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Article(id=1241522852827026250, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522846384583426, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202308423, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1692633600000, receivedDateStr=2023-08-22, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773931714493, onlineDateStr=2026-03-19, pubDate=1707494400000, pubDateStr=2024-02-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773931714493, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773931714493, creator=13701087609, updateTime=1773931714493, updator=13701087609, issue=Issue{id=1241522846384583426, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='3', pageStart='385', pageEnd='576', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773931712956, creator=13701087609, updateTime=1773931842201, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241523388544504301, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522846384583426, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241523388544504302, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522846384583426, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=407, endPage=411, ext={EN=ArticleExt(id=1241522853611361159, articleId=1241522852827026250, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Construction and verification of risk assessment model for cardiometabolic multimorbidity in China based on meta-analysis, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To establish a risk assessment model of cardiometabolic multimorbidity (CMM) and validate the model.

Methods

The influencing factors of CMM were determined by meta-analysis, and the population exposure rate was obtained by consulting a large cohort study.The CMM risk assessment model was constructed based on Harvard cancer risk index method. The model was verified by the data of China Health and Retirement Longitudinal Study (CHARLS). The SPSS 25.0 software was used to calculate the area under the receiver operator characteristic ROC curve (AUC), sensitivity, specificity, and Hosmer-Lemshow goodness-of-fit test to verify the effect of the model.

Results

A total of 20 articles were included in the meta- analysis. Age, sex, place of residence, education, economic level, BMI, waistline, night sleep, depression, irregular diet, and insufficient intake of vegetables and fruits were the risk factors of CMM. Concerning the model validation results, the area under the ROC curve was 0.712 (95%CI: 0.696-0.728), the Jordan index was 1.42, the sensitivity was 65%, the specificity was 66.1%, and the result of Hosmer-Lemshow goodness-of-fit test was χ2=10.652 (P=0.222).

Conclusion

The CMM risk assessment model based on meta-analysis has good predictive performance and can be used as a tool for CMM risk assessment to guide clinical identification of CMM high-risk groups and implement intervention measures.

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目的

构建心血管代谢性共病（CMM）风险评估模型，并进行模型验证。

方法

通过meta分析确定CMM发病的影响因素，并查阅大型队列研究获得人群暴露率，基于哈佛癌症风险指数法构建CMM风险评估模型。通过中国健康与养老追踪调查项目（CHARLS）数据进行模型验证。运用SPSS 25.0软件计算受试者工作特征（ROC）曲线下面积（AUC）、灵敏度、特异度及Hosmer-Lemeshow拟合优度检验验证模型效果。

结果

Meta分析共纳入20篇文献，年龄、性别、居住地、受教育程度、经济水平、BMI、腰围、夜间睡眠、抑郁、饮食不规律和蔬菜水果摄入不足是CMM危险因素。CMM风险评估模型验证ROC曲线下面积为0.712（95%CI：0.696～0.728），约登指数为1.42，灵敏度为65%，特异度为66.1%，Hosmer-Lemeshow拟合优度检验χ2=10.652，P=0.222。

结论

基于meta分析构建的CMM风险评估模型具有较好的预测性能，可作为CMM风险评估工具，指导临床识别CMM高危人群并实施干预措施。

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赵华，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=XAkPOwIq+r77SRTVDNOYnw==, magXml=P1omRIcpYJS1dKDTOyjWRg==, pdfUrl=null, pdf=xf2l3RjgxatHR6tsOY0ckg==, pdfFileSize=697370, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=4YTw/C/DgH0pEmhnrnv8Uw==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=vWXH/3rxskI1WZfP3hqr9A==, mapNumber=null, authorCompany=null, fund=null, authors=

贾铭（1998—），女，硕士在读，研究方向：内科护理

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贾铭（1998—），女，硕士在读，研究方向：内科护理

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贾铭（1998—），女，硕士在读，研究方向：内科护理

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The transformation criteria of cardiometabolic multimorbidity risk class index hazard score

