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Article(id=1320442647613919746, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320442616123081022, articleNumber=null, orderNo=null, doi=10.11847/zgggws1146384, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1731254400000, receivedDateStr=2024-11-11, revisedDate=1745251200000, revisedDateStr=2025-04-22, acceptedDate=1754064000000, acceptedDateStr=2025-08-02, onlineDate=1792747659828, onlineDateStr=2026-10-23, pubDate=1767974400000, pubDateStr=2026-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792747659828, onlineIssueDateStr=2026-10-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792747659828, creator=13701087609, updateTime=1792747659828, updator=13701087609, issue=Issue{id=1320442616123081022, tenantId=1146029695717560320, journalId=1319373219989389464, year='2026', volume='42', issue='1', pageStart='1', pageEnd='128', issueExtLink='null', onlineDate='null', pubDate='1767974400000', pubDateStr='2026-01-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792747652321, creator='13701087609', updateTime=1792748136930, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320444648791851086, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320442616123081022, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320444648791851087, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320442616123081022, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=89, endPage=95, ext={EN=ArticleExt(id=1320442647819440643, articleId=1320442647613919746, tenantId=1146029695717560320, journalId=1319373219989389464, language=EN, title=Differences of influencing factors on death between young and middle-aged tuberculosis patients and elderly tuberculosis patients and the construction of Nomgram model, columnId=1320427686514770171, journalTitle=Chinese Journal of Public Health, columnName=Survey and Report, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the difference of influencing factors on the death between young and middle-aged tuberculosis patients and elderly tuberculosis patients and construct an age-stratified nomogram prediction model for the death risk of tuberculosis patients.

Methods

A total of 100 811 confirmed cases of tuberculosis treated and closed in 14 designated tuberculosis hospitals in Hangzhou from January 2005 to December 2021 were included as the training set, and 7 669 confirmed cases treated and closed from January 2022 to June 2023 were included as the validation set. A multivariate Cox regression model was used to evaluate the independent risk predictors of death of young and middle-aged and elderly tuberculosis patients, and an age-stratified nomogram model was constructed. The predictive performance of the model was evaluated based on the index of consistency (C-index), area under the curve (AUC), and calibration curve.

Results

Age (HR = 1.056, 95%CI: 1.047–1.066), gender (HR = 1.353, 95%CI: 1.060–1.727), current residence (HR = 1.792, 95%CI: 1.434–2.240), pathogenic diagnosis results (HR = 2.338, 95%CI: 1.711–3.193), drug resistance (HR = 1.724, 95%CI: 1.098–2.707), human immunodeficiency virus (HIV) infection (HR = 4.342, 95%CI: 1.391–13.552), history of tuberculosis (HR = 1.627, 95%CI: 1.225–2.161), treatment plan (HR = 1.345, 95%CI: 1.054–1.716), use of fixed-dose combination (FDC) in the whole process or not (HR = 2.342, 95%CI: 1.310–4.188) were independent influencing factors for the death of young and middle-aged patients. Age (HR = 1.086, 95%CI: 1.080–1.092), gender (HR = 1.548, 95%CI: 1.390–1.724), pathogenic diagnosis results (HR = 2.144, 95%CI: 1.888–2.435), treatment management methods (HR = 1.182, 95%CI: 1.044–1.337), history of tuberculosis (HR = 1.217, 95%CI: 1.077–1.374), and use of FDC in the whole process or not (HR = 1.200, 95%CI: 1.010–1.426) were independent influencing factors for the death of elderly patients.

Conclusion

The factors influencing the death of young and middle-aged tuberculosis patients were different from those of the elderly. The age-stratified nomogram model constructed demonstrated good performance in predicting the death of tuberculosis patients.

, authors=Zijian FANG, Ruoqi DAI, Qingchun LI, Li XIE, Qingjun JIA, Yifei WU, Chunping HUANG, Qinglin CHENG, authorsList=Zijian FANG, Ruoqi DAI, Qingchun LI, Li XIE, Qingjun JIA, Yifei WU, Chunping HUANG, Qinglin CHENG, authorCompany=null, correspAuthors=Qinglin CHENG, authorNote=null, correspAuthorsNote=
CHENG Qinglin, E-mail:
, copyrightStatement=Copyright © 2026 Chinese Journal of Public Health. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1320442651820806680, articleId=1320442647613919746, tenantId=1146029695717560320, journalId=1319373219989389464, language=CN, title=中青年与老年结核病患者死亡影响因素差异：基于Nomgram模型构建, columnId=1320427686770622717, journalTitle=中国公共卫生, columnName=调查报告与分析, runingTitle=null, highlight=null, articleAbstract=
目的

