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Article(id=1241675632279613518, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241675628051755031, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202406346, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1718899200000, receivedDateStr=2024-06-21, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773968139953, onlineDateStr=2026-03-20, pubDate=1732464000000, pubDateStr=2024-11-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773968139953, onlineIssueDateStr=2026-03-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773968139953, creator=13701087609, updateTime=1773968139953, updator=13701087609, issue=Issue{id=1241675628051755031, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='22', pageStart='4033', pageEnd='4224', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773968138945, creator=13701087609, updateTime=1773968595676, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241677543783322543, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241675628051755031, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241677543783322544, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241675628051755031, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=4063, endPage=4069, ext={EN=ArticleExt(id=1241675632644517983, articleId=1241675632279613518, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Construction and validation of a nomogram predictive model for occupational blood exposures of medical staff, columnId=1228016570660745413, journalTitle=Modern Preventive Medicine, columnName=Environmental and Occupational Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To establish a nomogram predictive model for occupational blood exposures of medical staff and validate the model.

Methods

From July to December 2023, 20% of the medical staff of each sample hospital were selected for the study using multi-stage whole group probability sampling method and divided into training and validation groups in a 7:3 ratio. LASSO-Logistic regression was used to screen independent risk factors. R language was used to establish a nomogram model and verify it.

Results

A total of 2 251 medical staff were included. There was no significant difference in general data between the two groups (P>0.05). LASSO-Logistic regression analysis showed that post (doctor: OR=3.024, 95% CI: 1.313-6.963; nurses: OR=3.837, 95% CI: 1.739-8.467), professional title (intermediate: OR=1.926, 95% CI: 1.444-2.569); advanced: OR=1.684, 95% CI: 1.052-2.697), education level (undergraduate: OR=2.076, 95% CI: 1.445-2.983); master’s degree or above: OR= 1.767, 95% CI: 1.073-2.910), psychological quality (general: OR=0.658, 95% CI: 0.443-0.987; good: OR=0.568, 95% CI: 0.368-0.879), stress level (moderate: OR=1.348, 95% CI: 1.061-1.713; severe: OR=2.109, 95% CI: 1.457-3.055), protective awareness (general: OR=0.515, 95% CI: 0.332-0.799; good: OR=0.297, 95% CI: 0.186-0.474), protective behavior (sometimes: OR=0.589, 95% CI: 0.363-0.955; always: OR=0.424, 95% CI: 0.261-0.689) was an independent influencing factor of blood-borne occupational exposure. The results of ROC curve showed that the area under the curve of the training group was 0.821 (95% CI: 0.667-0.831), and the validation group was 0.716 (95% CI: 0.618-0.715). The Hosmer-Lemeshow test showed that the calibration of the model was good (P=0.568, 0.956). The calibration curve showed that the prediction curve was basically fitted with the standard curve, and the model prediction accuracy was high. The results of the decision curve showed that the model had the best applicability when the risk threshold was about 0.1-0.8.

Conclusion

The nomogram model can accurately identify high-risk groups of occupational blood exposures and provide a basis for personalized risk prevention and control.

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目的

构建医务人员血源性职业暴露列线图预测模型并验证。

方法

2023年7—12月，采用多阶段整群概率抽样法抽取各样本医院20%的医务人员为研究对象，以7∶3比例分为训练组和验证组。LASSO-Logistic回归筛选独立风险因素，R语言建立列线图模型并进行验证。

结果

共纳入2 251名医务人员，两组一般资料比较，差异均无统计学意义（P>0.05）。LASSO-Logistic回归分析显示，岗位（医生：OR=3.024，95% CI：1.313~6.963；护士：OR=3.837，95% CI：1.739~8.467）、职称（中级：OR=1.926，95% CI：1.444~2.569；高级：OR=1.684，95% CI：1.052~2.697）、文化程度（本科：OR=2.076，95% CI：1.445~2.983；硕士及以上：OR=1.767，95% CI：1.073~2.910）、心理素质（一般：OR=0.658，95% CI：0.443~0.987；良好：OR=0.568，95% CI：0.368~0.879）、压力程度（中度：OR=1.348，95% CI：1.061~1.713；重度：OR=2.109，95% CI：1.457~3.055）、防护意识（一般：OR=0.515，95% CI：0.332~0.799；良好：OR=0.297，95% CI：0.186~0.474）、防护行为（有时：OR=0.589，95% CI：0.363~0.955；总是：OR=0.424，95% CI：0.261~0.689）是血源性职业暴露的独立影响因素。ROC曲线结果显示，训练组曲线下面积为0.821（95% CI：0.667~0.831），验证组为0.716（95% CI：0.618~0.715）。Hosmer-Lemeshow检验表明，模型的校准度较好（P=0.568、0.956），校准曲线显示，预测曲线与标准曲线基本拟合，模型预测准确度较高。决策曲线结果显示，在风险阈值约为0.1~0.8时，模型适用性最佳。

