac mortality. Kaplan–Meier survival analysis based on the HGI quartiles and log-rank tests were employed to compare differences in primary and secondary endpoints. Additionally, restricted cubic spline (RCS) curves were used to explore nonlinear relationships between the HGI and endpoints, identifying inflection points. Subgroup analyses and interaction tests were conducted to assess the robustness of the findings.Results:In comparing the baseline characteristics of endpoints across all-cause mortality, cardiac mortality, and cardiovascular mortality, significantly higher mortality rates were observed in the high HGI quartile group (Q4) compared to the other three groups (Q1, Q2, and Q3) (p < 0.05). Kaplan–Meier curves demonstrated increased mortality risks in the high HGI group across all endpoints (p < 0.05). Multivariable Cox proportional hazards models indicated that high HGI levels were associated with all-cause mortality (Q4: hazard ratio (HR) (95% confidence interval (CI)) = 1.232 (1.065, 1.426); p = 0.005), cardiac mortality (HR (95% CI) = 1.516 (1.100, 2.088); p = 0.011) and cardiovascular mortality (HR (95% CI) = 1.334 (1.013, 1.756); p = 0.039). Low HGI was associated only with all-cause mortality (Q1: HR (95% CI) = 1.269 (1.082, 1.488); p = 0.003). RCS analysis confirmed a U-shaped relationship between the HGI and all three outcome events. Subgroup analyses and interaction tests supported the robustness of the conclusions.Conclusion:This study demonstrates a U-shaped association between the HGI and overall mortality, cardiac mortality, and cardiometabolic mortality in the general population. Specifically, the high HGI value represented a risk factor for all-cause, cardiac, and cardiovascular mortality. In contrast, low HGI values were associated only with all-cause mortality in the general population.

BackgroundThe hemoglobin glycation index (HGI), which quantifies the difference between observed and predicted hemoglobin A1c (HbA1c) levels, has been linked to adverse outcomes. However, its relationship with myocardial infarction (MI) in patients with diabetes mellitus (DM) remains unexplored. This study aimed to investigate the association between HGI and MI incidence in critically ill patients with diabetes mellitus (DM) using data from the MIMIC-IV database.MethodsLinear regression analysis of HbA1c and fasting blood glucose levels was conducted to calculate HGI. Subsequently, differences in MI incidence across HGI quartiles were assessed using the Kaplan-Meier survival analysis, with the log-rank test applied. Cox proportional hazards models and restricted cubic spline (RCS) analyses were conducted to estimate hazard ratios (HRs) for MI risk across HGI quartiles, with Q1 as the reference.ResultsA total of 8,055 DM patients with an initial ICU admission exceeding 24 h were included, with 21.5% of them presenting MI. Compared to HGI Q1 (-3.81, -1.236), the risk of MI increased by 1.26 times in Q2 (HR: 1.26, 95% confidence interval [CI]: 1.10–1.45), 1.48 times in Q3 (HR: 1.48, 95% CI: 1.29–1.69), and 1.39 times in Q4 (HR: 1.39, 95% CI: 1.21–1.60). RCS analysis showed a nonlinear positive association between HGI and outcome events that remained consistent across different subgroups as the stratified analysis suggested.ConclusionA significant correlation was revealed between HGI and the risk of MI in patients with DM, especially among those with elevated HGI levels, suggesting that HGI may serve as a potential biomarker for assessing MI risk in this population.

BackgroundThe hemoglobin glycation index (HGI) is defined as the difference between the observed and predicted values of glycosylated hemoglobin (HbA1c), which is closely associated with a variety of poor prognoses. However, the relationship between HGI and short-term mortality risk in patients with a first diagnosis of acute myocardial infarction (AMI) remains unclear. This study aims to provide a better understanding of the relationship between HGI and mortality risk in patients with a first diagnosis of AMI.MethodsWe conducted a cohort study using data from 1,961 patients with a first diagnosis of AMI from the Medical Information Mart for Intensive Care IV (MIMIC-IV; version 2.2) database. Patients were divided into four groups based on HGI quartiles. A Cox proportional hazards model and a two-segmented Cox proportional hazards model were used to elucidate the non-linear relationship between HGI in patients with a first diagnosis of AMI and mortality.ResultsOf the surveyed population, 175 patients (8.92%) died within 90 days, and 210 patients (10.71%) died within 180 days. A low HGI was significantly associated with 90-day mortality [HR, 1.99; 95% CI (1.22, 3.08); P < 0.001] and 180-day mortality [HR, 1.74; 95% CI (1.18, 2.43); P < 0.001] in patients with a first diagnosis of AMI in the completely adjusted Cox proportional risk model, showing a non-linear correlation with an inflection point at 0.16 and 0.44. In the subgroup analysis, patients with prediabetes mellitus (pre-DM) and lower HGI levels had increased 90-day [HR, 8.30; 95% CI (2.91, 23.68)] and 180-day mortality risks [HR, 6.84; 95% CI (2.86, 16.34)].ConclusionThere is a significant correlation between HGI and all-cause mortality in patients diagnosed with AMI, especially those with lower HGI. HGI can serve as a potential indicator for evaluating the 90 and 180-day death risk of such patients.

