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机器学习应用中的逻辑回归模型用于预测肾移植术后一年肾功能较差的老年肾移植受者:老年肾移植机器人(Elderly KTbot)

Logistic Regression Model in a Machine Learning Application to Predict Elderly Kidney Transplant Recipients with Worse Renal Function One Year after Kidney Transplant: Elderly KTbot.

作者信息

Elihimas Júnior Ubiracé Fernando, Couto Jamila Pinho, Pereira Wallace, Barros de Oliveira Sá Michel Pompeu, Tenório de França Eduardo Eriko, Aguiar Filipe Carrilho, Cabral Diogo Buarque Cordeiro, Alencar Saulo Barbosa Vasconcelos, Feitosa Saulo José da Costa, Claizoni Dos Santos Thais Oliveira, Dos Santos Elihimas Helen Conceição, Alves Emilly Pereira, José de Carvalho Lima Marcio, Branco Cavalcanti Frederico Castelo, Schwingel Paulo Adriano

机构信息

Programa de Pós-Graduação em Ciências da Saúde (PPGCS), Universidade de Pernambuco (UPE), Recife, PE 50100-130, Brazil.

Unidade de Nefrologia e Divisão de Transplante, Universidade Federal de Pernambuco (UFPE), Recife, PE 50670-901, Brazil.

出版信息

J Aging Res. 2020 Aug 19;2020:7413616. doi: 10.1155/2020/7413616. eCollection 2020.

DOI:10.1155/2020/7413616
PMID:32922997
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7453245/
Abstract

BACKGROUND

Renal replacement therapy (RRT) is a public health problem worldwide. Kidney transplantation (KT) is the best treatment for elderly patients' longevity and quality of life.

OBJECTIVES

The primary endpoint was to compare elderly versus younger KT recipients by analyzing the risk covariables involved in worsening renal function, proteinuria, graft loss, and death one year after KT. The secondary endpoint was to create a robot based on logistic regression capable of predicting the likelihood that elderly recipients will develop worse renal function one year after KT.

METHOD

Unicentric retrospective analysis of a cohort was performed with individuals aged ≥60 and <60 years old. We analysed medical records of KT recipients from January to December 2017, with a follow-up time of one year after KT. We used multivariable logistic regression to estimate odds ratios for elderly vs younger recipients, controlled for demographic, clinical, laboratory, data pre- and post-KT, and death.

RESULTS

18 elderly and 100 younger KT recipients were included. Pretransplant immune variables were similar between two groups. No significant differences ( > 0.05) between groups were observed after KT on laboratory data means and for the prevalences of diabetes mellitus, hypertension, acute rejection, cytomegalovirus, polyomavirus, and urinary infections. One year after KT, the creatinine clearance was higher ( = 0.006) in youngers (70.9 ± 25.2 mL/min/1.73 m) versus elderlies (53.3 ± 21.1 mL/min/1.73 m). There was no difference in death outcome comparison. Multivariable analysis among covariables predisposing chronic kidney disease epidemiology collaboration (CKD-EPI) equation <60 mL/min/1.73 m presented a statistical significance for age ≥60 years ( = 0.01) and reduction in serum haemoglobin ( = 0.03). The model presented goodness-fit in the evaluation of artificial intelligence metrics (precision: 90%; sensitivity: 71%; and score: 0.79).

CONCLUSION

Renal function in elderly KT recipients was lower than in younger KT recipients. However, patients aged ≥60 years maintained enough renal function to remain off dialysis. Moreover, a learning machine application built a robot (Elderly KTbot) to predict in the elderly populations the likelihood of worse renal function one year after KT.

