Association between the haemoglobin glycation index and 30-day and 365-day mortality in patients with heart failure admitted to the intensive care unit

Abstract

Background: The 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.

Methods: The 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.

Results: The 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.

Conclusions: In 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.

Keywords: All-cause mortality; Haemoglobin glycation index; Heart failure; MIMIC-IV database.

Fig. 1

The linear regression equation between FBG and HbA1c: predict HbA1c = 0.442 * FBG (mmol/L) + 3.12 (r² = 0.37 and P < 0.001)

Fig. 2

Selection of study population from MIMIC-IV database

Fig. 3

Kaplan–Meier survival analysis for 30-day (A) and 365-day (B) all-cause mortality. A table below the Kaplan–Meier curves shows the number of patients at risk at different time points (in days)

Fig. 4

RCS analysis of the association of HGI with 30-day (A) and 365-day (B) all-cause mortality. The horizontal dotted black line represents the HR = 1. The orange curve shows the value of HR. The orange shaded area represents the 95% CI. The adjustment strategy is the same as the Model 3. The plot demonstrates an "L-shaped" association, where the risk stabilizes below the inflection point (HGI = − 1.295) and increases sharply above this threshold. RCS, restricted cubic spline

Fig. 5

Forest plots of subgroup analysis of HGI and 30-day all-cause mortality. Adjusting for the same covariates as in Model 3 except for the stratification variables. HT: hypertension; DM: diabetes mellitus; CAD, coronary artery disease

Fig. 6

Forest plots of subgroup analysis of HGI and 365-day all-cause mortality. Adjusting for the same covariates as in Model 3 except for the stratification variables