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Article(id=1322168736564490335, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322168481081049284, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.09.007, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1753977600000, receivedDateStr=2025-08-01, revisedDate=1773936000000, revisedDateStr=2026-03-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1793159191503, onlineDateStr=2026-10-28, pubDate=1778342400000, pubDateStr=2026-05-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793159191503, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793159191503, creator=13701087609, updateTime=1793159191503, updator=13701087609, issue=Issue{id=1322168481081049284, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='9', pageStart='1425', pageEnd='1616', issueExtLink='null', onlineDate='null', pubDate='1778342400000', pubDateStr='2026-05-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793159130592, creator='13701087609', updateTime=1793159130592, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=1481, endPage=1487, ext={EN=ArticleExt(id=1322168736824537184, articleId=1322168736564490335, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Construction and validation of secondary ectopic risk prediction model for PICC catheters in preterm infants based on random forest algorithm, columnId=1220689484526572183, journalTitle=Chinese Nursing Research, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the key risk factors for secondary ectopics of catheter after central venous catheter insertion(PICC) through peripheral veins,and to construct and validate a risk prediction model based on the random forest algorithm.

Methods

The data of 590 preterm infants hospitalized in the department of neonatology of a tertiary specialty hospital in Shanxi province with PICC catheterization were retrospectively collected,and the data were randomly divided into training set(n=413) and validation set(n=177) in a ratio of 7∶3.Eighteen clinical indicators were selected as predictors for whether secondary ectopic of the catheter occurred at the tip after PICC catheterization in preterm infants.Based on the random forest algorithm,a risk prediction model for secondary ectopic ectopic of PICC catheters in preterm infants was constructed,the importance of risk factors was ranked,and a SHAP plot was plotted to explain the contribution of each variable to the prediction of model output.The confusion matrix analysis of the model was carried out using the validation set data,and the prediction effect of the model was evaluated by using the accuracy,sensitivity,specificity and receiver operating characteristic(ROC) curves.

Results

The incidence of secondary ectopic of PICC catheter in 413 preterm infants in the training set was 39.0%,and the key factors screened by the random forest algorithm were the first X-ray localization result,secondary catheter fixation,mechanical ventilation,and weight gain rate,respectively.In the validation set,the accuracy of the risk prediction model was 81.92%,the sensitivity was 71.21%,the specificity was 88.29%,the positive predictive value was 78.33%,the negative predictive value was 83.76%,and the AUC value was 0.870(95%CI:0.817-0.923).

Conclusions

The risk prediction model constructed based on the random forest algorithm demonstrates good predictive performance.It could potentially provide a scientific basis for the safe and efficient clinical use of PICC in premature infants.

, authors=Yongqin GUO, Yingying DOU, Jianli LI, Li WANG, Jing HU, authorsList=Yongqin GUO, Yingying DOU, Jianli LI, Li WANG, Jing HU, authorCompany=null, correspAuthors=Yongqin GUO, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, 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=1322168739546640492, articleId=1322168736564490335, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=基于随机森林算法的早产儿PICC导管继发性异位风险预测模型的构建与验证, columnId=1220689484681761433, journalTitle=护理研究, columnName=科研论著, runingTitle=null, highlight=null, articleAbstract=
目的

探讨早产儿经外周静脉置入中心静脉导管（PICC）后发生导管继发性异位的危险因素，基于随机森林算法构建并验证风险预测模型。

方法

回顾性收集在山西省某三级甲等专科医院新生儿科住院且行PICC置管的590例早产儿资料，将数据按7∶3的比例随机分为训练集（n=413）和验证集（n=177）。以早产儿PICC置管后尖端是否发生导管继发性异位为结局变量，选取首次X线定位结果、导管二次固定等18个临床指标为预测变量。在训练集内应用LASSO回归进行特征筛选，基于随机森林算法构建早产儿PICC导管继发性异位风险预测模型，对危险因素的重要性进行排序，并绘制沙普利可加解释图（SHAP），解释每个变量对模型输出预测的贡献。应用验证集数据对模型进行混淆矩阵分析，采用准确率、灵敏度、特异度和受试者工作特征（ROC）曲线评价预测模型的效能。

