背景:假伯克霍尔德菌,革兰氏阴性病原体,导致类鼻窦病。尽管存在各种临床实验室鉴定方法,基于文化的技术缺乏综合评价。因此,本系统综述和荟萃分析旨在评估基于培养的自动化和非自动化方法的诊断准确性.
方法:通过PubMed/MEDLINE收集数据,EMBASE,和Scopus使用特定的搜索策略。选定的研究使用QUADAS-2进行偏倚评估。计算敏感性和特异性,生成汇总估计。使用I2统计学评估异质性。
结果:该综述涵盖了20项研究,其中包括2988个假单胞菌样本和753个非B。假虫样本。基于自动化的方法,特别是在更新数据库时,表现出较高的合并敏感性(82.79%;95%CI64.44-95.85%)和特异性(99.94%;95%CI98.93-100.00%).亚组分析强调了更新数据库自动化的高敏感性(96.42%,95%CI90.01-99.87%)与非更新(3.31%,95%CI0.00-10.28%),而特异性仍然很高,为99.94%(95%CI98.93-100%)。非自动化方法显示出不同的灵敏度和特异性。内部乳胶凝集显示出最高的敏感性(100%;95%CI98.49-100%),其次是商业乳胶凝集(99.24%;95%CI96.64-100%)。然而,API20E的敏感性最低(19.42%;95%CI12.94-28.10%)。总的来说,非自动化工具的敏感性为88.34%(95%CI77.30-96.25%),特异性为90.76%(95%CI78.45-98.57%).
结论:该研究强调了自动化在准确识别假单胞菌方面的关键作用,支持基于证据的类lioidosis管理决策。自动化技术,尤其是那些更新数据库的人,提供可靠和高效的识别。
BACKGROUND: Burkholderia pseudomallei, a Gram-negative pathogen, causes
melioidosis. Although various clinical laboratory identification methods exist, culture-based techniques lack comprehensive evaluation. Thus, this systematic review and meta-analysis aimed to assess the diagnostic accuracy of culture-based automation and non-automation methods.
METHODS: Data were collected via PubMed/MEDLINE, EMBASE, and Scopus using specific search strategies. Selected studies underwent bias assessment using QUADAS-2. Sensitivity and specificity were computed, generating pooled estimates. Heterogeneity was assessed using I2 statistics.
RESULTS: The review encompassed 20 studies with 2988 B. pseudomallei samples and 753 non-B. pseudomallei samples. Automation-based methods, particularly with updating databases, exhibited high pooled sensitivity (82.79%; 95% CI 64.44-95.85%) and specificity (99.94%; 95% CI 98.93-100.00%). Subgroup analysis highlighted superior sensitivity for updating-database automation (96.42%, 95% CI 90.01-99.87%) compared to non-updating (3.31%, 95% CI 0.00-10.28%), while specificity remained high at 99.94% (95% CI 98.93-100%). Non-automation methods displayed varying sensitivity and specificity. In-house latex agglutination demonstrated the highest sensitivity (100%; 95% CI 98.49-100%), followed by commercial latex agglutination (99.24%; 95% CI 96.64-100%). However, API 20E had the lowest sensitivity (19.42%; 95% CI 12.94-28.10%). Overall, non-automation tools showed sensitivity of 88.34% (95% CI 77.30-96.25%) and specificity of 90.76% (95% CI 78.45-98.57%).
CONCLUSIONS: The study underscores automation\'s crucial role in accurately identifying B. pseudomallei, supporting evidence-based
melioidosis management decisions. Automation technologies, especially those with updating databases, provide reliable and efficient identification.