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Article(id=1241067204905595850, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241067197318091153, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202204520, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1651248000000, receivedDateStr=2022-04-30, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773823079563, onlineDateStr=2026-03-18, pubDate=1741536000000, pubDateStr=2025-03-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773823079563, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773823079563, creator=13701087609, updateTime=1773823079563, updator=13701087609, issue=Issue{id=1241067197318091153, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='5', pageStart='769', pageEnd='960', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773823077754, creator=13701087609, updateTime=1773823268053, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241067995544482681, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241067197318091153, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241067995544482682, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241067197318091153, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=812, endPage=817, ext={EN=ArticleExt(id=1241067208164569191, articleId=1241067204905595850, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis of factors influencing short-term poor prognosis in patients with acute exacerbation of chronic obstructive pulmonary disease and construction of a nomogram, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To construct a risk prediction model for short-term poor prognosis in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and to explore the influencing factors of poor short-term prognosis in these patients, providing a basis for early prediction and intervention.

Methods

AECOPD patients admitted to the Department of Respiratory Medicine at Cangzhou People’s Hospital in Hebei Province from October 2020 to September 2021 were selected as the study subjects. After 90 days, patients were divided into case group (n=167) and control group (n=250) based on prognosis outcomes. Patient demographics, disease conditions, the Modified Medical Research Council (mMRC) dyspnea scale, and the COPD Assessment Test (CAT) were collected at different time points. Binary logistic regression was used for factor analysis, and a nomogram prediction model was established. The diagnostic efficacy and calibration of the nomogram model were evaluated using ROC curves and calibration curves, while DCA was used to assess clinical utility.

Results

Multivariate analysis indicated that marital status (OR=2.111, 95%CI:1.076-4.141), severity of illness (OR=5.322, 95%CI: 2.908-9.740), history of frequent acute exacerbations (OR=8.217, 95%CI:4.569-14.778), nutritional risk (OR=2.463, 95%CI: 1.141-5.319), and PCT (OR=2.039, 95%CI: 1.116-3.727) were independent risk factors for poor short-term prognosis in AECOPD patients. The nomogram prediction model had an AUC of 0.894 (95%CI: 0.868-0.929), and the calibration curve demonstrated good consistency between predicted probabilities and actual outcomes, indicating high predictive accuracy.

Conclusion

The nomogram model can accurately identify the risk of poor short-term prognosis in AECOPD patients, providing a theoretical basis for personalized clinical management of AECOPD patients.

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目的

构建慢性阻塞性肺疾病急性加重（AECOPD）患者短期预后不良风险预测模型，探索AECOPD患者短期预后不良的影响因素，为早期预测和干预提供依据。

方法

选取2020年10月—2021年9月在河北省沧州市人民医院呼吸科所收治AECOPD患者为研究对象，90 d后根据预后效果分为病例组（n=167）和对照组（n=250）。不同时期分别搜集患者一般情况、疾病情况、呼吸困难问卷和COPD评估测试问卷等。采用二元logistic回归进行影响因素分析，建立列线图预测模型。采用ROC曲线和校准曲线评价列线图模型的诊断效能和校准度、DCA评估临床收益性。

结果

多因素结果分析显示，婚姻（OR=2.111 ,95%CI：1.076～4.141）、病情程度（OR=5.322 ,95%CI：2.908～9.740）、既往频繁急性加重史（OR=8.217,95%CI：4.569～14.778）、营养风险（OR=2.463 ,95%CI：1.141～5.319）和PCT（OR=2.039 ,95%CI：1.116～3.727）均为AECOPD短期预后不良的独立影响因素。列线图预测模型AUC为0.894（95%CI：0.868～0.929），校准曲线显示该模型预测概率与实际一致性良好，模型预测准确度较高。

结论

列线图模型可精准识别AECOPD患者短期预后不良的发生风险，为AECOPD患者开展个性化临床管理提供一定理论依据。

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赵雅宁，E-mail:
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=fza6PGpD09wMtBrgacVoNw==, magXml=HZveMSN3bejylY9wYEadNg==, pdfUrl=null, pdf=I+y0VruVIAHYPAb7aYfe6g==, pdfFileSize=756987, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=I/h+Fq7IDOCKoMKWRp1zUg==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=1g+0wfoCAGUW3/4QbAeVig==, mapNumber=null, authorCompany=null, fund=null, authors=