结果

训练集413例早产儿中PICC导管继发性异位发生率为39.0%，随机森林算法筛选出的关键因素按重要性排序分别为首次X线定位结果、导管二次固定、机械通气、体重增长率等。验证集中，风险预测模型的准确率为81.92%、灵敏度为71.21%、特异度为88.29%、阳性预测值为78.33%，阴性预测值为83.76%，ROC曲线下面积（AUC）值为0.870［95%CI（0.817，0.923）］。

结论

基于随机森林算法构建的风险预测模型的预测效能良好，可为早产儿临床安全、高效地使用PICC导管提供科学依据。

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GUO Yongqin, E-mail:
郭永琴，主任护师，本科，E-mail:
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unstructuredReference=罗飞翔.极低出生体重儿PICC导管尖端位置与体重增长相关性分析[D].杭州:浙江大学,2017., articleTitle=极低出生体重儿PICC导管尖端位置与体重增长相关性分析, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322530995719488315, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168736564490335, xref=1, ext=[AuthorCompanyExt(id=1322530995727876924, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168736564490335, companyId=1322530995719488315, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Changzhi Maternal and Child Health Care Hospital, Shanxi 046000 China), AuthorCompanyExt(id=1322530995736265533, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168736564490335, companyId=1322530995719488315, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1长治市妇幼保健院，山西 046000)])], figs=[ArticleFig(id=1322531002015138657, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168736564490335, language=EN, label=Figure 1, caption=LASSO regression analysis of ectopic predictors of PICC catheter tip in preterm infants, figureFileSmall=Y559s/n43fwJxGng4KB1ww==, figureFileBig=1xnRPEHvY0oagwEUPjLyrA==, tableContent=null), ArticleFig(id=1322531002094830434, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168736564490335, language=CN, label=图1, caption=早产儿PICC导管尖端异位预测因素的LASSO回归分析

(A为λ与变量的动态过程图；B为LASSO回归模型中λ的选择过程)

, figureFileSmall=Y559s/n43fwJxGng4KB1ww==, figureFileBig=1xnRPEHvY0oagwEUPjLyrA==, tableContent=null), ArticleFig(id=1322531003776746339, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168736564490335, language=EN, label=Figure 2, caption=Ranking of the importance of random forest variables, figureFileSmall=AFANv/thS1rsJHOMmDU8dA==, figureFileBig=83JP/BMD/umocOg5Ea0ZQQ==, tableContent=null), ArticleFig(id=1322531003885798244, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168736564490335, language=CN, label=图2, caption=随机森林变量重要性排序, figureFileSmall=AFANv/thS1rsJHOMmDU8dA==, figureFileBig=83JP/BMD/umocOg5Ea0ZQQ==, tableContent=null), ArticleFig(id=1322531003978072933, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168736564490335, language=EN, label=Figure 3, caption=SHAP visualization of secondary ectopic occurrence of PICC catheters in preterm infants, figureFileSmall=AHms+v5DJOIPsl4ay4cCUg==, figureFileBig=qrwj9sZO3aSBp++a42zePw==, tableContent=null), ArticleFig(id=1322531004053570406, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168736564490335, language=CN, label=图3, caption=早产儿PICC导管继发性异位发生的SHAP可视图, figureFileSmall=AHms+v5DJOIPsl4ay4cCUg==, figureFileBig=qrwj9sZO3aSBp++a42zePw==, tableContent=null), ArticleFig(id=1322531004120679271, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168736564490335, language=EN, label=Figure 4, caption=SHAP force plot of secondary ectopic occurrence of PICC catheter in preterm infants(single sample interpretation), figureFileSmall=khmEkwgjsLssAZQWL7pzmQ==, figureFileBig=bv+sYYIUz/gycRwg7jMlZg==, tableContent=null), ArticleFig(id=1322531004200371048, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168736564490335, language=CN, label=图4, caption=早产儿PICC导管继发性异位发生的SHAP力图（单个样本解释）

