Garcia Valencia Oscar A, Suppadungsuk Supawadee, Thongprayoon Charat, Ho Yuh-Shan, Siranart Noppachai, Wathanavasin Wannasit, Jadlowiec Caroline C, Mao Shennen A, Leeaphorn Napat, Soliman Karim M, Ali Hatem, Budhiraja Pooja, Miao Jing, Cheungpasitporn Wisit
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
Chakri Naruebodindra Medical Institute, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Ren Fail. 2025 Dec;47(1):2513007. doi: 10.1080/0886022X.2025.2513007. Epub 2025 Jun 9.
Kidney transplantation is the preferred treatment for end-stage kidney disease (ESKD) in the United States, yet access and outcomes vary by insurance type, race, and socioeconomic status. This systematic review synthesizes U.S.-based evidence on how insurance coverage influences transplant waitlisting, access, and outcomes. AI-assisted analysis was used to quantify disparities and propose policy recommendations.
A systematic review of MEDLINE, EMBASE, and the Cochrane Database (through November 2024) was conducted to identify studies on insurance-related disparities in U.S. kidney transplantation (PROSPERO: CRD42023484733). AI-assisted synthesis using o3-mini-high (2025) was employed to identify patterns and guide policy development.
Among 2,163 records, 14 studies met inclusion criteria. Patients with Medicare or Medicaid-particularly racial and ethnic minorities-had lower referral rates and higher transplant waitlist rejection compared to those with private insurance. Socioeconomic barriers such as low income and limited education further impaired access and worsened post-transplant outcomes. Publicly insured recipients had higher post-transplant mortality and graft failure rates. Loss of Medicare after 36 months was associated with reduced immunosuppressant adherence and increased rejection. Disparities were amplified by Medicaid expansion variability and inconsistent transplant center policies. AI-assisted analysis confirmed these disparities and generated policy proposals including standardized referral guidelines, lifelong immunosuppressant coverage, targeted financial aid, equity-linked incentives for transplant centers, and scalable digital health solutions.
Insurance type, race, and socioeconomic status significantly influence kidney transplant access and outcomes. AI-assisted analysis identified structural inequities and informed targeted policy strategies to advance transplant equity and support broader healthcare reform.
在美国，肾移植是终末期肾病（ESKD）的首选治疗方法，但获取途径和治疗结果因保险类型、种族和社会经济地位而异。本系统评价综合了美国国内关于保险覆盖范围如何影响移植等待名单、获取途径和治疗结果的证据。采用人工智能辅助分析来量化差异并提出政策建议。
对MEDLINE、EMBASE和Cochrane数据库（截至2024年11月）进行系统评价，以识别关于美国肾移植中与保险相关差异的研究（PROSPERO：CRD42023484733）。使用o3-mini-high（2025）进行人工智能辅助综合分析，以识别模式并指导政策制定。
在2163条记录中，14项研究符合纳入标准。与拥有私人保险的患者相比，参加医疗保险或医疗补助的患者，尤其是少数种族和族裔，转诊率较低，移植等待名单被拒率较高。低收入和教育程度有限等社会经济障碍进一步阻碍了获取途径，并使移植后结果恶化。参加公共保险的受者移植后死亡率和移植物失败率较高。36个月后失去医疗保险与免疫抑制剂依从性降低和排斥反应增加有关。医疗补助扩展的差异和移植中心政策的不一致加剧了差异。人工智能辅助分析证实了这些差异，并提出了政策建议，包括标准化转诊指南、终身免疫抑制剂覆盖、有针对性的经济援助、针对移植中心的公平相关激励措施以及可扩展的数字健康解决方案。
保险类型、种族和社会经济地位显著影响肾移植的获取途径和治疗结果。人工智能辅助分析识别了结构性不平等，并为推进移植公平和支持更广泛的医疗改革提供了有针对性的政策策略。
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