探讨中青年与老年结核病患者死亡的影响因素差异，构建年龄分层的结核病患者死亡风险列线图预测模型。

方法

选择2005年1月—2021年12月浙江省杭州市14家结核病定点医院收治并结案的100 811例结核病确诊病例作为训练集，2022年1月—2023年6月收治并结案的7 669例确诊病例作为验证集。应用多因素Cox回归模型分别评估中青年与老年结核病患者死亡的独立风险预测因子，并构建年龄分层的列线图模型。采用一致性指数（C-index）、受试者操作特征曲线下面积（AUC）和校正曲线评估模型预测效果。

结果

年龄（HR=1.056，95%CI=1.047～1.066）、性别（HR=1.353，95%CI=1.060～1.727）、现居住地（HR=1.792，95%CI=1.434～2.240）、病原学诊断结果（HR=2.338，95%CI=1.711～3.193）、耐药情况（HR=1.724，95%CI=1.098～2.707）、人类免疫缺陷病毒（HIV）感染（HR=4.342，95%CI=1.391～13.552）、结核病史（HR=1.627，95%CI=1.225～2.161）、治疗方案（HR=1.345，95%CI=1.054～1.716）、是否全程使用抗结核固定剂量复合制剂（FDC）（HR=2.342，95%CI=1.310～4.188）是中青年结核病患者死亡的独立影响因素。年龄（HR=1.086，95%CI=1.080～1.092）、性别（HR=1.548，95%CI=1.390～1.724）、病原学诊断结果（HR=2.144，95%CI=1.888～2.435）、治疗管理方式（HR=1.182，95%CI=1.044～1.337）、结核病史（HR=1.217，95%CI=1.077～1.374）、是否全程使用FDC（HR=1.200，95%CI=1.010～1.426）是老年结核病患者死亡的独立影响因素。

结论

杭州市中青年与老年结核病患者死亡的影响因素差异较大，需制定个性化防治措施。构建的年龄分层列线图模型对预测结核病患者死亡具有良好的效果。

, authors=方子健, 戴若骐, 李清春, 谢立, 贾庆军, 吴亦斐, 黄春萍, 程庆林*, *, authorsList=方子健, 戴若骐, 李清春, 谢立, 贾庆军, 吴亦斐, 黄春萍, 程庆林, authorCompany=null, correspAuthors=程庆林, authorNote=

方子健（1991—），主管医师，硕士，研究方向：传染病预防与控制

, correspAuthorsNote=
程庆林，E-mail：
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注：A为中青年患者训练集；B为老年患者训练集；C为中青年患者验证集；D为老年患者验证集

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注：A为中青年患者训练集；B为老年患者训练集；C为中青年患者验证集；D为老年患者验证集

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Comparison of basic information of patients between training set and validation set

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变量分类训练集验证集χ2值P值
例数百分比/%例数百分比/%
性别男性67 52166.98500165.2110.0440.002
女性33 29033.022 66834.79
户籍常住人口87 65086.94670287.391.2510.263
外来人口13 16113.0696712.61
民族汉族99 87999.08758598.902.2410.134
其他民族9320.92841.10
现居住地农村51 02950.62398651.985.2510.022
城镇49 78249.38368348.02
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训练集与验证集患者基本情况比较

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变量分类训练集验证集χ2值P值
例数百分比/%例数百分比/%
性别男性67 52166.98500165.2110.0440.002
女性33 29033.022 66834.79
户籍常住人口87 65086.94670287.391.2510.263
外来人口13 16113.0696712.61
民族汉族99 87999.08758598.902.2410.134
其他民族9320.92841.10
现居住地农村51 02950.62398651.985.2510.022
城镇49 78249.38368348.02
), ArticleFig(id=1320442657676055128, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320442647613919746, language=EN, label=Tab.2, caption=