结论

列线图模型可精准识别血源性职业暴露高风险人群，为开展个性化风险防控提供依据。

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林陶玉，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=+zKEgyUsalFwi4iS6oU4Uw==, magXml=Y+jn3mSI+/erO7qYvAQa9A==, pdfUrl=null, pdf=KUbE/fPyaW5zAMgQ+A+sSw==, pdfFileSize=1187673, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=yt0WsCp3idvr6JAa6ojPgw==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=bGO6nP6wvyWg3tH0LojYLw==, mapNumber=null, authorCompany=null, fund=null, authors=

郭晓颖（1999—），女，硕士在读，研究方向：护理管理、临床护理

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郭晓颖（1999—），女，硕士在读，研究方向：护理管理、临床护理

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郭晓颖（1999—），女，硕士在读，研究方向：护理管理、临床护理

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American Journal of Infection Control, 2016, 44(1): 85-90., articleTitle=Prevalence and risk factors of needlestick injuries, sharps injuries, and blood and body fluid exposures among operating room nurses in Thailand, refAbstract=null)], funds=[Fund(id=1241826551327686992, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, awardId=SKYD2023087, language=CN, fundingSource=苏州市科技发展计划项目(SKYD2023087), fundOrder=null, country=null), Fund(id=1241826551445127509, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, awardId=SGY2022D01, language=CN, fundingSource=苏州高新区人民医院科学创新基金联合项目(SGY2022D01), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241826542418984991, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, xref=1., ext=[AuthorCompanyExt(id=1241826542423179296, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, companyId=1241826542418984991, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Nursing, School of Medicine, Shihezi University, Shihezi, Xinjiang 832000, China), AuthorCompanyExt(id=1241826542431567904, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, companyId=1241826542418984991, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.石河子大学医学院　护理系，新疆　石河子　832000)]), AuthorCompany(id=1241826542561591339, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, xref=2., ext=[AuthorCompanyExt(id=1241826542569979948, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, companyId=1241826542561591339, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.苏州高新区人民医院　护理部)]), AuthorCompany(id=1241826542679031857, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, xref=3., ext=[AuthorCompanyExt(id=1241826542687420470, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, companyId=1241826542679031857, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.石河子大学第一附属医院　创伤外科)])], figs=[ArticleFig(id=1241826547875774720, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, language=EN, label=Fig.1, caption=Coefficient paths of LASSO regression, figureFileSmall=Go54nGMTfg/jyNNvpqg1vg==, figureFileBig=XAw/lS/dAR8XSp+n5Y9BFA==, tableContent=null), ArticleFig(id=1241826547989020934, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, language=CN, label=图1, caption=LASSO回归路径系数, figureFileSmall=Go54nGMTfg/jyNNvpqg1vg==, figureFileBig=XAw/lS/dAR8XSp+n5Y9BFA==, tableContent=null), ArticleFig(id=1241826548144210187, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, language=EN, label=Fig.2, caption=Results of LASSO regression validation, figureFileSmall=8s6kDTs7AttydyJz8VRIjg==, figureFileBig=GFSmp+VYB+WrpAe66BIVoA==, tableContent=null), ArticleFig(id=1241826548249067793, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, language=CN, label=图2, caption=LASSO回归验证结果, figureFileSmall=8s6kDTs7AttydyJz8VRIjg==, figureFileBig=GFSmp+VYB+WrpAe66BIVoA==, tableContent=null), ArticleFig(id=1241826548337148180, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, language=EN, label=Fig.3, caption=A nomogram model of occupational blood exposures in medical staff, figureFileSmall=00ggoafEeLV5hoODmfp+IQ==, figureFileBig=4SyhxJwqVn3FWRCpBRN47A==, tableContent=null), ArticleFig(id=1241826548492337434, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, language=CN, label=图3, caption=医务人员发生血源性职业暴露的列线图模型

注：**代表P<0.01，***代表P<0.001。

, figureFileSmall=00ggoafEeLV5hoODmfp+IQ==, figureFileBig=4SyhxJwqVn3FWRCpBRN47A==, tableContent=null), ArticleFig(id=1241826548630749472, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, language=EN, label=Fig.4a, caption=ROC curve of training group　Fig.4b　ROC curve of validation group, figureFileSmall=r6ZlTFe7XjTXTgiomSqPAA==, figureFileBig=PAlvZt7EsIAPfhUV6eGIzw==, tableContent=null), ArticleFig(id=1241826548756578598, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, language=CN, label=图4a, caption=训练组ROC曲线　图4b　验证组ROC曲线, figureFileSmall=r6ZlTFe7XjTXTgiomSqPAA==, figureFileBig=PAlvZt7EsIAPfhUV6eGIzw==, tableContent=null), ArticleFig(id=1241826548890796333, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, language=EN, label=Fig.5a, caption=Training group calibration curve　Fig.5b　Validation group calibration curve, figureFileSmall=XdJIOPiJ4JRGKardHexI5A==, figureFileBig=0naxgtuoqRLwo9MCYxYMCw==, tableContent=null), ArticleFig(id=1241826549020819760, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, language=CN, label=图5a, caption=训练组校准曲线图　图5b　验证组校准曲线, figureFileSmall=XdJIOPiJ4JRGKardHexI5A==, figureFileBig=0naxgtuoqRLwo9MCYxYMCw==, tableContent=null), ArticleFig(id=1241826549138260277, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, language=EN, label=Fig.6a, caption=Clinical decision curve of training group　Fig.6b　Clinical decision curve of validation group, figureFileSmall=24xaauTqWlC5l1a86TRJsw==, figureFileBig=byD6KXupXcUgbZWi+74vMA==, tableContent=null), ArticleFig(id=1241826549247312185, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, language=CN, label=图6a, caption=训练组临床决策曲线　图6b　验证组临床决策曲线, figureFileSmall=24xaauTqWlC5l1a86TRJsw==, figureFileBig=byD6KXupXcUgbZWi+74vMA==, tableContent=null), ArticleFig(id=1241826549360558395, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, language=EN, label=Table 1, caption=

