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Study on the link between neutrophil percentage to albumin ratio and acute kidney injury in severe ischemic stroke patients during hospitalization

Year: 2025
Type: article
Study design: Observational Study
Abstract: Background This study aimed to investigate the association between the neutrophil percentage-to-albumin ratio (NPAR) and the occurrence of acute kidney injury (AKI) in patients with severe ischemic stroke. Methods Based on the EICU Clinical Research Database (EICU-CRD), 1,027 patients with severe ischemic stroke were enrolled (AKI group: 137 cases, non-AKI group: 890 cases). Data description: Non-normally distributed variables were expressed as median (IQR), and categorical variables were presented as frequency (weighted percentage). Statistical analysis: Intergroup comparisons were performed using the Wilcoxon rank-sum test and Rao-Scott chi-square test. Multivariate logistic regression and trend analysis were employed to evaluate the predictive value of NPAR for AKI, with adjustments for confounding factors. Results 1. NPAR levels: The AKI group exhibited significantly higher NPAR than the control group (29 ± 10 vs. 24 ± 7, p < 0.001). 2. Risk prediction: After adjusting for confounding factors including liver function, electrolyte levels, blood cell count, history of renal insufficiency, furosemide use, and vital signs, NPAR remained an independent risk factor for AKI (OR = 1.041, 95% CI: 1.007–1.076, p-value = 0.0162). 3. Dose-effect relationship: A significant increase in AKI risk was observed with each one-quarter increase in NPAR (Q4 vs Q1, OR: 3.598, 95% CI: 1.482–9.12, p-value: 0.0056, p for trend: 0.0028). 4. Subgroup analysis: The impact of elevated NPAR on AKI risk was more pronounced in male patients. Conclusion Elevated NPAR levels significantly increase the risk of acute kidney injury in patients with severe ischemic stroke, demonstrating a clear dose–response relationship. These findings suggest that NPAR may serve as a potential biomarker.
Authors: Chao Dong, Zhaobing Tang, Xingang Bai, Fangxu Que, Ling Bai, Yi Huang, Sheng He, Rizhao Pang
Language: English
Subfield: Nephrology
Field: Medicine
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4414254941
FWCI: 0.62
Cites: 39
Cited by: 1
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