Multivariate Cox analysis of death factors in tuberculosis patients

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训练集变量对照组β $ S_{\overline{x}} $Wald χ2值P值HR值95%CI
中青年年龄0.0550.005141.443<0.0011.0561.047～1.066
男性女性0.3020.1255.8750.0151.3531.060～1.727
现居住地为农村城镇0.5840.11426.296<0.0011.7921.434～2.240
病原学诊断结果阳性阴性或未查0.8490.15928.487<0.0012.3381.711～3.193
耐药非耐药0.5450.2305.5930.0181.7241.098～2.707
HIV感染未感染1.4680.5816.3940.0114.3421.391～13.552
有结核病史无结核病史0.4870.14511.290<0.0011.6271.225～2.161
非STP方案STP方案0.2960.1245.6690.0171.3451.054～1.716
未全程使用FDC全程使用FDC0.8510.2968.2480.0042.3421.310～4.188
老年年龄0.0830.003875.229<0.0011.0861.080～1.092
男性女性0.4370.05563.309<0.0011.5481.390～1.724
病原学诊断结果阳性阴性或未查0.7630.065138.464<0.0012.1441.888～2.435
治疗管理方式为全程管理/督导方式其他方式0.1670.0637.0070.0081.1821.044～1.337
有结核病史无结核病史0.1960.0629.9940.0021.2171.077～1.374
未全程使用FDC全程使用FDC0.1820.0884.3030.0381.2001.010～1.426
), ArticleFig(id=1320442657743163993, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320442647613919746, language=CN, label=表2, caption=

结核病患者死亡因素的多因素Cox分析

, figureFileSmall=null, figureFileBig=null, tableContent=
训练集变量对照组β $ S_{\overline{x}} $Wald χ2值P值HR值95%CI
中青年年龄0.0550.005141.443<0.0011.0561.047～1.066
男性女性0.3020.1255.8750.0151.3531.060～1.727
现居住地为农村城镇0.5840.11426.296<0.0011.7921.434～2.240
病原学诊断结果阳性阴性或未查0.8490.15928.487<0.0012.3381.711～3.193
耐药非耐药0.5450.2305.5930.0181.7241.098～2.707
HIV感染未感染1.4680.5816.3940.0114.3421.391～13.552
有结核病史无结核病史0.4870.14511.290<0.0011.6271.225～2.161
非STP方案STP方案0.2960.1245.6690.0171.3451.054～1.716
未全程使用FDC全程使用FDC0.8510.2968.2480.0042.3421.310～4.188
老年年龄0.0830.003875.229<0.0011.0861.080～1.092
男性女性0.4370.05563.309<0.0011.5481.390～1.724
病原学诊断结果阳性阴性或未查0.7630.065138.464<0.0012.1441.888～2.435
治疗管理方式为全程管理/督导方式其他方式0.1670.0637.0070.0081.1821.044～1.337
有结核病史无结核病史0.1960.0629.9940.0021.2171.077～1.374
未全程使用FDC全程使用FDC0.1820.0884.3030.0381.2001.010～1.426
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中青年与老年结核病患者死亡影响因素差异：基于Nomgram模型构建
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方子健 , 戴若骐 , 李清春 , 谢立 , 贾庆军 , 吴亦斐 , 黄春萍 , 程庆林 *, *
中国公共卫生 | 调查报告与分析 2026,42(1): 89-95
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中国公共卫生 |调查报告与分析 2026 , 42 (1) : 89 -95
中青年与老年结核病患者死亡影响因素差异：基于Nomgram模型构建
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方子健（1991—），主管医师，硕士，研究方向：传染病预防与控制

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方子健, 戴若骐, 李清春, 谢立, 贾庆军, 吴亦斐, 黄春萍, 程庆林*, *
作者信息
  • 杭州市疾病预防控制中心 杭州市卫生监督所，杭州 310021
通讯作者:
程庆林，E-mail：
作者简介:

方子健（1991—），主管医师，硕士，研究方向：传染病预防与控制

Differences of influencing factors on death between young and middle-aged tuberculosis patients and elderly tuberculosis patients and the construction of Nomgram model
Zijian FANG, Ruoqi DAI, Qingchun LI, Li XIE, Qingjun JIA, Yifei WU, Chunping HUANG, Qinglin CHENG
Affiliations
  • Hangzhou Health Supervision Institution, Hangzhou Center for Disease Control and Prevention, Hangzhou 310021, China
    • Corresponding Author:
    CHENG Qinglin, E-mail:
出版时间: 2026-01-10 doi: 10.11847/zgggws1146384
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摘要
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目的

探讨中青年与老年结核病患者死亡的影响因素差异，构建年龄分层的结核病患者死亡风险列线图预测模型。

方法

选择2005年1月—2021年12月浙江省杭州市14家结核病定点医院收治并结案的100 811例结核病确诊病例作为训练集，2022年1月—2023年6月收治并结案的7 669例确诊病例作为验证集。应用多因素Cox回归模型分别评估中青年与老年结核病患者死亡的独立风险预测因子，并构建年龄分层的列线图模型。采用一致性指数（C-index）、受试者操作特征曲线下面积（AUC）和校正曲线评估模型预测效果。