Comparison of baseline data between the training group and the validation group ［case (%)］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量全组
（n=2 251）		训练组
（n=1 575）		验证组
（n=676）		χ2值P值
地区1.4910.222
东部1 078（47.9）741（47.0）337（49.9）
西部1 173（52.1）834（53.0）339（50.1）
性别0.5640.453
男397（17.6）284（18.0）113（16.7）
女1 854（82.4）1 291（82.0）563（83.3）
年龄（岁）2.4380.487
≤25193（8.6）129（8.2）64（9.5）
26~351 236（54.9）871（55.3）365（54.0）
36~45589（26.2）419（26.6）170（25.1）
≥46233（10.4）156（9.9）77（11.4）
岗位0.0040.998
医技87（3.9）61（3.9）26（3.8）
医生491（21.8）343（21.8）148（21.9）
护士1 673（74.3）1 171（74.3）502（74.3）
职称0.5370.764
初级999（44.4）705（44.8）294（43.5）
中级945（42.0）660（41.9）285（42.2）
高级307（13.6）210（13.3）97（14.3）
工作年限（年）4.8500.088
1~101 116（49.6）776（49.3）340（50.3）
11~20843（37.5）608（38.6）235（34.8）
>20292（13.0）191（12.1）101（14.9）
聘用类别0.0720.788
在编715（31.8）503（31.9）212（31.4）
合同1 536（68.2）1 072（68.1）464（68.6）
文化程度0.4890.783
大专及以下342（15.2）234（14.9）108（16.0）
本科1 555（69.1）1 091（69.3）464（68.6）
硕士及以上354（15.7）250（15.9）104（15.4）
婚姻状况0.0040.951
未婚508（22.6）356（22.6）152（22.5）
已婚1 743（77.4）1 219（77.4）524（77.5）
健康状况4.3430.114
较差198（8.8）142（9.0）56（8.3）
一般1 754（77.9）1 239（78.7）515（76.2）
良好299（13.3）194（12.3）105（15.5）
心理素质3.1920.203
较差239（10.6）179（11.4）60（8.9）
一般1 211（53.8）837（53.1）374（55.3）
良好801（35.6）559（35.5）242（35.8）
压力程度2.2130.331
轻度1 155（51.3）793（50.3）362（53.6）
中度863（38.3）619（39.3）244（36.1）
重度233（10.4）163（10.3）70（10.4）
防护意识2.5280.283
较差201（8.9）133（8.4）68（10.1）
一般1 040（46.2）754（47.9）322（47.6）
良好1 010（44.9）688（43.7）286（42.3）
防护行为0.8860.642
从不185（8.2）125（7.9）60（8.9）
有时692（30.7）480（30.5）212（31.4）
总是1 374（61.0）970（61.6）404（59.8）
), ArticleFig(id=1241826549452833085, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, language=CN, label=表1, caption=

训练组与验证组研究对象的基线资料比较［例（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量全组
（n=2 251）		训练组
（n=1 575）		验证组
（n=676）		χ2值P值
地区1.4910.222
东部1 078（47.9）741（47.0）337（49.9）
西部1 173（52.1）834（53.0）339（50.1）
性别0.5640.453
男397（17.6）284（18.0）113（16.7）
女1 854（82.4）1 291（82.0）563（83.3）
年龄（岁）2.4380.487
≤25193（8.6）129（8.2）64（9.5）
26~351 236（54.9）871（55.3）365（54.0）
36~45589（26.2）419（26.6）170（25.1）
≥46233（10.4）156（9.9）77（11.4）
岗位0.0040.998
医技87（3.9）61（3.9）26（3.8）
医生491（21.8）343（21.8）148（21.9）
护士1 673（74.3）1 171（74.3）502（74.3）
职称0.5370.764
初级999（44.4）705（44.8）294（43.5）
中级945（42.0）660（41.9）285（42.2）
高级307（13.6）210（13.3）97（14.3）
工作年限（年）4.8500.088
1~101 116（49.6）776（49.3）340（50.3）
11~20843（37.5）608（38.6）235（34.8）
>20292（13.0）191（12.1）101（14.9）
聘用类别0.0720.788
在编715（31.8）503（31.9）212（31.4）
合同1 536（68.2）1 072（68.1）464（68.6）
文化程度0.4890.783
大专及以下342（15.2）234（14.9）108（16.0）
本科1 555（69.1）1 091（69.3）464（68.6）
硕士及以上354（15.7）250（15.9）104（15.4）
婚姻状况0.0040.951
未婚508（22.6）356（22.6）152（22.5）
已婚1 743（77.4）1 219（77.4）524（77.5）
健康状况4.3430.114
较差198（8.8）142（9.0）56（8.3）
一般1 754（77.9）1 239（78.7）515（76.2）
良好299（13.3）194（12.3）105（15.5）
心理素质3.1920.203
较差239（10.6）179（11.4）60（8.9）
一般1 211（53.8）837（53.1）374（55.3）
良好801（35.6）559（35.5）242（35.8）
压力程度2.2130.331
轻度1 155（51.3）793（50.3）362（53.6）
中度863（38.3）619（39.3）244（36.1）
重度233（10.4）163（10.3）70（10.4）
防护意识2.5280.283
较差201（8.9）133（8.4）68（10.1）
一般1 040（46.2）754（47.9）322（47.6）
良好1 010（44.9）688（43.7）286（42.3）
防护行为0.8860.642
从不185（8.2）125（7.9）60（8.9）
有时692（30.7）480（30.5）212（31.4）
总是1 374（61.0）970（61.6）404（59.8）
), ArticleFig(id=1241826549587050818, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, language=EN, label=Table 2, caption=

