The Lactic Dehydrogenase-to-Albumin Ratio Predicts Acute Kidney Injury in Patients with Intracerebral Hemorrhage: A Multicenter Cohort Study

Taotao Dong¹Yangchun Xiao¹Xiang Yuan¹Peng Wang²Chao You²Fang Fang²Yu Zhang^1*

• ¹Affiliated Hospital and Clinical Medical College of Chengdu University, Chengdu, China
• ²West China Hospital, Sichuan University, Chengdu, Sichuan Province, China

Background: Acute kidney injury (AKI) is a common and serious complication in patients with intracerebral hemorrhage (ICH), contributing to poor clinical outcomes. The lactic dehydrogenase-to-albumin ratio (LAR) has emerged as a promising biomarker combining markers of cellular injury and systemic inflammation. However, its role in predicting AKI in ICH patients remains unexplored. Objective: This study aims to evaluate the predictive value of LAR for AKI in ICH patients and assess its potential to enhance predictive models for AKI risk. Methods: We conducted a retrospective, multicenter cohort study involving 6465 patients with spontaneous ICH from three hospitals in China. LAR was calculated from routine laboratory values (lactic dehydrogenase and albumin), and its association with AKI was analyzed using logistic regression and receiver operating characteristic (ROC) curves. Subgroup analyses were performed to explore heterogeneity in the relationship between LAR and AKI. Results: Higher LAR values were significantly associated with increased risk of AKI. Patients with LAR >5.61 had a higher odds ratio (OR 2.51, 95% CI 2.13-2.96) for developing AKI compared to those with lower LAR. A dose-response relationship was observed, with progressively higher AKI risk across LAR quartiles. Incorporating LAR into existing predictive models improved the accuracy from 0.73 to 0.76 (p < 0.001). Subgroup analysis revealed that age and hematoma volume influenced the strength of the LAR-AKI association. Conclusions: LAR is a reliable and cost-effective biomarker associated with AKI risk in ICH patients, with significant potential to enhance early detection and risk stratification; however, causality cannot be established due to the retrospective study design. Its incorporation into predictive models improves accuracy and provides a feasible tool for identifying high-risk patients. Further validation and long-term studies are needed to confirm its clinical utility across diverse populations.