结果

年龄（HR=1.056，95%CI=1.047～1.066）、性别（HR=1.353，95%CI=1.060～1.727）、现居住地（HR=1.792，95%CI=1.434～2.240）、病原学诊断结果（HR=2.338，95%CI=1.711～3.193）、耐药情况（HR=1.724，95%CI=1.098～2.707）、人类免疫缺陷病毒（HIV）感染（HR=4.342，95%CI=1.391～13.552）、结核病史（HR=1.627，95%CI=1.225～2.161）、治疗方案（HR=1.345，95%CI=1.054～1.716）、是否全程使用抗结核固定剂量复合制剂（FDC）（HR=2.342，95%CI=1.310～4.188）是中青年结核病患者死亡的独立影响因素。年龄（HR=1.086，95%CI=1.080～1.092）、性别（HR=1.548，95%CI=1.390～1.724）、病原学诊断结果（HR=2.144，95%CI=1.888～2.435）、治疗管理方式（HR=1.182，95%CI=1.044～1.337）、结核病史（HR=1.217，95%CI=1.077～1.374）、是否全程使用FDC（HR=1.200，95%CI=1.010～1.426）是老年结核病患者死亡的独立影响因素。

结论

杭州市中青年与老年结核病患者死亡的影响因素差异较大，需制定个性化防治措施。构建的年龄分层列线图模型对预测结核病患者死亡具有良好的效果。

结核病患者  /  死亡  /  老年  /  中青年  /  影响因素  /  杭州市
Objective

To explore the difference of influencing factors on the death between young and middle-aged tuberculosis patients and elderly tuberculosis patients and construct an age-stratified nomogram prediction model for the death risk of tuberculosis patients.

Methods

A total of 100 811 confirmed cases of tuberculosis treated and closed in 14 designated tuberculosis hospitals in Hangzhou from January 2005 to December 2021 were included as the training set, and 7 669 confirmed cases treated and closed from January 2022 to June 2023 were included as the validation set. A multivariate Cox regression model was used to evaluate the independent risk predictors of death of young and middle-aged and elderly tuberculosis patients, and an age-stratified nomogram model was constructed. The predictive performance of the model was evaluated based on the index of consistency (C-index), area under the curve (AUC), and calibration curve.

Results

Age (HR = 1.056, 95%CI: 1.047–1.066), gender (HR = 1.353, 95%CI: 1.060–1.727), current residence (HR = 1.792, 95%CI: 1.434–2.240), pathogenic diagnosis results (HR = 2.338, 95%CI: 1.711–3.193), drug resistance (HR = 1.724, 95%CI: 1.098–2.707), human immunodeficiency virus (HIV) infection (HR = 4.342, 95%CI: 1.391–13.552), history of tuberculosis (HR = 1.627, 95%CI: 1.225–2.161), treatment plan (HR = 1.345, 95%CI: 1.054–1.716), use of fixed-dose combination (FDC) in the whole process or not (HR = 2.342, 95%CI: 1.310–4.188) were independent influencing factors for the death of young and middle-aged patients. Age (HR = 1.086, 95%CI: 1.080–1.092), gender (HR = 1.548, 95%CI: 1.390–1.724), pathogenic diagnosis results (HR = 2.144, 95%CI: 1.888–2.435), treatment management methods (HR = 1.182, 95%CI: 1.044–1.337), history of tuberculosis (HR = 1.217, 95%CI: 1.077–1.374), and use of FDC in the whole process or not (HR = 1.200, 95%CI: 1.010–1.426) were independent influencing factors for the death of elderly patients.

Conclusion

The factors influencing the death of young and middle-aged tuberculosis patients were different from those of the elderly. The age-stratified nomogram model constructed demonstrated good performance in predicting the death of tuberculosis patients.