Univariate and multivariate logistic regression analyses of occupational blood exposures in medical staff

, figureFileSmall=null, figureFileBig=null, tableContent=
自变量单因素分析多因素分析
OR值（95% CI）P值OR值（95% CI）P值
地区（ref:东部）
西部0.966（0.756~1.234）0.782
性别（ref:男）
女0.818（0.657~1.020）0.074
年龄（岁，ref:≤25）
26~351.409（1.014~1.956）0.041
36~451.681（1.681~2.382）0.003
≥461.690（1.129~2.528）0.011
岗位（ref:医技）
医生4.054（2.091~7.859）<0.0013.024（1.313~6.963）0.009
护士4.112（2.160~7.828）<0.0013.837（1.739~8.467）0.001
职称（ref:初级）
中级1.687（1.404~2.028）<0.0011.926（1.444~2.569）<0.001
高级1.584（1.213~2.068）0.0011.684（1.052~2.697）0.030
工作年限（年，ref:1~10）
11~201.261（1.049~1.516）0.0140.852（0.641~1.132）0.269
>201.375（1.058~1.786）0.0171.074（0.696~1.658）0.746
聘用类别（ref:在编）
合同0.934（0.778~1.120）0.458
文化程度（ref:大专及以下）
本科2.162（1.645~2.844）<0.0012.076（1.445~2.983）<0.001
硕士及以上2.038（1.448~2.867）<0.0011.767（1.073~2.910）0.025
婚姻状况（ref:未婚）
已婚0.959（0.783~1.175）0.687
健康状况（ref:较差）
一般0.970（0.718~1.309）0.842
良好0.792（0.546~1.147）0.217
心理素质（ref:较差）
一般0.481（0.355~0.653）<0.0010.658（0.443~0.987）0.038
良好0.297（0.216~0.409）<0.0010.568（0.368~0.879）0.011
压力程度（ref:轻度）
中度1.527（1.274~1.832）<0.0011.348（1.061~1.713）0.015
重度2.483（1.845~3.340）<0.0012.109（1.457~3.055）<0.001
防护意识（ref:较差）
一般0.652（0.462~0.921）0.0150.515（0.332~0.799）0.003
良好0.330（0.233~0.467）<0.0010.297（0.186~0.474）<0.001
防护行为（ref:从不）
有时0.498（0.343~0.723）<0.0010.589（0.363~0.955）0.032
总是0.275（0.192~0.393）<0.0010.424（0.261~0.689）0.001
), ArticleFig(id=1241826549708685640, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241675632279613518, language=CN, label=表2, caption=

