Diagnostic accuracy of automation and non-automation techniques for identifying Burkholderia pseudomallei: A systematic review and meta-analysis
Issued Date
2024-07-01
Resource Type
ISSN
18760341
eISSN
1876035X
Scopus ID
2-s2.0-85194398021
Journal Title
Journal of Infection and Public Health
Volume
17
Issue
7
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Infection and Public Health Vol.17 No.7 (2024)
Suggested Citation
Songsri J., Chatatikun M., Wisessombat S., Mala W., Phothaworn P., Senghoi W., Palachum W., Chanmol W., Intakhan N., Chuaijit S., Wongyikul P., Phinyo P., Yamasaki K., Chittamma A., Klangbud W.K. Diagnostic accuracy of automation and non-automation techniques for identifying Burkholderia pseudomallei: A systematic review and meta-analysis. Journal of Infection and Public Health Vol.17 No.7 (2024). doi:10.1016/j.jiph.2024.04.022 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/98611
Title
Diagnostic accuracy of automation and non-automation techniques for identifying Burkholderia pseudomallei: A systematic review and meta-analysis
Corresponding Author(s)
Other Contributor(s)
Abstract
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%). Conclusion: 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.