（A为高风险样本的SHAP值贡献分解；B为低风险样本的SHAP值贡献分解）

, figureFileSmall=khmEkwgjsLssAZQWL7pzmQ==, figureFileBig=bv+sYYIUz/gycRwg7jMlZg==, tableContent=null), ArticleFig(id=1322531004254897001, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168736564490335, language=EN, label=Figure 5, caption=ROC of secondary ectopic risk prediction model of PICC catheter in preterm infants in the validation set, figureFileSmall=LWlvpKyS13wx5MofK0/VTQ==, figureFileBig=DgQNsmBz+GXXbXupxImlKA==, tableContent=null), ArticleFig(id=1322531004326200170, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168736564490335, language=CN, label=图5, caption=验证集早产儿PICC导管继发性异位风险预测模型的ROC图, figureFileSmall=LWlvpKyS13wx5MofK0/VTQ==, figureFileBig=DgQNsmBz+GXXbXupxImlKA==, tableContent=null), ArticleFig(id=1322531004389114731, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168736564490335, language=EN, label=Table 1, caption=

Univariate analysis of secondary PICC catheter ectopia in preterm infants in the training set

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类正常组(n=252)异位组(n=161)χ2值P
胎龄>32周91(36.1)43(26.7)
28~32周147(58.3)102(63.4)5.6850.058
<28周14(5.6)16(9.9)
出生体重>1 500 g88(34.9)40(24.8)
1 000~1 500 g152(60.3)106(65.8)6.8150.033
<1 000 g12(4.8)15(9.3)
5 min Apgar评分>7分219(86.9)130(80.7)2.3950.122
≤7分33(13.1)31(19.3)
性别男122(48.4)86(53.4)0.7940.373
女130(51.6)75(46.6)
机械通气否226(89.7)88(54.7)64.214<0.001
是26(10.3)73(45.3)
体重增长率<20%143(56.7)31(19.3)55.109<0.001
≥20%109(43.3)130(80.7)
导管材质硅胶93(36.9)49(30.4)1.5470.214
聚氨酯159(63.1)112(69.6)
置管时日龄1~3 d41(16.3)40(24.8)
>3~6 d121(48.0)64(39.8)5.1830.075
>6 d90(35.7)57(35.4)
置管部位右上肢115(45.6)72(44.7)
左上肢16(6.3)36(22.4)25.334<0.001
下肢121(48.0)53(32.9)
置管静脉贵要静脉115(45.6)79(49.1)12.3530.006
大隐静脉111(44.0)49(30.4)
肘正中静脉14(5.6)20(12.4)
其他静脉12(4.8)13(8.1)
置管耗时<30 min194(77.0)117(72.7)0.7640.382
≥30 min58(23.0)44(27.3)
导管外露长度≤2 cm116(46.0)59(36.6)3.1700.075
>2 cm136(54.0)102(63.4)
导管二次固定否60(23.8)108(67.1)74.443<0.001
是192(76.2)53(32.9)
每日评估次数≥2次240(95.2)146(90.7)2.6320.105
<2次12(4.8)15(9.3)
使用风险评估表否65(25.8)57(35.4)3.9090.048
是187(74.2)104(64.6)
导管留置时间<3周104(41.3)29(18.0)23.285<0.001
≥3周148(58.7)132(82.0)
每次摄片时体位一致132(52.4)54(33.5)13.336<0.001
不一致120(47.6)107(66.5)
首次X线定位结果T4~<T535(13.9)69(42.9)82.668<0.001
T5~T696(38.1)37(23.0)
T9~<T1075(29.8)5(3.1)
T10~T1146(18.3)48(29.8)
), ArticleFig(id=1322531004473000812, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168736564490335, language=CN, label=表1, caption=