医务人员发生血源性职业暴露的单因素、多因素logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
自变量单因素分析多因素分析
OR值（95% CI）P值OR值（95% CI）P值
地区（ref:东部）
西部0.966（0.756~1.234）0.782
性别（ref:男）
女0.818（0.657~1.020）0.074
年龄（岁，ref:≤25）
26~351.409（1.014~1.956）0.041
36~451.681（1.681~2.382）0.003
≥461.690（1.129~2.528）0.011
岗位（ref:医技）
医生4.054（2.091~7.859）<0.0013.024（1.313~6.963）0.009
护士4.112（2.160~7.828）<0.0013.837（1.739~8.467）0.001
职称（ref:初级）
中级1.687（1.404~2.028）<0.0011.926（1.444~2.569）<0.001
高级1.584（1.213~2.068）0.0011.684（1.052~2.697）0.030
工作年限（年，ref:1~10）
11~201.261（1.049~1.516）0.0140.852（0.641~1.132）0.269
>201.375（1.058~1.786）0.0171.074（0.696~1.658）0.746
聘用类别（ref:在编）
合同0.934（0.778~1.120）0.458
文化程度（ref:大专及以下）
本科2.162（1.645~2.844）<0.0012.076（1.445~2.983）<0.001
硕士及以上2.038（1.448~2.867）<0.0011.767（1.073~2.910）0.025
婚姻状况（ref:未婚）
已婚0.959（0.783~1.175）0.687
健康状况（ref:较差）
一般0.970（0.718~1.309）0.842
良好0.792（0.546~1.147）0.217
心理素质（ref:较差）
一般0.481（0.355~0.653）<0.0010.658（0.443~0.987）0.038
良好0.297（0.216~0.409）<0.0010.568（0.368~0.879）0.011
压力程度（ref:轻度）
中度1.527（1.274~1.832）<0.0011.348（1.061~1.713）0.015
重度2.483（1.845~3.340）<0.0012.109（1.457~3.055）<0.001
防护意识（ref:较差）
一般0.652（0.462~0.921）0.0150.515（0.332~0.799）0.003
良好0.330（0.233~0.467）<0.0010.297（0.186~0.474）<0.001
防护行为（ref:从不）
有时0.498（0.343~0.723）<0.0010.589（0.363~0.955）0.032
总是0.275（0.192~0.393）<0.0010.424（0.261~0.689）0.001
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医务人员血源性职业暴露列线图预测模型的构建与验证
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郭晓颖 1 , 林陶玉 2 , 李圆圆 1, 3 , 郭薇 1 , 张亚桃 1 , 陈彦丽 1
现代预防医学 | 环境与职业卫生 2024,51(22): 4063-4069
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现代预防医学 | 环境与职业卫生 2024, 51(22): 4063-4069
医务人员血源性职业暴露列线图预测模型的构建与验证
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郭晓颖1, 林陶玉2 , 李圆圆1, 3, 郭薇1, 张亚桃1, 陈彦丽1
作者信息
  • 1.石河子大学医学院　护理系，新疆　石河子　832000
  • 2.苏州高新区人民医院　护理部
  • 3.石河子大学第一附属医院　创伤外科

    • 郭晓颖（1999—），女，硕士在读，研究方向：护理管理、临床护理

通讯作者:

林陶玉，E-mail:
Construction and validation of a nomogram predictive model for occupational blood exposures of medical staff
Xiao-ying GUO1, Tao-yu LIN2 , Yuan-yuan LI1, 3, Wei GUO1, Ya-tao ZHANG1, Yan-li CHEN1
Affiliations
  • Department of Nursing, School of Medicine, Shihezi University, Shihezi, Xinjiang 832000, China
出版时间: 2024-11-25 doi: 10.20043/j.cnki.MPM.202406346
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摘要
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目的

构建医务人员血源性职业暴露列线图预测模型并验证。

方法

2023年7—12月，采用多阶段整群概率抽样法抽取各样本医院20%的医务人员为研究对象，以7∶3比例分为训练组和验证组。LASSO-Logistic回归筛选独立风险因素，R语言建立列线图模型并进行验证。

结果

共纳入2 251名医务人员，两组一般资料比较，差异均无统计学意义（P>0.05）。LASSO-Logistic回归分析显示，岗位（医生：OR=3.024，95% CI：1.313~6.963；护士：OR=3.837，95% CI：1.739~8.467）、职称（中级：OR=1.926，95% CI：1.444~2.569；高级：OR=1.684，95% CI：1.052~2.697）、文化程度（本科：OR=2.076，95% CI：1.445~2.983；硕士及以上：OR=1.767，95% CI：1.073~2.910）、心理素质（一般：OR=0.658，95% CI：0.443~0.987；良好：OR=0.568，95% CI：0.368~0.879）、压力程度（中度：OR=1.348，95% CI：1.061~1.713；重度：OR=2.109，95% CI：1.457~3.055）、防护意识（一般：OR=0.515，95% CI：0.332~0.799；良好：OR=0.297，95% CI：0.186~0.474）、防护行为（有时：OR=0.589，95% CI：0.363~0.955；总是：OR=0.424，95% CI：0.261~0.689）是血源性职业暴露的独立影响因素。ROC曲线结果显示，训练组曲线下面积为0.821（95% CI：0.667~0.831），验证组为0.716（95% CI：0.618~0.715）。Hosmer-Lemeshow检验表明，模型的校准度较好（P=0.568、0.956），校准曲线显示，预测曲线与标准曲线基本拟合，模型预测准确度较高。决策曲线结果显示，在风险阈值约为0.1~0.8时，模型适用性最佳。

结论

列线图模型可精准识别血源性职业暴露高风险人群，为开展个性化风险防控提供依据。

医务人员  /  血源性职业暴露  /  列线图预测模型
Objective

To establish a nomogram predictive model for occupational blood exposures of medical staff and validate the model.

Methods

From July to December 2023, 20% of the medical staff of each sample hospital were selected for the study using multi-stage whole group probability sampling method and divided into training and validation groups in a 7:3 ratio. LASSO-Logistic regression was used to screen independent risk factors. R language was used to establish a nomogram model and verify it.

