Stress hyperglycemia ratio as a biomarker for early mortality risk stratification in cardiovascular disease: a propensity-matched analysis.

Abstract

Background: Stress hyperglycemia ratio (SHR) has emerged as a potential prognostic marker in critical illness, but its association with mortality in cardiovascular disease remains incompletely characterized. This study investigated the relationship between SHR and all-cause mortality in critically ill patients with cardiovascular disease, adjusting for a variety of confounding factors using propensity score matching (PSM).

Methods: A cohort of 3,352 critically ill patients with cardiovascular disease was stratified by SHR quartiles (Q1-Q4). Baseline characteristics, comorbidities (e.g., heart failure, diabetes), and severity scores (OASIS, APSIII, SOFA) were extracted from a large database containing de-identified health data patients admitted to the intensive care units (ICUs) of Beth Israel Deaconess Medical Center. PSM (670 matched pairs) balanced covariates between high (SHR > 1.355) and low SHR groups. The associations between SHR and mortality risk (in-hospital, 28-day, 90-day, 365-day) were evaluated using Cox models, restricted cubic spline (RCS) analysis, and Kaplan-Meier survival curves. Cox proportional hazards models were implemented with three sequential adjustment levels: Model 1 (unadjusted); Model 2 (adjusted for demographic factors and comorbidities); and Model 3 (fully adjusted). Predictive performance of SHR combined with severity scores was assessed via area under the curve (AUC) improvement.

Results: Higher SHR quartiles exhibited greater comorbidity burden (e.g., acute kidney injury: 84.6% in Q4 vs. 79.7% in Q1, P < 0.001) and severity scores (P < 0.001). Unadjusted analysis showed a significant association between SHR and mortality, with Q4 having the highest in-hospital (Q4: 16.3% vs. Q1-Q3: 5.1-6.4%, P < 0.001) and 365-day mortality (Q4: 29.2% vs. Q1-Q3: 15.7-16.9%, P < 0.001). The RCS analysis revealed a U-shaped mortality risk, with average optimal SHR cutoffs of 1.355. After PSM, cox proportional hazard models confirmed that high SHR (Q4) remained associated with early mortality (in-hospital HR = 2.117, [95% CI: 1.223-3.665], P = 0.007; 28-day HR = 1.859, [95% CI: 1.100-3.141], P = 0.020) but not long-term outcomes (90-day mortality, P = 0.127; 365-day mortality, P = 0.123) in the Model 1. Similar trends were obtained after adjusting for demographic factors and comorbidities (Model 2) and in the fully adjusted model (Model 3). Adding SHR improved short-term mortality prediction performance (e.g., OASIS AUC: +0.034 for in-hospital, P < 0.001), though benefits diminished post-PSM (e.g., OASIS: +0.012 for in-hospital, P = 0.009). However, incorporating SHR did not enhance the predictive performance of OASIS and SAPSII for 90-day and 365-day mortality prediction after PSM.

Conclusion: Elevated SHR contributes to early mortality in patients with cardiovascular disease, even after rigorous confounder adjustment. The incremental predictive value of SHR support its utility for risk stratification, particularly for short-term outcomes, but its prognostic value fades for long-term mortalities. These findings highlight SHR as a favorable biomarker for clinical decision-making in acute cardiovascular care.