刘瑶（1992—），女，硕士，讲师，研究方向：呼吸病学

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Journal of Parasitic Biology, 2022, 17(1): 87-90,94.(In Chinese), articleTitle=Application value of combined detection of PCT and CRP in the diagnosis of chronic obstructive pulmonary disease complicated with bacterial infection, refAbstract=null)], funds=[Fund(id=1241067224119702182, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067204905595850, awardId=20180731, language=CN, fundingSource=河北省卫健委重点课题项目(20180731), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241067211717144914, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067204905595850, xref=null, ext=[AuthorCompanyExt(id=1241067211725533523, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067204905595850, companyId=1241067211717144914, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Nursing and Rehabilitation, North China University of Science and Technology, Tangshan, Hebei 063210, China), AuthorCompanyExt(id=1241067211733922132, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067204905595850, companyId=1241067211717144914, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=华北理工大学护理与康复学院，河北 唐山 063210)])], figs=[ArticleFig(id=1241067217845023297, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067204905595850, language=EN, label=Figure 1, caption=Nomogram of risk prediction of poor short-term prognosis in patients with AECOPD, figureFileSmall=pwxArwQWXQCZIVZEXME1Xw==, figureFileBig=I/h+Fq7IDOCKoMKWRp1zUg==, tableContent=null), ArticleFig(id=1241067217933103687, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067204905595850, language=CN, label=图1, caption=AECOPD患者短期预后不良风险预测列线图, figureFileSmall=pwxArwQWXQCZIVZEXME1Xw==, figureFileBig=I/h+Fq7IDOCKoMKWRp1zUg==, tableContent=null), ArticleFig(id=1241067221418570326, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067204905595850, language=EN, label=Figure 2, caption=ROC curve of nomogram model predicting the risk of poor short-term prognosis in patients with AECOPD, figureFileSmall=DPitcvlTqeSeJrunOdVvIA==, figureFileBig=urGra7bpIUuHCUDWbk54Kg==, tableContent=null), ArticleFig(id=1241067221561176670, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067204905595850, language=CN, label=图2, caption=列线图模型预测AECOPD患者短期预后不良风险的ROC曲线, figureFileSmall=DPitcvlTqeSeJrunOdVvIA==, figureFileBig=urGra7bpIUuHCUDWbk54Kg==, tableContent=null), ArticleFig(id=1241067221687005800, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067204905595850, language=EN, label=Figure 3, caption=Calibration curve of probability of poor short-term prognosis in patients with AECOPD, figureFileSmall=hGBOvqrh2vQHAHnmpXm9XA==, figureFileBig=VpjNHVfg5LZDDXd/7Yikxw==, tableContent=null), ArticleFig(id=1241067221821223536, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067204905595850, language=CN, label=图3, caption=AECOPD患者短期预后不良发生概率校准曲线, figureFileSmall=hGBOvqrh2vQHAHnmpXm9XA==, figureFileBig=VpjNHVfg5LZDDXd/7Yikxw==, tableContent=null), ArticleFig(id=1241067222009967224, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067204905595850, language=EN, label=Table 1, caption=

Univariate analysis of influencing factors of poor short-term prognosis of AECOPD ［n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
因素病例组对照组χ2值P值OR（95%CI）
性别0.4980.480
男89（53.3）142（56.8）1
女78（46.7）108（43.2）0.871（0.777～1.708）
年龄（岁）14.4200.001
<6012（7.2）42（16.8）1
60～7587（52.1）143（57.2）2.129（1.063～4.265）
>7566（40.7）67（26.0）0.0013.662（1.771～7.568）
文化程度1.1030.294
较高26（15.6）49（19.6）1
较低141（84.4）201（80.4）0.756（0.449～1.275）
婚姻13.787<0.001
在婚112（67.1）207（82.8）1
未在婚55（32.9）43（17.2）2.364（1.492～3.747）
营养风险53.9820.001
无风险19（11.4）114（45.6）1
有风险148（88.6）136（54.4）6.529（3.810～11.189）
心血管疾病9.7960.002
无8（4.8）36（14.4）1
有159（95.2）216（85.6）3.343（1.513～7.390）
PNI24.4460.001
正常63（37.7）156（62.4）1
异常104（62.3）92（37.6）2.740（1.829～4.104）
既往频繁急性加重史162.444<0.001
否31（19.2）202（80.8）1
是136（81.4）48（19.2）18.462（11.184～30.476）
SII8.4000.004
正常32（19.2）80（32.0）1
异常135（80.8）170（68.0）1.985（1.243～3.170）
病情严重程度127.561<0.001
轻度28（16.8）183（73.2）1
重度139（83.2）67（26.8）13.559（8.281～22.202）
CERWS15.984<0.001
低危6（3.6）30（12.0）1
中危74（44.3）131（52.4）2.824（1.124～7.099）
高危87（52.1）89（35.6）4.888（1.938～12.326）
CRP11.863<0.001
正常35（21.0）92（36.8）1
异常132（79.0）158（63.2）2.196（1.396～3.453）
PCT49.4100.001
正常43（25.7）152（60.8）1
异常124（74.3）98（39.2）4.473（2.910～6.875）
), ArticleFig(id=1241067222173545086, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067204905595850, language=CN, label=表1, caption=

