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Article(id=1241025203665433286, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241025201983508979, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202412204, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1734019200000, receivedDateStr=2024-12-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773813065686, onlineDateStr=2026-03-18, pubDate=1744214400000, pubDateStr=2025-04-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773813065686, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773813065686, creator=13701087609, updateTime=1773813065686, updator=13701087609, issue=Issue{id=1241025201983508979, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='7', pageStart='1153', pageEnd='1344', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773813065285, creator=13701087609, updateTime=1773815493878, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241035388320543403, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241025201983508979, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241035388320543404, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241025201983508979, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1158, endPage=1167, ext={EN=ArticleExt(id=1241025203992589000, articleId=1241025203665433286, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Establishment and validation of a risk prediction model for COPD and severe COPD among middle-aged and elderly residents in Chengdu community, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To understand the prevalence and influencing factors of Chronic Obstructive Pulmonary Disease (COPD) among middle-aged and elderly residents in the community, and to construct and validate a Nomogram prediction model to provide a theoretical basis for the prevention of COPD onset and deterioration.

Methods

This cross-sectional study involved 3 250 middle-aged and elderly community residents in Chengdu Eastern New District. Basic information and risk factors for COPD were collected through pulmonary function tests, physical examinations, and questionnaires. The study population was divided into training and validation sets. Elastic net analysis was performed in the training set to select coefficients, and LASSO regression and random forest models were utilized to analyze the influencing factors of COPD and severe COPD, leading to the establishment of a Nomogram prediction model.

Results

The detection rates of COPD and severe COPD among community residents in Chengdu were 26% and 5.38%, respectively. LASSO regression indicated that age, male, being unmarried, low income, childhood pneumonia hospitalization, childhood cough, and parental respiratory disease history were common risk factors for COPD and severe COPD. Unique risk factors for COPD included low BMI, occupation, dust exposure, allergy history, workplace smoking, family smoking, and personal smoking.Unique risk factors for severe COPD included low educational level, waist-to-hip ratio, and maternal smoking during pregnancy. A Nomogram risk prediction model for COPD and severe COPD was established based on these indicators, with AUC values of 69.34%(95%CI: 66.90%-71.79%) and 76.81% (95%CI: 72.71%-80.90%), respectively, demonstrating good predictive performance. Internal validation through 1 000 bootstrap resampling showed that the calibration curves of the Nomogram model closely matched the actual occurrence of COPD and severe COPD, indicating a good fit.

Conclusion

Gender, age, educational level, marital status, family income, waist-to-hip ratio, childhood pneumonia hospitalization history, childhood cough, maternal smoking, paternal respiratory disease history, and maternal respiratory disease history are independent risk factors for COPD and severe COPD among community residents in Chengdu. The constructed Nomogram prediction model can provide a concise and intuitive personalized risk assessment for COPD and severe COPD for community residents and high-risk populations.

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目的

了解社区中老年居民慢性阻塞性肺疾病（COPD）的流行情况和影响因素，并构建列线图（nomogram）预测模型验证，为预防COPD发病及COPD恶化提供理论依据。

方法

本研究为横断面研究，在成都市东部新区纳入3 250名中老年社区居民作为研究对象，通过肺功能检查、体格检查、问卷调查收集研究对象的基本情况和COPD的危险因素。将研究人群分为训练集和验证集，在训练集中开展弹性网分析筛选系数，利用最小绝对收缩选择算子（LASSO）回归和随机森林模型分析COPD及重度COPD的影响因素并建立nomogram预测模型。

结果

成都市社区居民COPD和重度COPD的检出率分别为26%和5.38%。经LASSO回归显示，年龄、性别男、未婚、低收入、童年肺炎住院、童年咳嗽、父母亲呼吸道病史是COPD和重度COPD共有的风险因素，低BMI、职业、粉尘暴露、过敏史、工作场所吸烟、家人吸烟及个人吸烟是COPD特有的风险因素，低受教育程度、腰臀比、母亲怀孕吸烟是重度COPD特有的风险因素。将以上指标建立社区居民患COPD以及重度COPD的nomogram风险预测模型，模型AUC分别为69.34%（95%CI：66.90%～71.79%）和76.81%（95%CI：72.71%～80.90%），具有较好的预测效能，经bootstrap重采样1 000次进行内部验证，该列线图模型预测COPD及重度COPD发生的校准曲线和实际曲线呈较好吻合。

结论

性别、年龄、教育程度、婚姻状况、家庭收入、腰臀比、童年肺炎住院史、童年咳嗽、母亲吸烟、父亲呼吸道病史和母亲呼吸道病史为成都市社区居民COPD及重度COPD的独立风险因素，绘制的nomogram预测模型能够简洁、直观地为社区居民及高风险人群提供个性化的COPD及重度COPD风险预测。

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杨淑娟， E-mail:;
李小波， E-mail:；
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杨波和祝金湘为共同第一作者

杨淑娟与李小波为共同通信作者

杨波（1981—），男，本科，研究方向：呼吸慢病管理；

祝金湘（2000—），女，硕士在读，研究方向：社会医学与卫生事业管理；

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tableContent=null), ArticleFig(id=1241025216290287975, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025203665433286, language=EN, label=Figure 2, caption=LASSO regression screening results for influencing factors of severe COPD, figureFileSmall=rRTZBgzpnOfGJ1lTwhCFFQ==, figureFileBig=cYFKCcBKF3SDPpAGmBnMcA==, tableContent=null), ArticleFig(id=1241025216399339889, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025203665433286, language=CN, label=图2, caption=重度COPD影响因素LASSO回归筛查结果, figureFileSmall=rRTZBgzpnOfGJ1lTwhCFFQ==, figureFileBig=cYFKCcBKF3SDPpAGmBnMcA==, tableContent=null), ArticleFig(id=1241025216500003192, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025203665433286, language=EN, label=Figure 3, caption=The ROC curve of COPD risk prediction model, figureFileSmall=1eN/Ersi2ypC4lUgW7AaUQ==, figureFileBig=ArW1IbxuD98u9+yC1C0A6A==, tableContent=null), ArticleFig(id=1241025216579694979, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025203665433286, language=CN, label=图3, caption=COPD风险预测模型ROC曲线分析

