Ge Yuanshuo, Hu Ding, Wang Zhe, Zhang Cheng
Department of General Surgery, General Hospital of Northern Theater Command (Formerly Called General Hospital of Shenyang Military Area), Shenyang, China.
Jinzhou Medical University, Jinzhou, Liaoning Province, China.
BMC Infect Dis. 2025 Aug 14;25(1):1021. doi: 10.1186/s12879-025-11429-w.
Sepsis is a life-threatening condition characterized by dysregulated immune responses and metabolic disturbances. The albumin-to-neutrophil-lymphocyte ratio (ANLR) is a novel composite biomarker integrating nutritional and inflammatory status. However, its prognostic significance in sepsis remains unclear. This study aims to evaluate the association between ANLR and mortality in sepsis patients using both traditional statistical methods and machine learning models.
A retrospective cohort study was conducted using the MIMIC-IV (v3.1) database. In this study, 6,288 patients diagnosed with sepsis and admitted to the ICU were analyzed, with participants stratified into quartiles according to their ANLR measurements. The primary endpoint was set as 30-day mortality, while 90-day mortality served as a secondary outcome. The association between ANLR and mortality was investigated through Kaplan-Meier survival analysis, Cox regression, and restricted cubic spline (RCS) modeling. Furthermore, the contribution of ANLR relative to other predictors was evaluated by developing machine learning models, with SHapley Additive exPlanations (SHAP) employed to determine variable importance.
A higher ANLR was independently associated with improved survival. In the fully adjusted Cox model, elevated ANLR predicted a lower risk of mortality at 30 days (HR 0.68, 95% CI 0.59-0.79, p < 0.001) and at 90 days (HR 0.85, 95% CI 0.76-0.94, p = 0.002). Machine learning analysis identified ANLR as the second most important variable influencing sepsis mortality. ANLR demonstrated superior predictive ability (AUC 0.66) compared to traditional markers, including SOFA, NLR, and albumin.
ANLR is a robust and independent predictor of sepsis-related mortality, outperforming conventional biomarkers. Incorporating ANLR into routine clinical workflows could improve risk assessment and facilitate individualized treatment strategies for patients with severe sepsis. Further prospective studies are needed to validate these findings and explore potential therapeutic implications.
脓毒症是一种危及生命的疾病，其特征为免疫反应失调和代谢紊乱。白蛋白与中性粒细胞淋巴细胞比值（ANLR）是一种整合营养和炎症状态的新型复合生物标志物。然而，其在脓毒症中的预后意义仍不明确。本研究旨在使用传统统计方法和机器学习模型评估脓毒症患者中ANLR与死亡率之间的关联。
使用MIMIC-IV（v3.1）数据库进行了一项回顾性队列研究。在本研究中，分析了6288例诊断为脓毒症并入住重症监护病房的患者，参与者根据其ANLR测量值分为四分位数。主要终点设定为30天死亡率，90天死亡率作为次要结局。通过Kaplan-Meier生存分析、Cox回归和受限立方样条（RCS）建模研究ANLR与死亡率之间的关联。此外，通过开发机器学习模型评估ANLR相对于其他预测因子的贡献，使用SHapley加性解释（SHAP）来确定变量重要性。
较高的ANLR与生存率提高独立相关。在完全调整的Cox模型中，ANLR升高预示30天时死亡风险较低（HR 0.68，95%CI 0.59-0.79，p<0.001），90天时死亡风险较低（HR 0.85，95%CI 0.76-0.94，p=0.002）。机器学习分析确定ANLR是影响脓毒症死亡率的第二重要变量。与包括序贯器官衰竭评估（SOFA）、中性粒细胞与淋巴细胞比值（NLR）和白蛋白在内的传统标志物相比，ANLR表现出卓越的预测能力（AUC 0.66）。
ANLR是脓毒症相关死亡率的有力且独立的预测因子，优于传统生物标志物。将ANLR纳入常规临床工作流程可改善风险评估，并促进重症脓毒症患者的个体化治疗策略。需要进一步的前瞻性研究来验证这些发现并探索潜在的治疗意义。
