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Article(id=1203053370102734852, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1203053366290113441, articleNumber=null, orderNo=null, doi=10.11855/j.issn.0577-7402.2023.03.0298, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1648742400000, receivedDateStr=2022-04-01, revisedDate=null, revisedDateStr=null, acceptedDate=1654704000000, acceptedDateStr=2022-06-09, onlineDate=1764759875082, onlineDateStr=2025-12-03, pubDate=1679932800000, pubDateStr=2023-03-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1764759875082, onlineIssueDateStr=2025-12-03, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1764759875082, creator=13701087609, updateTime=1764759875082, updator=13701087609, issue=Issue{id=1203053366290113441, tenantId=1146029695717560320, journalId=1189873630562394117, year='2023', volume='48', issue='3', pageStart='245', pageEnd='366', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1764759874174, creator=13701087609, updateTime=1764810242575, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1203264626747220064, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1203053366290113441, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1203264626747220065, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1203053366290113441, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=298, endPage=303, ext={EN=ArticleExt(id=1203053371969200204, articleId=1203053370102734852, tenantId=1146029695717560320, journalId=1189873630562394117, language=EN, title=Correlation analysis of inflammation index and idiopathic pulmonary interstitial fibrosis complicated with respiratory failure, columnId=1190310109000602400, journalTitle=Medical Journal of Chinese People’s Liberation Army, columnName=Clinical Research, runingTitle=null, highlight=null, articleAbstract=

Objective To analyze the correlation between inflammatory index and respiratory failure (RF) in patients with idiopathic pulmonary fibrosis (IPF). Methods The clinical data of 169 patients with IPF admitted in the First Hospital of Lanzhou University from January 2019 to October 2021 were collected, and then divided into the group with respiratory failure (RF group, n=84) and the group without respiratory failure (non-RF group, n=85) according to the arterial blood gas analysis. The clinical data such as blood routine and biochemical data were collected for comparison between the two groups. Logistic regression analysis was performed to screen the possible risk factors of RF in IPF patients; Spearman correlation analysis was used to explore the correlation between multiple inflammatory indicators and between PaO2 level and inflammatory indicators; The diagnostic value of inflammatory indicators was analyzed in IPF patients complicated with RF by using the ROC curve. Results NLR (neutrophil to lymphocyte ratio) and PLR (platelet to lymphocyte ratio) were significantly higher in RF group than in non-RF group, while LMR(lymphocyte to monocyte ratio) were significantly lower than those in non-RF group, the differences were statistically significant (P<0.05). Spearman correlation analysis was conducted on NLR, PLR, LMR and CRP, PCT and PaO2 levels in all patients, it was indicated that NLR, PLR were positively correlated with CRP and PCT (P<0.05), while LMR was negatively correlated with CRP and PCT (P<0.05), moreover, there was a negative correlation between NLR and PaO2 level (P<0.05), and a positive correlation between LMR and PaO2 level (P<0.05). Logistic multifactor regression analysis suggested that smoking and increased NLR were independent risk factors for RF in IPF patients. ROC analysis indicated that NLR and LMR could be effectively used in diagnosis of IPF complicated with RF, and the area under the curve was 0.738(95%CI 0.663-0.812) and 0.736(95%CI 0.660-0.812), respectively.PLR had limited diagnostic value for IPF complicated with RF, and the area under the curve was only 0.629(95%CI 0.545-0.714), while the combined diagnostic ability of NLR, PLR and LMR was higher than the three single indexes, and the area under the curve was 0.760(95%CI 0.689-0.832). Conclusions Elevated NLR is an independent risk factor for RF in IPF patients. NLR, PLR, LMR and their combination have certain diagnostic value for IPF patients complicated with RF, and the combined diagnostic ability of the three is better than the three single indicators alone.

, correspAuthors=Long Li, authorNote=null, correspAuthorsNote=
* E-mail:
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目的 分析炎性指标与特发性肺间质纤维化（IPF）患者并发呼吸衰竭（RF）的相关性。方法 收集2019年1月-2021年10月于兰州大学第一医院就诊的169例IPF患者的临床资料进行回顾性分析，按照动脉血气分析结果分为RF组（n=84例）和非RF组（n=85例）。收集血常规、生化等临床资料，比较两组患者的各项临床指标，采用回归分析筛选IPF患者发生RF可能的危险因素；采用Spearman相关性分析探讨多项炎性指标之间及炎性指标与动脉血氧分压（PaO2）的相关性。采用受试者工作特征（ROC）曲线分析炎性指标对IPF患者并发RF的诊断价值。结果 RF组中性粒细胞与淋巴细胞比值（NLR）、血小板与淋巴细胞比值（PLR）明显高于非RF组（P<0.05），淋巴细胞与单核细胞比值（LMR）明显低于非RF组（P<0.05）。将所有患者NLR、PLR、LMR与C反应蛋白（CRP）、降钙素原（PCT）、PaO2水平进行Spearman相关性分析，结果显示，NLR、PLR与CRP、PCT均呈正相关（P<0.05），LMR与CRP、PCT呈负相关（P<0.05），且NLR与PaO2水平呈负相关（P<0.05），LMR与PaO2水平呈正相关（P<0.05）；Logistic多因素回归分析显示，吸烟、NLR升高是IPF患者发生RF的独立危险因素。ROC曲线分析显示，NLR、LMR可用于诊断IPF并发RF，其曲线下面积（AUC）分别为0.738（95%CI 0.663~0.812）、0.736（95%CI 0.660~0.812）；PLR对IPF并发RF的诊断价值有限，AUC仅为0.629（95%CI 0.545~0.714）；而NLR、PLR、LMR三者联合诊断能力高于3个单项指标，AUC为0.760（95%CI 0.689~0.832）。结论 NLR升高是IPF患者发生RF的独立危险因素。NLR、PLR、LMR及三者联合对IPF患者并发RF有一定的诊断价值，且三者联合的诊断效力优于3个单项指标。