Results

A total of 2 251 medical staff were included. There was no significant difference in general data between the two groups (P>0.05). LASSO-Logistic regression analysis showed that post (doctor: OR=3.024, 95% CI: 1.313-6.963; nurses: OR=3.837, 95% CI: 1.739-8.467), professional title (intermediate: OR=1.926, 95% CI: 1.444-2.569); advanced: OR=1.684, 95% CI: 1.052-2.697), education level (undergraduate: OR=2.076, 95% CI: 1.445-2.983); master’s degree or above: OR= 1.767, 95% CI: 1.073-2.910), psychological quality (general: OR=0.658, 95% CI: 0.443-0.987; good: OR=0.568, 95% CI: 0.368-0.879), stress level (moderate: OR=1.348, 95% CI: 1.061-1.713; severe: OR=2.109, 95% CI: 1.457-3.055), protective awareness (general: OR=0.515, 95% CI: 0.332-0.799; good: OR=0.297, 95% CI: 0.186-0.474), protective behavior (sometimes: OR=0.589, 95% CI: 0.363-0.955; always: OR=0.424, 95% CI: 0.261-0.689) was an independent influencing factor of blood-borne occupational exposure. The results of ROC curve showed that the area under the curve of the training group was 0.821 (95% CI: 0.667-0.831), and the validation group was 0.716 (95% CI: 0.618-0.715). The Hosmer-Lemeshow test showed that the calibration of the model was good (P=0.568, 0.956). The calibration curve showed that the prediction curve was basically fitted with the standard curve, and the model prediction accuracy was high. The results of the decision curve showed that the model had the best applicability when the risk threshold was about 0.1-0.8.

Conclusion

The nomogram model can accurately identify high-risk groups of occupational blood exposures and provide a basis for personalized risk prevention and control.

