Frontiers in cellular and infection microbiology · 2026
A dual-phase strategy to integrate the VITEK MS RUO database into routine clinical practice: from validation of analytical performance to implementation of an automated workflow.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Nian Hua, Li Fushun, Wang Xingqi, Yu Xiaoou, Dai Jinna, Chu Yunzuo
Аннотация
BACKGROUND: Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is pivotal in clinical microbiology. The VITEK MS Research Use Only (RUO) database offers broader species coverage, yet its clinical adoption is hindered by insufficient performance validation against the approved in vitro diagnostic (IVD) database and inefficient manual operational workflows.
OBJECTIVE: This two-phase study first aimed to evaluate the concordance and supplementary performance of the RUO database compared to the IVD database, and subsequently to develop and validate an automated integrated workflow to enhance laboratory efficiency.
METHODS: In Phase 1 (Mar 2021-Jun 2022), parallel manual testing of 2,432 isolates was performed with both databases to analyze concordance. Based on these results, an in-house automated relay software was developed to parse IVD results and fully automate RUO reanalysis. Phase 2 (Jul-Nov 2022) prospectively incorporated 3,954 isolates to implement and assess the "IVD screening - automated RUO reanalysis" workflow.
RESULTS: Phase 1 demonstrated high RUO-IVD concordance (95.7% species/genus agreement). The RUO database correctly identified 98.9% of isolates and provided valid supplementary identification for 84.4% (108/128) of IVD-failed cases, with Tier 2 contributing 28.9%. Phase 2 revealed that the integrated workflow increased the overall identification rate from 95.5% to 98.7%, with Tier 2 contributing an additional 14.5%. The automated software reduced reanalysis turnaround time by > 75%, saving consumables and labor.
CONCLUSION: The VITEK MS RUO database is a reliable and complementary tool to the IVD database. Integration with automated software creates an efficient, compliant clinical workflow, providing a practical model to enhance pathogen identification for infectious disease management. This two-phase study first sought to evaluate the concordance and supplemental identification capacity of two databases (Phase 1). Based on these performance results, an automated integrated workflow was then developed, implemented, and validated for its diagnostic efficacy and laboratory efficiency in a clinical setting (Phase 2).
OBJECTIVE: This two-phase study first aimed to evaluate the concordance and supplementary performance of the RUO database compared to the IVD database, and subsequently to develop and validate an automated integrated workflow to enhance laboratory efficiency.
METHODS: In Phase 1 (Mar 2021-Jun 2022), parallel manual testing of 2,432 isolates was performed with both databases to analyze concordance. Based on these results, an in-house automated relay software was developed to parse IVD results and fully automate RUO reanalysis. Phase 2 (Jul-Nov 2022) prospectively incorporated 3,954 isolates to implement and assess the "IVD screening - automated RUO reanalysis" workflow.
RESULTS: Phase 1 demonstrated high RUO-IVD concordance (95.7% species/genus agreement). The RUO database correctly identified 98.9% of isolates and provided valid supplementary identification for 84.4% (108/128) of IVD-failed cases, with Tier 2 contributing 28.9%. Phase 2 revealed that the integrated workflow increased the overall identification rate from 95.5% to 98.7%, with Tier 2 contributing an additional 14.5%. The automated software reduced reanalysis turnaround time by > 75%, saving consumables and labor.
CONCLUSION: The VITEK MS RUO database is a reliable and complementary tool to the IVD database. Integration with automated software creates an efficient, compliant clinical workflow, providing a practical model to enhance pathogen identification for infectious disease management. This two-phase study first sought to evaluate the concordance and supplemental identification capacity of two databases (Phase 1). Based on these performance results, an automated integrated workflow was then developed, implemented, and validated for its diagnostic efficacy and laboratory efficiency in a clinical setting (Phase 2).