摘要

背景

肾脏替代疗法(RRT)是一个全球性的公共卫生问题。肾移植(KT)是改善老年患者寿命和生活质量的最佳治疗方法。

目的

主要终点是通过分析肾移植术后一年肾功能恶化、蛋白尿、移植肾丢失和死亡所涉及的风险协变量,比较老年与年轻肾移植受者。次要终点是基于逻辑回归创建一个机器人,能够预测老年受者肾移植术后一年肾功能恶化的可能性。

方法

对年龄≥60岁和<60岁的个体进行单中心队列回顾性分析。我们分析了2017年1月至12月肾移植受者的医疗记录,肾移植术后随访时间为一年。我们使用多变量逻辑回归来估计老年与年轻受者的比值比,并对人口统计学、临床、实验室、肾移植前后的数据以及死亡情况进行了控制。

结果

纳入了18名老年肾移植受者和100名年轻肾移植受者。两组移植前免疫变量相似。肾移植术后,两组在实验室数据均值以及糖尿病、高血压、急性排斥反应、巨细胞病毒、多瘤病毒和泌尿系统感染的患病率方面均未观察到显著差异(>0.05)。肾移植术后一年,年轻受者(70.9±25.2 mL/min/1.73m²)的肌酐清除率高于老年受者(53.3±21.1 mL/min/1.73m²)(P = 0.006)。死亡结局比较无差异。在慢性肾脏病流行病学协作组(CKD-EPI)方程<60 mL/min/1.73m²的协变量多变量分析中,年龄≥60岁(P = 0.01)和血清血红蛋白降低(P = 0.03)具有统计学意义。该模型在人工智能指标评估中表现出良好的拟合度(精度:90%;灵敏度:71%;F1分数:0.79)。

结论

老年肾移植受者的肾功能低于年轻肾移植受者。然而,年龄≥60岁的患者维持了足够的肾功能以避免透析。此外,一个学习机器应用程序构建了一个机器人(老年肾移植机器人)来预测老年人群肾移植术后一年肾功能恶化的可能性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56d1/7453245/48efd5c30ddf/JAR2020-7413616.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56d1/7453245/8dfb6b38a0b0/JAR2020-7413616.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56d1/7453245/d48fa74fc4fa/JAR2020-7413616.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56d1/7453245/48efd5c30ddf/JAR2020-7413616.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56d1/7453245/8dfb6b38a0b0/JAR2020-7413616.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56d1/7453245/d48fa74fc4fa/JAR2020-7413616.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56d1/7453245/48efd5c30ddf/JAR2020-7413616.003.jpg

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本文引用的文献

1
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BMJ. 2019 Sep 17;366:l4923. doi: 10.1136/bmj.l4923.
2
Kidney Transplantation in Septuagenarians: 70 Is the New 60!七十岁老人的肾移植:70 如今就是新的 60!
Kidney Int Rep. 2019 Mar 23;4(5):640-642. doi: 10.1016/j.ekir.2019.03.015. eCollection 2019 May.
3
Kidney Transplantation in Old Recipients From Old Donors: A Single-Center Experience.
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BJS Open. 2023 Mar 7;7(2). doi: 10.1093/bjsopen/zrad011.
4
Machine Learning for Renal Pathologies: An Updated Survey.机器学习在肾病变中的应用:最新研究综述。
Sensors (Basel). 2022 Jul 1;22(13):4989. doi: 10.3390/s22134989.
老年供者的肾脏移植给老年受者:单中心经验
Transplant Proc. 2019 Mar;51(2):405-407. doi: 10.1016/j.transproceed.2019.01.019. Epub 2019 Jan 4.
4
Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016.《全球疾病负担研究》分析强调了 1990 年至 2016 年期间全球、地区和国家慢性肾脏病流行病学的趋势。
Kidney Int. 2018 Sep;94(3):567-581. doi: 10.1016/j.kint.2018.04.011. Epub 2018 Aug 3.
5
Mortality in Elderly Waiting-List Patients Versus Age-Matched Kidney Transplant Recipients: Where is the Risk?老年等待名单患者与年龄匹配的肾移植受者的死亡率:风险在哪里?
Kidney Blood Press Res. 2018;43(1):256-275. doi: 10.1159/000487684. Epub 2018 Feb 22.
6
The Brazilian Longitudinal Study of Aging (ELSI-Brazil): Objectives and Design.巴西老龄化纵向研究(ELSI-Brazil):目标和设计。
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8
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