tuberculosis patients  /  death  /  the elderly  /  young and middle-aged adults  /  influencing factor  /  Hangzhou city
方子健, 戴若骐, 李清春, 谢立, 贾庆军, 吴亦斐, 黄春萍, 程庆林. 中青年与老年结核病患者死亡影响因素差异：基于Nomgram模型构建. 中国公共卫生, 2026 , 42 (1) : 89 -95 . DOI: 10.11847/zgggws1146384
Zijian FANG, Ruoqi DAI, Qingchun LI, Li XIE, Qingjun JIA, Yifei WU, Chunping HUANG, Qinglin CHENG. Differences of influencing factors on death between young and middle-aged tuberculosis patients and elderly tuberculosis patients and the construction of Nomgram model[J]. Chinese Journal of Public Health, 2026 , 42 (1) : 89 -95 . DOI: 10.11847/zgggws1146384
正文
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在全球范围内，结核病仍然是单一传染源导致死亡的主要原因[1]。研究显示，全球每年因结核病死亡人数约为160万[2]，不仅造成大量患者伤残，也造成较大经济负担。2006—2021年，我国结核病整体死亡率呈下降趋势，但结核病死亡人数仍约占传染病和寄生虫病总死亡人数的32%[3]。作为一种多系统疾病，结核病患者死亡风险受多种因素影响，国内外已有关于结核病患者死亡危险因素的相关研究[4–5]，也有部分研究探索并构建了结核病患者死亡预测列线图模型[6–8]。这些研究大多集中于全人群或老年患者群体的死亡影响因素，缺乏对不同年龄段结核病患者死亡影响因素的对比分析。由于中青年与老年结核病患者在生理、心理、疾病认知等方面存在较大差异[9]，直接构建全人群死亡预测模型可能无法在临床中准确预测和评估患者的死亡风险。为此，本研究探讨中青年与老年结核病患者死亡影响因素的差异，并基于Cox回归模型构建年龄分层的结核病患者死亡风险列线图预测模型，从而提供更精准的结核病患者死亡风险评估工具。结果报告如下。
1　对象与方法
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1.1　对象
为2005年1月—2023年6月杭州市14家结核病定点医院登记治疗并结案的结核病确诊病例，通过医院病历系统收集病例资料。纳入标准：（1） 结核病诊断符合《中国结核病分类WS 196—2017》[10]；（2） ≥15岁确诊患者；（3） 有明确生存或死亡结局登记患者。排除标准：（1） 未结案的结核病患者；（2） 诊断变更为非结核病患者；（3）≤14岁患者；（4）失访或数据丢失严重者；（5） 登记为因交通事故、溺水、自杀等外部因素死亡者[11]。训练集和验证集是模型训练与评估的2种关键数据集，其中训练集是模型学习的基础，常用于训练模型以进行预测；验证集则是从原始数据中独立划分出的一部分数据集，不参与模型的训练，而是用于测试已训练的模型[12]。本研究以2005年1月—2021年12月登记收治并结案的所有100 811例患者作为训练集，2022年1月—2023年6月登记收治并结案的所有7 669例患者作为验证集。训练集及验证集均通过“所有参与者”的设计原则，将满足所有纳入和排除标准后的患者确定为研究对象[13]。本研究通过杭州市疾病预防控制中心（杭州市卫生监督所）伦理委员会审核批准（2021-HS06）。由于本研究为回顾性研究，对研究涉及的所有数据均经过脱敏后纳入分析，且不会对受试者造成任何风险或伤害。经伦理委员会批准同意后对研究对象免除知情同意。
1.2　资料收集
通过医院病历系统收集资料：（1） 人口学特征：年龄、性别、户籍、民族、现居住地；（2） 临床特征：病例发现方式、合并其他基础疾病、合并≥2种结核病、人类免疫缺陷病毒（human immunodeficiency virus, HIV）感染、规范胸部X线检查结果、治疗管理方式、治疗方案、是否全程使用抗结核固定剂量复合制剂（fixed-dose combination formulations, FDC）；（3） 实验室特征：病原学诊断结果、耐药情况、痰涂片检查阳性频次、痰培养检查阳性频次；（4） 其他特征：结核病史、就诊延误、从就诊到诊断时间。所有调查资料均由经培训合格的杭州市疾病预防控制中心（杭州市卫生监督所）调查人员现场收集。
1.3　变量及定义