训练集早产儿PICC导管继发性异位发生的单因素分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类正常组(n=252)异位组(n=161)χ2值P
胎龄>32周91(36.1)43(26.7)
28~32周147(58.3)102(63.4)5.6850.058
<28周14(5.6)16(9.9)
出生体重>1 500 g88(34.9)40(24.8)
1 000~1 500 g152(60.3)106(65.8)6.8150.033
<1 000 g12(4.8)15(9.3)
5 min Apgar评分>7分219(86.9)130(80.7)2.3950.122
≤7分33(13.1)31(19.3)
性别男122(48.4)86(53.4)0.7940.373
女130(51.6)75(46.6)
机械通气否226(89.7)88(54.7)64.214<0.001
是26(10.3)73(45.3)
体重增长率<20%143(56.7)31(19.3)55.109<0.001
≥20%109(43.3)130(80.7)
导管材质硅胶93(36.9)49(30.4)1.5470.214
聚氨酯159(63.1)112(69.6)
置管时日龄1~3 d41(16.3)40(24.8)
>3~6 d121(48.0)64(39.8)5.1830.075
>6 d90(35.7)57(35.4)
置管部位右上肢115(45.6)72(44.7)
左上肢16(6.3)36(22.4)25.334<0.001
下肢121(48.0)53(32.9)
置管静脉贵要静脉115(45.6)79(49.1)12.3530.006
大隐静脉111(44.0)49(30.4)
肘正中静脉14(5.6)20(12.4)
其他静脉12(4.8)13(8.1)
置管耗时<30 min194(77.0)117(72.7)0.7640.382
≥30 min58(23.0)44(27.3)
导管外露长度≤2 cm116(46.0)59(36.6)3.1700.075
>2 cm136(54.0)102(63.4)
导管二次固定否60(23.8)108(67.1)74.443<0.001
是192(76.2)53(32.9)
每日评估次数≥2次240(95.2)146(90.7)2.6320.105
<2次12(4.8)15(9.3)
使用风险评估表否65(25.8)57(35.4)3.9090.048
是187(74.2)104(64.6)
导管留置时间<3周104(41.3)29(18.0)23.285<0.001
≥3周148(58.7)132(82.0)
每次摄片时体位一致132(52.4)54(33.5)13.336<0.001
不一致120(47.6)107(66.5)
首次X线定位结果T4~<T535(13.9)69(42.9)82.668<0.001
T5~T696(38.1)37(23.0)
T9~<T1075(29.8)5(3.1)
T10~T1146(18.3)48(29.8)
), ArticleFig(id=1322531004565275501, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168736564490335, language=EN, label=Table 2, caption=

Confusion matrix situation

, figureFileSmall=null, figureFileBig=null, tableContent=
导管尖端异位预测未发生预测发生
实际未发生9813
实际发生1947
), ArticleFig(id=1322531004628190062, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168736564490335, language=CN, label=表2, caption=

混淆矩阵情况

, figureFileSmall=null, figureFileBig=null, tableContent=
导管尖端异位预测未发生预测发生
实际未发生9813
实际发生1947
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基于随机森林算法的早产儿PICC导管继发性异位风险预测模型的构建与验证
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郭永琴 , 豆瑛瑛 , 李建丽 , 王莉 , 胡晶
护理研究 | 科研论著 2026,40(9): 1481-1487
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护理研究 |科研论著 2026 , 40 (9) : 1481 -1487
基于随机森林算法的早产儿PICC导管继发性异位风险预测模型的构建与验证
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郭永琴 , 豆瑛瑛, 李建丽, 王莉, 胡晶
作者信息
  • 1长治市妇幼保健院，山西 046000
通讯作者:
GUO Yongqin, E-mail:
郭永琴，主任护师，本科，E-mail:
Construction and validation of secondary ectopic risk prediction model for PICC catheters in preterm infants based on random forest algorithm
Yongqin GUO , Yingying DOU, Jianli LI, Li WANG, Jing HU
Affiliations
  • 1Changzhi Maternal and Child Health Care Hospital, Shanxi 046000 China
出版时间: 2026-05-10 doi: 10.12102/j.issn.1009-6493.2026.09.007
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摘要
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目的

探讨早产儿经外周静脉置入中心静脉导管（PICC）后发生导管继发性异位的危险因素，基于随机森林算法构建并验证风险预测模型。

方法

回顾性收集在山西省某三级甲等专科医院新生儿科住院且行PICC置管的590例早产儿资料，将数据按7∶3的比例随机分为训练集（n=413）和验证集（n=177）。以早产儿PICC置管后尖端是否发生导管继发性异位为结局变量，选取首次X线定位结果、导管二次固定等18个临床指标为预测变量。在训练集内应用LASSO回归进行特征筛选，基于随机森林算法构建早产儿PICC导管继发性异位风险预测模型，对危险因素的重要性进行排序，并绘制沙普利可加解释图（SHAP），解释每个变量对模型输出预测的贡献。应用验证集数据对模型进行混淆矩阵分析，采用准确率、灵敏度、特异度和受试者工作特征（ROC）曲线评价预测模型的效能。

