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Article(id=1239268422987600791, tenantId=1146029695717560320, journalId=1205117082300743687, issueId=1239268417962832543, articleNumber=null, orderNo=null, doi=10.14109/j.cnki.xyylc.2024.07.09, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1685462400000, receivedDateStr=2023-05-31, revisedDate=null, revisedDateStr=null, acceptedDate=1713974400000, acceptedDateStr=2024-04-25, onlineDate=1773394216533, onlineDateStr=2026-03-13, pubDate=1721836800000, pubDateStr=2024-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773394216533, onlineIssueDateStr=2026-03-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773394216533, creator=13701087609, updateTime=1773394216533, updator=13701087609, issue=Issue{id=1239268417962832543, tenantId=1146029695717560320, journalId=1205117082300743687, year='2024', volume='43', issue='7', pageStart='481', pageEnd='560', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773394215336, creator=13701087609, updateTime=1773394445099, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1239269381725810851, tenantId=1146029695717560320, journalId=1205117082300743687, issueId=1239268417962832543, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1239269381725810852, tenantId=1146029695717560320, journalId=1205117082300743687, issueId=1239268417962832543, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=529, endPage=534, ext={EN=ArticleExt(id=1239268423251841956, articleId=1239268422987600791, tenantId=1146029695717560320, journalId=1205117082300743687, language=EN, title=Potential medication safety risks and Lasso-logistic regression analysis in hypertension complicated with cerebral infarction elderly patients, columnId=1207314218647392369, journalTitle=Chinese Journal of New Drugs and Clinical Remedies, columnName=Original Article, runingTitle=null, highlight=null, articleAbstract=
AIM

To investigate the potentially inappropriate medications (PIM) in elderly patients with hypertension combined with cerebral infarction and to develop a nomogram model based on Lasso-logistic regression of the influencing factors.

METHODS

A total of 105 patients with hypertension combined with cerebral infarction and multi-drug therapy discharged from January to December 2020 were included，and the PIM was evaluated by the criteria of potentially inappropriate medications for older adults in China. The PIM influencing factors were analyzed by Lasso-logistic regression, Nomogram model of factors influencing the PIM risk was developed, and the discrimination and accuracy of the model were evaluated.

RESULTS

The incidence of PIM in 105 patients of hypertension complicated with cerebral infarction was 85 cases (81.0%) and 184 times, involving 26 drugs. The 1st drug was clopidogrel with 48 times (26.1%). A statistically significant effect of the number of drugs (P=0.003, OR=1.412, 95% CI: 0.582 to 2.868) and diabetes (P<0.001, OR=0.081, 95% CI: -3.899 to -1.129) on PIM was suggested by the Lasso-logistic regression. Of the model, a good accuracy and discrimination was suggested by the area under the ROC curve (AUC), which was 85.3% (95% CI: 0.772 to 0.933), and an acceptable predictive power was suggested by internal validation with Bootstrap method (B=1 000).

CONCLUSION

The nomogram model based on Lasso-logistic regression can better identify the risk of PIM. Elderly patients with hypertension complicated with cerebral infarction and multi-drug therapy have a high risk of PIM, and it is necessary to reduce the number of medications to reduce the risk of PIM.

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目的

探讨高血压合并脑梗死老年患者潜在不适当用药（PIM）情况并建立影响因素Lasso-logistic回归列线图模型。

方法

纳入2020年1月至12月高血压合并脑梗死多重用药治疗的出院患者105例，以《中国老年人潜在不适当用药判断标准》评价PIM情况，Lasso-logistic回归分析PIM风险影响因素，建立PIM风险影响因素列线图模型，评价模型区分度和准确度。

结果

105例患者PIM为85例（81.0%），涉及26种药品、184例次，排第1位是氯吡格雷48例次（26.1%）。Lasso-logistic回归提示，用药种数（P=0.003，OR=1.412，95%CI：0.582 ~ 2.868）、糖尿病（P<0.001，OR=0.081，95%CI：-3.899 ~ -1.129）对PIM影响显著。ROC曲线下面积（AUC）为85.3%（95%CI：0.772 ~ 0.933），提示模型精准度和区分度良好；Bootstrap法（重采样次数B=1 000）内部验证提示模型预测能力一般。