考虑到《中国结核病预防控制工作技术规范（2020版）》[14]（以下简称《技术规范》）虽在内容和技术手段上相较旧版规范或指南等有所优化，但其基本框架和核心定义未发生根本性改变。为确保结核病防控工作的连贯性和稳定性，本研究中的变量及定义目前均参照《技术规范》[14]。包括：（1）结核病病原学诊断指患者结核分枝杆菌病原学检查，包括涂片、培养和分子生物学检测等。诊断结果包括病原学阳性、病原学阴性和病原学未查。（2）治疗管理方式指结核病患者抗结核治疗期间的治疗管理方式，包括全程管理/督导，在结核病治疗过程中由督导人员（医务人员、家庭成员、志愿者等）或借助智能工具对其进行直接面试下督导服药及从诊断到随访复诊等的全疗程的管理；其他管理方式采用除全程管理/督导方式以外的管理方式，如间断性的医务人员或家庭成员管理、自我管理等。（3）FDC是将利福平、异烟肼、乙胺丁醇等≥2种抗结核药物按照固定剂量制成的复合制剂。（4）耐药情况指对任何一线药物（利福平、异烟肼、乙胺丁醇等）或二线药物（氧氟沙星、卡那霉素、阿米卡星等）耐药。（5）就诊延误指结核病患者从出现症状到就诊的时间间隔>14 d。（6）治疗方案包括标准化治疗方案（standardized treatment plan, STP）和非STP，非STP是指由于患者药物耐受性差、不良反应等原因，无法适用STP而采用的其他各类个体化方案。（7）本研究中生存分析结局变量为研究对象在抗结核治疗期间死亡。生存时间为研究对象经由结核病定点医疗机构诊断为结核病到由定点医疗机构、疾病预防控制机构或社区卫生服务中心在患者结束抗结核治疗时对其作出明确的生存或死亡判定的时间。删失值定义为研究对象在结核病治疗结束时定点医疗机构、疾病预防控制机构或社区卫生服务中心未作出明确的生存或死亡判定者，包括因诊断变更或转入利福平耐药治疗而停止治疗者、未登记治疗转归者。
1.4　统计分析
采用SPSS 22.0软件进行分析。定量资料服从偏态分布，采用中位数（四分位数间距）M（QR）描述。组间比较采用Wilcoxon秩和检验。定性资料以构成比表示，采用χ2检验。因素分析采用Cox分析。基于结核病患者死亡的独立危险因素，采用R 4.4.1的rms和survival包构建患者死亡风险预测列线图模型，并采用Bootstrap法和验证集分别进行内、外部验证。模型区分度用一致性指数（index of concordance, C-index）和受试者操作特征曲线下面积（area under the curve, AUC）进行评价，并绘制校准曲线。检验水准α=0.05（双尾）。C-index是用于评估生存模型预测性能的常用指标，主要用于评价模型预测结果与实际观察结果之间的一致性程度。C-index值介于0～1，当C-index>0.7时，通常认为模型具有较好的预测能力[15]。AUC常用于评估各种二分类模型性能，其值介于0～1，AUC值越大模型分类性能越好，当AUC>0.7时，通常认为模型具有较好的分类能力[15]。校准曲线通过将模型预测概率与实际事件发生频率进行比较来评估模型的校准效果，当模型预测概率与实际发生概率完全一致时，校准曲线将是一条与对角线重合的直线，校准曲线越接近对角线，说明模型的预测越准确[15]。列线图模型公式：$ h(t,x)=h_0(t)\exp(\beta_1x_1+\cdots+\beta_ix_i+\cdots+\beta_kx_k) $。其中$ h(t,x) $为t时的风险函数，t为生存时间，$ x=(x_1, x_2,\cdots x_k) $为可能与生存相关的协变量或交互项，$ {h_0}(t) $为t时刻的基线风险函数，$ \beta=（\beta_1，\beta_2，\cdots \beta_m） $为Cox模型的回归系数。
2　结　果
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2.1　结核病患者基本情况（表1）
根据纳入与排除标准，共纳入患者108 480例，其中训练集100 811例，验证集7 669例。训练集年龄为15～99岁，年龄中位数44岁。训练集中中青年（15～59岁）患者70 298例，占69.73%；老年（≥60岁）患者30 513例，占30.27%。验证集患者年龄为15～101岁，中位年龄39岁。验证集中中青年（15～59岁）患者4 295例，占56.0%；老年（≥60岁）患者3 374例，占44.0%。训练集与验证集患者年龄比较差异无统计学意义（Z=−1.379，P>0.01）。
2.2　结核病患者死亡影响因素单因素Cox回归分析
以研究对象抗结核治疗期间是否发生死亡（存活=0，死亡=1）和生存时间为因变量，以年龄、性别、户籍、民族、现居住地、病原学诊断结果、病例发现方式、耐药情况、合并其他基础疾病、治疗管理方式、合并≥2种结核病、HIV感染、结核病史、治疗方案、痰涂片检查阳性频次、痰培养检查阳性频次、规范胸部X线检查结果、就诊延误、从就诊到诊断时间、是否全程使用FDC作为自变量，进行单因素Cox回归分析。结果表明，中青年结核病患者的死亡在年龄、性别、户籍、现居住地、病原学诊断结果、耐药情况、治疗管理方式、HIV感染、结核病史、治疗方案、就诊延误、是否全程使用FDC方面的差异均有统计学意义（P<0.05）；老年结核病患者的死亡在年龄、性别、病原学诊断结果、耐药情况、治疗管理方式、结核病史、是否全程使用FDC方面的差异均有统计学意义（P<0.05）。