结果

训练集413例早产儿中PICC导管继发性异位发生率为39.0%，随机森林算法筛选出的关键因素按重要性排序分别为首次X线定位结果、导管二次固定、机械通气、体重增长率等。验证集中，风险预测模型的准确率为81.92%、灵敏度为71.21%、特异度为88.29%、阳性预测值为78.33%，阴性预测值为83.76%，ROC曲线下面积（AUC）值为0.870［95%CI（0.817，0.923）］。

结论

基于随机森林算法构建的风险预测模型的预测效能良好，可为早产儿临床安全、高效地使用PICC导管提供科学依据。

早产儿  /  PICC  /  继发性异位  /  随机森林算法  /  风险预测模型
Objective

To investigate the key risk factors for secondary ectopics of catheter after central venous catheter insertion(PICC) through peripheral veins,and to construct and validate a risk prediction model based on the random forest algorithm.

Methods

The data of 590 preterm infants hospitalized in the department of neonatology of a tertiary specialty hospital in Shanxi province with PICC catheterization were retrospectively collected,and the data were randomly divided into training set(n=413) and validation set(n=177) in a ratio of 7∶3.Eighteen clinical indicators were selected as predictors for whether secondary ectopic of the catheter occurred at the tip after PICC catheterization in preterm infants.Based on the random forest algorithm,a risk prediction model for secondary ectopic ectopic of PICC catheters in preterm infants was constructed,the importance of risk factors was ranked,and a SHAP plot was plotted to explain the contribution of each variable to the prediction of model output.The confusion matrix analysis of the model was carried out using the validation set data,and the prediction effect of the model was evaluated by using the accuracy,sensitivity,specificity and receiver operating characteristic(ROC) curves.

Results

The incidence of secondary ectopic of PICC catheter in 413 preterm infants in the training set was 39.0%,and the key factors screened by the random forest algorithm were the first X-ray localization result,secondary catheter fixation,mechanical ventilation,and weight gain rate,respectively.In the validation set,the accuracy of the risk prediction model was 81.92%,the sensitivity was 71.21%,the specificity was 88.29%,the positive predictive value was 78.33%,the negative predictive value was 83.76%,and the AUC value was 0.870(95%CI:0.817-0.923).

Conclusions

The risk prediction model constructed based on the random forest algorithm demonstrates good predictive performance.It could potentially provide a scientific basis for the safe and efficient clinical use of PICC in premature infants.