结论

基于Lasso-logistic回归构建的PIM风险影响因素列线图能较好识别PIM风险，高血压合并脑梗死多重用药治疗住院老年患者PIM风险高，有必要减少用药种数以降低PIM风险。

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叶根深，男，副主任药师，硕士，主要从事临床药学与药事管理的研究，E-mail：

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叶根深，男，副主任药师，硕士，主要从事临床药学与药事管理的研究，E-mail：

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叶根深，男，副主任药师，硕士，主要从事临床药学与药事管理的研究，E-mail：

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Chin J Geriatr, 2021,40（8）: 1045-1049., articleTitle=The clinical aplication value of potentially inappropriatedrug assesment system in elderly inpatients, refAbstract=null)], funds=[Fund(id=1239268430113722592, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, awardId=20210304, language=CN, fundingSource=2021年度蚌埠市科技创新指导类项目(20210304), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1239268424409469940, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, xref=a., ext=[AuthorCompanyExt(id=1239268424413664245, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, companyId=1239268424409469940, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=a.Department of Pharmacy, Bengbu First People’s Hospital, Bengbu ANHUI 233000, China), AuthorCompanyExt(id=1239268424426247159, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, companyId=1239268424409469940, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=a.蚌埠市第一人民医院 药学部，安徽 蚌埠　233000)]), AuthorCompany(id=1239268424535299071, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, xref=b., ext=[AuthorCompanyExt(id=1239268424547880960, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, companyId=1239268424535299071, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=b.Department of Neurology, Bengbu First People’s Hospital, Bengbu ANHUI 233000, China), AuthorCompanyExt(id=1239268424556269569, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, companyId=1239268424535299071, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=b.蚌埠市第一人民医院 神经内科，安徽 蚌埠　233000)])], figs=[ArticleFig(id=1239268428771545265, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, language=EN, label=null, caption=null, figureFileSmall=BnGr1MgBOjS6Pgc8sE8kvQ==, figureFileBig=WWPvGuaMPqFbp9B3xwX9TA==, tableContent=null), ArticleFig(id=1239268428855431348, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, language=CN, label=图1, caption=Lasso回归变量筛选

A：λ与模型误差，B：λ与变量的解路径。ADL：生活自理能力，Braden评分：压疮危险因素评估

, figureFileSmall=BnGr1MgBOjS6Pgc8sE8kvQ==, figureFileBig=WWPvGuaMPqFbp9B3xwX9TA==, tableContent=null), ArticleFig(id=1239268429023203515, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, language=EN, label=null, caption=null, figureFileSmall=nU8ofJGwQzr0kl+j2rcvrQ==, figureFileBig=i7KiitACfON2lZaxxWoFwg==, tableContent=null), ArticleFig(id=1239268429090312383, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, language=CN, label=图2, caption=潜在不适当用药（PIM）风险影响因素列线图模型, figureFileSmall=nU8ofJGwQzr0kl+j2rcvrQ==, figureFileBig=i7KiitACfON2lZaxxWoFwg==, tableContent=null), ArticleFig(id=1239268429182587077, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, language=EN, label=null, caption=null, figureFileSmall=BjBMFjz/tzW36u04xDq8zQ==, figureFileBig=TKzpOrSuzaF7fWL9lpPmsA==, tableContent=null), ArticleFig(id=1239268429371330760, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, language=CN, label=图3, caption=潜在不适当用药风险影响因素列线图模型受试者操作特征曲线（A）和校准曲线（B）, figureFileSmall=BjBMFjz/tzW36u04xDq8zQ==, figureFileBig=TKzpOrSuzaF7fWL9lpPmsA==, tableContent=null), ArticleFig(id=1239268429463605453, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
变量所有患者（n=105）无PIM（n=20）有PIM（n=85）
年龄/岁74.0（69.0，79.0）68.0（66.2，75.5）76.0（70.0，79.0）
性别（男）/例（%）37（35.2）7（35.0）30（35.3）
体重指数/kg·m-224.7±3.825.5±3.124.6±4.0
ADL评估/分80.0（70.0，90.0）85.0（75.0，95.0）75.0（70.0，90.0）
Braden评分/分22.0（21.0，23.0）23.0（22.0，23.0）22.0（21.0，23.0）
跌倒坠床风险评分/例（%）
轻度风险（0分~2分）20（19.1）4（20.0）16（18.8）
中重度风险（3分~6分）85（80.9）16（80.0）69（81.2）
用药种数/种11.0（8.0，13.0）9.5（6.0，11.8）11.0（9.0，14.0）
住院天数/d10.0（8.2，11.9）9.5（7.8，10.6）10.3（8.7，12.2）
出院诊断/种8.0（6.0，10.0）8.5（6.2，10.0）8.0（6.0，10.5）
糖尿病/例（%）40（38.1）14（70.0）26（30.6）
冠心病/例（%）49（46.7）7（35.0）42（49.4）
), ArticleFig(id=1239268429547491536, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, language=CN, label=表1, caption=