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RR值关联强度危险分数
0.9≤X<1.1不明显0
0.7≤X<0.9 1.1≤X<1.5弱5
0.4≤X<0.7 1.5≤X<3.0中等10
0.2≤X<0.4 3.0≤X<7.0强25
X<0.2 X≥7.0非常强50
), ArticleFig(id=1241680459453748164, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522852827026250, language=CN, label=表1, caption=

CMM风险等级指数危险分数转化标准

, figureFileSmall=null, figureFileBig=null, tableContent=
RR值关联强度危险分数
0.9≤X<1.1不明显0
0.7≤X<0.9 1.1≤X<1.5弱5
0.4≤X<0.7 1.5≤X<3.0中等10
0.2≤X<0.4 3.0≤X<7.0强25
X<0.2 X≥7.0非常强50
), ArticleFig(id=1241680459600548813, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522852827026250, language=EN, label=Table 2, caption=

Cardiometabolic multimorbidity risk assessment model variables and relative hazard magnitudes

, figureFileSmall=null, figureFileBig=null, tableContent=
危险因素OR值危险分数人群平均暴露率
大小来源
中年（45～59岁）3.80250.2452021年中国卫生统计年鉴
老年（≥60岁）2.82100.187全国第七次人口普查
性别（女）1.54100.487全国第七次人口普查
受教育程度较高（初中及以上）2.01100.650全国第七次人口普查
居住在城市1.4150.638全国第七次人口普查
经济水平较高1.2150.2002021年中国卫生统计年鉴
超重（24 kg/m2≤BMI<28 kg/m2）1.92100.304中国慢性病和危险因素监测研究
肥胖（BMI≥28 kg/m2）3.01250.109中国慢性病和危险因素监测研究
中心性肥胖（腰围男性≥90 cm，女性≥85 cm）1.70100.274中国健康与养老报告
吸烟1.2750.325中国慢性病和危险因素监测研究
饮酒1.2750.517中国慢性病和危险因素监测研究
夜间睡眠较少（≤7 h）1.1750.169中国睡眠研究报告2023
抑郁1.50100.106中国国民心理健康发展报告2022
), ArticleFig(id=1241680459726377939, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522852827026250, language=CN, label=表2, caption=

CMM风险评估模型变量及相对危险度大小

, figureFileSmall=null, figureFileBig=null, tableContent=
危险因素OR值危险分数人群平均暴露率
大小来源
中年（45～59岁）3.80250.2452021年中国卫生统计年鉴
老年（≥60岁）2.82100.187全国第七次人口普查
性别（女）1.54100.487全国第七次人口普查
受教育程度较高（初中及以上）2.01100.650全国第七次人口普查
居住在城市1.4150.638全国第七次人口普查
经济水平较高1.2150.2002021年中国卫生统计年鉴
超重（24 kg/m2≤BMI<28 kg/m2）1.92100.304中国慢性病和危险因素监测研究
肥胖（BMI≥28 kg/m2）3.01250.109中国慢性病和危险因素监测研究
中心性肥胖（腰围男性≥90 cm，女性≥85 cm）1.70100.274中国健康与养老报告
吸烟1.2750.325中国慢性病和危险因素监测研究
饮酒1.2750.517中国慢性病和危险因素监测研究
夜间睡眠较少（≤7 h）1.1750.169中国睡眠研究报告2023
抑郁1.50100.106中国国民心理健康发展报告2022
), ArticleFig(id=1241680459848012763, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522852827026250, language=EN, label=Table 3, caption=

Incidence of cardiometabolic multimorbidity by risk class

, figureFileSmall=null, figureFileBig=null, tableContent=
CMM风险等级发病人数（%）未发病人数（%）合计
较低4（3.4）112（96.6）116
低88（4.8）1 744（95.2）1 832
一般152（6.0）2 379（94.0）2 531
高600（8.2）6 674（91.7）7 274
较高183（15.2）1 019（84.8）1 202
合计1 02711 92812 955
), ArticleFig(id=1241680459940287458, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522852827026250, language=CN, label=表3, caption=

