# Evaluation of novel glucose-related blood biomarkers for predicting in-hospital mortality in patients with acute ischemic stroke

## TL;DR

The study evaluates new blood biomarkers related to glucose to predict severity and in-hospital mortality in acute ischemic stroke patients in Thailand.

## Contribution

The study introduces and evaluates novel glucose-related biomarkers for predicting outcomes in acute ischemic stroke patients.

## Key findings

- GAR outperformed other biomarkers in predicting severity with an AUC of 0.672.
- SHR was most effective in predicting in-hospital mortality with an AUC of 0.832.
- High GAR and SHR levels significantly increased the risk of death in AIS patients.

## Abstract

Acute Ischemic Stroke (AIS) is a major cause of death and disability worldwide. AIS patients with hyperglycemia demonstrate a potential risk to enhance severity and mortality rates. Glycemic markers, including blood glucose, hemoglobin A1c, and stress hyperglycemia ratio (SHR), have currently been reported to predict unfavorable outcomes in these patients. However, the ability of novel glucose-related blood biomarkers, such as the glucose to albumin ratio (GAR), glucose to estimated average glucose ratio (GAGR), and glucose to potassium ratio (GPR), to predict severe AIS patients and in-hospital mortality in Thailand remains unclear. This study aimed to investigate the utility of novel glucose related-blood biomarkers in predicting severity and in-hospital mortality among AIS patients. We conducted a retrospective single-center analysis of data from patients admitted to the Stroke Unit at Saraburi Hospital between January 1 and December 31, 2023. A total of 351 AIS patients were examined, with 191 (54.4%) presenting severe cases, and 31 (8.8%) died in the hospital. We demonstrated that the GAR was superior to SHR, GAGR, and GPR in predicting severity, showing an area under the curve (AUC) of 0.672 (95% CI: 0.614–0.731), yielding a sensitivity of 72.8% and a specificity of 56.6%. However, the SHR showed a highest AUC of 0.832 (95% CI: 0.734–0.930) in predicting in-hospital mortality, with sensitivity and specificity of 87.1% and 64.7%, respectively. Furthermore, AIS patients with GAR ≥ 30.0 and SHR ≥ 18.0 had a 12.761 and 12.365-fold increased risk of death (p < 0.001), respectively. Our study indicates that, besides SHR, GAR may serve as a predictive and cost-effective biomarker for predicting severe cases and in-hospital mortality of AIS, facilitating early triage even with limited resources.