肺纤维化风险预测的临床生物化学模型  

A functional laboratory biochemical model for the prediction of pulmonary fibrosis risk

作　　者：冷冬[1] 李桂芹[2] 王颖[1] 缪冉[3] 陈铎 黄骁舾[3] Leng Dong;Li Guiqin;Wang Ying;Miao Ran;Chen Duo;Huang Xiaoxi(Clinical Laboratory,Beijing Chaoyang Hospital,Capital Medical University,Beijing 100020,China;Office for Disease Prevention and Control,Chaoyang Hospital,Capital Medical University,Beijing 100020,China;Medical Research Center,Beijing Chaoyang Hospital,Capital Medical University,Beijing 100020,China;Department of Respiratory and Critical Care Medicine,Beijing Chaoyang Hospital,Capital Medical University,Beijing 100020,China)

机构地区：[1]首都医科大学附属北京朝阳医院检验科,北京100020 [2]首都医科大学附属北京朝阳医院疾病预防控制处,北京100020 [3]首都医科大学附属北京朝阳医院医学研究中心,北京100020 [4]首都医科大学附属北京朝阳医院呼吸与危重症医学科,北京100020

出　　处：《首都医科大学学报》2019年第6期875-880,共6页Journal of Capital Medical University

基　　金：国家自然科学基金青年项目(81700061,81300049);北京市医院管理局临床医学发展专项经费(ZYLX201811);国家临床重点专科建设项目。

摘　　要：目的应用数据挖掘技术分析肺纤维化(pulmonary fibrosis,PF)患者及对照组血清生物化学数据,为疾病的早期诊断提供有益线索。方法本研究收集PF患者(n=29)和健康对照(n=55)的生物化学检测数据,采用Z-计分和Log2转换方式将数据进行归一化处理,用主成分分析与贝叶斯回归分析方法提取特征指标,构建PF患者血清生物化学指标的风险预测模型,并进行受试者操作特征分析。结果与正常对照相比,PF患者血清中的α-羟丁酸脱氢酶、乳酸脱氢酶、白蛋白、白蛋白与球蛋白比值、前白蛋白及钙浓度差异有统计学意义(P<0.05)。其中α-羟丁酸脱氢酶是一种有效预测潜在PF风险的生物化学检测指标。结论本研究对PF血清生物化学数据进行挖掘分析,成功构建了PF血清生物化学预测模型。Objective To determine promising indices for early diagnosis of pulmonary fibrosis(PF)by data extraction and analysis.Methods Biochemical data from patients with PF(n=29)and healthy controls(n=55)were collected and normalized by Z-score indexation and Log2 transformation,followed by principal component and Bayesian probit regression analyses.Signature parameters for PF were identified and used for discriminative function modeling and receiver operating characteristic analysis.Results Theα-hydroxybutyric dehydrogenase(HBDH),lactic dehydrogenase,albumin,albumin:globulin ratio,prealbumin,and calcium parameters were significantly different between PF and healthy control samples(P<0.05),and discriminant functions of PF and health were constructed.HBDH was found to be the efficient parameter that could indicate the potential risk of PF.Conclusion This study is attempt to investigate PF serum biochemical characteristics by mining of clinical biochemical data.Discriminant functional model for predicting PF with signature biochemical parameters were constructed successfully.

关 键 词：肺纤维化 临床生物化学 数据建模 

分 类 号：R563[医药卫生—呼吸系统]