不同风险等级CMM发病情况

, figureFileSmall=null, figureFileBig=null, tableContent=
CMM风险等级发病人数（%）未发病人数（%）合计
较低4（3.4）112（96.6）116
低88（4.8）1 744（95.2）1 832
一般152（6.0）2 379（94.0）2 531
高600（8.2）6 674（91.7）7 274
较高183（15.2）1 019（84.8）1 202
合计1 02711 92812 955
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基于Meta分析的中国心血管代谢性共病发病风险评估模型的构建及验证
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贾铭 1 , 赵华 1 , 史秀山 2 , 郑宇娟 3 , 彭菊意 1, 4 , 刘星宇 1 , 刘宇丹 1
现代预防医学 | 流行病与统计方法 2024,51(3): 407-411
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现代预防医学 | 流行病与统计方法 2024, 51(3): 407-411
基于Meta分析的中国心血管代谢性共病发病风险评估模型的构建及验证
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贾铭1, 赵华1 , 史秀山2, 郑宇娟3, 彭菊意1, 4, 刘星宇1, 刘宇丹1
作者信息
  • 1.山西中医药大学护理学院，山西 晋中 030619
  • 2.山西白求恩医院心血管内科
  • 3.山西省人民医院心血管内科
  • 4.山西白求恩医院精神卫生科

    • 贾铭（1998—），女，硕士在读，研究方向：内科护理

通讯作者:

赵华，E-mail：
Construction and verification of risk assessment model for cardiometabolic multimorbidity in China based on meta-analysis
Ming JIA1, Hua ZHAO1 , Xiu-shan SHI2, Yu-juan ZHENG3, Ju-yi PENG1, 4, Xing-yu LIU1, Yu-dan LIU1
Affiliations
  • College of Nursing, Shanxi University of Chinese Medicine, Jinzhong, Shanxi 030619, China
出版时间: 2024-02-10 doi: 10.20043/j.cnki.MPM.202308423
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摘要
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目的

构建心血管代谢性共病（CMM）风险评估模型，并进行模型验证。

方法

通过meta分析确定CMM发病的影响因素，并查阅大型队列研究获得人群暴露率，基于哈佛癌症风险指数法构建CMM风险评估模型。通过中国健康与养老追踪调查项目（CHARLS）数据进行模型验证。运用SPSS 25.0软件计算受试者工作特征（ROC）曲线下面积（AUC）、灵敏度、特异度及Hosmer-Lemeshow拟合优度检验验证模型效果。

结果

Meta分析共纳入20篇文献，年龄、性别、居住地、受教育程度、经济水平、BMI、腰围、夜间睡眠、抑郁、饮食不规律和蔬菜水果摄入不足是CMM危险因素。CMM风险评估模型验证ROC曲线下面积为0.712（95%CI：0.696～0.728），约登指数为1.42，灵敏度为65%，特异度为66.1%，Hosmer-Lemeshow拟合优度检验χ2=10.652，P=0.222。

结论

基于meta分析构建的CMM风险评估模型具有较好的预测性能，可作为CMM风险评估工具，指导临床识别CMM高危人群并实施干预措施。

心血管代谢性共病  /  危险因素  /  风险评估模型  /  哈佛癌症风险指数法  /  Meta分析
Objective

To establish a risk assessment model of cardiometabolic multimorbidity (CMM) and validate the model.

Methods

The influencing factors of CMM were determined by meta-analysis, and the population exposure rate was obtained by consulting a large cohort study.The CMM risk assessment model was constructed based on Harvard cancer risk index method. The model was verified by the data of China Health and Retirement Longitudinal Study (CHARLS). The SPSS 25.0 software was used to calculate the area under the receiver operator characteristic ROC curve (AUC), sensitivity, specificity, and Hosmer-Lemshow goodness-of-fit test to verify the effect of the model.