注：图A为COPD患病风险预测模型的ROC曲线；图B为重度COPD患病风险预测模型的ROC曲线。

, figureFileSmall=1eN/Ersi2ypC4lUgW7AaUQ==, figureFileBig=ArW1IbxuD98u9+yC1C0A6A==, tableContent=null), ArticleFig(id=1241025216705524107, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025203665433286, language=EN, label=Figure 4, caption=The nomogram of COPD risk prediction model, figureFileSmall=q+31SGIRs5cXRMURzYDSLw==, figureFileBig=iIaO3TyiIwcWM/mEWl5Ufg==, tableContent=null), ArticleFig(id=1241025216848130449, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025203665433286, language=CN, label=图4, caption=COPD风险预测模型列线图

注：图A为COPD患病风险的预测模型列线图；图B为重度COPD患病风险的预测模型列线图。

, figureFileSmall=q+31SGIRs5cXRMURzYDSLw==, figureFileBig=iIaO3TyiIwcWM/mEWl5Ufg==, tableContent=null), ArticleFig(id=1241025216990736797, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025203665433286, language=EN, label=Figure 5, caption=The calibration curves of COPD risk prediction nomogram models, figureFileSmall=r+UlD1OQWlII5+d1fEVrPg==, figureFileBig=nA5Ci9MC7xgk1apHXnzDsA==, tableContent=null), ArticleFig(id=1241025217129148837, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025203665433286, language=CN, label=图5, caption=COPD风险预测列线图模型校正曲线

注：图A为COPD预测列线图模型的校正曲线；图B为重度COPD风险预测列线图模型的校正曲线。

, figureFileSmall=r+UlD1OQWlII5+d1fEVrPg==, figureFileBig=nA5Ci9MC7xgk1apHXnzDsA==, tableContent=null), ArticleFig(id=1241025217221423531, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025203665433286, language=EN, label=Table 1, caption=

Basic characteristics of research subjects

, figureFileSmall=null, figureFileBig=null, tableContent=
特征总人群(n=3 250)COPD(n=845)非COPD(n=2 405)
人口学基本特征
年龄（岁）67.99±9.8962.42±10.46
性别男1 272 (39.1)408 (48.3)864 (35.9)
女1 978 (60.9)437 (51.7)1 541 (64.1)
文化程度文盲718 (22.1)236 (27.9)482 (20.0)
小学1 398 (43.0)352 (41.7)1 046 (43.5)
初中963 (29.6)218 (25.8)745 (31.0)
高中及以上171 (5.3)39 (4.6)132 (5.5)
婚姻已婚2 720 (83.7)653 (77.3)2 067 (85.9)
未婚/丧偶/离婚/其他530 (16.3)192 (22.7)338 (14.1)
家庭收入（元/月）<1 000862 (26.5)261 (30.9)601 (25.0)
1 000～<3 0001 580 (48.6)422 (49.9)1 158 (48.1)
3 000～<5 000533 (16.4)101 (12.0)432 (18.0)
5 000275 (8.5)61 (7.2)214 (8.9)
当前职业农林劳作者2 202 (67.8)604 (71.5)1 598 (66.4)
工人劳动者395 (12.2)94 (11.1)301 (12.5)
其他职业653 (20.1)147 (17.4)506 (21.0)
测量特征指标
BMI（kg/m2）24.93±7.9925.96±12.24
腰臀比0.91±0.0690.90±0.066
血氧饱和度94.48±4.0994.68±4.53
家族史、个人史暴露
糖尿病患糖尿病490 (15.1)123 (14.6)367 (15.3)
未患糖尿病2 760 (84.9)722 (85.4)2 038 (84.7)
高血压患高血压1 602 (49.3)449 (53.1)1 153 (47.9)
未患高血压1 648 (50.7)396 (46.9)1 252 (52.1)
粉尘暴露暴露粉尘或者花粉等933 (28.7)253 (29.9)680 (28.3)
未暴露粉尘或者花粉等2 317 (71.3)592 (70.1)1 725 (71.7)
过敏存在过敏史205 (6.3)54 (6.4)151 (6.3)
未存在过敏史3 045 (93.7)791 (93.6)2 254 (93.7)
童年住院肺炎支气管炎90 (2.8)27 (3.2)63 (2.6)
未存在肺炎支气管炎/记不清3 160 (97.2)818 (96.8)2 342 (97.4)
童年咳嗽经常咳嗽135 (4.2)42 (5.0)93 (3.9)
不经常/记不清3 115 (95.8)803 ((95.8)2 312 (96.1)
母亲吸烟是110 (3.4)28 (3.3)82 (3.4)
否/记不清3 140 (96.6)817 (96.7)2 323 (96.6)
母亲二手烟暴露史是1 287 (39.6)304 (36.0)983 (40.9)
否/记不清1 963 (60.4)541 (64.0)1 422 (59.1)
父亲呼吸道病史父亲有呼吸道疾病史365 (11.2)108 (12.8)257 (10.7)
父亲没有呼吸道疾病史2 885 (88.8)737 ((87.2)2 148 (89.3)
母亲呼吸道病史母亲有呼吸道病史274 (8.4)87 (10.3)187 (7.8)
母亲没有呼吸道病史2 976 (91.6)758 (89.7)2 218 (92.2)
烟草类及环境污染物暴露房屋新装修（年）1108 (3.3)26 (3.1)82 (3.4)
1～<3260 (8.0)72 (8.5)188 (7.8)
3～<5188 (5.8)53 (6.3)135 (5.6)
5～<10423 (13.0)93 (11.0)330 (13.7)
102 271 (69.9)601 (71.1)1 670 (69.4)
炊具固体燃料暴露水平高暴露121 (3.7)30 (3.6)91 (3.8)
中暴露1 922 (59.1)515 (60.9)1 407 (58.5)
未暴露1 207 (37.1)300 (35.5)907 (37.7)
取暖固体燃料暴露取暖，有固体燃料暴露75 (2.3)19 (2.2)56 (2.3)
取暖，没有固体燃料暴露3 175 (97.7)826 (97.8)2 349 (97.7)
家人吸烟有1 731 (53.3)477 (56.4)1 254 (52.1)
无1 519 (46.7)368 (43.6)1 151 (47.9)
工作场所吸烟有952 (29.3)230 (27.2)722 (30.0)
无2 298 (70.7)615 (72.8)1 683 ((70.0)
个人吸烟不吸烟2 840 (87.4)709 (83.9)2 131 (88.6)
吸烟410 (12.6)136 (16.1)274 (11.4)
个人饮酒不饮酒2 492 (76.7)615 (72.8)1 877 (78.0)
饮酒758 (23.3)230 (27.2)528 (22.0)
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研究对象基本情况