AECOPD短期预后不良影响因素的单因素分析［n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
因素病例组对照组χ2值P值OR（95%CI）
性别0.4980.480
男89（53.3）142（56.8）1
女78（46.7）108（43.2）0.871（0.777～1.708）
年龄（岁）14.4200.001
<6012（7.2）42（16.8）1
60～7587（52.1）143（57.2）2.129（1.063～4.265）
>7566（40.7）67（26.0）0.0013.662（1.771～7.568）
文化程度1.1030.294
较高26（15.6）49（19.6）1
较低141（84.4）201（80.4）0.756（0.449～1.275）
婚姻13.787<0.001
在婚112（67.1）207（82.8）1
未在婚55（32.9）43（17.2）2.364（1.492～3.747）
营养风险53.9820.001
无风险19（11.4）114（45.6）1
有风险148（88.6）136（54.4）6.529（3.810～11.189）
心血管疾病9.7960.002
无8（4.8）36（14.4）1
有159（95.2）216（85.6）3.343（1.513～7.390）
PNI24.4460.001
正常63（37.7）156（62.4）1
异常104（62.3）92（37.6）2.740（1.829～4.104）
既往频繁急性加重史162.444<0.001
否31（19.2）202（80.8）1
是136（81.4）48（19.2）18.462（11.184～30.476）
SII8.4000.004
正常32（19.2）80（32.0）1
异常135（80.8）170（68.0）1.985（1.243～3.170）
病情严重程度127.561<0.001
轻度28（16.8）183（73.2）1
重度139（83.2）67（26.8）13.559（8.281～22.202）
CERWS15.984<0.001
低危6（3.6）30（12.0）1
中危74（44.3）131（52.4）2.824（1.124～7.099）
高危87（52.1）89（35.6）4.888（1.938～12.326）
CRP11.863<0.001
正常35（21.0）92（36.8）1
异常132（79.0）158（63.2）2.196（1.396～3.453）
PCT49.4100.001
正常43（25.7）152（60.8）1
异常124（74.3）98（39.2）4.473（2.910～6.875）
), ArticleFig(id=1241067222286791300, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067204905595850, language=EN, label=Table 2, caption=

Variable assignment

, figureFileSmall=null, figureFileBig=null, tableContent=
变量赋值
短期预后情况0=短期预后良好，1=短期预后不良
婚姻0=在婚，1=未在婚
年龄（岁）1=<60，2=60～75，3= >75
合并心血管疾病0=否，1=有
病情程度1=轻度，2=重度
既往是否有频繁加重史0=否，1=是
营养风险0=无风险，1=有风险
CERWS0=低危，1=中危，2=高危
PNI1=正常，2=异常
SII1=正常，2=异常
PCT1=正常，2=异常
CRP1=正常，2=异常
), ArticleFig(id=1241067222387454601, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067204905595850, language=CN, label=表2, caption=

变量赋值

, figureFileSmall=null, figureFileBig=null, tableContent=
变量赋值
短期预后情况0=短期预后良好，1=短期预后不良
婚姻0=在婚，1=未在婚
年龄（岁）1=<60，2=60～75，3= >75
合并心血管疾病0=否，1=有
病情程度1=轻度，2=重度
既往是否有频繁加重史0=否，1=是
营养风险0=无风险，1=有风险
CERWS0=低危，1=中危，2=高危
PNI1=正常，2=异常
SII1=正常，2=异常
PCT1=正常，2=异常
CRP1=正常，2=异常
), ArticleFig(id=1241067222483923601, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067204905595850, language=EN, label=Table 3, caption=