Results

A total of 20 articles were included in the meta- analysis. Age, sex, place of residence, education, economic level, BMI, waistline, night sleep, depression, irregular diet, and insufficient intake of vegetables and fruits were the risk factors of CMM. Concerning the model validation results, the area under the ROC curve was 0.712 (95%CI: 0.696-0.728), the Jordan index was 1.42, the sensitivity was 65%, the specificity was 66.1%, and the result of Hosmer-Lemshow goodness-of-fit test was χ2=10.652 (P=0.222).

Conclusion

The CMM risk assessment model based on meta-analysis has good predictive performance and can be used as a tool for CMM risk assessment to guide clinical identification of CMM high-risk groups and implement intervention measures.

Cardiovascular metabolic comorbidities  /  Risk factors  /  Risk assessment model  /  Harvard cancer risk index  /  Meta-analysis
贾铭, 赵华, 史秀山, 郑宇娟, 彭菊意, 刘星宇, 刘宇丹. 基于Meta分析的中国心血管代谢性共病发病风险评估模型的构建及验证. 现代预防医学, 2024 , 51 (3) : 407 -411 . DOI: 10.20043/j.cnki.MPM.202308423
Ming JIA, Hua ZHAO, Xiu-shan SHI, Yu-juan ZHENG, Ju-yi PENG, Xing-yu LIU, Yu-dan LIU. Construction and verification of risk assessment model for cardiometabolic multimorbidity in China based on meta-analysis[J]. Modern Preventive Medicine, 2024 , 51 (3) : 407 -411 . DOI: 10.20043/j.cnki.MPM.202308423
正文
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随着危险因素和不良生活行为的增加，我国心脑血管疾病、糖尿病和高血压等慢性病发病率逐年上升，对居民健康水平产生较大影响，并且多种慢性病往往同时共存。心血管代谢性共病（cardiometabolic multimorbidity, CMM）是多种共病模式下最为稳定的聚集模式之一，指个体同时患有两种及以上的心血管代谢性疾病，包括缺血性心脏病、脑卒中和糖尿病等疾病[1]。2022年一项流行病学调查显示，CMM的发病率为14.4%[2]，研究发现与没有共病的患者相比，患CMM的死亡风险增加了两倍[3]，CMM不仅影响患者康复进程，而且影响疾病预后，增加医疗负担，严重影响患者的生活质量[4]。由此可见，CMM已成为影响我国居民身体健康和生活的主要公共卫生问题，加强对CMM健康管理至关重要。风险评估作为慢病健康管理的核心环节，对于提高患者生活质量，降低患病率和死亡率具有重要的意义。目前，国内外关于CMM的研究较少，且缺乏有效的风险评估工具。因此，本研究以CMM患者影响因素的meta分析为基础，采用哈佛癌症风险指数法构建CMM发病风险评估模型，旨在为我国CMM人群的健康管理提供依据。
