Objectives Stage ≥ 3 chronic kidney disease (CKD) affects ∼25% of people with gout. The effects of urate-lowering therapy (ULT) on CKD incidence and progression have remained inconclusive. Here, we assessed the impact of a gout urate-lowering therapy (ULT) clinic intervention using artificial intelligence (AI) on CKD incidence and achievement of serum urate target. Methods An observational study compared propensity score-matched gout cohorts from an AI-based Gout Intelligent Management System (GIMS) and a standard Electronic Medical Records system (EMRS) clinic database. The GIMS included a mobile application, data fusion interface, and modules for expert consultation and laboratory results management. All patients had gout and a starting eGFR > 60 ml/min. Using a 1:2 propensity score (PS)-matched cohort study design we assessed the impact of the AI-based system on CKD outcomes and ULT effectiveness over 4 years of follow-up. Results Compared with EMRS, GIMS was associated with reduced incidence of CKD stage ≥ 3. Specifically, 169/4117 new onset CKD stage ≥ 3 (incidence 4.1 per 100 person-years) with GIMS compared with 164/2128 with EMRS (incidence 7.7 per 100 person-years) during follow-up. More participants achieved serum urate <6.0 mg/dl with GIMS vs EMRS during follow-up (49.8% vs 25.9%, p < 0.001). Conclusion Application of the AI-based GIMS was associated with lower incidence of CKD stage ≥ 3 and superior target serum urate achievement in people with gout. The AI-based GIMS represents a novel approach to improve real-world renal outcomes and ULT success in gout.

中文翻译：

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基于人工智能的痛风管理系统减少了慢性肾病的发生，提高了目标血清尿酸盐的实现


目标 ≥ 期 3 慢性肾脏病 （CKD） 影响 ∼25% 的痛风患者。降尿酸疗法 （ULT） 对 CKD 发病率和进展的影响仍无定论。在这里，我们评估了使用人工智能 （AI） 的痛风降尿酸疗法 （ULT） 临床干预对 CKD 发生率和血清尿酸盐目标实现的影响。方法 一项观察性研究比较了来自基于 AI 的痛风智能管理系统 （GIMS） 和标准电子病历系统 （EMRS） 诊所数据库的倾向评分匹配的痛风队列。GIMS 包括移动应用程序、数据融合界面以及用于专家咨询和实验室结果管理的模块。所有患者均患有痛风，起始 eGFR > 为 60 ml/min。使用 1：2 倾向评分 （PS） 匹配的队列研究设计，我们评估了基于 AI 的系统在 4 年随访中对 CKD 结果和 ULT 有效性的影响。结果 与 EMR 相比，GIMS 与 CKD ≥ 3 期发病率降低相关。具体而言，在随访期间，GIMS 新发 CKD 分期为 169/4117 ≥ 3 级（发生率为 4.1/100 人年），而 EMRS 为 164/2128 例（发生率为 7.7/100 人年）。在随访期间，GIMS 与 EMR 相比，更多的参与者达到血清尿酸盐 <6.0 mg/dl （49.8% vs 25.9%，p < 0.001）。结论 基于 AI 的 GIMS 的应用与痛风患者 CKD ≥ 3 期发生率降低和血清尿酸目标达到率较高相关。基于 AI 的 GIMS 代表了一种改善痛风真实肾脏结局和 ULT 成功率的新方法。