ORIGINAL RESEARCH article
Front. Immunol.
Sec. Autoimmune and Autoinflammatory Disorders: Autoinflammatory Disorders
Neutrophil-to-Lymphocyte Ratio Predicts Inpatient Gout Recurrence: A Large-Scale Multicenter Retrospective Cohort with Machine-Learning Validation
Provisionally accepted
Hui Zhang1
Jiani Liu2Ruifeng Lin2Danning Xie1Wenxia Lin3Qingqing Zhang2Fei Zhong1Shixian Chen1
Qin Huang1Min Zhang1Yixin Chen4Xiaoling Chen4Zhipeng Chen4Jiabao Xu5Li Cai2Xinhao Xia4Yaqi Chen4Ziwen Xu6Yi Yuan1
Meng Li1
Juan Li1*- 1Department of Rheumatology and Immunology, Nanfang Hospital, Southern Medical University, Guangzhou, China, Guangzhou, China
- 2Department of Traditional Chinese Internal Medicine, School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China, Guangzhou, China
- 3School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China, Guangzhou, China
- 4The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
- 5School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China., Guangzhou, China
- 6the school of Integrated Traditional Chinese and Western Medicine, Guangzhou Medical University, Guangzhou, China
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Abstract 5 Background: The neutrophil-to-lymphocyte ratio (NLR) is an accessible marker of 6 systemic inflammation. However, its prognostic value for inpatient gout recurrence, 7 particularly in comparison with traditional biomarkers, remains unclear. This study 8 aims to investigate the association of NLR with inpatient gout recurrence, and 9 compare its performance with traditional markers. 10 11 Methods: In this international, multicenter retrospective cohort study, hospitalized 12 patients with gout were enrolled from the GoutRe cohort (China, 2010-2025) and 13 MIMIC-IV cohort (USA, 2008-2019). Restricted cubic spline, Cox regression and 14 competing risk models were deployed to visualize and assess the association of NLR 15 with inpatient gout recurrence risk. Model performance was evaluated using the 16 C-statistic, net reclassification improvement, and decision curve analysis. Multiple 17 machine learning algorithms were employed for external validation. 18 19 Results: Among 7,603 patients (GoutRe: 5,584; MIMIC-IV: 2,019), elevated NLR 20 (>2.69) was independently associated with a higher inpatient gout recurrence risk 21 (GoutRe: HR=2.05; MIMIC-IV: HR=2.84; both P <0.001). NLR correlated with 22 systemic inflammation, comorbidities, and use of diuretics/β-blockers. It 23 outperformed serum uric acid (UA) and C-reactive protein (CRP) in predicting 24 inpatient gout recurrence(AUC: 0.62 vs. 0.59 and 0.61, respectively), with improved 25 accuracy when combined with UA (AUC=0.65, P <0.01). Predictive value remained 26
Keywords: Neutrophil-to-lymphocyte ratio, Gout, Recurrence, machine learning, Model
Received: 19 Aug 2025; Accepted: 27 Oct 2025.
Copyright: © 2025 Zhang, Liu, Lin, Xie, Lin, Zhang, Zhong, Chen, Huang, Zhang, Chen, Chen, Chen, Xu, Cai, Xia, Chen, Xu, Yuan, Li and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Juan Li, lijuan@smu.edu.cn
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