Multivariate logistic regression analysis of poor short-term prognosis in AECOPD (n=167)

, figureFileSmall=null, figureFileBig=null, tableContent=
因素β WaldP值OR（95%CI）
婚姻0.7470.3444.7230.0302.111（1.076～4.141）
年龄（岁）0.0270.987
60～75-0.0490.5330.0080.9270.952（0.335～2.706）
>75-0.0900.5940.0230.8800.914（0.286～2.926）
合并心血管疾病0.6410.5671.2790.2581.899（0.625～5.771）
病情程度1.6720.30829.402<0.0015.322（2.908～9.740）
既往频繁急性加重史2.1060.29949.466<0.0018.217（4.569～14.778）
营养风险0.9010.3935.2680.0222.463（1.141～5.319）
CREWS等级0.1420.932
中危-0.0510.6240.0070.9350.950（0.279～3.231）
高危-0.1530.6290.0590.8070.858（0.250～2.941）
PNI0.2950.3020.9530.3291.343（0.743～2.428）
SII0.3630.3640.9940.3191.438（0.704～2.934）
PCT0.7130.3085.3670.0212.039（1.116～3.727）
CRP0.1620.3400.2270.6341.176（0.604～2.291）
), ArticleFig(id=1241067222618141338, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067204905595850, language=CN, label=表3, caption=

AECOPD短期预后不良多因素logistic回归分析（n=167）

, figureFileSmall=null, figureFileBig=null, tableContent=
因素β WaldP值OR（95%CI）
婚姻0.7470.3444.7230.0302.111（1.076～4.141）
年龄（岁）0.0270.987
60～75-0.0490.5330.0080.9270.952（0.335～2.706）
>75-0.0900.5940.0230.8800.914（0.286～2.926）
合并心血管疾病0.6410.5671.2790.2581.899（0.625～5.771）
病情程度1.6720.30829.402<0.0015.322（2.908～9.740）
既往频繁急性加重史2.1060.29949.466<0.0018.217（4.569～14.778）
营养风险0.9010.3935.2680.0222.463（1.141～5.319）
CREWS等级0.1420.932
中危-0.0510.6240.0070.9350.950（0.279～3.231）
高危-0.1530.6290.0590.8070.858（0.250～2.941）
PNI0.2950.3020.9530.3291.343（0.743～2.428）
SII0.3630.3640.9940.3191.438（0.704～2.934）
PCT0.7130.3085.3670.0212.039（1.116～3.727）
CRP0.1620.3400.2270.6341.176（0.604～2.291）
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慢阻肺急性加重患者短期预后不良影响因素分析及列线图的构建
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刘瑶 , 王源 , 赵雅宁 , 赵旭 , 赵大业
现代预防医学 | 流行病与统计方法 2025,52(5): 812-817
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现代预防医学 | 流行病与统计方法 2025, 52(5): 812-817
慢阻肺急性加重患者短期预后不良影响因素分析及列线图的构建
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刘瑶, 王源, 赵雅宁 , 赵旭, 赵大业
作者信息
  • 华北理工大学护理与康复学院，河北 唐山 063210

    • 刘瑶（1992—），女，硕士，讲师，研究方向：呼吸病学

通讯作者:

赵雅宁，E-mail:
Analysis of factors influencing short-term poor prognosis in patients with acute exacerbation of chronic obstructive pulmonary disease and construction of a nomogram
Yao LIU, Yuan WANG, Ya-ning ZHAO , Xu ZHAO, Da-ye ZHAO
Affiliations
  • School of Nursing and Rehabilitation, North China University of Science and Technology, Tangshan, Hebei 063210, China
出版时间: 2025-03-10 doi: 10.20043/j.cnki.MPM.202204520
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摘要
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目的