2.3　结核病患者死亡影响因素多因素Cox回归分析（表2）
以研究对象抗结核治疗期间是否发生死亡（存活=0，死亡=1）和生存时间为因变量，将单因素分析中P<0.05的变量纳入多因素Cox分析。结果显示，年龄、性别、现居住地、病原学诊断结果、耐药情况、HIV感染、结核病史、治疗方案、是否全程使用FDC是中青年结核病患者死亡的独立影响因素；年龄、性别、病原学诊断结果、治疗管理方式、结核病史、是否全程使用FDC是老年结核病患者死亡的影响因素。
2.4　预测模型建立（图1）
根据多因素Cox回归分析确定模型最终入组变量，构建年龄分层的结核病患者死亡风险列线图预测模型。该模型可通过垂直线在列线图顶端分值线上得到不同变量相应积分（0～100.0分）。根据所有变量积分的总和在列线图底部预测线上分别得到结核病患者在抗结核治疗1年内死亡率的预测值。例如现有1例确诊结核病患者，年龄75岁，男性，有结核病治疗史，本次就诊被诊断为病原学阳性肺结核，并具有FDC使用禁忌证，由所构建的列线图模型计算该个体抗结核治疗期间出现非意外死亡的总体评分为37.5+13.5+5+5+23.5=84.5分，据图1可知，预测该患者1年抗结核治疗期间存活概率约为86%。
2.5　模型预测效能评估（图2、3）
中青年训练集和验证集C-index分别为0.787（95%CI=0.763～0.813）、0.806（95%CI=0.728～0.884）；老年训练集和验证集分别为0.731（95%CI=0.721～0.742）、0.764（95%CI=0.729～0.800）。中青年训练集和验证集患者抗结核治疗1年内死亡风险ROC曲线下面积为0.772（95%CI=0.748～0.796 ）、0.752（95%CI=0.717～0.787）；老年训练集和验证集为0.749（95%CI=0.738～0.760）、0.705（95%CI=0.678～0.732）。采用1 000次Bootstrap自抽样内部验证中青年训练集和验证集C-index分别为0.787（95%CI=0.762～0.811）、0.806（95%CI=0.727～0.883）；老年训练集和验证集为0.731（95%CI=0.721～0.742）、0.764（95%CI=0.729～0.799）。根据中青年与老年结核病患者死亡情况绘制模型校准度曲线，结果显示模型具有良好的准确性。
3　讨　论
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结核病是我国感染性疾病导致死亡的第三大原因[3]，降低结核病死亡率对于实现全球健康目标至关重要。本研究对杭州市中青年及老年2个年龄段结核病患者死亡风险的影响因素进行分析。结果表明，年龄、性别、结核病史及病原学结果阳性均为中青年和老年结核病患者死亡的影响因素，与天津市[16]、湖南省[17]的研究结果相似。在死亡危险因素中，HIV感染是导致中青年患者死亡的最主要原因。Yamamura M等[18]的研究也表明，受HIV影响，年轻结核病患者较≥60岁的患者更可能死于结核病。对于老年患者，由于自身免疫系统已较为脆弱，HIV感染对其免疫功能的抑制也许并未显著加重感染或并发症。因此，对老年患者的死亡率造成的影响较小。本研究中，耐药对老年患者的死亡并未产生显著影响，但耐药的中青年患者死亡率约为非耐药患者的1.72倍，这与天津市[16]、湖南省[17]两地的研究结果有所不同。Laycock K M等[19]的研究结果显示，受结核病羞耻感和社会孤立感知等影响，中青年耐药患者的失访率明显高于老年患者，而患者因失访导致的耐药治疗失败，可能是导致中青年患者死亡风险升高的重要原因。
本研究结果显示，现居住地为农村的中青年结核病患者死亡风险较高，约为城镇患者的1.79倍，该结果与李政等[3]的研究一致。农村医疗卫生资源相对匮乏及患者健康意识薄弱可能是死亡风险增加的主要原因。但农村老年患者因常合并多种其他疾病[20]，结核病的影响可能易被其他健康问题所掩盖。根据《技术规范》等相关文件要求，临床医生在结核病患者就诊时会首选STP方案进行治疗，未采用STP方案通常是由于患者对药物耐受性较差或发生严重不良反应。这种情况在老年患者中较为常见，但在中青年患者中，此类情况的出现往往提示病情更为复杂。另一方面，非STP方案常常会使用二线或三线抗结核药物，这些药物副作用和耐受问题可能在中青年患者中更为显著，导致治疗中断或失败，从而增加患者死亡风险。而老年患者由于临床用药更为谨慎，在药物不良反应的管理上可能更加受到关注。因此，未必出现相同程度的治疗失败及死亡风险。此外，Podlekareva D N 等[21]的研究结果表明，开始抗结核治疗时，使用少于3种有效成分的抗结核药与患者更高的结核病相关死亡有关。本研究中，中青年及老年患者中未使用FDC复合制剂者亦出现了更高的死亡风险。FDC不仅能通过提高患者依从性、减少漏服药物风险以及降低耐药菌株发生率等在提升患者治疗成功率上起到积极作用，还能通过优化药物间的协同作用，增强抗结核效果。在不同年龄段患者中推广FDC的全程使用有助于降低结核病相关死亡率并改善治疗结局。