研究对象特征

, figureFileSmall=null, figureFileBig=null, tableContent=
变量所有患者（n=105）无PIM（n=20）有PIM（n=85）
年龄/岁74.0（69.0，79.0）68.0（66.2，75.5）76.0（70.0，79.0）
性别（男）/例（%）37（35.2）7（35.0）30（35.3）
体重指数/kg·m-224.7±3.825.5±3.124.6±4.0
ADL评估/分80.0（70.0，90.0）85.0（75.0，95.0）75.0（70.0，90.0）
Braden评分/分22.0（21.0，23.0）23.0（22.0，23.0）22.0（21.0，23.0）
跌倒坠床风险评分/例（%）
轻度风险（0分~2分）20（19.1）4（20.0）16（18.8）
中重度风险（3分~6分）85（80.9）16（80.0）69（81.2）
用药种数/种11.0（8.0，13.0）9.5（6.0，11.8）11.0（9.0，14.0）
住院天数/d10.0（8.2，11.9）9.5（7.8，10.6）10.3（8.7，12.2）
出院诊断/种8.0（6.0，10.0）8.5（6.2，10.0）8.0（6.0，10.5）
糖尿病/例（%）40（38.1）14（70.0）26（30.6）
冠心病/例（%）49（46.7）7（35.0）42（49.4）
), ArticleFig(id=1239268429635571922, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
药品风险警示级别风险强度例（%）
氯吡格雷A低43（26.5）
西咪替丁B低19（11.7）
螺内酯A低19（11.7）
艾司唑仑A低18（11.1）
异丙嗪B低14（8.6）
尼麦角林A低11（6.8）
地西泮B高7（4.3）
胰岛素（sliding scale）A低5（3.1）
硝苯地平B低4（2.5）
布洛芬A低4（2.5）
复方利血平B低4（2.5）
阿托品B高3（1.9）
胺碘酮A低2（1.2）
庆大霉素B低2（1.2）
山莨菪碱B高2（1.2）
门冬胰岛素（sliding scale）A低1（0.6）
奥氮平A低1（0.6）
重组赖脯胰岛素（sliding scale）A低1（0.6）
华法林A低1（0.6）
氯硝西泮B高1（0.6）
合计162（100）
), ArticleFig(id=1239268429736235222, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, language=CN, label=表2, caption=

与药物相关潜在不适当用药

, figureFileSmall=null, figureFileBig=null, tableContent=
药品风险警示级别风险强度例（%）
氯吡格雷A低43（26.5）
西咪替丁B低19（11.7）
螺内酯A低19（11.7）
艾司唑仑A低18（11.1）
异丙嗪B低14（8.6）
尼麦角林A低11（6.8）
地西泮B高7（4.3）
胰岛素（sliding scale）A低5（3.1）
硝苯地平B低4（2.5）
布洛芬A低4（2.5）
复方利血平B低4（2.5）
阿托品B高3（1.9）
胺碘酮A低2（1.2）
庆大霉素B低2（1.2）
山莨菪碱B高2（1.2）
门冬胰岛素（sliding scale）A低1（0.6）
奥氮平A低1（0.6）
重组赖脯胰岛素（sliding scale）A低1（0.6）
华法林A低1（0.6）
氯硝西泮B高1（0.6）
合计162（100）
), ArticleFig(id=1239268429828509911, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
药品建议例（%）
氯吡格雷谨慎使用5（22.7）
布洛芬换用对乙酰氨基酚或阿司匹林，密切监测血压4（18.2）
低分子量肝素谨慎使用4（18.2）
地塞米松谨慎使用2（9.1）
塞来昔布换用对乙酰氨基酚或阿司匹林，密切监测血压2（9.1）
艾司唑仑谨慎使用1（4.5）
异丙嗪避免用于男性1（4.5）
厄贝沙坦氢氯噻嗪换用其他降压药1（4.5）
醋氯芬酸换用对乙酰氨基酚或阿司匹林，密切监测血压1（4.5）
肝素谨慎使用1（4.5）
合计22（100）
), ArticleFig(id=1239268429904007384, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, language=CN, label=表3, caption=