BackgroundGlycemic variability is increasingly recognized as a critical factor influencing outcomes in intensive care, yet its prognostic role remains unclear. The Hemoglobin Glycation Index (HGI), which reflects individual glycemic variation, has not been thoroughly studied in critically ill populations.AimTo evaluate the association between HGI and all-cause mortality in critically ill patients using data from a large intensive care unit (ICU) cohort.MethodsWe conducted a retrospective cohort study using the MIMIC-IV database. The primary outcomes were 30-, 90-, and 365-day all-cause mortality; in-hospital mortality was secondary. Kaplan-Meier analysis, Cox regression, and restricted cubic spline (RCS) modeling were used to assess mortality risk across HGI levels. Propensity score matching (PSM) and subgroup analyses were performed to ensure robustness.ResultsAmong 9,695 patients, those with low HGI (< − 0.40) had significantly higher mortality (P < 0.001). RCS analysis showed a nonlinear association between HGI and 30-day mortality. Higher HGI values were independently associated with reduced risk of death at all time points, with hazard ratios ranging from 0.43 to 0.76 (P < 0.001). These associations persisted after multivariable adjustment and PSM. Subgroup analyses showed consistent results across patient characteristics.ConclusionsLower HGI values are associated with increased short- and long-term mortality in critically ill patients. HGI may serve as a valuable prognostic biomarker for risk stratification in ICU settings.

The aim of this study was to explore the relationship between the hemoglobin glycation index (HGI) of Congestive Heart Failure (CHF) patients and their risk of mortality within 365 days. The Medical Information Mart for Intensive Care (MIMIC-IV) database supplied the patient data for this study, which was categorized into quartiles based on the HGI. The primary endpoint was all-cause mortality within a 365-day period. Kaplan-Meier (K-M) analysis was utilized to compare this primary endpoint across the four aforementioned groups. The relationship between the HGI and the endpoint was examined using restricted cubic splines (RCS) and a Cox proportional hazards analysis. A total of 985 patients were included in this study. HGI was significantly associated with 30 days mortality (15.9%; HR, 0.79; 95% CI, (0.67~0.92); P=0.003) and 60 days mortality (19.3%; HR, 0.83; 95% CI, (0.72~0.96); P=0.011) and 90 days mortality (22.1%; HR, 0.86; 95% CI, (0.75~0.99); P=0.031) and 365 days mortality (30.7%; HR, 0.97; 95% CI, (0.86~1.09); P=0.611) in patients with critical CHF in the completely adjusted Cox proportional risk model. RCS analysis revealed a U-shaped relationship between HGI and outcome events. KM curves survival analysis suggests a correlation between 30 days and 365 days mortality in HGI and CHF patients. A higher HGI has a more protective effect than a low HGI for patients with CHF and was directly associated with short-term mortality rates. These findings may be helpful in the management of patients with CHF.

BackgroundGlycemic control is critical for managing transcatheter aortic valve replacement (TAVR) patients, especially those in intensive care units (ICUs). Emerging metrics such as the hemoglobin glycation index (HGI), stress hyperglycemia ratio (SHR), and glycemic variability (GV) offer advanced insights into glucose metabolism. However, their prognostic implications for short- and long-term outcomes post-TAVR remain underexplored.MethodsThis retrospective cohort study analyzed 3342 ICU-admitted TAVR patients via the MIMIC-IV database. Patients were stratified into tertiles for HGI, SHR, and GV levels. Survival analyses, including Kaplan‒Meier curves, Cox proportional hazards models and restricted cubic splines (RCSs), were used to assess associations between glycemic control metrics and 30-day and 365-day all-cause mortality in these patients. Sensitivity analyses, subgroup assessments, and external validation were also performed to verify the study findings.ResultsDuring follow-up, 1.6% and 6.9% of patients experienced 30-day and 365-day mortality after TAVR, respectively. In the fully adjusted cox regression model, lower HGI (HR 1.48, 95% CI 1.05–2.09, P = 0.025) and higher SHR (HR 1.63, 95% CI 1.15–2.32, P = 0.006) were most significantly associated with an increased risk of 365-day mortality. Higher SHR was also significantly associated with an increased risk of 30-day mortality in patients (HR 2.92, 95% CI 1.32–6.45, P = 0.008). Both lower (HR 0.59, 95% CI 0.38–0.92, P = 0.019) and higher GV levels (HR 1.43, 95% CI 1.06–1.93, P = 0.020) were associated with the risk of 365-day mortality.ConclusionsIn critically ill TAVR patients, glycemic control metrics are closely associated with long-term all-cause mortality. The HGI, SHR, and GV provide prognostic insights into clinical outcomes that surpass conventional glucose measurements. These findings highlight the importance of personalized glycemic management strategies in improving TAVR patient outcomes.