, correspAuthors=李龙, authorNote=null, correspAuthorsNote=
李龙，E-mail：
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焦欢，硕士研究生，主要从事间质性肺疾病相关的临床研究

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焦欢，硕士研究生，主要从事间质性肺疾病相关的临床研究

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Respir Med, 2022, 191:106686., articleTitle=The clinical relevance of lymphocyte to monocyte ratio in patients with idiopathic pulmonary fibrosis (IPF), refAbstract=null)], funds=[Fund(id=1203053377442767191, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, awardId=ldyyn2021-34, language=EN, fundingSource=Innovation Fund of the First Hospital of Lanzhou University(ldyyn2021-34), fundOrder=null, country=null), Fund(id=1203053377556013403, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, awardId=ldyyn2021-34, language=CN, fundingSource=兰州大学第一医院立项基金(ldyyn2021-34), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1203053373068107915, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, xref=1, ext=[AuthorCompanyExt(id=1203053373080690828, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, companyId=1203053373068107915, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1The First Clinical Medical College of Lanzhou University, Lanzhou, Gansu 730000, China), AuthorCompanyExt(id=1203053373110050962, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, companyId=1203053373068107915, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1兰州大学第一临床医学院，甘肃兰州 730000)]), AuthorCompany(id=1203053373202325658, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, xref=2, ext=[AuthorCompanyExt(id=1203053373210714267, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, companyId=1203053373202325658, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2Department of Respiratory and Critical Care Medicine, the First Hospital of Lanzhou University, Lanzhou, Gansu 730000, China), AuthorCompanyExt(id=1203053373214908572, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, companyId=1203053373202325658, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2兰州大学第一医院呼吸与危重症医学科，甘肃兰州 730000)])], figs=[ArticleFig(id=1203053375962177811, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, language=EN, label=Fig. 1, caption=Correlation matrix of spearman correlation of inflammation index in IPF patients, figureFileSmall=AsoHY7nfmzlIde+ZQ9n/zw==, figureFileBig=9xbAAynUPwFIhizM9BqULA==, tableContent=null), ArticleFig(id=1203053376067035415, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, language=CN, label=图1, caption=IPF患者炎性指标Spearman相关性的相关矩阵

IPF. 特发性肺间质纤维化；NLR. 中性粒细胞与淋巴细胞比值；PLR. 血小板与淋巴细胞比值；LMR. 淋巴细胞与单核细胞比值；CRP. C反应蛋白；PCT. 降钙素原；PaO2. 动脉血氧分压

, figureFileSmall=AsoHY7nfmzlIde+ZQ9n/zw==, figureFileBig=9xbAAynUPwFIhizM9BqULA==, tableContent=null), ArticleFig(id=1203053376264167717, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, language=EN, label=Fig. 2, caption=ROC curve of NLR, PLR, LMR and their combination in diagnosing IPF complicated with RF, figureFileSmall=P0hd07cJw+46Ic1Tf26JYw==, figureFileBig=w3/A1kn+IwxpjU/xeJDAmA==, tableContent=null), ArticleFig(id=1203053376352248105, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, language=CN, label=图2, caption=NLR、PLR、LMR单项及三者联合诊断IPF患者并发RF的ROC曲线分析

NLR. 中性粒细胞与淋巴细胞比值；PLR. 血小板与淋巴细胞比值；LMR. 淋巴细胞与单核细胞比值；IPF. 特发性肺间质纤维化；RF. 呼吸衰竭

, figureFileSmall=P0hd07cJw+46Ic1Tf26JYw==, figureFileBig=w3/A1kn+IwxpjU/xeJDAmA==, tableContent=null), ArticleFig(id=1203053376461300014, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, language=EN, label=Tab. 1, caption=