premature infants  /  PICC  /  secondary ectopia  /  random forest algorithm  /  risk prediction model
郭永琴, 豆瑛瑛, 李建丽, 王莉, 胡晶. 基于随机森林算法的早产儿PICC导管继发性异位风险预测模型的构建与验证. 护理研究, 2026 , 40 (9) : 1481 -1487 . DOI: 10.12102/j.issn.1009-6493.2026.09.007
Yongqin GUO, Yingying DOU, Jianli LI, Li WANG, Jing HU. Construction and validation of secondary ectopic risk prediction model for PICC catheters in preterm infants based on random forest algorithm[J]. Chinese Nursing Research, 2026 , 40 (9) : 1481 -1487 . DOI: 10.12102/j.issn.1009-6493.2026.09.007
正文
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早产儿各器官、系统发育尚未成熟，无法依靠自身从外界获得足够营养，缺乏维持生命的物质基础，常需依赖持续静脉营养和药物治疗以维持其生长发育需求[1]。经外周静脉置入中心静脉导管（PICC）现已广泛应用于新生儿尤其是早产儿，是护理危重患儿的重要技术，但在PICC使用过程中由于机体差异、静脉系统的复杂性等，较易出现多种并发症，其中导管继发性异位是较常见的并发症之一[2]。相关研究显示，国内新生儿PICC导管异位发生率为16.83%~40.10%[3]，国外为8.1%~56.0%[4]。若导管尖端异位则可引起渗漏、疼痛，诱发穿刺肢体静脉炎，出现乳糜胸、PICC相关性心包积液及合并心脏压塞等[5-6]。因此，准确预测早产儿PICC导管继发性异位的风险因素，对于提前采取相应的干预措施具有重要意义。近年来，机器学习算法在医疗领域中的应用日益增多，其中随机森林算法凭借其高准确性和可解释性受到广泛关注。随机森林是一种集成学习方法，通过构建多个决策树并对其结果进行综合，能够高效地处理高维数据及复杂关系建模问题，具备较高的预测准确性[7-8]。然而，现阶段关于早产儿PICC导管继发性异位的随机森林风险预测模型尚未见相关报道。基于此，本研究基于随机森林算法构建早产儿PICC导管继发性异位的风险预测模型，并对模型的预测性能进行验证，以期为早产儿临床安全、高效地使用 PICC 导管提供科学依据。
1　对象与方法
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1.1　研究对象
回顾性选取2023年5月—2025年4月山西省某三级甲等专科医院新生儿科住院且行PICC置管的590例早产儿资料作为研究对象。入选标准：胎龄≤36周，出生体重<2 000 g；在我院行PICC 置管；首次置管且一次置管成功；置管操作由我院PICC专科护士完成。排除标准：PICC使用期间出现严重并发症，如气胸、胸腔积液等；X线摄片不清，导管尖端位置模糊；原始数据缺失率>20%者。本研究将样本量设置为10倍事件每变量比（events per variable,EPV）[9]，初步纳入18个相关因素，导管异位发生率为40.10%[3]，考虑10%的样本流失率，本研究的样本量至少为494例，本研究最终纳入590例。按7∶3的比例随机分为训练集（n=413）和验证集（n=177）。本研究已通过医院伦理委员会审批（审批号：CZSFYLL2025字022号）。
1.2　研究方法
1.2.1　临床资料收集
由2名经过培训的研究者登录医院临床信息系统，导入患儿的病例资料，包括胎龄、出生体重、5 min Apgar评分、性别、机械通气、体重增长率。体重增长率=[（置管2周时体重-置管时体重）/置管时体重]×100%。从护理文书中收集患儿PICC导管置入及维护资料，包括导管材质、置管时日龄、置管部位、置管静脉、置管耗时、导管外露长度、导管二次固定、每日评估次数、使用风险评估表、导管留置时间、导管留置期间摄片时体位以及首次X线定位结果。
1.2.2　PICC尖端位置的判定标准
PICC置管位置的判定以胸部X片为标准，经上肢静脉置管时导管尖端需在上腔静脉内，即胸椎T4~T6水平；经下肢静脉置管时，导管尖端需在下腔静脉内，即胸椎T9~T11水平。若导管尖端不在此范围内即称为异位[2]。
1.2.3　数据预处理
根据原始数据，所有无效条目均被移除，对于缺失率低于20%的变量，使用随机森林迭代多重填补法统一处理缺失数据，经过预处理后最终保留了590例早产儿数据。此外，所有连续变量均根据文献中临床相关阈值或临界值进行分类，以方便模型构建。
1.2.4　统计学方法