与疾病或症状相关潜在不适当用药

, figureFileSmall=null, figureFileBig=null, tableContent=
药品建议例（%）
氯吡格雷谨慎使用5（22.7）
布洛芬换用对乙酰氨基酚或阿司匹林，密切监测血压4（18.2）
低分子量肝素谨慎使用4（18.2）
地塞米松谨慎使用2（9.1）
塞来昔布换用对乙酰氨基酚或阿司匹林，密切监测血压2（9.1）
艾司唑仑谨慎使用1（4.5）
异丙嗪避免用于男性1（4.5）
厄贝沙坦氢氯噻嗪换用其他降压药1（4.5）
醋氯芬酸换用对乙酰氨基酚或阿司匹林，密切监测血压1（4.5）
肝素谨慎使用1（4.5）
合计22（100）
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变量ΒSEWaldPOR95%CI
常数项-6.4683.8082.8860.0890.002—
年龄0.0780.0512.3160.1281.081-0.223~1.774
用药种数0.3450.1178.7430.0031.4120.582~2.868
糖尿病-2.5140.70712.662<0.0010.081-3.899~-1.129
), ArticleFig(id=1239268430034030813, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239268422987600791, language=CN, label=表4, caption=

潜在不适当用药风险多因素logistic回归

, figureFileSmall=null, figureFileBig=null, tableContent=
变量ΒSEWaldPOR95%CI
常数项-6.4683.8082.8860.0890.002—
年龄0.0780.0512.3160.1281.081-0.223~1.774
用药种数0.3450.1178.7430.0031.4120.582~2.868
糖尿病-2.5140.70712.662<0.0010.081-3.899~-1.129
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高血压合并脑梗死老年患者潜在用药安全风险及Lasso-logistic回归分析
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叶根深 a , 吴纪霞 b , 余兴群 a , 宦娣 a , 刘忠明 a , 李佳会 a , 忻志鸣 a
中国新药与临床杂志 | 论著 2024,43(7): 529-534
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中国新药与临床杂志 | 论著 2024, 43(7): 529-534
高血压合并脑梗死老年患者潜在用药安全风险及Lasso-logistic回归分析
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叶根深a , 吴纪霞b, 余兴群a, 宦娣a, 刘忠明a, 李佳会a, 忻志鸣a
作者信息
  • a.蚌埠市第一人民医院 药学部，安徽 蚌埠　233000
  • b.蚌埠市第一人民医院 神经内科，安徽 蚌埠　233000

    • 叶根深，男，副主任药师，硕士，主要从事临床药学与药事管理的研究，E-mail：

Potential medication safety risks and Lasso-logistic regression analysis in hypertension complicated with cerebral infarction elderly patients
Gen-shen YEa , Ji-xia WUb, Xing-qun YUa, Di HUANa, Zhong-ming LIUa, Jia-hui LIa, Zhi-ming XINa
Affiliations
  • a.Department of Pharmacy, Bengbu First People’s Hospital, Bengbu ANHUI 233000, China
  • b.Department of Neurology, Bengbu First People’s Hospital, Bengbu ANHUI 233000, China
出版时间: 2024-07-25 doi: 10.14109/j.cnki.xyylc.2024.07.09
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摘要
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目的

探讨高血压合并脑梗死老年患者潜在不适当用药（PIM）情况并建立影响因素Lasso-logistic回归列线图模型。

方法

纳入2020年1月至12月高血压合并脑梗死多重用药治疗的出院患者105例，以《中国老年人潜在不适当用药判断标准》评价PIM情况，Lasso-logistic回归分析PIM风险影响因素，建立PIM风险影响因素列线图模型，评价模型区分度和准确度。