1　资料与方法
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1.1　Meta分析
检索中国期刊全文数据库、维普咨讯中文期刊服务平台、万方数据知识服务平台、中国生物医学文献数据库、PubMed、Web of Science、Cochrane Library、Embase、CINAHL和Medline，收集建库至2023年3月20日公开发表的关于CMM危险因素的文献。采用主题词与自由词结合的检索方法，中文检索词为“心血管代谢性疾病” “共病” “心血管代谢性共病” “危险因素” “影响因素” “风险因素” “相关因素” “预测因素”；英文检索词为“cardiometabolic diseases” “multimorbidity” “comorbidity” “multiple chronic conditions” “cardiometabolic multimorbidity”“risk factor” “ influence factor” “related factor” “predictor”。文献纳入标准：（1）关于CMM患者的队列、病例对照和横断面研究；（2）研究对象为经过二级甲等及以上医院确诊的患有缺血性心脏病、脑卒中和糖尿病中两种及以上的患者；（3）原始文献采用多因素logistic回归分析得到危险因素的OR/RR值及其95%CI；（4）可以获得全文的中文或英文文献。文献排除标准：（1）重复发表的研究；（2）无法获取完整数据的研究；（3）文献质量评价为低等级的研究。队列研究和病例对照研究使用纽卡斯尔-渥太华量表（Newcastle-Ottawa scale, NOS）[5]，横断面研究文献使用美国卫生保健质量和研究机构（Agency for Healthcare Research and Quality, AHRQ）[6]对纳入文献的质量进行评价。采用RevMan 5.4和Stata 16.0软件进行meta分析。
1.2　模型构建
本研究采用哈佛癌症风险指数法构建CMM风险评估模型。哈佛癌症风险指数法根据疾病风险因素的相对危险度和个体的暴露水平来评估疾病的发生风险，它将发病危险分为七个等级：很低、较低、低、一般、高、较高和很高，该方法常被用于对个体慢性病和癌症进行评估和预测[7]。具体步骤如下：（1）通过meta分析确定各危险因素的相对危险度（relative risk, RR）的大小，由于我国CMM发病率小于10%，因此使用OR值近似估算RR值，参照转化标准见表1，将RR值转化为危险分数；（2）基于中国大型队列研究，确定各危险因素的暴露率；（3）计算人群平均危险分数=∑(各危险因素的危险分数×各危险因素人群暴露率）；（4）计算个体CMM风险总分数，若存在该危险因素即加相应的危险分数，若不存在即为0；（5）计算个体风险比值R=个体CMM危险分数/人群平均危险分数；（6）计算个体风险水平划分等级，其中包括：很低：R<0、较低R=0或R<0.5、低0.5≤R<0.9、一般0.9≤R<1.1、高1.1≤R<2.0、较高2.0≤R<5.0、很高R≥5.0，共七个级别。
1.3　模型验证
本研究基于中国健康与养老追踪调查项目（China Health and Retirement Longitudinal Study, CHARLS），该项目由北京大学国家发展研究院牵头、北京大学中国社会科学调查中心负责执行，在全国28个省级单位、150个县级单位和450个村级单位开展调查，旨在收集我国45岁及以上中老年人个人和家庭数据，包括健康状况与功能、家庭结构和医疗保险等内容，具有较好的代表性。本研究根据meta分析内容筛选相关变量，将CMM定义为个体同时患有两种及以上的心血管代谢性疾病（包括糖尿病、心脏病和脑卒中）。使用SPSS 25.0进行数据整理和分析。并运用受试者工作特征（receiver operating characteristic, ROC）曲线下面积（area under the curve,AUC）来评估模型区分度。取得最大的约登指数时，将其作为最优截断值，用于计算模型的灵敏度和特异度。使用Hosmer-Lemeshow检验评价拟合度。检验水准α=0.05。
2　结果
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2.1　CMM影响因素
