Article(id=1309635235290112349, tenantId=1146029695717560320, journalId=1227999776411738115, issueId=1309635220643607372, articleNumber=null, orderNo=null, doi=10.12048/j.issn.1674-229X.2026.02.008, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1745337600000, receivedDateStr=2025-04-23, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790170971939, onlineDateStr=2026-09-23, pubDate=1771948800000, pubDateStr=2026-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790170971939, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790170971939, creator=13701087609, updateTime=1790170971939, updator=13701087609, issue=Issue{id=1309635220643607372, tenantId=1146029695717560320, journalId=1227999776411738115, year='2026', volume='36', issue='2', pageStart='81', pageEnd='160', issueExtLink='null', onlineDate='null', pubDate='1771948800000', pubDateStr='2026-02-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790170968446, creator='13701087609', updateTime=1790219084169, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309837032671772868, tenantId=1146029695717560320, journalId=1227999776411738115, issueId=1309635220643607372, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309837032671772869, tenantId=1146029695717560320, journalId=1227999776411738115, issueId=1309635220643607372, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=131, endPage=135, ext={EN=ArticleExt(id=1309635235499827550, articleId=1309635235290112349, tenantId=1146029695717560320, journalId=1227999776411738115, language=EN, title=Construction and Application of a Management Model for Antibiotic Usage Intensity Corrected by CMI, columnId=null, journalTitle=Pharmacy Today, columnName=null, runingTitle=null, highlight=null, articleAbstract=
OBJECTIVE To explore the correlation between Case Mix Index (CMI) and Antimicrobial Usage Density (AUD), construct a tiered management model for AUD, optimize the management of antimicrobial agents in the entire hospital, and improve the quality of antimicrobial management.
METHODS Data on CMI, AUD, and other antimicrobial usage-related indicators from 41 clinical departments of a tertiary general hospital in 2023 were collected. Linear regression models and multivariate regression analysis were used to investigate the relationship between CMI and AUD and other antimicrobial usage indicators. The optimal model was determined using the Rank Sum Ratio (RSR), and tiered management of AUD was implemented based on the this optimal model.
RESULTS The fitting coefficient (R2) of the CMI×Antimicrobial Usage Rate×AUD model was 0.522, which was significantly higher than that of other AUD linear regression models (P <0.05), indicating a superior fit. Using this model, the RSR was calculated, and a linear regression equation was established:y =0.120 3x-0.102 6, with R2=0.899. Analysis of variance showed F =357.112 and P < 0.001, indicating that the regression equation constructed with the RSR based on this model was well-established and statistically significant. After adjusting AUD with CMI and antimicrobial usage rate, the tiered results of 10 departments (24.40%) changed, with 5 departments being upgraded and 5 downgraded in management level. Following the implementation of tiered AUD management, the overall antimicrobial usage intensity in the hospital decreased by 4.22% in 2024 compared to 2023. The 8 strictly controlled departments showed an average AUD reduction of 5.89%, indicating a satisfactory overall management effect.
CONCLUSION Adjusting AUD with CMI and antimicrobial usage rate can enhance the accuracy of antimicrobial usage evaluation and provide a scientific basis for hospital antimicrobial management.
, authors=Weijuan LI, Lusi MAI
*, Yuanmei YAN, Chengyin XIAO, Xin DENG, Huili CHANG, authorsList=Weijuan LI, Lusi MAI, Yuanmei YAN, Chengyin XIAO, Xin DENG, Huili CHANG, authorCompany=null, correspAuthors=Lusi MAI, 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=1309635236418380127, articleId=1309635235290112349, tenantId=1146029695717560320, journalId=1227999776411738115, language=CN, title=CMI校正抗菌药物使用强度管理模型的构建与应用, columnId=1228268588277105398, journalTitle=今日药学, columnName=临床药学, runingTitle=null, highlight=null, articleAbstract=
目的 研究病例组合指数（CMI）与抗菌药物使用强度（AUD）的关系，构建AUD分档管理模型，优化全院抗菌药物管理，提升管理质量。
方法 收集某三甲综合医院2023年41个科室的CMI、AUD等抗菌药物使用相关指标。通过线性回归模型和多变量回归分析，探讨CMI与AUD等指标的关系。利用秩和比（RSR）确定最佳模型，并据此进行AUD分档管理。
结果 CMI×抗菌药物使用率×AUD模型拟合系数（R2）为0.522，显著优于其他模型（P < 0.05）。基于此模型计算RSR，建立直线回归方程：y =0.120 3x-0.102 6，R2 =0.899，方差分析显示F =357.112，P<0.001，表明模型构建良好，差异有统计学意义。引入CMI和抗菌药物使用率调整AUD后，10个科室（24.40%）的分档结果发生变化，其中5个科室管控级别上调，5个科室下调。实施AUD分档管理后，全院2024年抗菌药物使用强度较2023年下降4.22%，8个严格管控科室AUD平均降幅达5.89%，管控效果显著。
结论 结合CMI和抗菌药物使用率校正AUD，可提高抗菌药物使用评价的准确性，为医院抗菌药物管理提供科学依据。
, authors=李伟娟, 麦露丝
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