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  • 梁鲲,刘勇.定量CT对骨质疏松症诊断价值的meta分析[J].同济大学学报(医学版),2017,38(3):70-75.    [点击复制]
  • LIANG Kun,LIU Yong.Diagnosis value of quantitative CT in osteoporosis: a meta-analysis[J].同济大学学报(医学版),2017,38(3):70-75.   [点击复制]
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定量CT对骨质疏松症诊断价值的meta分析
梁鲲,刘勇
0
(同济大学附属第十人民医院放射科,上海 200072)
摘要:
目的 对定量CT在骨质疏松症中的诊断价值进行meta分析。方法 按照循证医学诊断meta分析的相关原则,对PubMed、Google学术、中国知网、百度学术中定量CT诊断骨质疏松症的相关文献进行检索,时间与语言不限。对筛选出的文献分别进行双变量混合效应及层次综合受试者工作曲线模型分析,并对其异质性进行分析。结果 共筛选出13篇文献,15组研究,两种分析方法均表明纳入文献的总敏感性为0.84(95% CI: 0.68~0.93),总特异性为0.78(95% CI: 0.66~0.87),SROC曲线下面积为0.88(95% CI: 0.84~0.90),表明该诊断方法具有较高的诊断价值。结论 定量CT骨质疏松症的筛查中具有较高的价值,在疾病的确诊中尚需要结合其他临床资料进行综合诊断。
关键词:  定量CT  骨质疏松症  双变量混合效应模型  层次综合受试者工作曲线模型
DOI:10.16118/j.1008-0392.2017.03.014
投稿时间:2016-12-31
基金项目:
Diagnosis value of quantitative CT in osteoporosis: a meta-analysis
LIANG Kun,LIU Yong
(Dept. of Radiology, Tenth People's Hospital, Tongji University, Shanghai 200072, China)
Abstract:
Objective To evaluate the application of quantitative CT in diagnosis of osteoporosis with meta-analysis. Methods According to the principle of diagnosis test in meta-analysis and QUADAS-2 scale, we searched Pubmed, Google Scholar, CNKI, Baidu Scholar for papers on osteoporosis diagnosed by quantitative CT. We calculated the combined sensitivity and specificity based on bivariate mixed effects models and hierarchical summary receiver operating curve model and took tests for heterogeneity. Results Fifteen data sets from 13 articles were included for meta-analysis. The combined estimation for sensitivity was 0.84 (95% CI: 0.68-0.93); for specificity was 0.78 (95% CI: 0.66-0.87). The area under summary receiver operating curve (SROC) was 0.88 (95% CI: 0.84-0.90). The value of diagnosis has been proved in upper middle level. Conclusion Quantitative CT may be used in screening of osteoporosis for general population, however, the confirmed diagnosis of osteoporosis should be made on the basis of comprehensive investigations.
Key words:  quantitative CT  osteoporosis  bivariate mixed effects models  hierarchical summary receiver operating curve model

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