初期检索494篇文献，最终纳入20篇。经过meta分析，得到中年（45～59岁）、老年（≥60岁）、性别（女）、受教育程度较高（初中及以上）、经济水平较高、居住在城市、吸烟、饮酒、超重（24 kg/m2≤BMI<28 kg/m2）、肥胖（BMI≥28 kg/m2）、中心性肥胖（腰围男性≥90 cm，女性≥85 cm）、夜间睡眠较少（≤7 h）、抑郁、蔬菜水果摄入不足和饮食不规律为CMM的危险因素，缺乏规律运动、空气污染不是CMM的危险因素，无统计学意义（P>0.05）[8]。
2.2　CMM风险评估模型构建
根据meta分析结果确定风险因素OR值，参照表1将危险因素转化为危险分数，结果见表2。12 955例慢性病患者平均分数为37分。
2.3　个体发病风险预测实例分析
某女性，54岁，居住在城镇，文化程度为大专，经济收入较高；身高156 cm，体重65 kg，腰围80 cm；有高血压病史和糖尿病病史；不吸烟，不喝酒，夜间睡眠情况较差为6 h，使用抑郁量表（CESD-10）评分为12分≥10分。该个体的心血管代谢性共病危险等级计算如下：BMI=65/2.433 6=26.71≥24，为超重，个体危险分数得分情况=女性（5分）+年龄54岁（25分）+居住在城市（5分）+文化程度为大专（10分）+经济收入较高（5分）+超重（10分）+夜间睡眠较少（5分）+抑郁（10分）=75分，个体危险得分与人群平均危险分数比值R为2.03，即该个体发生心血管代谢性共病的风险等级为较高风险。
2.4　CMM风险评估模型验证
研究数据来自CHARLS 2015年Biomarker和2018年Demographic Backgrounds，Health Status and Functioning和Cognition and Depression模块内容。剔除相关数据缺失的样本后，最终纳入12 955名中老年人作为研究对象。本研究将调查对象的教育水平分为三个类别：文盲、小学及以下、初中及以上。衡量收入的指标是家庭人均年收入，根据中国统计年鉴[9]，将调查对象按照人均可支配收入水平从低到高排序，并将其平均分为五个等份，在这个分类中，处于最低20%的收入群体被定为为低收入，其次为较低收入组，中等收入组，较高收入组和高收入组。体重指数（BMI）分为三组：18.5～23.9 kg/m2范围为正常，24.0～27.9 kg/m2范围为超重，≥28.0 kg/m2为肥胖。男性腰围≥90 cm，女性腰围≥85 cm则被视为中心型肥胖。有抑郁症状的标准是抑郁量表（CESD-10）得分≥10分。
使用哈佛癌症风险指数法构建模型分析结果表明：在患有CMM的人中，较低风险等级4人，低风险等级88人，一般风险等级152人，高风险等级600人，较高风险等级183人，不同CMM风险等级中的CMM患病数量有所不同（χ2=129.209，P<0.001）。χ2趋势检验表明，随着CMM风险等级的增加，CMM患病人数也呈增加趋势（P<0.001）,见表3、图1。根据模型评估结果，绘制了ROC曲线进行验证，具体结果如下：ROC曲线下面积（AUC）为0.712（95%CI：0.690～0.728），灵敏度为65%，特异度为66.1%，在最佳截断值R=1.42处达到最优，见图2。对Hosmer- Lemeshow拟合优度进行检验，结果显示χ2=10.652，P=0.222。结果显示模型拟合准确度较好，预测结果与实际结果的符合度较高。
3　讨论
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中年（45～59岁）、老年（≥60岁）、性别（女）、受教育程度较高（初中及以上）、居住在城市和经济水平较高为CMM发病的一般因素，难以进行干预；肥胖（BMI≥ 28kg/m2）、超重（24 kg/m2≤BMI<28 kg/m2）、中心性肥胖（腰围男性≥ 90cm、女性≥ 85cm）、抑郁、饮酒、吸烟、夜间睡眠较少（≤7 h）、饮食不规律和蔬菜水果摄入不足是CMM发病的生活行为因素，可通过加大宣传力度，减少不良生活行为的产生，提高居民健康素养。因此，临床可通过定期开展宣教讲座，设立减重、戒烟、心理疏导门诊等方式，指导患者科学减重、戒烟戒酒、缓解心理压力，以降低CMM的发病率[10-11]。