, figureFileSmall=null, figureFileBig=null, tableContent=
特征总人群(n=3 250)COPD(n=845)非COPD(n=2 405)
人口学基本特征
年龄（岁）67.99±9.8962.42±10.46
性别男1 272 (39.1)408 (48.3)864 (35.9)
女1 978 (60.9)437 (51.7)1 541 (64.1)
文化程度文盲718 (22.1)236 (27.9)482 (20.0)
小学1 398 (43.0)352 (41.7)1 046 (43.5)
初中963 (29.6)218 (25.8)745 (31.0)
高中及以上171 (5.3)39 (4.6)132 (5.5)
婚姻已婚2 720 (83.7)653 (77.3)2 067 (85.9)
未婚/丧偶/离婚/其他530 (16.3)192 (22.7)338 (14.1)
家庭收入（元/月）<1 000862 (26.5)261 (30.9)601 (25.0)
1 000～<3 0001 580 (48.6)422 (49.9)1 158 (48.1)
3 000～<5 000533 (16.4)101 (12.0)432 (18.0)
5 000275 (8.5)61 (7.2)214 (8.9)
当前职业农林劳作者2 202 (67.8)604 (71.5)1 598 (66.4)
工人劳动者395 (12.2)94 (11.1)301 (12.5)
其他职业653 (20.1)147 (17.4)506 (21.0)
测量特征指标
BMI（kg/m2）24.93±7.9925.96±12.24
腰臀比0.91±0.0690.90±0.066
血氧饱和度94.48±4.0994.68±4.53
家族史、个人史暴露
糖尿病患糖尿病490 (15.1)123 (14.6)367 (15.3)
未患糖尿病2 760 (84.9)722 (85.4)2 038 (84.7)
高血压患高血压1 602 (49.3)449 (53.1)1 153 (47.9)
未患高血压1 648 (50.7)396 (46.9)1 252 (52.1)
粉尘暴露暴露粉尘或者花粉等933 (28.7)253 (29.9)680 (28.3)
未暴露粉尘或者花粉等2 317 (71.3)592 (70.1)1 725 (71.7)
过敏存在过敏史205 (6.3)54 (6.4)151 (6.3)
未存在过敏史3 045 (93.7)791 (93.6)2 254 (93.7)
童年住院肺炎支气管炎90 (2.8)27 (3.2)63 (2.6)
未存在肺炎支气管炎/记不清3 160 (97.2)818 (96.8)2 342 (97.4)
童年咳嗽经常咳嗽135 (4.2)42 (5.0)93 (3.9)
不经常/记不清3 115 (95.8)803 ((95.8)2 312 (96.1)
母亲吸烟是110 (3.4)28 (3.3)82 (3.4)
否/记不清3 140 (96.6)817 (96.7)2 323 (96.6)
母亲二手烟暴露史是1 287 (39.6)304 (36.0)983 (40.9)
否/记不清1 963 (60.4)541 (64.0)1 422 (59.1)
父亲呼吸道病史父亲有呼吸道疾病史365 (11.2)108 (12.8)257 (10.7)
父亲没有呼吸道疾病史2 885 (88.8)737 ((87.2)2 148 (89.3)
母亲呼吸道病史母亲有呼吸道病史274 (8.4)87 (10.3)187 (7.8)
母亲没有呼吸道病史2 976 (91.6)758 (89.7)2 218 (92.2)
烟草类及环境污染物暴露房屋新装修（年）1108 (3.3)26 (3.1)82 (3.4)
1～<3260 (8.0)72 (8.5)188 (7.8)
3～<5188 (5.8)53 (6.3)135 (5.6)
5～<10423 (13.0)93 (11.0)330 (13.7)
102 271 (69.9)601 (71.1)1 670 (69.4)
炊具固体燃料暴露水平高暴露121 (3.7)30 (3.6)91 (3.8)
中暴露1 922 (59.1)515 (60.9)1 407 (58.5)
未暴露1 207 (37.1)300 (35.5)907 (37.7)
取暖固体燃料暴露取暖，有固体燃料暴露75 (2.3)19 (2.2)56 (2.3)
取暖，没有固体燃料暴露3 175 (97.7)826 (97.8)2 349 (97.7)
家人吸烟有1 731 (53.3)477 (56.4)1 254 (52.1)
无1 519 (46.7)368 (43.6)1 151 (47.9)
工作场所吸烟有952 (29.3)230 (27.2)722 (30.0)
无2 298 (70.7)615 (72.8)1 683 ((70.0)
个人吸烟不吸烟2 840 (87.4)709 (83.9)2 131 (88.6)
吸烟410 (12.6)136 (16.1)274 (11.4)
个人饮酒不饮酒2 492 (76.7)615 (72.8)1 877 (78.0)
饮酒758 (23.3)230 (27.2)528 (22.0)
), ArticleFig(id=1241025218865590717, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025203665433286, language=EN, label=Table 2, caption=

