Neutrophil-to-lymphocyte ratio predicts inpatient gout recurrence: a large-scale multicenter retrospective cohort with machine-learning validation

医学 痛风 回顾性队列研究 高尿酸血症 内科学 生物标志物 队列 队列研究 梅德林 急诊医学 多中心研究 重症监护医学 试验预测值 共病 物理疗法 疾病严重程度 风险评估 儿科 肿瘤科
作者
Hui Zhang,Jiani Liu,Ruifeng Lin,Danning Xie,Wenxia Lin,Qingqing Zhang,Fei Zhong,Shixian Chen,Qin Huang,Min Zhang,Yixin Chen,Xiaoling Chen,Zhipeng Cheng,Jiabao Xu,Cai Li,Xinhao Xia,Yaqi Chen,Zheng Xu,Yi Yuan,Meng Li
出处
期刊:Frontiers in Immunology [Frontiers Media]
卷期号:16: 1688516-1688516
标识
DOI:10.3389/fimmu.2025.1688516
摘要

Background The neutrophil-to-lymphocyte ratio (NLR) is an accessible marker of systemic inflammation. However, its prognostic value for inpatient gout recurrence, particularly in comparison with traditional biomarkers, remains unclear. This study aims to investigate the association of NLR with inpatient gout recurrence, and compare its performance with traditional markers. Methods In this international, multicenter retrospective cohort study, hospitalized patients with gout were enrolled from the GoutRe cohort (China, 2010-2025) and MIMIC-IV cohort (USA, 2008-2019). Restricted cubic spline, Cox regression and competing risk models were deployed to visualize and assess the association of NLR with inpatient gout recurrence risk. Model performance was evaluated using the C-statistic, net reclassification improvement, and decision curve analysis. Multiple machine learning algorithms were employed for external validation. Results Among 7,603 patients (GoutRe: 5,584; MIMIC-IV: 2,019), elevated NLR (>2.69) was independently associated with a higher inpatient gout recurrence risk (GoutRe: HR = 2.05; MIMIC-IV: HR = 2.84; both P < 0.001). NLR correlated with systemic inflammation, comorbidities, and use of diuretics/β-blockers. It outperformed serum uric acid (UA) and C-reactive protein (CRP) in predicting inpatient gout recurrence (AUC: 0.62 vs. 0.59 and 0.61, respectively), with improved accuracy when combined with UA (AUC = 0.65, P < 0.01). Predictive value remained consistent across subgroups, including those with normal UA, no tophus, and ongoing anti-inflammatory or urate-lowering therapy. Machine learning models, particularly XGBoost, confirmed NLR’s predictive strength. Incorporation of NLR into baseline models improved discrimination and reclassification. Decision curve analysis showed greater net clinical benefit with NLR-based models. Biological plausibility analysis revealed that elevated NLR reflected neutrophilia and lymphopenia, indicative of systemic inflammation during the intercritical period. Conclusions Elevated NLR is a robust, accessible biomarker independently associated with inpatient gout recurrence. Its integration into clinical risk models enhances prediction accuracy and supports personalized inpatient gout recurrence prevention strategies.
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