上海交通大学学报（医学版）

上海交通大学学报（医学版） >

2021 , Vol. 41 >Issue 9: 1228 - 1232

DOI: https://doi.org/10.3969/j.issn.1674-8115.2021.09.014

论著 · 临床研究

肝硬化并发轻微型肝性脑病的筛查模型建立与评价

  • 钱珠萍 ,
  • 杨艳
展开
  • 1.上海交通大学医学院附属瑞金医院护理部，上海 200025
    2.上海交通大学护理学院，上海 200025
钱珠萍（1985—），女，主管护师，硕士；电子信箱：gelico@163.com。
杨艳，电子信箱：renji_yy@126.com。

收稿日期: 2021-01-18

  网络出版日期: 2021-08-24

基金资助

上海交通大学医学院护理研究项目(JYHZ2035)

收起

Establishment and evaluation of screening model of minimal hepatic encephalopathy in patients with liver cirrhosis

  • Zhu-ping QIAN ,
  • Yan YANG
Expand
  • 1.Nursing Department, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
    2.School of Nursing, Shanghai Jiao Tong University, Shanghai 200025, China
YANG Yan, E-mail: renji_yy@126.com.

Received date: 2021-01-18

  Online published: 2021-08-24

Supported by

Nursing Research Project of Shanghai Jiao Tong University School of Medicine(JYHZ2035)

Fold

摘要

目的·建立并评价肝硬化患者并发轻微型肝性脑病的筛查模型。方法·以2017年6月—2019年11月住院的404例肝硬化患者为研究对象，采集其临床资料。基于Logistic回归分析和人工神经网络分别建立轻微型肝性脑病筛查模型，对2种模型的判别能力进行评价和比较。结果·Logistic回归分析提示，肝硬化并发轻微型肝性脑病的独立危险因素为年龄、糖尿病史、感染、肾功能不全、营养风险、总胆红素>24 μmol/L、血氨>47 μmol/L、国际标准比值≥1.5（均P<0.05）。人工神经网络模型与Logistic回归模型的受试者操作特征曲线（receiver operator characteristic curve，ROC curve）的曲线下面积（area under the curve，AUC）分别为0.814和0.737（Z=4.208，P=0.000），灵敏度分别为72.4%、69.9%，特异度分别为76.7%、67.8%。结论·人工神经网络模型对轻微型肝性脑病的筛查效能优于Logistic回归模型。

关键词： 轻微型肝性脑病; 人工神经网络; 筛查模型

本文引用格式

钱珠萍 , 杨艳 . 肝硬化并发轻微型肝性脑病的筛查模型建立与评价[J]. 上海交通大学学报（医学版）, 2021 , 41(9) : 1228 -1232 . DOI: 10.3969/j.issn.1674-8115.2021.09.014

Abstract

Objective

·To establish and evaluate a screening model of liver cirrhosis patients complicated with minimal hepatic encephalopathy (MHE).

Methods

·A total of 404 patients with liver cirrhosis who were hospitalized from June 2017 to November 2019 were selected as the research subjects, and the clinical data of them were collected. Based on Logistic regression analysis and artificial neural network (ANN), the MHE screening models were established, and the discriminant ability of the two models was evaluated and compared.

Results

·The Logistic regression model showed that age, history of diabetes mellitus, infection, renal insufficiency, nutritional risk, total bilirubin>24 μmol/L, blood ammonia>47 μmol/L and international normalized ratio (INR)≥1.5 were the significant risk factors (all P<0.05). The area under the curve (AUC) of receiver operator characteristic curve (ROC curve) of ANN model and Logistic regression model were 0.814 and 0.737 (Z=4.208, P=0.000), respectively. The sensitivities were 72.4% and 69.9%, and the specificities were 76.7% and 67.8%, respectively.

Conclusion

·The ANN model is more effective than the Logistic regression model in MHE screening.

Key words： minimal hepatic encephalopathy (MHE); artificial neural network (ANN); screening model

参考文献

1 中华医学会消化病学分会, 中华医学会肝病学分会. 中国肝性脑病诊治共识意见(2013年,重庆)[J]. 中国医学前沿杂志(电子版), 2014, 6(2): 81-93.
2 Thomsen KL, Macnaughtan J, Tritto G, et al. Clinical and pathophysiological characteristics of cirrhotic patients with grade 1 and minimal hepatic encephalopathy[J]. PLoS One, 2016, 11(1): e0146076.
3 Ampuero J, Montoliú C, Simón-Talero M, et al. Minimal hepatic encephalopathy identifies patients at risk of faster cirrhosis progression[J]. J Gastroenterol Hepatol, 2018, 33(3): 718-725.
4 Bajaj JS, Thacker LR, Heuman DM, et al. The Stroop smartphone application is a short and valid method to screen for minimal hepatic encephalopathy[J]. Hepatology, 2013, 58(3): 1122-1132.
5 Sharma P, Sharma BC. A survey of patterns of practice and perception of minimal hepatic encephalopathy: a nationwide survey in India[J]. Saudi J Gastroenterol, 2014, 20(5): 304-308.
6 马瑜, 嵇承栋, 赵静, 等. 筛查模型与效果评价的研究进展[J]. 公共卫生与预防医学, 2014, 25(4): 70-74.
7 中华医学会肝病学分会, 中华医学会感染病学分会. 慢性乙型肝炎防治指南(2015年版)[J]. 实用肝脏病杂志, 2015, 19(3): 5-16.
8 Yuan LT, Chuah SK, Yang SC, et al. Multiple bacterial infections increase the risk of hepatic encephalopathy in patients with cirrhosis[J]. PLoS One, 2018, 13(5): e0197127.
9 朱彬, 邹聪聪, 郑昕. 终末期肝病的营养不良评价体系和营养支持治疗[J]. 临床肝胆病杂志, 2017, 33(9): 1699-1706.
10 Pang Y, Kartsonaki C, Turnbull I, et al. Diabetes, plasma glucose, and incidence of fatty liver, cirrhosis, and liver cancer: a prospective study of 0.5 million people[J]. Hepatology, 2018, 68(4): 1308-1318.
11 佘倩, 陈明锴. 不同血糖代谢状态患者肝硬化的临床特征[J]. 武汉大学学报(医学版), 2019, 40(6): 982-986.
12 张浩彬, 周伟珠. IBM SPSS Modeler18.0数据挖掘权威指南[M]. 北京: 人民邮电出版社, 2019.
13 秦平, 张镏琢, 赵晓雯, 等. BP神经网络在代谢综合征影响因素分析中的应用[J]. 实用预防医学, 2011, 18(10): 1819-1822.
14 陈杰, 周勤, 陈进, 等. 人工神经网络在疾病预后研究中的应用进展[J]. 中国胸心血管外科临床杂志, 2013, 20(1): 95-99.
15 许人, 石张镇, 张文龙, 等. Logistic多元回归和人工神经网络在判断进展期胃癌淋巴结转移中的应用[J]. 中国老年学杂志, 2016,36(16): 3980-3982.
16 中华医学会肝病学分会. 肝硬化肝性脑病诊疗指南[J]. 传染病信息, 2018, 31(5): 403-420.
17 李倩倩, 牛小霞, 徐碧海,等. 肝硬化失代偿期患者主要照顾者对延续性护理需求的质性研究[J]. 解放军护理杂志, 2018, 35(1): 25-28.
Options
文章导航

/

〈 〉