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Introduction <p>Bloodstream infections represent a major medical emergency, in which rapid and accurate pathogen identification is essential for optimizing therapy. This study evaluates the analytical performance of the Autof MS2600 and Biotyper Sirius (Bruker) for the identification of microorganisms directly from positive blood culture.</p> Methods <p>A prospective study was conducted over 7 weeks at the Brussels University Hospital laboratory, including all new episodes of bacteremia. Practical aspects and three preparation protocols were compared: a commercial kit and an in-house pretreatment protocol on the Autof MS2600, and an in-house pretreatment protocol on the Sirius system.</p> Results <p>A total of 157 bloodstream infection episodes were analyzed, including 64 Gram-negative bacteria, 90 Gram-positive bacteria, and 3 yeasts. Correct species-level identification rates were 62.4% using the Autobio kit, 73.2% using the Autof MS2600 with the in-house protocol, and 72.6% using the Sirius system. In-house protocols performed significantly better than the commercial kit (<i>p</i> &lt; 0.05), with no significant difference between the two in-house protocols (<i>p</i> = 1.000). Performance was higher for Gram-negative bacteria (85.9% with in-house vs. 75.0% with the kit) than for Gram-positive bacteria (66.7% vs. 55.6%). The Sirius system showed the highest non-identification rate (25.5%), compared with the Autof MS2600 (11.5% with the in-house and 16.6% with the kit). The in-house pretreatment was faster than the kit-based protocol (19 vs. 40&#xa0;min).</p> Conclusion <p>In-house pretreatment protocols significantly improve and accelerate direct microbial identification from positive blood cultures, with comparable performance between platforms. Differences in non-identification rates mainly reflect system-specific scoring thresholds and algorithmic approaches. In this context, the choice of platform may be based on practical considerations, although this should be interpreted in light of the study design and sample size.</p>

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Comparative analytical performance of Autof MS2600 and Biotyper Sirius for rapid MALDI-TOF MS identification of microorganisms from positive blood cultures

  • 期刊论文

  • 发表日期：

    2026年5月18日

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  • Délaissée Chimène Nyamessameye,
  • Marine Piron,
  • Aissatou Barry,
  • Souad Mohammed,
  • Ethel Seyll,
  • Delphine Martiny

摘要

Introduction

Bloodstream infections represent a major medical emergency, in which rapid and accurate pathogen identification is essential for optimizing therapy. This study evaluates the analytical performance of the Autof MS2600 and Biotyper Sirius (Bruker) for the identification of microorganisms directly from positive blood culture.

Methods

A prospective study was conducted over 7 weeks at the Brussels University Hospital laboratory, including all new episodes of bacteremia. Practical aspects and three preparation protocols were compared: a commercial kit and an in-house pretreatment protocol on the Autof MS2600, and an in-house pretreatment protocol on the Sirius system.

Results

A total of 157 bloodstream infection episodes were analyzed, including 64 Gram-negative bacteria, 90 Gram-positive bacteria, and 3 yeasts. Correct species-level identification rates were 62.4% using the Autobio kit, 73.2% using the Autof MS2600 with the in-house protocol, and 72.6% using the Sirius system. In-house protocols performed significantly better than the commercial kit (p < 0.05), with no significant difference between the two in-house protocols (p = 1.000). Performance was higher for Gram-negative bacteria (85.9% with in-house vs. 75.0% with the kit) than for Gram-positive bacteria (66.7% vs. 55.6%). The Sirius system showed the highest non-identification rate (25.5%), compared with the Autof MS2600 (11.5% with the in-house and 16.6% with the kit). The in-house pretreatment was faster than the kit-based protocol (19 vs. 40 min).

Conclusion

In-house pretreatment protocols significantly improve and accelerate direct microbial identification from positive blood cultures, with comparable performance between platforms. Differences in non-identification rates mainly reflect system-specific scoring thresholds and algorithmic approaches. In this context, the choice of platform may be based on practical considerations, although this should be interpreted in light of the study design and sample size.