Comparison of the clinical data of IPF patients in RF group and non-RF group

, figureFileSmall=null, figureFileBig=null, tableContent=
项目RF组(n=84)非RF组(n=85)χ2/t/UP
性别[例(%)]  1.8080.179
 男54(64.3)46(54.1)
 女30(35.7)39(45.9)
年龄($\bar{x}±s$，岁)68.36±10.9666.01±10.671.4100.161
吸烟指数($\bar{x}±s$)216.67±369.2590.59±192.492.7880.006
BMI($\bar{x}±s$, kg/m2)23.14±3.6823.28±3.69–0.2440.807
高血压病史[例(%)]  1.8030.179
 有22(26.2)15(17.6)
 无62(73.8)70(82.4)
糖尿病病史[例(%)]  0.4400.507
 有15(17.9)12(14.1)
 无69(82.1)73(85.9)
ALB($\bar{x}±s$, g/L)36.08±5.3638.83±4.42–3.646<0.001
TC($\bar{x}±s$, mmol/L)3.61±1.134.09±0.97–2.9920.003
TG($\bar{x}±s$, mmol/L)1.29±0.581.35±0.69–0.6140.540
HDL-C($\bar{x}±s$, mmol/L)0.92±0.280.99±0.28–1.5530.122
LDL-C($\bar{x}±s$, mmol/L)2.33±0.832.68±0.76–2.9040.004
WBC($\bar{x}±s$, ×109/L)8.87±4.195.96±1.955.769<0.001
NEUT($\bar{x}±s$,%)71.09±15.0159.68±14.295.063<0.001
NLR($\bar{x}±s$)8.70±15.612.74±2.193.4650.001
PLR($\bar{x}±s$)210.10±215.67134.39±73.613.0470.003
LMR($\bar{x}±s$)3.11±2.404.47±2.21–3.832<0.001
CRP($\bar{x}±s$, mg/L)30.70±36.758.32±19.984.911<0.001
PCT($\bar{x}±s$, ng/ml)0.60±3.430.06±0.131.4390.154
), ArticleFig(id=1203053376582934834, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, language=CN, label=表1, caption=

RF组和非RF组IPF患者临床资料比较

, figureFileSmall=null, figureFileBig=null, tableContent=
项目RF组(n=84)非RF组(n=85)χ2/t/UP
性别[例(%)]  1.8080.179
 男54(64.3)46(54.1)
 女30(35.7)39(45.9)
年龄($\bar{x}±s$，岁)68.36±10.9666.01±10.671.4100.161
吸烟指数($\bar{x}±s$)216.67±369.2590.59±192.492.7880.006
BMI($\bar{x}±s$, kg/m2)23.14±3.6823.28±3.69–0.2440.807
高血压病史[例(%)]  1.8030.179
 有22(26.2)15(17.6)
 无62(73.8)70(82.4)
糖尿病病史[例(%)]  0.4400.507
 有15(17.9)12(14.1)
 无69(82.1)73(85.9)
ALB($\bar{x}±s$, g/L)36.08±5.3638.83±4.42–3.646<0.001
TC($\bar{x}±s$, mmol/L)3.61±1.134.09±0.97–2.9920.003
TG($\bar{x}±s$, mmol/L)1.29±0.581.35±0.69–0.6140.540
HDL-C($\bar{x}±s$, mmol/L)0.92±0.280.99±0.28–1.5530.122
LDL-C($\bar{x}±s$, mmol/L)2.33±0.832.68±0.76–2.9040.004
WBC($\bar{x}±s$, ×109/L)8.87±4.195.96±1.955.769<0.001
NEUT($\bar{x}±s$,%)71.09±15.0159.68±14.295.063<0.001
NLR($\bar{x}±s$)8.70±15.612.74±2.193.4650.001
PLR($\bar{x}±s$)210.10±215.67134.39±73.613.0470.003
LMR($\bar{x}±s$)3.11±2.404.47±2.21–3.832<0.001
CRP($\bar{x}±s$, mg/L)30.70±36.758.32±19.984.911<0.001
PCT($\bar{x}±s$, ng/ml)0.60±3.430.06±0.131.4390.154
), ArticleFig(id=1203053376691986743, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, language=EN, label=Tab. 2, caption=

Univariate logistic regression analysis of IPF patients complicated with RF

, figureFileSmall=null, figureFileBig=null, tableContent=
项目βWald χ2POR95%CI
年龄0.0201.9630.1611.0210.992~1.050
吸烟指数0.0026.8260.0091.0021.000~1.003
BMI–0.0100.0600.8060.9900.911~1.075
高血压病史0.5041.7850.1821.6560.790~3.471
糖尿病病史0.2790.4380.5081.3220.578~3.025
ALB–0.11611.5440.0010.8900.833~0.952
TC–0.4578.0610.0050.6330.462~0.868
TG–0.1500.3780.5390.8600.532~1.390
HDL-C–0.9752.3660.1240.3770.109~1.307
LDL-C–0.5817.6320.0060.5590.370~0.845
WBC0.31022.031<0.0011.3641.198~1.553
NEUT0.06019.527<0.0011.0611.034~1.090
NLR0.26314.405<0.0011.3011.136~1.491
PLR0.0069.8620.0021.0061.002~1.009
LMR–0.32812.370<0.0010.7200.600~0.865
CRP0.06017.678<0.0011.0611.032~1.091
PCT0.0064.7540.0291.0061.001~1.011
), ArticleFig(id=1203053376792650046, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, language=CN, label=表2, caption=