Variable coding

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变量编码
1234
性别男女
年龄
当前职业农林劳作者工人劳作者其他职业
受教育程度文盲小学初中高中及以上
婚姻已婚未婚/离婚/丧偶/其他
家庭收入（元/月）<1 0001 000～<3 0003 000～<5 0005 000
腰臀比
BMI
粉尘暴露有无
过敏史有无
童年肺炎住院有无
童年咳嗽经常咳嗽不经常/记不清
母亲吸烟是否/记不清
父亲呼吸道病史有无
母亲呼吸道病史有无
家人吸烟有无
工作场所吸烟有无
个人吸烟否是
COPD级别轻度中度重度极重度
), ArticleFig(id=1241025218987225541, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025203665433286, language=CN, label=表2, caption=

变量编码情况

, figureFileSmall=null, figureFileBig=null, tableContent=
变量编码
1234
性别男女
年龄
当前职业农林劳作者工人劳作者其他职业
受教育程度文盲小学初中高中及以上
婚姻已婚未婚/离婚/丧偶/其他
家庭收入（元/月）<1 0001 000～<3 0003 000～<5 0005 000
腰臀比
BMI
粉尘暴露有无
过敏史有无
童年肺炎住院有无
童年咳嗽经常咳嗽不经常/记不清
母亲吸烟是否/记不清
父亲呼吸道病史有无
母亲呼吸道病史有无
家人吸烟有无
工作场所吸烟有无
个人吸烟否是
COPD级别轻度中度重度极重度
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成都中老年社区居民COPD及重度COPD的风险预测模型的建立和验证
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杨波 1 , 祝金湘 2 , 刘文斌 1 , 戴琴 1 , 余彬 4, 1 , 杨玉婷 1 , 傅春梅 1 , 曾珍 1 , 李灵艳 1 , 唐庆 3 , 贾鹏 5, 6, 7, 8 , 李小波 3 , 杨淑娟 2, 8
现代预防医学 | 流行病与统计方法 2025,52(7): 1158-1167
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现代预防医学 | 流行病与统计方法 2025, 52(7): 1158-1167
成都中老年社区居民COPD及重度COPD的风险预测模型的建立和验证
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杨波1, 祝金湘2, 刘文斌1, 戴琴1, 余彬4, 1, 杨玉婷1, 傅春梅1, 曾珍1, 李灵艳1, 唐庆3, 贾鹏5, 6, 7, 8, 李小波3 , 杨淑娟2, 8
作者信息
  • 1.成都东部新区第二人民医院，四川 成都 641419
  • 2.四川大学华西公共卫生学院/华西第四医院，四川 成都 610041
  • 3.成都市第七人民医院呼吸科，四川 成都 610299
  • 4.四川大学-香港理工大学灾后重建与管理学院
  • 5.武汉大学资源与环境科学学院
  • 6.武汉大学人民医院
  • 7.武汉大学公共卫生学院
  • 8.武汉大学空间全生命周期健康国际研究中心，湖北 武汉 430072

    • 杨波（1981—），男，本科，研究方向：呼吸慢病管理；

    祝金湘（2000—），女，硕士在读，研究方向：社会医学与卫生事业管理；

通讯作者:

杨淑娟， E-mail:;
李小波， E-mail:；
Establishment and validation of a risk prediction model for COPD and severe COPD among middle-aged and elderly residents in Chengdu community
Bo YANG1, Jin-xiang ZHU2, Wen-bin LIU1, Qin DAI1, Bin YU4, 1, Yu-ting YANG1, Chun-mei FU1, Zhen ZENG1, Ling-yan LI1, Qing TANG3, Peng JIA5, 6, 7, 8, Xiao-bo LI3 , Shu-juan YANG2, 8
Affiliations
  • Second People’s Hospital of Chengdu Eastern New District, Chengdu, Sichuan 641419, China
出版时间: 2025-04-10 doi: 10.20043/j.cnki.MPM.202412204
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摘要
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目的

了解社区中老年居民慢性阻塞性肺疾病（COPD）的流行情况和影响因素，并构建列线图（nomogram）预测模型验证，为预防COPD发病及COPD恶化提供理论依据。

方法

本研究为横断面研究，在成都市东部新区纳入3 250名中老年社区居民作为研究对象，通过肺功能检查、体格检查、问卷调查收集研究对象的基本情况和COPD的危险因素。将研究人群分为训练集和验证集，在训练集中开展弹性网分析筛选系数，利用最小绝对收缩选择算子（LASSO）回归和随机森林模型分析COPD及重度COPD的影响因素并建立nomogram预测模型。

结果

成都市社区居民COPD和重度COPD的检出率分别为26%和5.38%。经LASSO回归显示，年龄、性别男、未婚、低收入、童年肺炎住院、童年咳嗽、父母亲呼吸道病史是COPD和重度COPD共有的风险因素，低BMI、职业、粉尘暴露、过敏史、工作场所吸烟、家人吸烟及个人吸烟是COPD特有的风险因素，低受教育程度、腰臀比、母亲怀孕吸烟是重度COPD特有的风险因素。将以上指标建立社区居民患COPD以及重度COPD的nomogram风险预测模型，模型AUC分别为69.34%（95%CI：66.90%～71.79%）和76.81%（95%CI：72.71%～80.90%），具有较好的预测效能，经bootstrap重采样1 000次进行内部验证，该列线图模型预测COPD及重度COPD发生的校准曲线和实际曲线呈较好吻合。