结果

105例患者PIM为85例（81.0%），涉及26种药品、184例次，排第1位是氯吡格雷48例次（26.1%）。Lasso-logistic回归提示，用药种数（P=0.003，OR=1.412，95%CI：0.582 ~ 2.868）、糖尿病（P<0.001，OR=0.081，95%CI：-3.899 ~ -1.129）对PIM影响显著。ROC曲线下面积（AUC）为85.3%（95%CI：0.772 ~ 0.933），提示模型精准度和区分度良好；Bootstrap法（重采样次数B=1 000）内部验证提示模型预测能力一般。

结论

基于Lasso-logistic回归构建的PIM风险影响因素列线图能较好识别PIM风险，高血压合并脑梗死多重用药治疗住院老年患者PIM风险高，有必要减少用药种数以降低PIM风险。

高血压  /  脑梗死  /  潜在不当用药清单  /  logistic模型  /  列线图
AIM

To investigate the potentially inappropriate medications (PIM) in elderly patients with hypertension combined with cerebral infarction and to develop a nomogram model based on Lasso-logistic regression of the influencing factors.

METHODS

A total of 105 patients with hypertension combined with cerebral infarction and multi-drug therapy discharged from January to December 2020 were included，and the PIM was evaluated by the criteria of potentially inappropriate medications for older adults in China. The PIM influencing factors were analyzed by Lasso-logistic regression, Nomogram model of factors influencing the PIM risk was developed, and the discrimination and accuracy of the model were evaluated.

RESULTS

The incidence of PIM in 105 patients of hypertension complicated with cerebral infarction was 85 cases (81.0%) and 184 times, involving 26 drugs. The 1st drug was clopidogrel with 48 times (26.1%). A statistically significant effect of the number of drugs (P=0.003, OR=1.412, 95% CI: 0.582 to 2.868) and diabetes (P<0.001, OR=0.081, 95% CI: -3.899 to -1.129) on PIM was suggested by the Lasso-logistic regression. Of the model, a good accuracy and discrimination was suggested by the area under the ROC curve (AUC), which was 85.3% (95% CI: 0.772 to 0.933), and an acceptable predictive power was suggested by internal validation with Bootstrap method (B=1 000).

CONCLUSION

The nomogram model based on Lasso-logistic regression can better identify the risk of PIM. Elderly patients with hypertension complicated with cerebral infarction and multi-drug therapy have a high risk of PIM, and it is necessary to reduce the number of medications to reduce the risk of PIM.