使用SPSS 26.0与R4.2.2软件进行统计学分析。用软件SPSS 26.0对临床资料进行分析，组间比较采用χ2检验，定性资料以例数、百分比(%)描述，以P<0.05为差异有统计学意义。采用R4.2.2软件的glmnet程序包执行LASSO特征筛选，基于训练集数据采用randomForest包构建随机森林模型，并通过十折交叉验证法调试 mtry 值和 ntree 值以获得最佳预测性能；通过计算某变量置换前后随机森林模型预测精准度的平均下降值，对危险因素的重要性进行排序；采用ranger包绘制沙普利可加解释图（SHAP），对模型进行预测变量重要程度的解释。最后，使用验证集数据构建混淆矩阵，评价模型性能，包括准确率、灵敏度、特异度、阳性预测值、阴性预测值、受试者工作特征（ROC ）曲线下面积(area under the curve，AUC)。
2　结果
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2.1　纳入患儿的基本情况及早产儿发生导管继发性异位的单因素分析
经过筛选后共纳入早产儿590例，其中227例（38.5%）发生导管继发性异位。训练集与验证集一般资料比较差异均无统计学意义(P>0.05)。训练集413例早产儿中，161例（39.0%）早产儿发生导管继发性异位。正常组与异位组患儿出生体重、机械通气、体重增长率、置管部位、置管静脉、导管二次固定、使用风险评估表、导管留置时间、每次摄片时体位、首次X线定位结果比较差异均有统计学意义（P<0.05），见表1。
2.2　早产儿PICC导管继发性异位特征筛选
根据单因素分析结果，将差异有统计学意义的10个变量通过LASSO回归进行降维处理，由最优λ（λ取值为0.025）筛选出最具有代表性的9个预测因子，分别为出生体重、机械通气、体重增长率、置管部位、导管二次固定、使用风险评估单、导管留置时间、每次摄片时体位、首次X线定位结果，见图1。
2.3　早产儿PICC导管继发性异位风险预测模型的构建及重要性
2.3.1　基于随机森林算法的风险预测模型的建立
将LASSO回归筛选出的9项特征变量带入随机森林模型，训练集中十折交叉验证结果显示，当ntree=500、mtry=3时差错率最低，早产儿PICC导管尖端异位的风险预测模型的预测性能最佳，准确率为91.04%，灵敏度为88.20%，特异度为92.86%，阳性预测值为88.75%，阴性预测值为92.49%，AUC值为0.960[95%CI(0.941，0.979)]。
2.3.2　危险因素的重要性
根据随机森林模型预测精度平均下降量（即准确性指数）对随机森林模型各变量的重要程度进行排序，其中首次X线定位结果、导管二次固定、机械通气、体重增长率对模型准确率的贡献较大，见图2。
2.3.3　随机森林模型的SHAP解释
为进一步直观解释所选变量对随机森林模型的影响，采用SHAP进行说明，Shapley值分布范围越广的指标对模型预测结果的重要性越高；Shapley 值越大（即颜色越偏黄），提示患儿越有可能发生导管继发性异位。SHAP汇总图显示，导管二次固定、首次X线定位结果、机械通气、体重增长率是预测早产儿PICC导管继发性异位的较显著的4个关键预测变量。其中，首次X线定位结果、使用机械通气、体重增长率（≥20%）对模型预测结果产生正贡献，表明具有这些特征患儿的PICC导管发生异位的风险较高。相对而言，使用导管二次固定则对模型预测结果表现出负贡献，表明此项措施可以改善PICC导管异位的发生，其SHAP值为0.136，可降低导管异位的发生率。见图3。
同时，SHAP图中的条形长度可视化了这些特征的重要性：较长的条形表示较高的SHAP值，突出显示该变量对模型预测的更强贡献（见图4)，首次X线定位结果成为最具影响力的预测因子。
2.4　早产儿PICC导管继发性异位风险预测模型的验证
根据混淆矩阵结果计算，模型整体判断准确率为81.92%，灵敏度为71.21%，特异度为88.29%；阳性预测值为78.33%，阴性预测值为83.76%，AUC为0.870 [95%CI(0.817，0.923)]，混淆矩阵结果见表2，验证集中早产儿PICC导管继发性异位风险预测模型的ROC图见图5。
3　讨论
收起
3.1　早产儿PICC导管继发性异位随机森林模型的预测效能
早产儿PICC导管尖端位置合理是《新生儿经外周置入中心静脉导管实践指南（第4版）》中明确强调的核心操作规范要求[10]。同时，降低导管继发性异位也是衡量中心静脉导管护理质量的关键指标之一，并符合国家卫生健康委关于“患者安全目标”中对管路管理的要求。已有研究证实，相较于中心位置，导管尖端异位可导致并发症发生率升高达4倍[11]。本研究中早产儿PICC导管继发性异位发生率达38.5%，与既往研究结果[3]相一致。本研究从一般资料、导管相关因素以及定位情况3个方面构建预测指标，经LASSO回归进行特征筛选后，基于随机森林算法构建了早产儿PICC导管继发性异位的风险预测模型。模型验证结果显示，预测模型AUC为0.870[95%CI(0.817，0.923)]，表明其具有良好的区分能力与预测效能。混淆矩阵结果进一步显示,模型准确率达81.92%，分类准确性较高；灵敏度为71.21%，特异度为88.29%，表示模型对导管异位患儿的识别能力较强；阳性预测值为78.33%，阴性预测值为83.76%，说明模型对病例的判别能力良好，漏判率及误判率较低。综合上述指标，说明本研究构建的随机森林模型展现出较好的预测效能，同时模型呈现出的各危险因素重要性排序，有助于医务人员更直观地把握风险因素，为初步筛查提供可靠的参考，与游晓青等[12]采用的Logistic回归分析相比，本模型在结果可读性、可视化呈现以及操作便捷性方面更具优势。