构建慢性阻塞性肺疾病急性加重（AECOPD）患者短期预后不良风险预测模型，探索AECOPD患者短期预后不良的影响因素，为早期预测和干预提供依据。

方法

选取2020年10月—2021年9月在河北省沧州市人民医院呼吸科所收治AECOPD患者为研究对象，90 d后根据预后效果分为病例组（n=167）和对照组（n=250）。不同时期分别搜集患者一般情况、疾病情况、呼吸困难问卷和COPD评估测试问卷等。采用二元logistic回归进行影响因素分析，建立列线图预测模型。采用ROC曲线和校准曲线评价列线图模型的诊断效能和校准度、DCA评估临床收益性。

结果

多因素结果分析显示，婚姻（OR=2.111 ,95%CI：1.076～4.141）、病情程度（OR=5.322 ,95%CI：2.908～9.740）、既往频繁急性加重史（OR=8.217,95%CI：4.569～14.778）、营养风险（OR=2.463 ,95%CI：1.141～5.319）和PCT（OR=2.039 ,95%CI：1.116～3.727）均为AECOPD短期预后不良的独立影响因素。列线图预测模型AUC为0.894（95%CI：0.868～0.929），校准曲线显示该模型预测概率与实际一致性良好，模型预测准确度较高。

结论

列线图模型可精准识别AECOPD患者短期预后不良的发生风险，为AECOPD患者开展个性化临床管理提供一定理论依据。

慢性阻塞性肺疾病  /  列线图预测模型  /  危险因素
Objective

To construct a risk prediction model for short-term poor prognosis in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and to explore the influencing factors of poor short-term prognosis in these patients, providing a basis for early prediction and intervention.

Methods

AECOPD patients admitted to the Department of Respiratory Medicine at Cangzhou People’s Hospital in Hebei Province from October 2020 to September 2021 were selected as the study subjects. After 90 days, patients were divided into case group (n=167) and control group (n=250) based on prognosis outcomes. Patient demographics, disease conditions, the Modified Medical Research Council (mMRC) dyspnea scale, and the COPD Assessment Test (CAT) were collected at different time points. Binary logistic regression was used for factor analysis, and a nomogram prediction model was established. The diagnostic efficacy and calibration of the nomogram model were evaluated using ROC curves and calibration curves, while DCA was used to assess clinical utility.

Results

Multivariate analysis indicated that marital status (OR=2.111, 95%CI:1.076-4.141), severity of illness (OR=5.322, 95%CI: 2.908-9.740), history of frequent acute exacerbations (OR=8.217, 95%CI:4.569-14.778), nutritional risk (OR=2.463, 95%CI: 1.141-5.319), and PCT (OR=2.039, 95%CI: 1.116-3.727) were independent risk factors for poor short-term prognosis in AECOPD patients. The nomogram prediction model had an AUC of 0.894 (95%CI: 0.868-0.929), and the calibration curve demonstrated good consistency between predicted probabilities and actual outcomes, indicating high predictive accuracy.

Conclusion

The nomogram model can accurately identify the risk of poor short-term prognosis in AECOPD patients, providing a theoretical basis for personalized clinical management of AECOPD patients.