IPF患者并发RF的单因素logistic回归分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
项目βWald χ2POR95%CI
年龄0.0201.9630.1611.0210.992~1.050
吸烟指数0.0026.8260.0091.0021.000~1.003
BMI–0.0100.0600.8060.9900.911~1.075
高血压病史0.5041.7850.1821.6560.790~3.471
糖尿病病史0.2790.4380.5081.3220.578~3.025
ALB–0.11611.5440.0010.8900.833~0.952
TC–0.4578.0610.0050.6330.462~0.868
TG–0.1500.3780.5390.8600.532~1.390
HDL-C–0.9752.3660.1240.3770.109~1.307
LDL-C–0.5817.6320.0060.5590.370~0.845
WBC0.31022.031<0.0011.3641.198~1.553
NEUT0.06019.527<0.0011.0611.034~1.090
NLR0.26314.405<0.0011.3011.136~1.491
PLR0.0069.8620.0021.0061.002~1.009
LMR–0.32812.370<0.0010.7200.600~0.865
CRP0.06017.678<0.0011.0611.032~1.091
PCT0.0064.7540.0291.0061.001~1.011
), ArticleFig(id=1203053376889119041, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, language=EN, label=Tab. 3, caption=

Multivariate logistic regression analysis of IPF patients complicated with RF

, figureFileSmall=null, figureFileBig=null, tableContent=
项目βWald χ2POR95%CI
吸烟指数0.0014.0120.0451.0011.000~1.003
ALB–0.0602.0450.1530.9420.868~1.022
NLR0.2518.2070.0041.2861.083~1.527
PLR–0.0030.9670.3260.9970.992~1.003
LMR–0.1041.1690.2800.9020.747~1.088
LDL-C–0.4710.2480.6180.6250.098~3.976
TC0.2320.1000.7521.2610.299~5.326
), ArticleFig(id=1203053377035919685, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, language=CN, label=表3, caption=

IPF患者并发RF的多因素logistic回归分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
项目βWald χ2POR95%CI
吸烟指数0.0014.0120.0451.0011.000~1.003
ALB–0.0602.0450.1530.9420.868~1.022
NLR0.2518.2070.0041.2861.083~1.527
PLR–0.0030.9670.3260.9970.992~1.003
LMR–0.1041.1690.2800.9020.747~1.088
LDL-C–0.4710.2480.6180.6250.098~3.976
TC0.2320.1000.7521.2610.299~5.326
), ArticleFig(id=1203053377153360203, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, language=EN, label=Tab. 4, caption=

Correlation analysis of NLR, PLR, LMR and CRP,PCT, PaO2 levels

, figureFileSmall=null, figureFileBig=null, tableContent=
炎性指标CRPPCTPaO2
rPrPrP
NLR0.448<0.0010.424<0.001–0.463<0.001
PLR0.376<0.0010.334<0.001–0.298<0.001
LMR–0.467<0.001–0.357<0.0010.425<0.001
), ArticleFig(id=1203053377291772237, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1203053370102734852, language=CN, label=表4, caption=

IPF患者炎性指标NLR、PLR、LMR与CRP、PCT、PaO2水平的相关性分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
炎性指标CRPPCTPaO2
rPrPrP
NLR0.448<0.0010.424<0.001–0.463<0.001
PLR0.376<0.0010.334<0.001–0.298<0.001
LMR–0.467<0.001–0.357<0.0010.425<0.001
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炎性指标与特发性肺间质纤维化并发呼吸衰竭的相关性分析
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焦欢 1 , 李龙 1, 2, *
解放军医学杂志 | 临床研究 2023,48(3): 298-303
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解放军医学杂志 | 临床研究 2023, 48(3): 298-303
炎性指标与特发性肺间质纤维化并发呼吸衰竭的相关性分析
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焦欢1, 李龙1, 2, *
作者信息
  • 1兰州大学第一临床医学院，甘肃兰州 730000
  • 2兰州大学第一医院呼吸与危重症医学科，甘肃兰州 730000

    • 焦欢，硕士研究生，主要从事间质性肺疾病相关的临床研究

通讯作者:

李龙，E-mail：
Correlation analysis of inflammation index and idiopathic pulmonary interstitial fibrosis complicated with respiratory failure
Huan Jiao1, Long Li1, 2, *
Affiliations
  • 1The First Clinical Medical College of Lanzhou University, Lanzhou, Gansu 730000, China
  • 2Department of Respiratory and Critical Care Medicine, the First Hospital of Lanzhou University, Lanzhou, Gansu 730000, China
出版时间: 2023-03-28 doi: 10.11855/j.issn.0577-7402.2023.03.0298
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摘要
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目的 分析炎性指标与特发性肺间质纤维化（IPF）患者并发呼吸衰竭（RF）的相关性。方法 收集2019年1月-2021年10月于兰州大学第一医院就诊的169例IPF患者的临床资料进行回顾性分析，按照动脉血气分析结果分为RF组（n=84例）和非RF组（n=85例）。收集血常规、生化等临床资料，比较两组患者的各项临床指标，采用回归分析筛选IPF患者发生RF可能的危险因素；采用Spearman相关性分析探讨多项炎性指标之间及炎性指标与动脉血氧分压（PaO2）的相关性。采用受试者工作特征（ROC）曲线分析炎性指标对IPF患者并发RF的诊断价值。结果 RF组中性粒细胞与淋巴细胞比值（NLR）、血小板与淋巴细胞比值（PLR）明显高于非RF组（P<0.05），淋巴细胞与单核细胞比值（LMR）明显低于非RF组（P<0.05）。将所有患者NLR、PLR、LMR与C反应蛋白（CRP）、降钙素原（PCT）、PaO2水平进行Spearman相关性分析，结果显示，NLR、PLR与CRP、PCT均呈正相关（P<0.05），LMR与CRP、PCT呈负相关（P<0.05），且NLR与PaO2水平呈负相关（P<0.05），LMR与PaO2水平呈正相关（P<0.05）；Logistic多因素回归分析显示，吸烟、NLR升高是IPF患者发生RF的独立危险因素。ROC曲线分析显示，NLR、LMR可用于诊断IPF并发RF，其曲线下面积（AUC）分别为0.738（95%CI 0.663~0.812）、0.736（95%CI 0.660~0.812）；PLR对IPF并发RF的诊断价值有限，AUC仅为0.629（95%CI 0.545~0.714）；而NLR、PLR、LMR三者联合诊断能力高于3个单项指标，AUC为0.760（95%CI 0.689~0.832）。结论 NLR升高是IPF患者发生RF的独立危险因素。NLR、PLR、LMR及三者联合对IPF患者并发RF有一定的诊断价值，且三者联合的诊断效力优于3个单项指标。

特发性肺间质纤维化  /  呼吸衰竭  /  中性粒细胞与淋巴细胞比值  /  血小板与淋巴细胞比值  /  淋巴细胞与单核细胞比值

Objective To analyze the correlation between inflammatory index and respiratory failure (RF) in patients with idiopathic pulmonary fibrosis (IPF). Methods The clinical data of 169 patients with IPF admitted in the First Hospital of Lanzhou University from January 2019 to October 2021 were collected, and then divided into the group with respiratory failure (RF group, n=84) and the group without respiratory failure (non-RF group, n=85) according to the arterial blood gas analysis. The clinical data such as blood routine and biochemical data were collected for comparison between the two groups. Logistic regression analysis was performed to screen the possible risk factors of RF in IPF patients; Spearman correlation analysis was used to explore the correlation between multiple inflammatory indicators and between PaO2 level and inflammatory indicators; The diagnostic value of inflammatory indicators was analyzed in IPF patients complicated with RF by using the ROC curve. Results NLR (neutrophil to lymphocyte ratio) and PLR (platelet to lymphocyte ratio) were significantly higher in RF group than in non-RF group, while LMR(lymphocyte to monocyte ratio) were significantly lower than those in non-RF group, the differences were statistically significant (P<0.05). Spearman correlation analysis was conducted on NLR, PLR, LMR and CRP, PCT and PaO2 levels in all patients, it was indicated that NLR, PLR were positively correlated with CRP and PCT (P<0.05), while LMR was negatively correlated with CRP and PCT (P<0.05), moreover, there was a negative correlation between NLR and PaO2 level (P<0.05), and a positive correlation between LMR and PaO2 level (P<0.05). Logistic multifactor regression analysis suggested that smoking and increased NLR were independent risk factors for RF in IPF patients. ROC analysis indicated that NLR and LMR could be effectively used in diagnosis of IPF complicated with RF, and the area under the curve was 0.738(95%CI 0.663-0.812) and 0.736(95%CI 0.660-0.812), respectively.PLR had limited diagnostic value for IPF complicated with RF, and the area under the curve was only 0.629(95%CI 0.545-0.714), while the combined diagnostic ability of NLR, PLR and LMR was higher than the three single indexes, and the area under the curve was 0.760(95%CI 0.689-0.832). Conclusions Elevated NLR is an independent risk factor for RF in IPF patients. NLR, PLR, LMR and their combination have certain diagnostic value for IPF patients complicated with RF, and the combined diagnostic ability of the three is better than the three single indicators alone.