3.2　早产儿PICC 导管继发性异位随机森林模型临床特征的重要性
在临床应用中，机器学习模型需兼顾预测性能与可解释性，其中临床特征贡献度的解析至关重要[13]。本研究构建的随机森林模型中，每项危险因素均有其对应的预测重要性排序，医务人员可根据置管病人导管留置期间的临床信息进行评估，判断此患儿是否为导管继发性异位的高危人群，继而有助于异位并发症的预防。本研究通过特征重要性矩阵图和SHAP分析，确定了首次X线定位结果、导管二次固定、机械通气、体重增长率是预测早产儿PICC导管继发性异位的关键因素。胸部X线片被认为是PICC尖端定位的“金标准”[14]，也是我国目前常规定位方式，研究证实，导管尖端位于理想位置是预防继发性异位的关键 [15-16]，不同定位部位的继发性异位发生率存在显著差异。本研究结果显示，导管尖端首次定位在T9~<T10组的继发性异位发生率最低（3.1%），T4~<T5组最高（42.9%），这与陈晓春等[17]的研究结果相一致。同时本研究也提示，新生儿下肢静脉PICC置管较上肢静脉更具优势。但对于下肢静脉穿刺条件不佳的患儿，若选择上肢静脉置管，建议首次定位时应将导管尖端置于T5~T6锥体水平，以延长导管使用时间并减少异位发生风险。其次，早产儿哭闹或烦躁时肢体不可控的活动会增加导管异位甚至脱管风险。牢固地固定导管是预防新生儿PICC导管尖端继发性异位的重要措施[18]。同时，外导管的二次固定与穿刺点固定同等重要，缺乏二次固定的缓冲会导致穿刺点固定快速失效，进而引发导管移位。恰当的二次固定联合舒适护理，不仅可以减少患儿的不适感，降低肢体活动频率，还能减少导管移位及静脉炎的发生[19]，本研究SHAP解释图也进一步支持了这一结论。此外，机械通气产生的正压会增加胸腔内压力，可能导致PICC导管在血管内发生离心性位移，从而引发异位，高频振荡通气模式的气流震荡可能进一步加剧导管漂移。本研究显示，使用机械通气的患儿发生PICC导管继发性异位的风险是未使用者的2.5倍。多项研究也证实，机械通气患儿更易发生PICC导管继发性异位[20-22]。所以，在临床工作中，对于使用机械通气的患儿要进行更加严密的监测，已及时发现并处理导管异位情况。生长发育是新生儿导管继发性异位区别于成人的特有因素。当体格增长，PICC导管会逐渐且不可避免地移动出中心静脉[23]。本研究对象以出生胎龄28~32周的早产儿为主，此类患儿住院期间常经历追赶性生长。罗飞翔[24]研究发现，当患儿体重增长超过基础体重的10%时，导管尖端即出现不同程度位移；随着体重增长分别达到基础体重的40%、70%和100%时，导管尖端位移量可相应达到约2、3、4个椎体水平。这表明住院期间患儿的快速体重增长是导致PICC继发性异位的重要因素。本研究当体重增长率≥20%时，导管继发性异位发生率显著升高。SHAP解释图也说明体重增长率越高的患儿，发生PICC导管继发性异位的风险越高。所以，在置管后期应根据患儿的体重增长情况及X线定位结果制定个性化导管位置追踪方案。
3.3　本研究的局限性
首先，本研究采用回顾性病例对照分析，病历资料存在一定的信息偏倚；同时，未能收集与早产儿PICC置管期间异位伴随相关症状的变量，如心律失常、血栓等实时资料；其次，尽管模型在内部验证中表现出色，但尚未进行进一步的外部验证。未来可进行多中心合作和外部验证，以提高预测模型的普遍性和通用性。
4　小结
收起
综上所述，本研究通过收集较易获得的临床指标，基于随机森林算法构建了早产儿PICC导管继发性异位风险预测模型，首次X线定位结果、导管二次固定、机械通气、体重增长率为早产儿PICC导管继发性异位发生的重要危险因素。模型预测性能较好，可以帮助临床护理人员早期识别导管继发性异位发生的潜在群体，提醒医护人员做好科学、有效的干预措施，有助于降低早产儿导管异位发生率，提升中心静脉导管质量管理水平。

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doi: 10.12102/j.issn.1009-6493.2026.09.007
  • 接收时间：2025-08-01
  • 首发时间：2026-10-28
  • 出版时间：2026-05-10
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  • 修回日期：2026-03-20
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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