结论

性别、年龄、教育程度、婚姻状况、家庭收入、腰臀比、童年肺炎住院史、童年咳嗽、母亲吸烟、父亲呼吸道病史和母亲呼吸道病史为成都市社区居民COPD及重度COPD的独立风险因素，绘制的nomogram预测模型能够简洁、直观地为社区居民及高风险人群提供个性化的COPD及重度COPD风险预测。

社区居民  /  慢阻肺  /  影响因素  /  Nomogram
Objective

To understand the prevalence and influencing factors of Chronic Obstructive Pulmonary Disease (COPD) among middle-aged and elderly residents in the community, and to construct and validate a Nomogram prediction model to provide a theoretical basis for the prevention of COPD onset and deterioration.

Methods

This cross-sectional study involved 3 250 middle-aged and elderly community residents in Chengdu Eastern New District. Basic information and risk factors for COPD were collected through pulmonary function tests, physical examinations, and questionnaires. The study population was divided into training and validation sets. Elastic net analysis was performed in the training set to select coefficients, and LASSO regression and random forest models were utilized to analyze the influencing factors of COPD and severe COPD, leading to the establishment of a Nomogram prediction model.

Results

The detection rates of COPD and severe COPD among community residents in Chengdu were 26% and 5.38%, respectively. LASSO regression indicated that age, male, being unmarried, low income, childhood pneumonia hospitalization, childhood cough, and parental respiratory disease history were common risk factors for COPD and severe COPD. Unique risk factors for COPD included low BMI, occupation, dust exposure, allergy history, workplace smoking, family smoking, and personal smoking.Unique risk factors for severe COPD included low educational level, waist-to-hip ratio, and maternal smoking during pregnancy. A Nomogram risk prediction model for COPD and severe COPD was established based on these indicators, with AUC values of 69.34%(95%CI: 66.90%-71.79%) and 76.81% (95%CI: 72.71%-80.90%), respectively, demonstrating good predictive performance. Internal validation through 1 000 bootstrap resampling showed that the calibration curves of the Nomogram model closely matched the actual occurrence of COPD and severe COPD, indicating a good fit.

Conclusion

Gender, age, educational level, marital status, family income, waist-to-hip ratio, childhood pneumonia hospitalization history, childhood cough, maternal smoking, paternal respiratory disease history, and maternal respiratory disease history are independent risk factors for COPD and severe COPD among community residents in Chengdu. The constructed Nomogram prediction model can provide a concise and intuitive personalized risk assessment for COPD and severe COPD for community residents and high-risk populations.

