Ahmed Mohammed Alrumaihi¹*, Mohammed Yahya Alqahtani², Sami Mohammed Alamri³, Mohammed Saud Faqihi³, Jawaher Alhumaidi Alanazi³ and Waad Qabbal Saad Alotaibi³

¹Medical Laboratory Scientific Officer, Virology Lab, Central Military Medical Laboratory and Blood Bank, Prince Sultan Military Medical City, Riyadh, Saudi Arabia
²Specialist of Lab, Prince Sultan Military Medical City, Riyadh, Saudi Arabia
³Medical Laboratory Specialist, Prince Sultan Military Medical City, Riyadh, Saudi Arabia

*Corresponding Author: Ahmed Mohammed Alrumaihi, Medical Laboratory Scientific Officer, Virology Lab, Central Military Medical Laboratory and Blood Bank, Prince Sultan Military Medical City, Riyadh, Saudi Arabia.

Received: June 16, 2025; Published: July 02, 2025

Abstract

Background: Bloodstream infections (BSIs) are a major cause of morbidity and mortality worldwide, which require accurate diagnosis for effective management. Blood culture is the gold standard for detecting BSIs, and the choice between automated and manual systems is a challenge, mainly in resource-limited settings. In this systematic review we aim to compare the diagnostic performance of automated versus manual blood culture systems in detecting bloodstream infections, focusing on yield, time to detection, and clinical impact.

Methods: Our review identified relevant studies which compare automated and manual blood culture systems. The review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Data were extracted and synthesized from 5 studies. Outcomes assessed included detection rate, contamination rate, time to detection (TTD), and pathogen identification efficiency.

Results: Automated systems show higher detection rates, shorter TTD, and improved pathogen identification. These systems improved clinical decision making and supported antimicrobial management through faster organism recovery. Manual systems is widely used in low and middle income countries due to cost and infrastructure limitations.

Conclusion: Automated blood culture systems better manual methods in most diagnostic metrics, but implementation in low resource settings need strategies to address the barriers.

 Keywords: Bloodstream Infections; Blood Culture; Automated Systems; Manual Systems; Diagnostic Yield; Time to Detection

References

1. Dunbar SA., et al. “Diagnosis and Management of Bloodstream Infections With Rapid, Multiplexed Molecular Assays”. Frontiers in Cellular and Infection Microbiology 12 (2022).
2. von Laer A., et al. “Implementation of Automated Blood Culture With Quality Assurance in a Resource-Limited Setting”. Frontiers in Medicine 8 (2021).
3. Ombelet S., et al. “Best Practices of Blood Cultures in Low- and Middle-Income Countries”. Frontiers in Medicine 6 (2019).
4. Buchan BW. “Commentary: Can Automated Blood Culture Systems Be Both New and Improved?” Journal of Clinical Microbiology 4 (2022).
5. Bharadwaj R., et al. “Blood Stream Infections”. Biomed Research International (2014): 1-3.
6. Halperin AV., et al. “Impact of Automated Blood Culture Systems on the Management of Bloodstream Infections: Results from a Crossover Diagnostic Clinical Trial”. Microbiology Spectrum5 (2022).
7. Qin Y., et al. “Comparative evaluation of BacT/ALERT VIRTUO and BACTEC FX400 blood culture systems for the detection of bloodstream infections”. Microbiology Spectrum1 (2025).
8. Syed Khizar Abbas., et al. “Comparison between Manual Blood Culture and Automated Blood Culture System in Cardiology Institute”. Medical Journal of South Punjab2 (2020).
9. Isaac EW., et al. “Bacterial Blood Isolates in Children: Conventional vs. Bactec Automated Blood Culture System in a Tertiary Health Centre in Gombe, North East Nigeria”. Open Journal of Medical Microbiology 3 (2021): 101-116.
10. Chowdhury RN., et al. “Comparison of Conventional and Automated Blood Culture Methods for The Diagnosis of Neonatal Septicemia”. Bangladesh Journal of Medical Microbiology2 (2021): 12-18.
11. Timsit JF., et al. “Bloodstream infections in critically ill patients: an expert statement”. Intensive Care Medicine2 (2020): 266-284.
12. Menchinelli G., et al. “In vitro Evaluation of BACT/ALERT® VIRTUO®, BACT/ALERT 3D®, and BACTECTM FX Automated Blood Culture Systems for Detection of Microbial Pathogens Using Simulated Human Blood Samples”. Frontiers in Microbiology (2019): 10.
13. Aydemir Ö., et al. “Comparison of time-to-detection of Mindray TDR and BacT/ALERT®3D blood culture systems using simulated blood cultures”. Acta Clinica Belgica3 (2024): 168-173.
14. Stewart AG., et al. “Central line associated and primary bloodstream infections”. Current Opinion in Critical Care5 (2023): 423-429.
15. Shirin M., et al. “Bacterial etiology and antibiotic resistance pattern of neonatal sepsis: a study in a tertiary care hospital, in Bangladesh”. International Journal of Contemporary Pediatrics5 (2019): 1839.
16. Simonsen KA., et al. “Early-Onset Neonatal Sepsis”. Clinical Microbiology Reviews1 (2014): 21-47.
17. Al-Hasan MN and Rac H. “Transition from intravenous to oral antimicrobial therapy in patients with uncomplicated and complicated bloodstream infections”. Clinical Microbiology Infectious3 (2020): 299-306.
18. Wellbelove Z., et al. “Comparing scoring systems for prediction of mortality in patients with bloodstream infection”. QJM: An International Journal of Medicine2 (2021): 105-110.

Citation

Citation: Ahmed Mohammed Alrumaih., et al. “Performance of Automated Versus Manual Blood Culture Systems in Detecting Bloodstream Infections; Systematic Review”.Acta Scientific Medical Sciences 9.8 (2025): 01-07 .

Copyright

Copyright: © 2025 Ahmed Mohammed Alrumaih., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.