idiopathic pulmonary interstitial fibrosis  /  respiratory failure  /  neutrophil to lymphocyte ratio  /  platelet to lymphocyte ratio  /  lymphocyte to monocyte ratio
焦欢, 李龙. 炎性指标与特发性肺间质纤维化并发呼吸衰竭的相关性分析. 解放军医学杂志, 2023 , 48 (3) : 298 -303 . DOI: 10.11855/j.issn.0577-7402.2023.03.0298
Huan Jiao, Long Li. Correlation analysis of inflammation index and idiopathic pulmonary interstitial fibrosis complicated with respiratory failure[J]. Medical Journal of Chinese People’s Liberation Army, 2023 , 48 (3) : 298 -303 . DOI: 10.11855/j.issn.0577-7402.2023.03.0298
正文
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特发性肺间质纤维化（idiopathic pulmonary fibrosis，IPF）是一种以进行性发展为特征的慢性肺部疾病，多数IPF患者因出现呼吸衰竭（respiratory failure，RF）在确诊后2~5年死亡[1]。RF是IPF患者病情发展的一个常见表现，也是部分患者就诊的主要原因。已有研究显示细菌感染和炎症反应可能参与了肺纤维化的发展[2]。中性粒细胞、单核细胞和淋巴细胞均可参与全身炎症反应。中性粒细胞与淋巴细胞比值（neutrophil to lymphocyte ratio，NLR）、淋巴细胞与单核细胞比值（lymphocyte to monocyte ratio，LMR）、血小板与淋巴细胞比值（platelet to lymphocyte ratio，PLR）是基于外周血的中性粒细胞、淋巴细胞、单核细胞和血小板计数计算得到的指标[3]，被称为全身炎症生物标志物。目前国内对NLR、PLR、LMR与IPF并发RF相关性的研究较少。本研究旨在通过分析IPF患者的感染相关指标，进一步探讨炎性指标与IPF并发RF的相关性。
1 资料与方法
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1.1 研究对象
收集2019年1月-2021年10月就诊于兰州大学第一医院的169例IPF患者的临床资料进行回顾性分析，按照其入院时的动脉血气分析结果分为RF组（n=84例）和非RF组（n=85例）。IPF的诊断参照ATS/ERS/JRS/ALAT联合推出的IPF国际指南：无外科肺活检者，高分辨率CT（HRCT）诊断为普通型间质性肺炎（usual interstitial pneumonia，UIP）；接受外科肺活检者，结合HRCT表现和组织病理学所见[4]。RF的诊断参照WHO的诊断标准：根据入院时静息状态、未吸氧条件下的动脉血气分析结果，动脉血氧分压（PaO2）<60 mmHg，伴或不伴二氧化碳分压（PaCO2）>50 mmHg[5]。排除以下患者：（1）已知原因（如环境暴露、结缔组织病、药物等）引起的间质性肺疾病（interstitial lung disease，ILD）；（2）合并肿瘤、胸部外伤及其他呼吸系统疾病；（3）合并严重的心血管、消化等其他系统疾病；（4）临床资料不全；（5）入院前3个月内接受过抗生素治疗。
1.2 临床资料收集
收集所有患者的一般情况、血常规、生化检查结果等资料。（1）一般情况：性别、年龄、体重指数（BMI）、吸烟指数、高血压病和糖尿病病史；（2）入院后第1次动脉血气分析结果：PaO2、PaCO2水平；（3）入院后第1次实验室检查（空腹采血）结果：白细胞计数（WBC）、中性粒细胞百分比（NEUT）、C反应蛋白（CRP）、降钙素原（PCT）、白蛋白（ALB）、总胆固醇（TC）、三酰甘油（TG）、低密度脂蛋白胆固醇（LDL-C）、高密度脂蛋白胆固醇（HDL-C），计算相应的NLR、PLR、LMR值。
1.3 方法
比较两组患者的各项临床指标，筛选IPF患者发生RF可能的危险因素。采用相关性分析探讨多项炎性指标之间及炎性指标与PaO2的相关性。采用受试者工作特征（ROC）曲线探讨炎性指标诊断IPF患者并发RF的价值。
1.4 统计学处理
采用SPSS 25.0软件进行统计分析。计量资料符合正态分布时以$\bar{x}±s$表示，两组间比较采用t检验，呈偏态分布时以M（Q1，Q3）表示，两组间比较采用U检验；计数资料以例（%）表示，两组间比较采用χ2检验。将单因素分析中差异有统计学意义的变量使用多因素logistic回归分析探讨IPF并发RF的危险因素；采用Spearman方法进行双变量相关性分析。P<0.05为差异有统计学意义。
2 结果
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2.1 两组临床资料比较