Community residents  /  Chronic obstructive pulmonary disease  /  Influencing factors  /  Nomogram
杨波, 祝金湘, 刘文斌, 戴琴, 余彬, 杨玉婷, 傅春梅, 曾珍, 李灵艳, 唐庆, 贾鹏, 李小波, 杨淑娟. 成都中老年社区居民COPD及重度COPD的风险预测模型的建立和验证. 现代预防医学, 2025 , 52 (7) : 1158 -1167 . DOI: 10.20043/j.cnki.MPM.202412204
Bo YANG, Jin-xiang ZHU, Wen-bin LIU, Qin DAI, Bin YU, Yu-ting YANG, Chun-mei FU, Zhen ZENG, Ling-yan LI, Qing TANG, Peng JIA, Xiao-bo LI, Shu-juan YANG. Establishment and validation of a risk prediction model for COPD and severe COPD among middle-aged and elderly residents in Chengdu community[J]. Modern Preventive Medicine, 2025 , 52 (7) : 1158 -1167 . DOI: 10.20043/j.cnki.MPM.202412204
正文
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慢性阻塞性肺病（chronic obstructive pulmonary disease，COPD）是一种可预防和治疗的慢性呼吸系统疾病，其主要特征为持续性的气流阻塞和呼吸道症状，如咳嗽、呼吸困难、咳痰等[1]。COPD与吸入的有害气体及颗粒物的异常炎症反应有关，这种反应导致了气流阻塞，并且随着病情恶化，可能进一步发展为肺心病和呼吸衰竭的慢性疾病，具有较高的致残率和死亡率[2]。COPD的危险因素不仅包括常见的烟草暴露和职业暴露[3-5]，遗传因素、社会经济状态及发育因素也被认为是COPD的重要风险因素[6-7]。以往对COPD的研究多聚焦于住院患者，而对社区人群的COPD研究相对较少。近年来，COPD的研究重点主要在其危险因素、发病机制、病理学改变以及预防和治疗策略。美国学者利用吸烟者队列的基线数据和随访肺功能数据，建立了COPD预测模型，这有助于识别COPD的高风险人群[8]。然而，现有的COPD预测模型主要依赖于临床生化指标[9-12]，而未能充分考虑家庭和社区环境以及个人健康行为的影响，针对中国社区人群的COPD及重度COPD风险预测研究相对较少。因此，构建COPD及重度COPD预测模型不仅能够早期识别出可能发展为COPD的高风险人群，还能进一步识别可进展为重度COPD的人群，从而利于早期预防并减少疾病的发生和发展[8]。本研究将基于社区COPD筛查人群，综合考虑个体居家社区环境和个人健康行为等因素，旨在建立COPD及重度COPD的预测模型，并探讨疾病在不同病程阶段的风险因素。
1　资料与方法
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1.1　研究对象
本研究于2023年10—11月在成都东部新区贾家镇采用整群抽样方法和按照70%比例抽取40岁及以上的常住居民，在征求知情同意后，开展COPD筛查。纳入标准：（1）40岁及以上的当地常驻居民；（2）自愿参加，同意生物样本采集并签署知情同意书。排除标准：（1）精神性疾患、恶性肿瘤及其他相关疾病；（2）肺功能检查绝对禁忌症：心梗、肺栓塞、脑卒中、休克；心功能不全、心率失常、心绞痛；大咯血；严重高血压（收缩压大于200 mm Hg，舒张压大于100 mm Hg）及高血压危象患者；胸腹主动脉瘤患者及严重甲状腺功能亢进；（3）肺功能检查相对禁忌症：静息心率>120次/min；咯血、气胸，严重肺大泡患者；孕妇；鼓膜穿孔；近四周呼吸道感染；免疫力低下者；危重疾病患者、血氧饱和度低于92%需要氧疗的患者；气管切开患者；活动性肺结核以及所有呼吸性传染病患者；（4）无法配合肺功能检查者，近三个月做过纤支镜；（5）肺功能严重减退患者；高热、剧咳患者；（6）消化道出血活动期患者；（7）颞颌关节易脱臼；（8）偏瘫、面瘫、失智等无法配合者。
总共抽取了4 144例40岁以上的居民开展COPD调查，在排除508例存在肺通气功能检查的禁忌症、359例不配合肺功能检查者以及135例不愿意参加肺通气功能检查的居民后，由调查员对3 277（79.08%）例符合条件的研究对象进行肺功能检查以及面对面问卷调查。
本研究通过成都市东部新区第二人民医院伦理委员会批准（KY2023001）。所有研究对象均自愿参加，签署知情同意书。
1.2　COPD诊断
采用问卷调查40岁以上人群，在问卷中结合既往史排除结核、肿瘤等疾病的患者，对研究对象采用赛克公司生产的X1型号便携式肺功能仪进行肺通气功能检查，FEV1/FVC<0.7，则通过储物罐吸入沙丁胺醇气雾剂400 μg，15 min后，采用便携式肺功能仪进行支气管舒张试验，FEV1/FVC<0.7，初筛诊断为COPD。
参考COPD全球倡议(GOLD)标准[13]，将FEV1/FVC <0.7诊断为COPD病人，并按照FEV1对COPD病人进行分级：FEV1≥80%为轻度，50%预计值≤FEV1<80%预计值为中度，30%预计值≤FEV1<50%预计值为重度，FEV1<30%预计值为极重度。
1.3　候选因子
本研究中的候选因子包括人口学特征、工作和生活环境暴露史、既往史和家族史、生活方式、健康状况等方面的因素。（1）人口学特征：包括性别（男性、女性）、婚姻状况（已婚、未婚/离婚/丧偶/其他）、家庭收入（<1 000、1 000～<3 000、3 000～<5 000和5 000元/月）、职业（农林劳作者、工人劳作者、其他职业）。（2）工作和生活环境暴露史：包括职业暴露粉尘、烟尘和空气污染物暴露（是、否）、居住房屋最近装修时间（新装修<1、1～<3、3～<5、5～<10和10年）、曾经是否有固体燃料进行烹饪或取暖（是、否）、家庭内二手烟暴露史（是、否）、工作场二手烟暴露史（是、否）、个人吸烟史（是、否，研究对象每天吸烟≥15支为吸烟）、饮酒史（是、否）。（3）既往史和家族史：是否具有对某些食物及物品过敏的历史；童年肺炎住院史（14岁前因肺炎或支气管炎住院与否）；童年咳嗽史（经常咳嗽、未咳嗽/记不清）；母亲孕期吸烟史（母亲怀孕时是否吸烟）；母亲孕期二手烟暴露史（母亲怀孕时是否接触二手烟）；父亲和母亲呼吸道病史（是、否），呼吸道疾病包括COPD、肺气肿、慢支炎、支气管扩张、哮喘、间质性肺病、过敏性鼻炎、肺癌、呼吸睡眠暂停综合征、肺结核和其他相关疾病。（4）健康状况：包括身体质量指数（body mass index，BMI），BMI=体重(kg)/身高的二次方(m2)计算得出；腰臀比=腰围/臀围；指尖血氧饱和度；血糖升高，定义空腹血糖浓度≥7.0 mmol/L；血压升高，SDP≥140 mm Hg，以及DDP≥90 mm Hg。
