Direct MALDI-TOF MS identification from positive blood culture bottles: a time-saving approach to reduce diagnostic delays
DOI:
https://doi.org/10.3855/jidc.21690Keywords:
bacteremia, candidemia, stewardship, diagnostics, septicemia, automationAbstract
Introduction: Bloodstream infections are a leading cause of mortality worldwide, necessitating prompt identification and treatment to improve patient outcomes. Conventional methods are time-consuming, often delaying appropriate antimicrobial therapy. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has emerged as a revolutionary tool for rapid microbial identification. This study aimed to evaluate the utility of direct microbial identification from positive blood cultures using MALDI-TOF MS in reducing diagnostic delays and improving pathogen detection for effective clinical management.
Methodology: This prospective study analyzed 416 monomicrobial positive blood cultures obtained between October 2022 and October 2024 from a tertiary care teaching hospital in North India. After Gram staining and initial processing, direct microbial identification was performed using MALDI-TOF MS. Conventional subculture-based methods were employed as the gold standard for comparison.
Results: Of the 416 isolates, 89% were correctly identified at the species level using the direct method. Among 204 Gram positive cocci, 91.6% were correctly identified, with species-specific rates of 100% for Staphylococcus aureus and Staphylococcus ureilyticus. Out of 160 Gram negative bacilli, 88.1% were accurately identified, with Acinetobacter baumannii showing 97.5% accuracy. Candida species showed a correct identification rate of 80.8%, with 100% identification for Candida albicans and Candida krusei.
Conclusions: Direct microbial identification using MALDI-TOF MS is a rapid, cost-effective alternative to conventional methods, enabling faster pathogen detection and supporting antimicrobial stewardship.
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Copyright (c) 2026 Jyotsna Agarwal, Nikhil Raj, Vikramjeet Singh, Avneesh Sharma, Anupam Das, Manodeep Sen

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