Chronic obstructive pulmonary disease  /  Nomogram prediction model  /  Risk factors
刘瑶, 王源, 赵雅宁, 赵旭, 赵大业. 慢阻肺急性加重患者短期预后不良影响因素分析及列线图的构建. 现代预防医学, 2025 , 52 (5) : 812 -817 . DOI: 10.20043/j.cnki.MPM.202204520
Yao LIU, Yuan WANG, Ya-ning ZHAO, Xu ZHAO, Da-ye ZHAO. Analysis of factors influencing short-term poor prognosis in patients with acute exacerbation of chronic obstructive pulmonary disease and construction of a nomogram[J]. Modern Preventive Medicine, 2025 , 52 (5) : 812 -817 . DOI: 10.20043/j.cnki.MPM.202204520
正文
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慢性阻塞性肺部疾病（chronic obstructive pulmonary disease,COPD）是一种高死亡率、高病残率、高慢病负担的公共疾患。全国每年因COPD死亡的人数达128万人，我国40岁以上人群中COPD患病率为8.2%，每年致残人数达500～1 000万，致死人数达100万[1]，给患者和社会带来严重的经济负担。COPD是一种长期反复发作的疾病，在病程发展中慢性阻塞性肺部疾病急性加重（acute exacerbation of chronic obstructive pulmonary disease，AECOPD）成为导致患者不良预后结局的重要原因，是COPD的一种急性加重状态。AECOPD患者会出现超过日常基础的疾病症状，如呼吸困难、气喘、咳嗽咳痰、胸闷、喘息加重等症状持续恶化，病情突然加重[2]。AECOPD由于呼吸道症状的急性恶化，活动耐受力明显下降，对呼吸功能、心血管健康以及精神健康等多方面的严重影响，可能会引发多种并发症，从而导致患者丧失日常生活能力、生活质量下降。以往有学者调查发现超过25%的COPD患者在急性加重后35 d呼气峰流速量无法恢复到加重前的基线水平，肺功能出现持续性恶化的现象[3]，由于AECOPD患者异质性较大，病情多变，因此早期识别AECOPD患者短期预后不良风险并构建风险预测模型，并及时给予个性化风险干预治疗，从而增加患者的生存率和生活质量至关重要。列线图也称诺莫图（Nomogram），可根据患者的特征来预测结局事件概率的风险评估工具，通过对影响因素进行量化，将结果发生概率进行客观的可视化呈现，列线图在风险预测、辅助决策、病情监测与管理以及患者教育与沟通等方面具有广泛的应用价值[4-5]。本研究利用列线图探究AECOPD患者短期预后的独立影响因素，为临床医护人员制定精准康复干预策略、减低患者再住院率、改善病程结局提供依据。
1　对象与方法
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1.1　研究对象
整群纳入2020年10月—2021年9月在河北省沧州市人民医院呼吸科诊收治的AECOPD患者。根据90 d随访结果，将167例短期预后不良者归为病例组，250例短期预后良好者归为对照组。研究方案已由华北理工大学医学伦理委员会审批（审批号：2021076），所有患者均签署了知情同意书。纳入标准：（1）入院时符合GOLD2020版[3]中的COPD诊断标准；（2）临床资料完整。排除标准：（1）合并其他极重度肺部疾病的患者，如伴有严重感染、恶病质、使用有创通气、多重耐药菌感染等；（2）无法完成肺功能测试者；（3）住院、随访期间新发其他疾病并因此转科的患者；（4）语言表达不清或沟通障碍患者。
1.2　资料收集及评价标准
1.2.1　资料收集方法
采用现场调查和病历资料收集相结合的方法。患者的一般资料、临床资料等均从医院病历系统中收集，患者出院90 d后进行随访调查。
1.2.2　影响因素的构建
本研究通过大量文献查阅、临床观察构建AECOPD患者的短期预后影响因素，主要包括人口学相关因素及疾病相关因素。（1）人口学相关因素：性别、年龄、文化程度。（2）疾病相关因素：①既往频繁急性加重史：既往无频繁急性加重史为近1年内患者急性加重次数<2次，既往有频繁急性加重史为近1年内患者急性加重次数≥2次[6]；②病情严重程度：病情程度轻度判定标准为患者无呼吸衰竭，呼吸20～30 次/min，无意识改变，无辅助呼吸肌群参与，低氧血症可通过鼻导管低流量吸氧改善，病情程度重度判定标准为呼吸衰竭，呼吸>30 次/min，无意识改变，伴有辅助呼吸肌群参与，低氧血症吸入28%～35%氧浓度可改善，二氧化碳在50～60 mm Hg[7]；③营养风险评估：采用NRS2002 营养风险评估量表，≥3分为有营养风险，<3 分为无营养风险[8]；④有无心血管疾病：包括高血压、心脏病、心衰等；⑤小野寺预后营养指数（PNI）正常为PNI≥45，异常为PNI<45 [9]；⑥免疫炎症指数（SII）：正常为SII≤488.56，异常为SII>488.56；⑦慢性呼吸系统早期预警评分(CREWS)：CREWS评分包括呼吸、血氧饱和度、是否吸氧、体温、收缩压、脉搏、意识水平7项评分指标，其中是否吸氧评分为0～2分，其他6项评分均为0～3分，总分为0～20分；低危组为0～4分、中危组为>4～<7分或任一单项评分达到3分、高危组为≥7分[10]；⑧C反应蛋白（CRP）：正常参考值≤10 mg/L，>10 mg/L为异常；⑨降钙素原（PCT）：正常值范围是<0.05 ng/ml，一旦>0.05 ng/ml则为偏高。