hypertension  /  brain infarction  /  potentially inappropriate medication list  /  logistic models  /  nomogram
叶根深, 吴纪霞, 余兴群, 宦娣, 刘忠明, 李佳会, 忻志鸣. 高血压合并脑梗死老年患者潜在用药安全风险及Lasso-logistic回归分析. 中国新药与临床杂志, 2024 , 43 (7) : 529 -534 . DOI: 10.14109/j.cnki.xyylc.2024.07.09
Gen-shen YE, Ji-xia WU, Xing-qun YU, Di HUAN, Zhong-ming LIU, Jia-hui LI, Zhi-ming XIN. Potential medication safety risks and Lasso-logistic regression analysis in hypertension complicated with cerebral infarction elderly patients[J]. Chinese Journal of New Drugs and Clinical Remedies, 2024 , 43 (7) : 529 -534 . DOI: 10.14109/j.cnki.xyylc.2024.07.09
正文
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高血压、脑梗死作为慢性病，都有患病率高、致残率高、致死率高的特点，严重威胁健康，引起经济负担，且近年来患病率呈上升趋势[1,2]。目前，慢性病患者的潜在不适当用药（potentially inappropriate medications，PIM）已成为临床安全研究中的热点与趋势[3]。PIM在罹患多种疾病的多重用药慢性病患者中难以避免，并增加老年患者再入院、死亡等风险[1,3,4]。部分研究报道分析了不同慢性病患者的PIM风险因素，但结论不一致[5-7]，笔者尚未见高血压合并脑梗死老年患者的PIM风险因素研究。本研究应用《中国老年人潜在不适当用药判断标准》[8]（以下简称《中国标准》），回顾性分析本院2020年高血压合并脑梗死患者PIM及相关因素数据，探讨多重用药患者的PIM发生率、分布及风险因素，并建立PIM风险影响因素列线图模型，为临床合理用药提供科学依据。
资料与方法
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研究对象
本研究为单中心、回顾性研究，经医院伦理学委员会批准同意开展课题，并同意免除签署知情同意书（批件号为BBYY202121）。采用完全随机方法，选取本院2020年1月至12月高血压合并脑梗死多重用药的出院患者。
纳排标准
纳入标准：（1）入院诊断中有高血压、脑梗死；（2）年龄≥65岁。排除标准：（1）住院时间<48 h；（2）死亡；（3）住院期间曾入住重症监护室；（4）住院期间有转科；（5）未用降压药；（6）未用脑梗死治疗药；（7）用药种数（不含中成药、中草药）<5种；（8）信息不完整；（9）因精神、意识障碍等不能确切主诉。
样本量估算
多重用药老年患者PIM发生率可高达79.4%~90.4%[5,6]，前期预试验中，按排除标准，约50%病例可纳入研究；预期可能纳入logistic回归分析的因素为6个，结合10倍每变量事件数（events per variable，EVP）原则[9]，估算按纳入标准分配样本量应不少于200例，最终纳入研究样本量应不少于100例。
资料收集
设计调查表，内容包括：（1）基本信息，包括年龄、性别、体重、身高等。（2）入院时进行生活自理能力（ADL）评估、压疮危险因素评估（Braden评分）、跌倒坠床风险评分。（3）用药种数等情况。（4）病案首页的出入院时间，计算住院天数。（5）出院诊断。未将心功能分级列为独立诊断的，予以校正。（6）同一病例中，同一品种药品同一类型风险点PIM计为1例次（可累计）。同一病例中PIM计为1例。
质量控制
数据录入、PIM评价后抽样交叉核对。课题组人员熟悉《中国标准》[8]后进行PIM评价。经沟通讨论后由高年资药师裁决、解决分歧。
统计学分析
应用SPSS 25.0及R 4.2.0进行统计分析。计数资料以频数（百分率或构成比）表示，正态分布计量资料以均数±标准差表示，偏态分布计量资料以M（Q1，Q3）表示。采用glmnet程序包进行Lasso-logistic回归分析，采用rms程序包绘制PIM风险影响因素列线图，采用受试者操作特征曲线（ROC）计算曲线下面积（AUC），评价区分度，采用Hosmer-Lemeshow检验绘制校准曲线评估准确度。P<0.05为有显著差异。
结果
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研究对象特征
随机选取高血压合并脑梗死患者225例，按纳排标准最终纳入105例患者，年龄为74.0（69.0，79.0）岁，男性37例（35.2%），体重指数（24.7±3.8）kg·m-2。患者同时罹患的其他疾病中，前两位为冠心病49例（46.7%）、糖尿病40例（38.1%）。见表1。
PIM分布情况
有85例（81.0%）患者发生PIM，共184例次，与药物相关PIM 162例次，其中与疾病或症状相关PIM 22例次，见表2、表3。PIM涉及26种药品，排第1位是氯吡格雷48例次（26.1%）；与药物相关PIM，风险主要为A级警示级别、低风险强度。
PIM风险影响因素的Lasso-logistic回归分析