1.4　统计学分析
服从正态分布的定量资料采用（均数±标准差）进行统计描述，定性资料采用频数（百分比）描述。结局变量为是否患COPD，以及是否患重度COPD。COPD和重度COPD的预测模型构建时，将研究对象按照7∶3比例分为训练集和验证集，在训练集中开展弹性网分析筛选系数，采用最小绝对收缩选择算子（LASSO）回归和随机森林分析筛出有统计学意义的关联因素，进一步纳入nomogram风险预测模型中。采用R软件的survival包和rms包构建社区居民COPD及重度COPD的风险预测模型及评分列线图，并采用pROC包，绘制社区居民重度COPD发生率预测模型的受试者工作特征曲线（receiver operating characteristic curve， ROC），进一步结合bootstrap重复抽样法进行内部验证。检验水准为α=0.05。本研究采用R（version4.3.3）进行统计分析。
2　结果
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2.1　研究对象基本情况
本研究共纳入3 277人，无效样本共27例，有效样本共3 250例。COPD患者845人，COPD检出率26%（845/3 250）。COPD正常组平均年龄为（62.42±10.46）岁，低于COPD组的（67.99±9.89）岁，男性COPD检出率为32.08%，高于女性的22.09%。COPD重度及以上者175人，重度COPD检出率为5.38%（175/3 250）。COPD轻中度及正常组平均年龄为（63.50±10.55）岁，低于重度COPD组的（70.38±9.28）岁；男性重度COPD检出率为7.38%，高于女性的4.10%。性别、年龄、文化程度、婚姻、家庭收入、当前职业、BMI、腰臀比、高血压史、粉尘暴露史、母亲二手烟史、父母亲呼吸道病史、家人吸烟、工作场所吸烟、个人吸烟、个人饮酒在COPD和非COPD之间有差异。变量性别、年龄、文化程度、婚姻、受教育程度、家庭收入、腰臀比、血氧饱和度、童年住院、童年咳嗽、父母亲呼吸道病史在重度COPD和非重度COPD之间具有差异。见表1。
2.2　COPD影响因素筛选
为了确保模型能够在未知数据上具有良好的泛化能力，本研究按7∶3比例将研究人群随机划分为训练集（2 271例）和测试集（979例）。剔除资料缺失>30%的影响因素，研究共纳入包括人口学资料、相关疾病、个人史及生活方式以及暴露相关的26个潜在的影响因素，在训练集中开展弹性网分析筛选系数，经LASSO回归对这26个变量进行降维处理，在选择最优Lambda参数时选择10倍交叉验证，以交叉验证误差最小时的Lambda值为模型最优模型，统计此时的非零回归系数的变量，见图1。
结果显示，以是否患COPD作为结局变量，通过LASSO回归共筛选出15个预测变量纳入模型，分别为性别、年龄、BMI、婚姻状况、当前职业、家庭收入、童年肺炎住院史、童年咳嗽史、过敏史、父亲呼吸道病史和母亲呼吸道病史、工作场所吸烟、粉尘暴露、家人吸烟、个人吸烟。以是否患重度及以上COPD作为结局变量，共筛选出11个预测变量纳入模型，分别为性别、年龄、教育程度、婚姻状况、家庭收入、腰臀比、童年肺炎住院史、童年咳嗽、母亲吸烟、父亲呼吸道病史和母亲呼吸道病史。可将以上变量分别纳入nomogram预测模型，作为COPD和重度COPD风险预测模型的因子。见图1、图2。
2.3　COPD风险异常预测模型构建
按是否患有COPD构建预测模型，在训练集人群中，通过LASSO回归筛选出的15个变量作为预测因子构建nomogram预测模型，预测公式为P=-0.05×points+ 2.19， points=44.73+1.43×(年龄)-0.29×(BMI)+9.13×(性别1)+6.20×(婚姻状况2)+0.80×(当前职业1)+ 2.45×(当前职业2)+7.15×(家庭收入1)+6.34×(家庭收入2)+7.07×(家庭收入4)+2.82×(是否粉尘暴露1)+4.58×(是否过敏1)+3.74×(是否童年肺炎住院1)+ 9.12×(是否童年经常咳嗽1)+10.77×(是否母亲有呼吸道病史1)+10.66×(是否父亲有呼吸道病史1)+ 4.35×(工作场所有无吸烟2)+9.10×(家人有无吸烟1)+7.68×(自己是否吸烟2)，所得模型AUC为69.34%（95%CI: 66.90%～71.79%），具有较好的预测效能。见图3。
按是否患有重度COPD构建预测模型，将LASSO回归筛选出的11个变量作为预测因子，构建nomogram预测模型，预测公式为P=-0.07×points+3.73，points=-41.26+1.43×(年龄)+9.36×(性别1)+19.66×(受教育程度1)+23.93×(受教育程度2)+ 18.93×(受教育程度3)+9.52×(婚姻状况2)+8.03×(家庭收入1)+6.29×(家庭收入2)+0.42×(家庭收入3)+27.75×(腰臀比)+6.60×(童年肺炎住院1)+17.11×(童年咳嗽1)+15.19×(母亲吸烟1)+11.89×(母亲呼吸道病史1)+18.66×(父亲呼吸道病史1)。本研究所得模型AUC为76.81%（95%CI：72.71%～80.90%），具有较好的预测效能，见图3。各变量编码见表2。
根据构建的COPD患病风险预测模型列线图，每个变量不同数值对应不同评分，评分越高代表发生COPD的几率越高。COPD患病风险较大的五个因素分别是年龄、性别、BMI、父亲呼吸道病史和母亲呼吸道病史。重度COPD患病风险较大的三个因素分别是年龄、腰臀比、受教育程度，见图4。采用bootstrap对COPD和重度COPD风险预测模型进行验证，重抽样1 000次，该列线图模型预测COPD和重度COPD发生的校准曲线和实际曲线基本吻合，拟合效果良好，见图5。
3　讨论
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本研究中40岁及以上社区人群的COPD检出率为26%，重度COPD检出率为5.38%。COPD风险预测模型纳入了年龄、性别、婚姻、BMI、职业、家庭收入、粉尘暴露、过敏史、童年肺炎住院史、童年咳嗽、父母有呼吸道病史、个人吸烟、家庭及工作场所吸烟；重度COPD风险预测模型纳入了年龄、性别、婚姻、教育程度、家庭收入、腰臀比、童年肺炎住院史、童年咳嗽、母亲吸烟、父母呼吸道病史；两个模型的AUC分别为69.3%和76.8%，列线图模型预测COPD和重度COPD发生的校准曲线和实际曲线基本吻合，拟合效果良好。
