# Associations between blood glucose-lipid levels and post-intravenous thrombolysis outcomes in stroke patients: a retrospective study utilizing logistic regression analysis

## TL;DR

This study finds that blood glucose and lipid levels are linked to outcomes in stroke patients treated with intravenous thrombolysis.

## Contribution

The study introduces a predictive model using blood glucose and lipid levels to assess post-thrombolysis stroke outcomes.

## Key findings

- Higher glucose and lipid levels are independent risk factors for poor stroke outcomes.
- The model showed strong predictive accuracy at 30 and 90 days post-treatment.
- Modifiable parameters like glucose and lipids may help improve risk-stratified stroke management.

## Abstract

This study investigates the relationship between blood glucose and lipid profiles and the functional outcomes of acute ischemic stroke (AIS) patients treated with intravenous thrombolysis.

We retrospectively analyzed data from AIS patients admitted to a tertiary hospital in Chengdu between September 2023 and March 2025. Patients were grouped according to post-thrombolysis modified Rankin scale (mRS) scores into good outcome (0–2) and poor outcome (3–6) categories. Multivariable logistic regression and nomogram modeling were used to identify predictors of 30- and 90-day outcomes.

The final predictive model included 11 variables, and the results showed higher age, smoking, diabetes, higher systolic blood pressure (SBP), higher diastolic blood pressure (DBP), higher total cholesterol (TC), lower high-density lipoprotein cholesterol (HDL), higher low-density lipoprotein cholesterol (LDL), higher haemoglobin A1c (HbA1c), and higher fasting plasma glucose (FPG) were independent risk factors. This model demonstrated robust predictive performance and accuracy across all timepoints (training dataset at 30 days, the AUC of 0.821, 95% CI: 0.765–0.877; training dataset at 90 days, the AUC of 0.871, 95% CI: 0.824–0.919).

Blood glucose and lipid levels are linked to outcomes after intravenous thrombolysis in stroke. Despite model limitations, these modifiable parameters may support risk-stratified management, potentially improving functional outcomes post-thrombolysis.