The glycemic gap as a prognostic indicator in cardiogenic shock: a retrospective cohort study

Abstract

Background: Stress-induced hyperglycemia (SIH) is associated with poor outcomes in cardiogenic shock (CS), and there have been inconsistent results among patients with or without diabetes mellitus (DM). The glycemic gap (GG) is derived by subtracting A1c-derived average glucose from blood glucose levels; it is a superior indicator of SIH. We aimed to explore the role of GG in the outcomes of patients with CS.

Methods: Data on patients diagnosed with CS were extracted from the MIMIC-IV v2.0 database to investigate the relationship between GG and 30-day mortality (Number of absolute GG subjects = 359; Number of relative GG subjects = 357). CS patients from the Second Affiliated Hospital of Wenzhou Medical University were enrolled to explore the correlation between GG and lactic acid (Number of absolute GG subjects = 252; Number of relative GG subjects = 251). Multivariate analysis, propensity score-matched (PSM) analysis, inverse probability treatment weighting (IPTW), and Pearson correlation analysis were applied.

Results: Absolute GG was associated with 30-day all-cause mortality in CS patients (HR_adjusted: 1.779 95% CI: 1.137-2.783; HR_PSM: 1.954 95% CI: 1.186-3.220; HR_IPTW: 1.634 95% CI: 1.213-2.202). The higher the absolute GG level, the higher the lactic acid level (β_adjusted: 1.448 95% CI: 0.474-2.423). A similar trend existed in relative GG (HR_adjusted: 1.562 95% CI: 1.003-2.432; HR_PSM: 1.790 95% CI: 1.127-2.845; HR_IPTW: 1.740 95% CI: 1.287-2.352; β_adjusted:1.294 95% CI: 0.369-2.219). Subgroup analysis showed that the relationship existed irrespective of DM. The area under the curve of GG combined with the Glasgow Coma Scale (GCS) for 30-day all-cause mortality was higher than that of GCS (absolute GG: 0.689 vs. 0.637; relative GG: 0.688 vs. 0.633). GG was positively related to the triglyceride-glucose index. Kaplan-Meier curves revealed that groups of higher GG with DM had the worst outcomes. The outcomes differed among races and GG levels (all P < 0.05).

Conclusions: Among patients with CS, absolute and relative GGs were associated with increased 30-day all-cause mortality, regardless of DM. The relationship was stable after multivariate Cox regression analysis, PSM, and IPTW analysis. Furthermore, they reflect the severity of CS to some extent. Hyperlactatemia and insulin resistance may underlie the relationship between stress-induced hyperglycemia and poor outcomes in CS patients. They both improve the predictive efficacy of the GCS.

Keywords: Cardiogenic shock; Glycemic gap; Inverse probability treatment weighting; Prognosis; Propensity score-matched analysis.

Fig. 1

Flow chart of study population of absolute glycemic gap. Notes: (a) Flow chart of study population of absolute glycemic gap from MIMIC-IV v2.0; (b) Flow chart of study population of absolute glycemic gap from the Second Affiliated Hospital of Wenzhou Medical University. Abbreviations: ICD, International Classification of Diseases; ICU, intensive care unit; IPTW, inverse probability treatment weight; MIMIC-IV v2.0, medical information mart for intensive care-IV database version 2.0; PSM, propensity score match

Fig. 2

Survival analysis. Notes: (a) Survival analysis across populations among different level of absolute glycemic gap; (b) Survival analysis across populations among different level of absolute glycemic gap with different glucose status; (c) Survival analysis across populations among different level of absolute glycemic gap with different ethnics. Abbreviations: DM, diabetes mellitus; GG, glycemic gap

Fig. 3

Subgroup analysis. Notes: (a) Subgroup analysis of the associations between absolute glycemic gap as a continuous variable and 30-day all-cause mortality among patients with cardiogenic shock; (b) Subgroup analysis of the associations between absolute glycemic gap as a categorical variable and 30-day all-cause mortality among patients with cardiogenic shock. Abbreviations: CI, confidence interval; DM, diabetes mellitus; HR, hazard ratio

Fig. 4

ROC curve analysis. Notes: Model1 only for GCS.Model2 was for GCS plus absolute glycemic gap. The area under the curve of GCS + glycemic gap was higher than that of GCS (0.689 vs. 0.637,P = 0.0221). Abbreviations: AUC, area under curve; GCS, Glasgow coma scale; ROC, receiver operating characteristic

Fig. 5

Pearson analysis between absolute glycemic gap and TyG. Notes: Scatter plot showed a positive correlation between absolute glycemic gap and TyG in Fig. 5. [R_ab = 0.5900 in total study population (a), R_ab = 0.4817 in diabetes (b), R_ab = 0.6965 in non-diabetes (c)]. Abbreviations: TyG, triglyceride-glucose index