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利用机器学习识别糖尿病肝移植受者长期生存的可调节预测因子。

Identifying Modifiable Predictors of Long-Term Survival in Liver Transplant Recipients With Diabetes Mellitus Using Machine Learning.

机构信息

Department of Computer Science University of Toronto Toronto Ontario Canada Genetics and Genome Biology SickKids Research Institute Toronto Ontario Canada Vector Institute Toronto Ontario Canada Interdepartmental Division of Critical Care Medicine Toronto Ontario Canada Multi Organ Transplant Program and Division of Gastroenterology University Health Network Toronto Ontario Canada Division of Gastroenterology and Hepatology University Health Network and University of Toronto Toronto Ontario Canada Division of Gastroenterology University of Toronto Toronto Ontario Canada.

出版信息

Liver Transpl. 2021 Apr;27(4):536-547. doi: 10.1002/lt.25930. Epub 2021 Feb 2.

DOI:10.1002/lt.25930
PMID:33113221
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8248095/
Abstract

Diabetes mellitus (DM) significantly impacts long-term survival after liver transplantation (LT). We identified survival factors for LT recipients who had DM to inform preventive care using machine-learning analysis. We analyzed risk factors for mortality in patients from across the United States using the Scientific Registry of Transplant Recipients (SRTR). Patients had undergone LT from 1987 to 2019, with a follow-up of 6.47 years (standard deviation [SD] 5.95). Findings were validated on a cohort from the University Health Network (UHN) from 1989 to 2014 (follow-up 8.15 years [SD 5.67]). Analysis was conducted with Cox proportional hazards and gradient boosting survival. The training set included 84.67% SRTR data (n = 15,289 patients), and the test set included 15.33% SRTR patients (n = 2769) and data from UHN patients (n = 1290). We included 18,058 adults (12,108 [67.05%] men, average age 54.21 years [SD 9.98]) from the SRTR who had undergone LT and had complete data for investigated features. A total of 4634 patients had preexisting DM, and 3158 had post-LT DM. The UHN data consisted of 1290 LT recipients (910 [70.5%] men, average age 54.0 years [SD 10.4]). Increased serum creatinine and hypertension significantly impacted mortality with preexisting DM 1.36 (95% confidence interval [CI], 1.21-1.54) and 1.20 (95% CI, 1.06-1.35) times, respectively. Sirolimus use increased mortality 1.36 times (95% CI, 1.18-1.58) in nondiabetics and 1.33 times (95% CI, 1.09-1.63) in patients with preexisting DM. A similar effect was found in post-LT DM, although it was not statistically significant (1.38 times; 95% CI, 1.07-1.77; P = 0.07). Survival predictors generally achieved a 0.60 to 0.70 area under the receiver operating characteristic for 5-year mortality. LT recipients who have DM have a higher mortality risk than those without DM. Hypertension, decreased renal function, and sirolimus for maintenance immunosuppression compound this mortality risk. These predisposing factors must be intensively treated and modified to optimize long-term survival after transplant.

摘要

糖尿病(DM)显著影响肝移植(LT)后的长期生存率。我们确定了患有 DM 的 LT 受者的生存因素,以便通过机器学习分析提供预防护理。我们使用科学移植受者登记处(SRTR)分析了来自美国各地的患者死亡的风险因素。患者在 1987 年至 2019 年期间接受了 LT,随访时间为 6.47 年(标准差[SD] 5.95)。在 1989 年至 2014 年期间,在来自大学健康网络(UHN)的队列中验证了研究结果(随访时间为 8.15 年[SD 5.67])。使用 Cox 比例风险和梯度提升生存进行分析。训练集包括 84.67%的 SRTR 数据(n=15289 名患者),测试集包括 15.33%的 SRTR 患者(n=2769 名)和 UHN 患者的数据(n=1290 名)。我们纳入了来自 SRTR 的 18058 名成年人(12108[67.05%]名男性,平均年龄 54.21 岁[SD 9.98]),他们接受了 LT 并具有调查特征的完整数据。共有 4634 名患者患有预先存在的 DM,3158 名患者患有 LT 后 DM。UHN 数据包括 1290 名 LT 受者(910[70.5%]名男性,平均年龄 54.0 岁[SD 10.4])。血清肌酐升高和高血压显著增加了预先存在的 DM 的死亡率,分别为 1.36(95%CI,1.21-1.54)和 1.20(95%CI,1.06-1.35)倍。西罗莫司在非糖尿病患者中使死亡率增加了 1.36 倍(95%CI,1.18-1.58),在患有预先存在的 DM 的患者中增加了 1.33 倍(95%CI,1.09-1.63)。在 LT 后 DM 中也发现了类似的效果,尽管没有统计学意义(1.38 倍;95%CI,1.07-1.77;P=0.07)。生存预测因素通常在 5 年死亡率方面达到 0.60 到 0.70 的接收器操作特征曲线下面积。患有 DM 的 LT 受者的死亡率风险高于没有 DM 的受者。高血压、肾功能下降和西罗莫司用于维持免疫抑制会增加这种死亡率风险。这些易患因素必须得到强化治疗和调整,以优化移植后的长期生存。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb6a/8248095/5f0413cbed09/LT-27-536-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb6a/8248095/a11eb9905387/LT-27-536-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb6a/8248095/59b3251ea425/LT-27-536-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb6a/8248095/09f2859e5ade/LT-27-536-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb6a/8248095/5f0413cbed09/LT-27-536-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb6a/8248095/a11eb9905387/LT-27-536-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb6a/8248095/59b3251ea425/LT-27-536-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb6a/8248095/09f2859e5ade/LT-27-536-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb6a/8248095/5f0413cbed09/LT-27-536-g003.jpg

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