实用器官移植电子杂志 ›› 2022, Vol. 10 ›› Issue (5): 401-407.DOI: 10.3969/j.issn.2095-5332.2022.05.005

• 论著 • 上一篇    下一篇

肝移植后糖尿病发病相关因素分析及预后影响

李畅 1 ,张慧 1 ,李长贤 1 ,吴晓峰 1 ,张峰 1 ,杨涛 2 ,张梅 2 ,李相成   

  1. 1. 南京医科大学第一附属医院肝胆中心,江苏 南京 210029;

    2. 南京医科大学第一附属医院内分泌科,江苏 南京 210029)

  • 出版日期:2022-09-20 发布日期:2022-09-20
  • 基金资助:

    江苏省社会发展重点病种规范化诊疗项目(BE2016789) 

Analysis of factors associated with the development of post-transplantation diabetes mellitus andits impact on prognosis 

Li Chang1 , Zhang Hui1 , Li Changxian1 , Wu Xiaofeng1 , Zhang Feng1 , Yang Tao2 , Zhang Mei2 ,Li Xiangcheng1 .   

  1. 1.TheHepatobiliary Center,The First Affiliated Hospital of Nanjing Medical University,Nanjing 210029,Jiangsu,China

    2.Department of Endocrinology,The First Affiliated Hospital of Nanjing Medical University,Nanjing 210029,Jiangsu,China

  • Online:2022-09-20 Published:2022-09-20

摘要:

目的 探讨影响肝移植后糖尿病(posttransplantation diabetes mellitus,PTDM)发生的相关危险因素,并分析 PTDM 对预后的影响。方法 选取 2015 年 1 月至 2020 年 9 月于南京医科大学第一附属医院肝胆中心行公民逝世后器官捐献(donation after citizen’s death,DCD)肝移植术患者,将患者分为PTDM 组与非 PTDM 组,比较两组患者的基线特征、术中信息及术后并发症情况,采用竞争风险模型识别影响 PTDM 发生的相关危险因素,采用 Cox 回归分析 PTDM 对患者预后的影响。结果 肝移植术后 1、3、5 年糖尿病的发病率分别为 13.3%16.9%27.0%2 组间患者年龄、术后住院时长、免疫排斥反应差异有统计学意义。考虑死亡为竞争风险事件后,年龄每增加 10 岁,发生 PTDM 的风险增加 45%(SHR 1.45,95% CI 1.17 ~ 1.80);有免疫排斥反应患者 PTDM 的发病风险是无免疫排斥反应患者的 2.06 倍(95% CI 1.00 ~ 4.24);

术后住院超过 1 个月患者 PTDM 的发病风险增加 62%(SHR 1.62,95% CI 1.01 ~ 2.59)。 PTDM 患者发生晚期感染并发症的风险为非 PTDM 患者的 2.48 倍(95% CI 1.22 ~ 5.04),而两组患者晚期胆道并发症的发生率无统计学差异(RR 1.58,95% CI 0.77 ~ 3.26)。控制疾病诊断后,PTDM 组患 者死亡风险是非 PTDM 患者的 1.73 倍(95% CI 1.07 ~ 2.77)。结论 PTDM 发病率与患者年龄、免疫排斥反应、术后住院时长相关,且 PTDM 患者总体生存较差,术后晚期感染风险上升。移植术后应早期筛查血糖,防治 PTDM,以期减少 PTDM 的发生,改善患者预后。

关键词:

肝移植  , 肝移植术后糖尿病  , 累积发生函数  , 竞争风险模型

Abstract:

Objective To investigate the associated risk factors affecting the development ofposttransplantation diabetes mellitus (PTDM) and to analyze the effect of PTDM on patient outcomes. Methods We selected patients who underwent liver transplantation from DCD donors at the hepatobiliary center of the First Affiliated Hospital of Nanjing Medical University between January 2015 and September 2020. The patients were divided into PTDM and non PTDM groups, and the baseline characteristics, intraoperative information and postoperative complications werecompared between the two groups. A competing risk model was used to identify relevant risk factors affecting the occurrence of PTDM, and Cox regression was used to analyze the effect of PTDM on patient outcomes. Results The incidence of diabetes at 1,3 and 5 years after liver transplantation was 13.3%,16.9%,27.0%, respectively. There were statistically significant differences in patient age, length of postoperative hospital stay, and immune rejection between groups. Afteraccounting for death as a competing risk event,every 10-year increase in age was associated with a 45% increased risk of developing PTDM (SHR = 1.45,95% CI = 1.17 ~ 1.80); The risk of PTDM in patients with immune rejection was 2.06 times higher than that in patients without immune rejection (95% CI = 1.00 ~ 4.24); Patients hospitalized for more than one month postoperatively had a 62% increased risk of developing PTDM (SHR = 1.62,95% CI = 1.01 ~ 2.59). The risk of late infectious complications in PTDM patients was 2.48 times higher than non PTDM patients (95% CI =1.22 ~ 5.04); Whereas the incidence of late biliary complications was not statistically different between the two groups (RR= 1.58,95% CI= 0.77~ 3.26). After controlling for disease diagnosis, patients in the PTDM group had a 1.73-fold (95% CI = 1.07 ~ 2.77) higher risk of death than non PTDM patients. Conclusion The incidence of PTDM is associated with patient age, immune rejection, and long postoperative hospitalization, and the overall survival of PTDM patients is poor, with a rising risk of late postoperative infection. After transplantation, blood glucose should be screened early to prevent PTDM, in the hope of reducing the occurrence of PTDM and improving patient outcomes.

Key words:

Liver transplantation ; , Posttransplantation diabetes mellitus ; , Cumulative incidence function ;Competing risk model