RF组IPF患者吸烟指数、WBC、NEUT、NLR、PLR、CRP值明显高于非RF组（P<0.05），ALB、TC、LDL-C、LMR值明显低于非RF组（P<0.05）；两组性别、年龄、BMI、高血压病和糖尿病病史比较差异无统计学意义（P>0.05，表1）。
2.2 IPF患者并发RF的危险因素分析
2.2.1 IPF患者并发RF的单因素logistic回归分析结果
对IPF患者发生RF的潜在危险因素进行单因素logistic回归分析，结果显示，吸烟指数、ALB、TC、LDL-C、WBC、NEUT、NLR、PLR、LMR、CRP、PCT值均有统计学意义（P<0.05，表2）。
2.2.2 IPF患者并发RF的多因素logistic回归分析结果
将单因素分析中有统计学意义的变量进行多因素logistic回归分析，包括吸烟指数（P=0.009）、ALB（P=0.001）、NLR（P<0.001）、PLR（P=0.002）、LMR（P<0.001）、LDL-C（P=0.006）、TC（P=0.005）。因WBC、NEUT、CRP、PCT与NLR存在相关关系（r分别为0.468、0.943、0.448、0.424，P<0.001），且本文主要研究NLR、PLR、LMR，故剔除WBC、NEUT、CRP、PCT后行多因素logistic回归分析，结果显示，吸烟指数、NLR升高是IPF患者并发RF的独立危险因素（P<0.05，表3）。
2.3 IPF患者炎性指标NLR、PLR、LMR与CRP、PCT、PaO2水平的相关性分析
将所有患者NLR、PLR、LMR与CRP、PCT、PaO2水平进行Spearman相关性分析，结果显示，NLR、PLR与CRP、PCT均呈正相关（P<0.05），LMR与CRP、PCT呈负相关（P<0.05），且NLR与PaO2水平呈负相关（P<0.05），LMR与PaO2水平呈正相关（P<0.05，图1、表4）。
2.4 NLR、PLR、LMR及三者联合诊断IPF患者并发RF的ROC曲线分析
将NLR、PLR、LMR单项及三者联合进行ROC曲线分析，结果显示，NLR、PLR、LMR诊断IPF患者并发RF的曲线下面积（AUC）分别为0.738（95%CI 0.663~0.812）、0.629（95%CI 0.545~0.714）、0.736（95%CI 0.660~0.812），而三者联合的诊断能力强于3个单项指标，其AUC为0.760（95%CI 0.689~0.832），敏感度为0.702，特异度为0.706（P<0.05，图2）。
3 讨论
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IPF患者并发RF后，病情往往进行性加重，严重影响其生活质量。疾病进展引起的RF是IPF患者常见的死亡原因，也是患者晚期或急性恶化后较常见的并发症。RF根据发病时间可分为急性RF（ARF）和慢性RF（CRF），ARF以呼吸困难和低氧血症的进行性恶化为特征，而CRF的发展通常是隐匿的且进展缓慢，CRF也可是IPF急性恶化的结果。研究显示，ARF和CRF均与IPF较高的病死率相关[6]。因而，深入探索影响肺纤维化进展的危险因素，努力识别和治疗可逆转的RF触发因素，有利于延缓IPF的疾病进展。IPF的相关研究显示，肺微生物组的改变可能导致肺纤维化进展或急性加重，提示细菌感染和炎症反应可能参与了肺纤维化的发展[2]。Invernizzi和Molyneaux[7]的研究显示，IPF患者较高的总细菌负荷与其无进展生存时间缩短有关，随着肺部细菌负担的增加，死亡风险增高。IPF患者肺微生物组的破坏可预测其疾病进展，微生物群多样性与肺泡炎症指数和促纤维化细胞因子增加相关[8]。
越来越多的证据显示，与单个细胞相比，通过计算外周血中性粒细胞、淋巴细胞、单核细胞和血小板计数的比值得出的指标与慢性炎症的相关性更加密切。与单一细胞计数值相比，计算不同血细胞计数的比值可减少脱水、补液和血液标本处理等对结果的影响，对炎症的预测价值可能更高。研究显示，NLR、LMR与其他炎症标志物如CRP、红细胞沉降率（ESR）呈正相关，且在多种临床情况下具有较好的预测价值；在危重患者中，NLR与疾病严重程度和病死率密切相关，并可比其他标志物更准确地预测菌血症和疾病的严重程度[9]。在慢性阻塞性肺疾病（chronic obstructive pulmonary diseases，COPD）患者中，NLR可预测疾病的严重程度和恶化的可能性；在肿瘤、心血管疾病等多种慢性疾病患者中，NLR、LMR被认为是一种有价值的预后标志物[10]。此外，有研究显示检测NLR、LMR的变化对新型冠状病毒疾病患者也有重要的临床意义[11]。研究显示，高PLR是老年人全因病死的独立预测因子；在多种疾病状态下，高PLR与病死率增高和预后不良有关[12]。PLR不仅可预测全身炎症情况的预后，在多种恶性肿瘤、心血管疾病、自身免疫性疾病中也可能是预后和治疗反应的有效预测因子[13]。本研究Spearman相关分析结果显示，NLR、PLR与CRP、PCT均呈正相关，LMR与CRP、PCT呈负相关，且NLR与PaO2水平呈负相关，LMR与PaO2水平呈正相关，与既往研究一致。这一结果提示炎性指标NLR、LMR、PLR在评估IPF患者炎症方面具有一定的价值。