Medical staff  /  Occupational blood exposures  /  Nomogram predictive model
郭晓颖, 林陶玉, 李圆圆, 郭薇, 张亚桃, 陈彦丽. 医务人员血源性职业暴露列线图预测模型的构建与验证. 现代预防医学, 2024 , 51 (22) : 4063 -4069 . DOI: 10.20043/j.cnki.MPM.202406346
Xiao-ying GUO, Tao-yu LIN, Yuan-yuan LI, Wei GUO, Ya-tao ZHANG, Yan-li CHEN. Construction and validation of a nomogram predictive model for occupational blood exposures of medical staff[J]. Modern Preventive Medicine, 2024 , 51 (22) : 4063 -4069 . DOI: 10.20043/j.cnki.MPM.202406346
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血源性职业暴露（Occupational blood exposures, OBEs）是指医务人员在从事诊疗、护理活动中，因接触被病原体感染的血液、体液或其他潜在性物质，可能感染血源性传染病的状态[1]。目前，血源性职业暴露仍是医疗机构内最主要的职业暴露类型。据统计，世界范围内每年因针刺伤感染乙型肝炎病毒、丙型肝炎病毒和人类免疫缺陷病毒的医务人员分别高达6.6万、1.6万和0.1万[2]。由此可知，医务人员因血源性职业暴露引发血源性病原体感染率极高。然而，机构层面惩罚性文化和繁琐的报告制度导致医务人员漏报现象严重[3-4]，不利于洞察关键风险要素并追根溯源，使医疗保健环境中血源性病原体相互传播的风险增高。既往研究证实，岗位、职称、工作年限、文化程度及压力负荷程度是血源性职业暴露的危险因素[5-6]；个人职业防护知信行水平同样对血源性职业暴露产生影响，高水平的防护意识使个体本能的采取防护行为，有效避免血源性职业暴露[7-8]。但既往研究结果间异质性大，且多聚焦于探究风险间的关系，对风险预测方面关注不足。因此，亟需从个人视角全面探索血源性职业暴露风险因素，精准划分风险要素等级，构建风险预测模型，以有效捕捉高风险人群并开展个性化风险防控。列线图是一种风险评估工具，可将复杂的回归分析结果可视化，并计算结局事件的预测概率[9]，引入网页计算器可显著提升列线图模型的实用性与适用性[10]。鉴于此，本研究聚焦于医务人员血源性职业暴露，基于知信行和认知负荷理论设计问卷剖析成因，应用LASSO-Logistic回归分析风险因素，借助列线图模型构建可视化风险预测工具，并部署网页计算器，提高血源性职业暴露早期风险筛查的准确性和特异性，以期为个性化风险防控的开展提供依据。
1　对象与方法
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1.1　调查对象
1.1.1　样本量计算
采用PS（power and sample size calculation）3.1进行样本量计算[11]：n=400×Q/P，Q=1-P。据文献，三甲医院锐器伤发生率约为16.26%[6]，此处，P取0.162 6，考虑10%的无效样本，本研究最小样本量n=2 266。
1.1.2　样本选择
2023年7—12月，采用多阶段整群概率抽样法，首先，根据地理位置并结合调查数据的可得性、同质性，随机抽取东、西部两城市区县级地区为样本地区，其次，整群抽取样本地区三级公立医院为样本医院，最后，采用按规模大小成比例的概率抽样法随机抽取各样本医院20%的医务人员为研究对象，开展横断面流行病学调查，包括：（1）从事临床工作的医务人员；（2）工作年限≥1年；（3）自愿参与本研究并签署知情同意书。排除进修生、实习生、调查期不在岗位的医务人员。本研究已获得苏州高新区人民医院医学伦理委员会批准（2023-136）。
1.2　研究方法
1.2.1　调查工具
基于知信行、认知负荷理论，在文献研究和专家咨询的基础上设计问卷。①社会人口学特征：包括性别、年龄、工作岗位、工作年限等。②职业暴露情况：采用Likert 2级评分法（0-否、1-是）。③心理素质情况：结合《成人心理素质问卷》[12]，包括突发事件、个人品质、适应能力3个维度。④压力程度情况：参考《中国护士工作压力源问卷》[13]，包括工作负荷、医疗纠纷、工作家庭平衡3个维度。⑤防护意识情况：在《护士职业防护态度问卷》[14]的基础上，结合专家咨询结果调整，包括主动获取防护信息、事先了解患者病史、定期参加相关培训3个维度。⑥防护行为情况：结合《护士职业防护行为问卷》[14]，包括风险操作戴手套、严格遵守操作规范、接触患者后洗手、4小时更换口罩、手掰安瓿垫纱布、锐器损伤后规范处理6个维度。采用Likert 5级评分法，依据总分进行分级，等级越高表示程度越高。预调查显示，问卷Cronbach α=0.804，KMO检验系数为0.883，Bartlett球形检验P<0.001，信效度良好。
1.2.2　资料收集与质量控制
使用问卷星发放问卷2 300份，并进行后台质量控制，设置匿名提交，每部手机仅可答题1次，限时<10 min，全部完成后方可提交。剔除无效问卷，最终获得2 251份问卷，有效回收率为97.8%。
1.3　统计学方法
统计学分析通过SPSS 25.0和R 4.3.3软件实现。计数资料以例（%）表示，组间比较采用χ2检验或Fisher确切概率法。单因素logistic回归、LASSO回归进行变量筛选，多因素logistic回归分析验证。R 4.3.3软件构建预测模型并绘制Nomogram（列线图），受试者工作（ROC）曲线、拟合优度检验、校准曲线以及决策曲线（DCA）评估列线图的预测效能。网页计算器布置使用“DynNom”软件包。检验水准α=0.05。
2　结果
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2.1　调查对象基本情况
2 251名医务人员中，年龄21~62岁，平均（34.580±7.437）岁，发生过血源性职业暴露的医务人员共869人（38.6%）。使用ca Tools函数包的split函数按7:3比例随机划分，分别归入训练组1 575名（70%）和验证组676名（30%）。训练组和验证组医务人员基线资料中，变量间差异均无统计学意义（P>0.05），表明验证组与训练组在基线特征上具有一致性，可用于模型的验证；见表1。
2.2　医务人员发生血源性职业暴露的单因素分析
单因素logistic分析结果显示，年龄、岗位、职称、工作年限、文化程度、心理素质、压力程度、防护意识、防护行为是医务人员血源性职业暴露的影响因素（P<0.05）；见表2。
2.3　血源性职业暴露风险因素筛选
将单因素logistic回归筛选的变量纳入LASSO回归。LASSO回归系数分布图显示，随着惩罚系数λ的增加，变量系数不断被压缩为0，见图1，结合10折交叉验证图，选择lambda.min=0.002 4为最佳λ值，筛选出岗位、职称、工作年限、文化程度、心理素质、压力程度、防护意识、防护行为共8个风险因素；见图1和图2。
2.4　医务人员发生血源性职业暴露的多因素分析