以年龄、体重指数、ADL评估、Braden评分、用药种数、住院天数、出院诊断（赋值均为实测值）、性别（赋值：男=1，女=2）、跌倒坠床风险评分［赋值：轻度风险（0分~ 2分）=1，中重度风险（3分~ 6分）=2］、糖尿病、冠心病（赋值：否= 0，是= 1）为自变量，PIM（赋值：否=0，是= 1）为因变量，进行Lasso-logistic回归分析。经5折Lasso交叉验证得到最优λ值；λ.min=0.032 663 32，λ.1se=0.087 939 7；见图1A，采用λ.1se筛选得到自变量，即图1B中与灰色虚纵线相交的系数变化曲线，分别为年龄、用药种数、糖尿病。回归分析显示，用药种数（P=0.003）、糖尿病（P<0.001）对PIM有显著影响，见表4。
PIM风险影响因素列线图模型的建立
预测PIM风险影响因素的列线图模型见图2。AUC为85.3%（95%CI：0.772~0.933），提示模型精准度和区分度良好。经Bootstrap法（重采样次数B=1 000）进行模型内部验证，结果提示模型预测能力一般，见图3。
讨论
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本研究选用基于药品不良反应监测和临床数据的《中国标准》，借鉴欧美和亚洲等多国及中国台湾地区PIM评价标准，检出率高于Beers标准及STOPP标准[6,7,10]。《中国标准》已用于多项老年患者PIM研究[7,10,11]，尤其是涵盖了Beers标准未包含的某些心脑血管疾病用药，如氯吡格雷。该标准第一部分包括13大类72种／类药物，每种／类药物附有用药风险点；第二部分涵盖27种疾病状态下44种／类药物[8]。
本研究中PIM发生率为81.0%，高于HUANG等[7]的研究（50.64%）和田艳等[12]针对社区老年患者的研究（29.66%），与MA等[6]报道的高龄住院患者PIM发生率（80.19%）接近。
引起PIM药物中，氯吡格雷（26.1%）、艾司唑仑（10.3%）居于前列，与报道[6,7]基本一致。氯吡格雷可引起血液、神经系统不良反应，且增加抗凝或凝血障碍患者出血风险，尽管风险级别较低，仍建议谨慎使用[8]。氯吡格雷发生主要不良心血管事件和非致命性心肌梗死的风险低于阿司匹林[13,14]。对确须联用氯吡格雷、阿司匹林的[15]，可考虑加用质子泵抑制剂，但可能会增加心血管不良事件风险，特别是应避免选用艾司奥美拉唑[16]。尽管替格瑞洛未列入《中国标准》[8]，但其引起大出血发生率与氯吡格雷相比无显著差异[17]，因此，双抗方案中以替格瑞洛替代氯吡格雷，不一定能降低出血风险。
艾司唑仑常用于失眠患者，但可引起过度镇静、嗜睡、诱发跌倒等[18]。失眠需要综合性治疗措施[19]，应充分评估患者失眠原因和类型，并可通过综合心理及认知行为治疗等措施[20]减少艾司唑仑应用，降低PIM风险。
Lasso回归可用于较小样本量、特别是有多个潜在共线性因子时变量筛选的研究，得到更相关、更可解释的变量[21]。与传统的logistic回归方法相比，Lasso-logistic回归模型在变量拟合、预测效果方面具有优势，可得到区分度、准确度更优的模型[22]。Bootstrap方法可用于较小样本量logistic回归模型校准度的内部验证。本研究显示，校准度曲线中有反S形曲线，提示模型可能有一定的风险，可能与样本量较小有关，而实际曲线与偏差纠正后曲线较为一致，提示验证结果较好。因而，模型总体预测能力一般。
本研究发现，用药种数可能是PIM风险增加的促进因素，每增加一种用药，PIM暴露风险可能增加4.12%左右，提示有必要通过药物重整等方式精简处方，降低老年患者PIM风险[23]。
本研究中，冠心病未进入模型，可能原因为冠心病用药与高血压合并脑梗死用药高度重叠，从而对PIM影响无显著意义。糖尿病似乎是PIM暴露风险的“阻碍”因素（P<0.001）；但本研究中降血糖药的PIM仅为7例（3.8%），易于被其他更多PIM例次药物“翻转”。因此，尚不能简单认为糖尿病是PIM的“阻碍”因素。
综上，高血压合并脑梗死患者PIM风险高达81.0%，Lasso-logistic回归模型提示用药种数可能是PIM风险增加的促进因素，而疾病如糖尿病对PIM的影响仍待探究。但本研究尚存在不足：样本量相对较小，未对模型进行外部验证；尝试探讨了处于共存疾病前两位的冠心病、糖尿病对PIM的影响，但未探索更多疾病因素。因《中国标准》未涉及中成药，因而也未探讨中成药对PIM的影响。此外，对于高血压合并脑梗死患者，Beers标准能否发现更多PIM，PIM与严重药品不良事件关系如何，都值得关注。
基金
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  • 2021年度蚌埠市科技创新指导类项目(20210304)
文献
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2024年第43卷第7期
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doi: 10.14109/j.cnki.xyylc.2024.07.09
  • 接收时间：2023-05-31
  • 首发时间：2026-03-13
  • 出版时间：2024-07-25
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  • 收稿日期：2023-05-31
  • 录用日期：2024-04-25
基金
2021年度蚌埠市科技创新指导类项目(20210304)
作者信息
    a.蚌埠市第一人民医院 药学部，安徽 蚌埠　233000
    b.蚌埠市第一人民医院 神经内科，安徽 蚌埠　233000
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