Hemoglobin glycation index predicts reduced mortality in critically ill patients with chronic kidney disease.

Nonlinear association between hemoglobin glycation index and mortality in ischemic stroke Patients: Insights from the MIMIC-IV database.

BackgroundThe Hemoglobin Glycation Index (HGI) quantifies the difference between observed and predicted glycated hemoglobin (HbA1c) values, and has connections to multiple adverse outcomes. However, the relationship between HGI and the risk of diabetic nephropathy (DN) in patients with type 2 diabetes mellitus (T2DM) remains underexplored. The objective of this study was to examine the relationship between baseline HGI and the risk of DN development among patients with T2DM through a retrospective cohort study.MethodsA single-center retrospective study was conducted on 1050 newly diagnosed T2DM patients with normal renal function at baseline. Participants were categorized into quartiles based on HGI values. The primary outcome was DN development, defined as persistent proteinuria or reduced estimated glomerular filtration rate (eGFR). Multivariable logistic regression, restricted cubic spline (RCS) analysis, and threshold effect models were employed to assess the association between HGI and DN risk. Subgroup and sensitivity analyses were conducted to validate the robustness of our findings, while mediation analysis was employed to explore potential underlying mechanisms.ResultsThe study revealed a U-shaped relationship between HGI and DN risk. Both excessively low and high HGI levels were associated with an increased risk of DN, with the lowest risk observed at an HGI threshold of −0.648. In fully adjusted models, the highest HGI quartile (Q4) demonstrated a significantly increased risk of DN (OR = 1.54, 95% CI: 1.03–2.30, P = 0.036), while the lowest HGI quartile (Q1) also showed a trend toward higher risk (OR = 1.40, 95% CI: 0.92–2.14, P = 0.115). However, fasting plasma glucose (FPG) (P for overall = 0.217) and glycated hemoglobin (HbA1c) (P for overall = 0.529) did not show an association with the risk of DN. Subgroup and sensitive analyses confirmed the consistency of this U-shaped association across different patient demographics. Mediation analysis indicated that C-reactive protein (CRP) mediated 11.1% of the effect of |HGI| on DN.ConclusionIn T2DM patients, baseline HGI exhibits a U-shaped association with DN risk, serving as a potential indicator for assessing DN risk.

BackgroundThe hemoglobin glycation index (HGI), an indicator of individual differences in glucose metabolism. This study undertakes a detailed 10-year cohort analysis to investigate the potential association between HGI and all-cause mortality in a Chinese adult population.MethodsBaseline data encompassing lifestyle and metabolic parameters were collected from 10,008 participants, with a subsequent 10-year follow-up. Following exclusions based on predefined criteria, 9,084 individuals were included in the final analysis. Participants were categorized into quartiles based on their HGI values. A suite of statistical tools, including Kaplan-Meier survival analysis, Cox proportional hazards models, restricted cubic splines (RCS), threshold effect models, and subgroup analyses, was employed to investigate the association between HGI and all-cause mortality.ResultsDuring the 10-year follow-up period, a total of 514 all-cause mortality cases were recorded. Kaplan-Meier survival analysis identified the Q2 group as having the lowest mortality rate. Fully adjusted Cox proportional hazards models demonstrated significant associations, indicating higher all-cause mortality risks in participants with both extremely low and high HGI levels compared to the Q2 group. RCS analysis further illustrated a U-shaped relationship between HGI and all-cause mortality.ConclusionsIn the Chinese population, both markedly elevated and significantly reduced HGI levels are associated with adverse impacts on long-term survival.Core tipThe aim of this study was to assess the association of Hemoglobin Glycation Index(HGI) with all-cause mortality in non-type 2 diabetic patients based on a 10-year cohort study from China. After COX regression, restricted cubic spline analysis, and subgroup analyses, it was found that a significant increase or decrease in HGI adversely affected long-term survival.