1.3　AECOPD患者短期预后判定
依据既往文献调查及临床观察，将AECOPD患者短期预后的观察时间确定为90 d。AECOPD患者短期预后情况应用mMRC问卷和CAT问卷进行判定，患者出院后90 d经mMRC症状评级≥3级且CAT分>20分判定为AECOPD短期预后不良。出院后90 d经mMRC症状评级<3级且CAT得分≤20分者判定为AECOPD短期预后良好。
1.4　统计学方法
采用SPSS 25.0软件进行统计学分析，采用χ2检验进行单因素分析，二元logistic回归进行多因素分析。应用rms程序包构建AECOPD患者短期预后不良风险预测模型，并绘制相应的列线图，受试者工作特征（ROC）曲线判断列线图模型效度，bootstrap法、Brier值等结果评价模型的校准度。校准曲线及决策曲线（DCA）评估模型预测效能。检验水准α=0.05。
2　结果
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2.1　AECOPD短期预后不良影响因素的单因素分析
单因素分析发现，年龄、CERWS、营养风险评估、心血管疾病、PNI、既往频繁急性加重史、SII、病情严重程度、C反应蛋白和PCT两组间差异性比较有统计学意义（P<0.05)。年龄60～75岁（OR=2.129，95%CI：1.063～4.265）、年龄>75岁（OR=3.662，95%CI：1.771～7.568）、未在婚（OR=2.364，95%CI：1.492～3.747）、有营养风险（OR=6.529，95%CI：3.810～11.189）、有心血管疾病（OR=3.343，95%CI：1.513～7.390）、PNI异常（OR=2.740，95%CI：1.829～4.104）、既往有频繁急性加重史（OR=18.462，95%CI：11.184～30.476）、SII异常（OR=1.985，95%CI：1.243～3.170）、病情严重（OR=13.559，95%CI：8.281～22.202）、CERWS为中危（OR=2.824，95%CI：1.124～7.099）、CERWS高危（OR=4.888，95%CI：1.938～12.326）、CRP异常（OR=2.196，95%CI：1.396～3.453）和PCT异常（OR= 4.473，95%CI：2.910～6.875）是AECOPD患者发生短期预后不良的影响因素（P<0.05)。见表1。
2.2　AECOPD短期预后不良影响因素的多因素分析
将单因素分析中有统计学意义的变量纳入二元logistic回归分析，结果显示，婚姻（OR=2.111，95%CI：1.076～4.141）、病情程度（OR=5.322，95%CI：2.908～9.740）、既往频繁急性加重史（OR=8.217，95%CI：4.569～14.778）、营养风险（OR=2.463，95%CI：1.141～5.319）、PCT（OR=2.039，95%CI：1.116～3.727）是AECOPD短期预后不良的独立危险因素，见表2。
2.3　AECOPD患者短期预后不良风险列线图预测模型的建立
将AECOPD患者短期预后不良风险的影响因素包括婚姻、病情程度、既往频繁急性加重史、营养风险、PCT作为自变量，构建AECOPD患者短期预后不良风险列线图预测模型，见图1、表3。
2.4　列线图预测模型的验证
采用pROC程序包绘制列线图模型的ROC曲线，列线图预测模型的AUC值为0.894（95%CI：0.868～0.929），约登指数为66.1%，特异性为74.4%，灵敏度为91.2%，表明模型判别度较高，见图2。使用bootstrap验证方法从原始数据中重复抽样1 000次评价模型预测能力，bootstrap校准后的C-index为0.904，该列线图模型的校准度均较好，结果见图3。列线图预测模型的Hosmer-Lemeshow检验P=0.636，具有较好的拟合度，列线图预后不良的预测曲线与标准曲线基本拟合，一致性良好。
3　讨论