本研究基于meta分析方法，纳入了影响CMM发病的主要危险因素，不仅扩大了样本量，而且弥补了无法开展大型横断面或队列研究的问题。基于哈佛癌症风险指数法构建CMM风险评估模型，该方法的优点是能够充分运用最新的研究成果，对慢性疾病风险进行评估，并考虑生活行为因素，符合慢性病预防治理的原则，目前被广泛应用[12]。齐元涛等[13]利用哈佛癌症指数方法，构建我国中老年癌症共病风险评估模型并进行验证，可针对癌症共病高危人群开展个性化的防控措施。Zhu等[7]建立了适用于江西省中老年社区居民的脑血管病风险预测模型，并为进一步开展脑血管病防治工作提供了科学依据。美国一项队列研究[14]使用基于哈佛癌症风险指数开发的评估工具（your disease risk）评估中年女性冠心病发病风险，结果表明该模型具有较好的预测性。以上研究均表明基于哈佛癌症风险指数法构建风险评估模型较为可靠，可指导临床进行分层管理。
目前国际上报道了多项研究共病患者的预测模型，但这些研究普遍针对的是共病患者的认知衰弱和死亡风险预测[15-16]，缺乏相关的CMM患者发病的风险预测模型。并且国内研究对于共病患者的评估大多使用查尔森合并症指数（CCI）和Elixhauser共病指数[17]，仅包含对于疾病的风险值评估，并不包含其他危险因素的评估。因此，基于风险因素构建CMM发病风险评估模型研究具有实际应用价值。
本研究基于CHARLS队列数据进行验证。验证结果显示：模型的ROC曲线下面积为0.712，灵敏度为65%，特异度为66.1%，Hosmer-Lemeshow拟合优度检验χ2=10.652，P=0.222。表明CMM风险评估模型具有较高的真实性和良好的拟合度。但风险等级结果只形成五个等级，较低和较高两个风险等级并未出现，原因是：（1）前期meta分析中仅纳入危险因素，未纳入保护性因素，所有危险因素得分都是正数，相应的个体风险分数/人群平均分数的值都不会是负数，因此不会出现“很低”风险等级。（2）本研究各危险因素的OR值均低于4.0，仅有两项危险因素危险分数为25分，无强相关因素（危险分数50分），即个体风险分数不会很高，因此很难出现“很高”风险等级。
本研究存在以下不足：（1）纳入meta分析的研究并未限定为中国人群，而危险因素的暴露率来自中国大型队列研究，提示模型可能并未完全反映中国CMM的发病风险，可能对模型的效果有一定的影响。（2）由于CHARLS队列数据的有限性，存在部分危险因素（如饮食行为因素）未纳入模型中并进行验证。
综上所述，本研究基于meta分析构建的CMM发病风险评估模型具有较好的预测效果，在社区卫生服务机构和医疗机构中可直接使用，这不仅有助于慢性病患者自行评估CMM发病的风险，实现“全民健康”，而且有助于医护人员尽早识别CMM高危人群，及早采取相应干预措施，以降低CMM的发病率。
基金
收起
  • 山西省社会科学界联合会2023至2024年度重点课题(SSKLZDKT2023117)
  • 山西省研究生实践创新项目(2023SJ272)
  • 山西中医药大学2023年度科技创新能力培育计划软科学研究专项项目(2023PY-RKX-03)
  • 山西中医药大学研究生教育创新项目(2023CX050)
文献
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2024年第51卷第3期
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doi: 10.20043/j.cnki.MPM.202308423
  • 接收时间：2023-08-22
  • 首发时间：2026-03-19
  • 出版时间：2024-02-10
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  • 收稿日期：2023-08-22
基金
山西省社会科学界联合会2023至2024年度重点课题(SSKLZDKT2023117)
山西省研究生实践创新项目(2023SJ272)
山西中医药大学2023年度科技创新能力培育计划软科学研究专项项目(2023PY-RKX-03)
山西中医药大学研究生教育创新项目(2023CX050)
作者信息
    1.山西中医药大学护理学院，山西 晋中 030619
    2.山西白求恩医院心血管内科
    3.山西省人民医院心血管内科
    4.山西白求恩医院精神卫生科

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