本研究根据中青年与老年患者死亡的影响因素，构建了年龄分层的结核病患者死亡风险列线图预测模型。列线图模型作为一种经典的数学工具，通过视觉化方式展示多个变量之间的关系。在考虑多变量影响的基础上，能根据患者的具体特征计算个体风险，并提供简单、个性化的预测结果。结核病具有发病率高、病程长、死亡率高等特点，通过预测结核病患者死亡风险，可以更好评估患者病情、制定高风险患者早期干预措施和个性化的治疗方案等。相较于中国湖南省[6]、天津市[7]和印度[8]的研究，本研究所构建年龄分层的结核病患者死亡风险列线图预测模型，除了考虑到中青年与老年患者在死亡风险上的差异外，还具有以下几个显著优势。首先，本研究具有较大的样本量，并且经过了外部验证。较大的样本量为研究的科学性与统计学意义提供了有力保障[22]。同时，外部验证的实施进一步提升了模型的泛化能力和可信度，从而有助于提高预测结果的准确性和可靠性。其次，尽管本研究未能收集到体质量指数、吸烟、饮酒等可能影响结核病患者死亡的混杂因素信息，但与中国湖南省、天津市和印度3项研究相比，本研究在数据收集过程中纳入了更为丰富的变量，综合考虑了患者的人口学特征、临床特征、实验室特征及结核病史、就诊延误等在内的其他特征。从而能够更全面地评估结核病患者的死亡风险，进一步提高模型预测的准确性。此外，本研究特别排除了由于交通事故、溺水、自杀等外部因素导致的死亡事件，有效避免这些非结核病相关因素对模型结果的干扰，确保所观察到的死亡事件与结核病的关联更加直接和准确，从而提高了死亡风险预测模型的可靠性，使其更能真实反映结核病本身对患者生存的影响。本研究构建的年龄分层的死亡风险列线图预测模型在中青年和老年患者的死亡风险预测中均展现出良好的预测能力、区分能力和可靠性。但结合C-index和AUC结果，模型在老年患者中表现略逊于中青年患者，提示可能有待进一步优化。该列线图模型可用于结核病临床实践，辅助医务人员针对不同年龄段患者进行有效的风险分层，并据此制定更具针对性的随访与干预策略。此外，列线图以图示方式呈现复杂的回归模型，便于患者及其家属、志愿者（如教师、学生及社区工作人员）等非专业人士理解和应用。尽管该模型在外部验证及临床适应性等方面仍需进一步研究与优化，但其在公共卫生管理与临床实践中的应用值得深入探索。
本研究存在一些局限性。首先，本研究数据来自医院病历系统，信息可能存在一定的疏漏与错误。其次，作为回顾性研究，可能存在一些潜在的混杂因素，如患者的不良生活习惯、社会经济状况、精神健康状况、药物不良反应等，这些因素未被纳入分析，可能对最终模型的预测效果产生潜在的影响。因此，未来的研究中应考虑纳入更多相关变量，以全面评估这些因素对模型性能的影响。此外，本研究中所构建的预测模型主要基于当前样本数据，尚需进一步通过外部数据集进行验证或开展大样本量、多中心的前瞻性研究，以进一步优化模型的准确性和稳健性。
本研究表明，中青年与老年结核病患者的死亡影响因素存在显著差异。针对这些差异制定个性化的防治措施，对降低结核病患者的死亡率具有重要意义。本研究构建的基于年龄分层的列线图模型在不同年龄段结核病患者的死亡预测中表现良好。建立更精确的死亡预测模型，有助于早期识别高风险患者，从而实施更有效的干预措施，以降低结核病患者的死亡风险。未来仍需进一步深入研究，以优化模型并提升其临床应用价值。

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doi: 10.11847/zgggws1146384
  • 接收时间：2024-11-11
  • 首发时间：2026-10-23
  • 出版时间：2026-01-10
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  • 收稿日期：2024-11-11
  • 修回日期：2025-04-22
  • 录用日期：2025-08-02
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    杭州市疾病预防控制中心 杭州市卫生监督所，杭州 310021

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2种不同金属材料的力学参数
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Family		 属数
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
species (%)
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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