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研究发现，超过30%的COPD患者患者因为相同疾病在30 d内再入院[11]，COPD患者一旦发生急性加重，就会导致更易发生下一次急性加重，故AECOPD患者会频繁发生急性加重。目前大多关于AECOPD患者短期预后情况的研究是以肺功能测定进行报导，急性加重期患者呼吸困难，配合度下降，肺功能检查得到的数据可能无法准确反映患者的实际肺功能状态，研究结果间异质性大，对风险预测方面关注不足。因此，建立一种AECOPD患者短期预后不良发生风险预测模型对于降低COPD急性加重后的再次加重率，及时予以最佳治疗并促使患者获得良好预后至关重要。列线图可优化个体预测精度，AECOPD患者短期预后情况列线图模型较少报导。在本研究中，根据多因素分析的结果获得一些选定的指标构建列线图，探究AECOPD患者短期预后的独立影响因素。
本研究结果显示，婚姻状况是患者短期预后不良的影响因素。AECOPD患者大多伴有长期、反复发作并逐渐加重的呼吸困难、耐力受限、全身不适及恐惧焦虑情绪等，情感支持、合理的饮食和良好的生活方式对患者的预后有很重要意义。有配偶者家庭照顾、家庭成员的关心能够帮助患者减轻心理及身体负担并增加抗压能力，帮助患者共同制定科学合理的康复治疗方法。本研究结果显示疾病严重程度是患者短期预后不良的独立影响因素。病情发展到较为严重的阶段，急性加重的发作次数较多，通常会引起呼吸困难等症状，治疗难度和并发症风险增加，预后较差，死亡率增加。同时病情较重的AECOPD患者易因感染、吸烟等因素诱发急性加重，恶化风险增大，呼吸衰竭很难纠正[12]。频繁急性加重的患者死亡风险增加了4.3倍，频繁发作会加速COPD的疾病进程，每次发作都可能造成患者的气道炎症加剧、气流受限加重，从而使得患者的FEV1（第一秒用力呼气量）降低，加重患者的肺功能损害。同时反复发作会使患者肺部弹性回缩力下降，肺部过度充气，肺总量下降，导致肺功能下降不易恢复。患者频繁发作还容易引发如呼吸衰竭、心脏病、肺感染等的其他并发症，会进一步加重患者的病情，造成预后不良。本研究发现营养风险会造成AECOPD患者短期预后不良风险增加，营养不良直接损害COPD患者的呼吸肌力量和膈肌功能，呼吸肌萎缩明显，呼吸肌纤维的重量及力量较低，降低呼吸肌肌力和耐力，肺部通气功能下降，患者易发生呼吸疲劳，这种呼吸肌功能的受损使得患者在面对感染或其他应激情况时，更难以维持有效的通气，咳嗽咳痰胸闷症状也更为明显，从而增加急性加重的风险[13-15]。营养不良会导致患者免疫功能的紊乱，抗感染能力降低，增加患者感染次数和感染严重度，加重肺功能的损害，易诱发呼吸衰竭。国内学者郎彤调查了80例COPD患者营养状况和临床指标后发现，营养不良的COPD患者更易发生呼吸衰竭，提高营养不良COPD患者的血清总胆固醇及低密度脂蛋白水平可能会改善患者的预后。PCT是降钙素的前体蛋白，主要是源自甲状腺C细胞，在健康人体血液中的浓度非常低。PCT是一种重要的感染标志物，当机体感染细菌后，特别是在肺部感染，细菌的内毒素刺激甲状腺细胞合成并分泌PCT，其水平的升高可能提示存在严重的感染或其他炎症性疾病。在COPD患者中，当疾病处于稳定期时，PCT水平通常保持相对稳定，但在细菌感染诱发的AECOPD中，血浆PCT水平会明显升高。因此PCT的表达水平可以判断AECOPD患者是否发生细菌感染及患者细菌感染程度。因此，PCT在评估AECOPD患者肺功能以及预测疾病预后等方面具有一定的临床价值，可作为AECOPD患者的不良预后的诊断指标之一[16-17]。
因此，本研究为AECOPD患者构建了可靠的短期预后风险评估模型，具有较强的临床指导作用。我们通过多种验证确定了本模型的可靠性和准确性，可及性与适应性强表现出对AECOPD患者短期预后不良具有较好的预测性能。列线图模型的建立有利于医护人员对患者进行合理分流及危险分层，以及提出针对性的康复护理治疗方案，从而防止患者的病情进一步恶化，有效改善患者的能力和健康状况。
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  • 河北省卫健委重点课题项目(20180731)
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2025年第52卷第5期
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doi: 10.20043/j.cnki.MPM.202204520
  • 接收时间：2022-04-30
  • 首发时间：2026-03-18
  • 出版时间：2025-03-10
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  • 收稿日期：2022-04-30
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河北省卫健委重点课题项目(20180731)
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    华北理工大学护理与康复学院，河北 唐山 063210

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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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