将是否发生血源性职业暴露（0-否，1-是）作为因变量，LASSO筛选的变量纳入多因素logistic回归。自变量赋值如下：岗位：1-医技，2-医生，3-护士；职称：1-初级，2-中级，3-高级；工作年限：1-1~10年，2-11~20年，3->20年；文化程度：1-大专及以下，2-本科，3-硕士及以上；心理素质：1-较差，2-一般，3-良好；压力程度：1-轻度，2-中度，3-重度；防护意识：1-较差，2-一般，3-良好；防护行为：1-从不，2-有时，3-总是。多分类变量以哑变量形式纳入模型并以第一类别为参照。结果显示，岗位、职称、文化程度、压力程度是医务人员血源性职业暴露的危险因素，心理素质、防护意识、防护行为是保护因素；见表2。
2.5　列线图预测模型的构建
R 4.3.3软件绘制列线图预测模型，评估临床医务人员血源性职业暴露的风险概率。由图3可知，列线图预测模型（网页链接：https://gxy1999.shinyapps.io/DynNomapp/）评分范围为200～450分，对应的风险概率为0.02～0.90。将各因素对应的分值相加得到总分，总分对应在风险轴上的位置即发生血源性职业暴露的风险系数。
2.6　列线图预测模型的验证
2.6.1　列线图模型的区分度检验
R 4.3.3软件pROC包绘制列ROC曲线，评估模型的准确性，结果显示训练组曲线下面积为0.821（95% CI：0.667～0.831），验证组为0.716（95% CI：0.618～0.715），模型区分度良好；见图4。
2.6.2　列线图模型的校准度检验
Hosmer-Lemeshow检验表明，该列线图模型的校准度均较好（P=0.568、0.956）。Bootstrap法重复抽样1 000次评价模型预测能力，结果如图5所示，训练组、验证组预测曲线与标准曲线基本拟合，一致性良好。
2.6.3　列线图模型的临床收益分析
R 4.3.3软件rmda、ggplot2包绘制决策曲线，验证临床应用价值，结果显示，在风险阈值约为0.1～0.8时，模型适用性最佳；见图6。
3　讨论
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医务人员血源性职业暴露涉及的影响因素众多，既往关于血源性职业暴露相关因素的研究大多在单因素分析基础上进行多因素logistic回归分析[5]，存在多重共线性的风险。为有效克服多重共线弊端，本研究运用LASSO回归筛选变量，在此基础上进行多因素logistic回归，可通过压缩回归系数，保留子集收缩而解决单因素logistic回归分析时复共线性数据有偏估计，使变量更具代表性[15]。
本研究结果显示，岗位、职称、文化程度是医务人员血源性职业暴露的危险因素。与既往研究一致[16-17]，医护人员仍是血源性职业暴露高风险群体。本研究发现，护士血源性职业暴露风险是医技的3.837倍，医生是医技的3.024倍。考虑护士接触病患频次和有创护理操作较多有关，一线护理工作者更易发生职业暴露[18-19]；医生参与危急手术及侵入性操作机会较多。此外，本科学历血源性职业暴露风险最高，可能与本科学历构成中护士占主体，而护士又是血源性职业暴露高风险人群有关。既往研究证实初级职称血源性职业暴露发生率高[5, 20]，但本研究发现随着职称增高，承担任务的风险指数、难度系数增加，导致面临血源性职业暴露风险增高，而中级职称在科室中发挥承上启下的作用，实践领域的宽度、任务的繁重性以及与患者接触环节、频次高于高级职称，因此，中级职称暴露风险最高。
本研究发现，防护意识、防护行为是血源性职业暴露的保护因素，且防护意识的保护作用高于防护行为。知信行理论认为个体通过学习获取知识，进行思考和分析，树立积极信念和态度，最后落实在具体的行动中形成积极行为[21]。防护意识是健康行为改变的动力，防护意识淡薄会显著降低防护行为依从性，从而导致血源性职业暴露。既往研究同样证实，加强相关培训锐器伤发生率可由76.90%降至23.10%[22]，进一步说明加强防护意识、落实防护行为是预防血源性职业暴露的重要手段。
尽管已有研究观察到锐器伤的发生与工作状态认知、心理素质有关[17]，但鲜有研究探讨其与血源性职业暴露的作用关系。本研究发现，医务人员心理素质一般及以上的占总人群的89.40%，是预防血源性职业暴露的关键因素。而压力程度与血源性职业暴露发生风险呈正相关，本研究中48.70%的医务人员处于中等及以上压力程度。这可能与医务人员工作性质有关，繁忙的工作量导致头脑需要处理的信息量过大，压力程度较高的医务人员，在超负荷的运转状态下易出现躯体、情感、认知的综合衰竭状态，导致其职业防护意识下降，标准预防落实不到位，更易发生血源性职业暴露。
我们开发的风险预测模型包括岗位、职称、文化程度、心理素质、压力程度、防护意识、防护行为，该模型对医务人员血源性职业暴露发生率具有良好的预测效果。根据回归系数标准差衡量，岗位对结局事件贡献度最大，其次是防护意识、防护行为。提示管理部门可通过对以上三个因素进行判断，快速筛查高风险人群（医护群体、防护意识较差、防护行为依从性较差），并针对上述特征的医护群体开展个性化风险防控培训，提高其职业防护知信行水平，以降低血源性职业暴露发生率。本研究样本选自东、西部地区两城市，未发现地域异质性，模型经内、外部验证及效果评价，均表现出较好的特异度、灵敏度以及临床应用价值，说明模型可为预测医务人员血源性职业暴露风险提供参考。
本研究的优点在于首次将医务人员血源性职业暴露的潜在风险因素进行整合，并构建可视化预测模型。医院管理部门可直观地对风险程度进行量化分析并精挖掘血源性职业暴露高风险人群，对未来构建针对性的风险防控方案及给予个性化的风险防控培训具有重要意义。但本研究也存在一些局限，一是只纳入了临床特征作为预测因子，对医务人员暴露风险的判断应在行为学、操作规范性等多领域结合的基础上，二是采用横断面研究，无法确定血源性职业暴露与危险因素间的因果关系，三是研究仅从个人视角展开，可能存在组织因素影响个人进而影响结果的情况。因此，未来的研究重点考虑设计多中心、多维度和纵向研究，以开发出更实用、可靠的多模态风险预测模型。
基金
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  • 苏州市科技发展计划项目(SKYD2023087)
  • 苏州高新区人民医院科学创新基金联合项目(SGY2022D01)
文献
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2024年第51卷第22期
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doi: 10.20043/j.cnki.MPM.202406346
  • 接收时间：2024-06-21
  • 首发时间：2026-03-20
  • 出版时间：2024-11-25
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  • 收稿日期：2024-06-21
基金
苏州市科技发展计划项目(SKYD2023087)
苏州高新区人民医院科学创新基金联合项目(SGY2022D01)
作者信息
    1.石河子大学医学院　护理系，新疆　石河子　832000
    2.苏州高新区人民医院　护理部
    3.石河子大学第一附属医院　创伤外科

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林陶玉，E-mail:
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2种不同金属材料的力学参数
科
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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