在COPD模型中我们发现烟草暴露、个人史、家族史和遗传因素变量具有较好的预测性。前期研究表明，吸烟是COPD最显著的环境风险因素[13-15]。吸入烟草烟雾中的有害颗粒或气体，如焦油、丙烯醛、氢氰酸等，能够通过多种生物学途径引发气道的氧化应激、炎症反应以及蛋白酶和抗蛋白酶之间的失衡，这些因素共同促成了COPD的发展[14，16-19]。在COPD的气道炎症过程中，多种炎症细胞发挥着作用，如Tc1、Th1、Th17和ILC3等[20]。激活的炎症细胞释放多种炎性介质于气道上皮细胞，诱导上皮细胞杯状化生和气道黏液高分泌[21-22]；慢性炎症刺激气道上皮细胞释放生长因子，促进气道周围平滑肌和成纤维细胞增生，导致小气道重塑[23]；巨噬细胞基质金属蛋白酶和中性粒细胞弹性蛋白酶等引起肺结缔组织中的弹性蛋白破坏，Tc1淋巴细胞释放颗粒酶穿孔素损伤肺泡上皮、导致不可逆性肺损伤，引发肺气肿[24-25]。此外，自身免疫调控机制、遗传危险因素以及肺发育相关因素也可能在COPD的发生发展中起到重要作用[17，26]。上述机制可能共同作用导致COPD的形成。此外有研究表明非烟草因素也发挥着重要作用，包括空气污染、职业暴露、社会和遗传因素等[27-30]，早期暴露引起的儿童期肺受损与成年后COPD患病风险有关[31]。中国的一项研究结果显示，除吸烟外，污染物暴露、低BMI、父母呼吸道病史及童年咳嗽是COPD的促进因素[32]，这与本研究发现相一致。McGeachie， M.J和Hernandez-Pacheco， N.等人的研究表明，患有持续性哮喘和肺功能发育不良的儿童在成年早期发生固定气流阻塞和COPD风险增加[7，33]。一项长期纵向队列研究表明，早期患有肺炎的儿童成年后出现肺功能降低和用力呼气量（FEV1）缺陷的风险增加[34]，儿童期肺炎和哮喘的结合可能构成更大的COPD风险[35]。在本研究中，我们发现无论是个体吸烟还是工作环境或家庭的烟草暴露都是轻中度COPD的重要危险因素，此外，具有童年肺炎住院史、过敏史、咳嗽及父母呼吸道病史的个体患COPD的概率更大。
在重度COPD模型中我们发现个人史和家族史变量具有较好的预测性，然而吸烟及烟草暴露变量对重度COPD的风险效应较小，除了与COPD相同的预测因子外，我们还发现高腰臀比、低学历和母亲吸烟对个体患重度COPD具有促进作用。轻度COPD表现为肺功能轻微受限，而重度COPD则表现为严重的气流受限[36]，本研究认为在确诊COPD后，患者往往会自发戒烟，减少烟草暴露的频率，因而烟草暴露并未作为危险因子纳入到预测模型中。Perret， J等[37]的队列研究和Li， J等人[38]的研究均表明母亲吸烟史是儿童成年后患COPD的潜在危险因素，产前和围产期对肺部发育至关重要，孕期母亲吸烟与COPD风险增加相关显著[31，39]，但潜在机制仍未完全阐述。本研究显示，母亲吸烟对重度COPD风险具有明显的促进作用，而对轻中度COPD患病风险较小，这一差异需进一步深入探讨。
本研究存在一定局限。一方面本研究采用横断面研究设计，难以得出影响因素和COPD患病之间的因果关系；另一方面由于该研究仅对成都东部新区居民进行了研究，这限制了研究结果的外推性。本研究的优势首先是对轻度COPD和重度以上COPD风险均做了相关分析并建立列线图，结果具有对比性，可根据每项危险因素的评分预测COPD轻中度及重度患病风险，列线图模型简单易懂，具有较高实践价值。其次，本研究纳入了3 250多名社区筛查居民作为COPD预测模型的研究人群，有效弥补了以往研究中样本量不足的问题，同时也克服了研究人群过于集中于医院患者的局限性。
综上所述，COPD的病程包括从早期的气流受限到后期的严重肺功能损害，本文构建了COPD不同病程阶段的风险预测模型和评分列线图，可根据相应风险因素预测个体COPD的病程阶段，可较好地预测成都市居民COPD患病风险，为筛查目标人群COPD患病情况和不同病程阶段COPD患者制定个体化治疗方案提供依据。
基金
收起
  • 国家重点研发计划(2023YFC3604702)
  • 四川省医学科研课题计划(S22070)
  • 四川省卫生信息中心“卫生健康数字化+临床应用发展”专项课题(2023ZXKY06005)
  • 成都市科技局重点研发项目(2024-YF05-02552-SN)
  • 成都东部新区卫生健康领域“揭榜挂帅”项目——慢阻肺风险人群识别模式构建及全病程管理路径研究(H231222)
文献
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2025年第52卷第7期
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文章信息
doi: 10.20043/j.cnki.MPM.202412204
  • 接收时间：2024-12-13
  • 首发时间：2026-03-18
  • 出版时间：2025-04-10
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  • 收稿日期：2024-12-13
基金
国家重点研发计划(2023YFC3604702)
四川省医学科研课题计划(S22070)
四川省卫生信息中心“卫生健康数字化+临床应用发展”专项课题(2023ZXKY06005)
成都市科技局重点研发项目(2024-YF05-02552-SN)
成都东部新区卫生健康领域“揭榜挂帅”项目——慢阻肺风险人群识别模式构建及全病程管理路径研究(H231222)
作者信息
    1.成都东部新区第二人民医院，四川 成都 641419
    2.四川大学华西公共卫生学院/华西第四医院，四川 成都 610041
    3.成都市第七人民医院呼吸科，四川 成都 610299
    4.四川大学-香港理工大学灾后重建与管理学院
    5.武汉大学资源与环境科学学院
    6.武汉大学人民医院
    7.武汉大学公共卫生学院
    8.武汉大学空间全生命周期健康国际研究中心，湖北 武汉 430072

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2种不同金属材料的力学参数
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Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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