NLR、LMR、PLR与肺纤维化的相关研究显示，NLR≥5是比CRP更好的指标，能够以更低的成本预测是否存在感染，NLR≥5.9时可预测IPF的病死率，应引起临床医师的注意[14]。此外，1年内NLR或PLR变化较大也与临床结果较差相关[15]。炎症和免疫反应在IPF中发挥着关键作用，炎症诱导中性粒细胞和血小板数量增加，淋巴细胞减少，因而它们的比值可间接评估炎症状态及细胞介导的免疫功能。呼吸道定植的微生物就像是炎症的触发器，当微生物组发生变化时可能触发中性粒细胞的趋化活性。有研究显示，中性粒细胞弹性蛋白酶可通过促进转化生长因子-β（transforming growth factor-β，TGF-β）活化、成纤维细胞增殖和肌成纤维细胞分化，推动肺纤维化的发展[16]。单核细胞作为促纤维化巨噬细胞和纤维细胞的祖细胞，通过促进肺泡炎症反应和释放促纤维化细胞因子参与IPF的发病机制[17]。单核细胞增多是对慢性感染的反应，单核细胞在组织修复、纤维化和组织再生机制中均发挥着重要作用。中性粒细胞的激活还可刺激白介素-1（IL-1）等炎性介质的产生，从而刺激巨核细胞产生血小板；血小板的表面受体能够识别病原体和免疫复合物，在免疫系统中发挥至关重要的作用[18]。虽然还需要进一步研究来阐明这种相关性的机制，但中性粒细胞、单核细胞、血小板等参与肺纤维化的病理生理机制已被业界广泛接受。已有文献报道，因为机体的慢性适应性会阻止中性粒细胞等细胞数量的增加，所以NLR、PLR、LMR指标对疾病急性炎症反应灵敏，而对慢性炎症状态反应欠佳[9]。本研究结果显示，RF组IPF患者NLR、PLR明显高于非RF组，LMR明显低于非RF组；多因素logistic回归分析结果显示，吸烟、NLR升高是IPF患者并发RF的独立危险因素。
据报道，NLR还可作为反映内皮功能障碍和氧化应激严重程度的参数[15]。一项校正年龄、性别、BMI和吸烟情况的研究结果显示，NLR、LMR、PLR与IPF患者的肺功能参数显著相关，特别是用力肺活量百分比（FVC%）与NLR、LMR，一氧化碳弥散量百分比（DLCO%）与NLR、LMR之间存在较强的相关性[9]。也有研究显示，绝对单核细胞计数升高可能是间质性肺疾病急性加重的一个独立危险因素[19]。此外，还有研究发现，诊断时高单核细胞计数是抗纤维化治疗第一年后肺功能下降的独立预测因素，并与诊断时的肺功能呈负相关[20]。对于严重RF需要机械通气的患者，NLR也可能是预测脱机失败的一个有用指标，当NLR>11时脱机风险较大，应谨慎考虑[10]。本研究ROC曲线分析结果显示，NLR、PLR、LMR及三者联合对IPF患者并发RF有一定的诊断价值，其中NLR诊断IPF患者并发RF的特异度高达0.906，且三者联合的诊断能力高于3个单项指标，提示NLR、PLR升高及LMR降低可用于IPF患者并发RF的诊断及病情评估。不仅NLR、PLR、LMR作为炎性指标与机体的炎症反应相关，中性粒细胞、单核细胞等也可通过免疫系统参与肺纤维化的进展，肺组织的炎症和纤维化导致气体有效交换障碍，推动弥漫性肺实质异常发展，可导致患者出现咳嗽、咳痰、进行性RF。
综上所述，本研究结果显示，NLR升高是IPF并发RF的独立危险因素；NLR、PLR、LMR及三者联合对IPF并发RF有一定的诊断价值，且三者联合的诊断效力优于3个单项指标。本研究存在一定的局限性，如为单中心回顾性研究，且样本量较小，结果可能存在偏倚，期待未来有更多大样本的前瞻性研究，以探索这些炎症标志物及其动态变化与肺纤维化进展的相关性。
基金
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  • 兰州大学第一医院立项基金(ldyyn2021-34)
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参考文献 引证文献
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2023年第48卷第3期
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doi: 10.11855/j.issn.0577-7402.2023.03.0298
  • 接收时间：2022-04-01
  • 首发时间：2025-12-03
  • 出版时间：2023-03-28
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  • 收稿日期：2022-04-01
  • 录用日期：2022-06-09
基金
Innovation Fund of the First Hospital of Lanzhou University(ldyyn2021-34)
兰州大学第一医院立项基金(ldyyn2021-34)
作者信息
    1兰州大学第一临床医学院，甘肃兰州 730000
    2兰州大学第一医院呼吸与危重症医学科，甘肃兰州 730000

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
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红菇属 Russula 17 8.13
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